system

Abstract

We provide the system. [Solution] A user registration means for elderly people to register in the system, A means for receiving consultations for the elderly person to input the details of the consultation, A response generation means that analyzes the aforementioned consultation content and generates an appropriate response, A follow-up means for sending follow-up messages to the aforementioned elderly person on a regular basis, In an emergency, an emergency contact system is in place to notify registered contacts and the appropriate authorities. A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The elderly often have anxieties about their health and daily life, but the means to receive appropriate support for those anxieties are limited. Also, regular follow-up and emergency response are often lacking, increasing the risk of dying alone. In the current system, no comprehensive measures have been taken for these problems, so an environment in which the elderly can live with peace of mind has not been provided. The purpose of this invention is to provide a system for solving these problems.

Means for Solving the Problems

[0005] This invention provides a user registration means and a consultation reception means that allow elderly people to register with the system and input their consultation details. It also includes a response generation means that analyzes the consultation content and generates appropriate answers. Furthermore, it provides a system that includes a follow-up means that sends follow-up messages to elderly people periodically, and an emergency contact means that notifies registered contacts and appropriate authorities in case of emergency. This can alleviate the anxieties and worries of elderly people and reduce the risk of dying alone.

[0006] The "user registration method" is a function that allows elderly people to enter information such as their name, contact information, address, and emergency contact information and register it in the system.

[0007] The "consultation reception method" is a function that allows elderly people to input their consultation details and send them to the system.

[0008] The "response generation means" is a function that analyzes the input consultation content and generates an appropriate response.

[0009] A "follow-up method" is a function that sends follow-up messages to elderly individuals periodically based on the content of their consultation.

[0010] "Emergency contact means" refers to a function that notifies registered contacts and appropriate authorities in the event of an emergency.

[0011] The "analysis method" is a function that classifies the input consultation content and divides it into appropriate categories.

[0012] The "reminder setting method" is a function that allows you to set up notifications for important matters to be sent periodically based on the consultation record. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a support system that enables elderly people to live with peace of mind, and includes a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact.

[0035] User registration method

[0036] Terminal: The elderly person accesses the system and displays the user registration screen. They enter information such as their name, contact information, address, and emergency contact information, and then submit it.

[0037] Server: Receives the transmitted information and stores it in the database.

[0038] Specific example: When Mr. Tanaka (the user) enters his name, phone number, and address and clicks the register button, the server saves that information.

[0039] Consultation methods

[0040] Terminal: Displays a screen where the user can enter their consultation details. The user enters their consultation details and presses the send button.

[0041] Server: Receives the input consultation content and analyzes it using natural language processing.

[0042] Specific example: When Mr. Tanaka types "I can't sleep lately" and presses the send button, the server receives and analyzes it.

[0043] Answer generation means

[0044] Server: Based on the analysis of the consultation content, it searches the database for an appropriate answer or automatically generates an appropriate answer.

[0045] Terminal: Displays the generated response to the user.

[0046] Specific example: The server retrieves information about "sleep disorders" from its database and displays the following response on Tanaka's terminal: "If you've been having trouble sleeping recently, it's important to avoid consuming caffeine before bed and to create a relaxing environment."

[0047] Follow-up methods

[0048] Server: Saves a history of consultations and generates follow-up messages at appropriate times. It also sends messages to users periodically based on reminder settings.

[0049] Terminal: Displays a follow-up message to the user.

[0050] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?" and sends it to Mr. Tanaka's device.

[0051] Emergency contact methods

[0052] User: Enter the details of your urgent need for help. For example, enter something like, "My chest hurts."

[0053] Server: Detects emergency keywords, immediately triggers an emergency alert, and notifies registered emergency contacts and the appropriate authorities.

[0054] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[0055] For example, if Ms. Tanaka enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify Ms. Tanaka's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on Ms. Tanaka's device.

[0056] These measures can quickly and appropriately alleviate the anxieties and worries of the elderly, reducing the risk of dying alone. The comprehensive support provided by the system enables elderly people to live with peace of mind.

[0057] The following describes the processing flow.

[0058] User registration method

[0059] Step 1:

[0060] Device: The user opens the system's website or app and displays the user registration screen.

[0061] Step 2:

[0062] User: Enter your name, contact information (phone number or email address), address, emergency contact information, etc.

[0063] Step 3:

[0064] Terminal: Checks the entered information and performs a simple check to ensure it is entered correctly.

[0065] Step 4:

[0066] Terminal: Sends information to the server.

[0067] Step 5:

[0068] Server: Receives the information and validates it to ensure there are no problems with its format or content.

[0069] Step 6:

[0070] Server: Saves information that passed validation to the database.

[0071] Consultation methods

[0072] Step 1:

[0073] Terminal: The user logs in and displays the consultation request screen.

[0074] Step 2:

[0075] User: Enter your inquiry in text format.

[0076] Step 3:

[0077] Terminal: Sends the entered consultation details to the server.

[0078] Step 4:

[0079] Server: Receives the consultation content and performs analysis using a natural language processing (NLP) engine.

[0080] Step 5:

[0081] Server: Based on the analysis results, classify the consultation content into the appropriate category.

[0082] Answer generation means

[0083] Step 1:

[0084] Server: Searches for the appropriate answer from a database related to the category.

[0085] Step 2:

[0086] Server: If an adequate response is not available, it will use a response generation algorithm to generate an appropriate response.

[0087] Step 3:

[0088] Terminal: Displays the generated response to the user.

[0089] Follow-up methods

[0090] Step 1:

[0091] Server: Stores the consultation details and corresponding responses in a database.

[0092] Step 2:

[0093] Server: Sets follow-up reminders based on saved consultation history.

[0094] Step 3:

[0095] Server: Generates a follow-up message when the reminder time is reached.

[0096] Step 4:

[0097] Terminal: Sends the generated follow-up message to the user.

[0098] Emergency contact methods

[0099] Step 1:

[0100] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[0101] Step 2:

[0102] Terminal: Sends urgent information to the server.

[0103] Step 3:

[0104] Server: Based on the received information, it detects emergency keywords and determines the need for emergency response.

[0105] Step 4:

[0106] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[0107] Step 5:

[0108] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[0109] Based on the above procedures, a system will be established to receive consultations from the elderly, provide appropriate answers, and implement necessary follow-up and emergency response. This will create a comprehensive support system that enables the elderly to live with peace of mind.

[0110] (Example 1)

[0111] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0112] Conventional support systems for the elderly have often struggled to provide prompt and appropriate responses to the concerns of the elderly, and their emergency response capabilities have frequently been inadequate. In particular, there is a need for solutions to daily anxieties and worries, as well as prompt responses in emergencies, but systems that meet these needs are limited. This invention aims to solve these problems by providing a comprehensive support system that enables the elderly to live their daily lives with peace of mind.

[0113] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0114] In this invention, the server includes a user registration means for elderly people to register with the system, a consultation reception means for elderly people to input their consultation details, a response generation means for analyzing the consultation details and generating appropriate answers, a follow-up means for periodically sending follow-up messages to the elderly people, an emergency contact means for notifying registered contacts and appropriate authorities in the event of an emergency, and a natural language processing means for detecting emergencies by receiving and analyzing the consultation details and taking necessary measures. This enables a comprehensive support system that can not only respond quickly and appropriately to the consultation details of elderly people but also respond quickly to emergencies.

[0115] "User registration method" refers to a method by which elderly individuals can access the system, enter information such as their name, contact information, address, and emergency contact information, and send this information to the server.

[0116] A "consultation reception method" is a means of providing an interface for elderly people to input their consultation details and transmitting that input to a server.

[0117] "Answer generation means" refers to a means of analyzing the received consultation content and either searching for an appropriate answer from a database based on the analysis results, or automatically generating an answer using a generation AI model.

[0118] A "follow-up method" is a means of generating periodic follow-up messages based on the content of the consultation and the history of the response, and sending these messages to the user.

[0119] An "emergency contact method" is a system that analyzes the content of inquiries entered during an emergency and, when an emergency keyword is detected, promptly notifies registered emergency contacts and the appropriate authorities.

[0120] "Natural language processing means" are tools for analyzing received consultation content, understanding keywords and meaning within the text, classifying the consultation content, and taking appropriate action.

[0121] This invention provides a support system that enables elderly people to live with peace of mind, and it is equipped with various functions. Specific embodiments are described below.

[0122] This system primarily consists of three components: a server, a terminal, and a user. The following describes in detail how each component works together to provide support for the elderly.

[0123] User registration method

[0124] Terminal: When a user accesses the system, a user registration screen is displayed on the terminal. This screen is built using HTML, CSS, and JavaScript (registered trademark). On this screen, the user enters information such as their name, contact information, address, and emergency contact information, and clicks the submit button.

[0125] Server: Receives information sent from the terminal and saves it to a database (MySQL or PostgreSQL) using a server-side program (e.g., Python or Java®).

[0126] Specific example: When a user enters "Taro Yamada", phone number "090-1234-5678", address "Chiyoda-ku, Tokyo", and emergency contact "090-8765-4321", and presses the submit button, the server saves this information to the database.

[0127] Consultation methods

[0128] Terminal: When a user logs in, a screen is displayed where they can enter their inquiry details. A text input field and a submit button are displayed using HTML, CSS, and JavaScript.

[0129] User: Enter your question and press the submit button. For example, enter "I haven't been able to sleep lately."

[0130] Server: Receives submitted consultation content and analyzes it using natural language processing libraries (e.g., spaCy or NLTK). This analysis extracts key keywords and themes.

[0131] Specific example: When a user types "I can't sleep lately" and presses the send button, the server receives the message and uses natural language processing to extract and analyze the keyword "can't sleep."

[0132] Answer generation means

[0133] Server: Based on the analysis results, it searches the database for the appropriate answer or automatically generates an answer using a generative AI model (e.g., GPT-4®). In doing so, the AI ​​model considers past data and question format to generate the most appropriate response.

[0134] Terminal: The generated response is sent to the user's terminal and displayed on the screen. It is displayed using HTML and JavaScript.

[0135] Specific example: The server retrieves information about "sleep disorders" from the database, generates a response such as "If you've been having trouble sleeping recently, it's important to avoid consuming caffeine before bed and create a relaxing environment," and displays it on the user's device.

[0136] Follow-up methods

[0137] Server: Stores consultation content and response history in a database and periodically generates follow-up messages.

[0138] Terminal: The generated follow-up message is sent to the user and displayed on the screen. It is displayed using HTML and JavaScript.

[0139] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?", sends it to the user's device, and the device displays the message.

[0140] Emergency contact methods

[0141] User: Enter the details of your urgent inquiry and press the send button. For example, enter "My chest hurts."

[0142] Server: If it detects an emergency keyword and determines that an emergency has occurred, it will notify registered emergency contacts and the appropriate authorities.

[0143] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[0144] For example, if a user enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify the user's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on the user's device.

[0145] Through the functions described above, the system of the present invention can quickly and appropriately alleviate the anxieties and worries of the elderly and respond quickly to emergencies. This enables comprehensive support for the elderly to live with peace of mind.

[0146] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0147] Step 1: Display the user registration screen.

[0148] Terminal: When a user accesses the system, the user registration screen is automatically displayed. The user registration screen is built using HTML, CSS, and JavaScript and includes input fields for name, contact information, address, emergency contact information, etc.

[0149] Input: None

[0150] Output: Display of user registration screen

[0151] Specific operation: The device displays a registration screen to the user via a browser and prompts them to enter user information.

[0152] Step 2: Enter and submit information

[0153] User: Enter the required information such as your name, contact information, address, and emergency contact information, then click the register button.

[0154] Input: Name, contact information, address, emergency contact information, etc.

[0155] Output: Entered user information

[0156] Specific operation: The user enters information into the displayed fields and presses the registration button to send the information to the device.

[0157] Step 3: Receiving and saving information

[0158] Server: Receives user information sent from the terminal and saves it to a database (MySQL or PostgreSQL) using a server-side program (e.g., Python or Java).

[0159] Input: Submitted user information

[0160] Output: User information stored in the database

[0161] Specific operation: The server analyzes the received information and saves it to the database in the appropriate format.

[0162] Step 4: Display of the consultation details input screen

[0163] Terminal: When a user logs in, a screen is displayed where they can enter their inquiry details. A text input field and a submit button are displayed using HTML, CSS, and JavaScript.

[0164] Input: None

[0165] Output: Display of the consultation input screen

[0166] Specific operation: The terminal displays an interface for the user to input their inquiry.

[0167] Step 5: Enter and submit your consultation details.

[0168] User: Enter your question and press the submit button. For example, enter "I haven't been able to sleep lately."

[0169] Input: Consultation details

[0170] Output: Input consultation content

[0171] Specific operation: The user enters information into the consultation field and presses the send button to send the information to the device.

[0172] Step 6: Receiving and analyzing the consultation content

[0173] Server: Receives the submitted consultation content and analyzes it using a natural language processing library (e.g., spaCy or NLTK).

[0174] Input: Submitted consultation details

[0175] Output: Analysis results (main keywords, meaning, etc.)

[0176] Specific operation: The server analyzes the received consultation content using a natural language processing library and extracts important keywords and meaning.

[0177] Step 7: Generate and submit your response.

[0178] Server: Based on the analysis results, it generates the optimal answer using a generative AI model (e.g., GPT-4). It then sends the generated answer to the user's device.

[0179] Input: Analysis results (main keywords, meaning of sentences, etc.)

[0180] Output: Generated answer

[0181] Specific operation: The server uses a generative AI model to generate an appropriate response and sends it to the user's terminal.

[0182] Step 8: Display the generated response

[0183] Terminal: Displays the generated response received from the server to the user. It is displayed on the screen using HTML and JavaScript.

[0184] Input: Generated answer

[0185] Output: Display of responses to the user

[0186] Specific action: The terminal displays the received response and provides it to the user.

[0187] Step 9: Generate and send follow-up messages

[0188] Server: Stores consultation content and response history in a database, periodically generates follow-up messages, and sends them to the user.

[0189] Input: Consultation details and response history

[0190] Output: Follow-up message

[0191] Specific operation: The server generates a follow-up message based on the information stored in the database and sends it to the user.

[0192] Step 10: Display a follow-up message

[0193] Terminal: Displays received follow-up messages to the user. Displayed using HTML and JavaScript.

[0194] Input: Follow-up message

[0195] Output: Display of follow-up message

[0196] Specific action: The device displays the received follow-up message to the user.

[0197] Step 11: Enter and submit details of your emergency consultation.

[0198] User: Enter the details of your urgent inquiry and press the send button. For example, enter "My chest hurts."

[0199] Input: Emergency consultation details

[0200] Output: Sending of urgent consultation details

[0201] Specific action: The user enters information into the emergency field and presses the send button to send the information to the terminal.

[0202] Step 12: Detect and notify of urgent keywords

[0203] Server: If it detects an emergency keyword and determines that an emergency has occurred, it will notify registered emergency contacts and the appropriate authorities.

[0204] Input: Submitted emergency consultation details

[0205] Output: Emergency notification

[0206] Specific operation: The server analyzes the received emergency call content and, if it contains emergency keywords, notifies the emergency contact and the appropriate authorities.

[0207] Step 13: Displaying an emergency message

[0208] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[0209] Input: Emergency notification

[0210] Output: Display of emergency message

[0211] Specific action: The terminal receives an emergency notification from the server and displays the emergency message to the user.

[0212] (Application Example 1)

[0213] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0214] When elderly people live alone, they often face anxieties in daily life and difficulty dealing with emergencies. Therefore, support is needed to ensure that elderly people can live with peace of mind. However, conventional support systems have limited means for elderly people to initiate consultations or emergency contact, which can result in delays in appropriate responses. Furthermore, the analysis of consultation content and the generation of responses are not sufficiently automated, making it difficult to quickly alleviate the anxieties of elderly people.

[0215] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0216] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, and an application installed on a robot for supporting the lives of the elderly, wherein the robot performs voice input and voice output, and analyzes the consultation content and generates a response using a natural language processing and generative AI model. This enables the elderly to easily interact with the system by voice and to respond quickly in emergencies.

[0217] A "user registration method" is an interface that allows elderly people to register their information in the system.

[0218] A "consultation reception method" is an interface that allows elderly people to input the details of their consultation.

[0219] "Answer generation means" refers to a means for analyzing the input consultation content and generating an appropriate answer.

[0220] A "follow-up method" refers to a means of sending follow-up messages to elderly individuals on a regular basis.

[0221] "Emergency contact methods" refer to means of notifying registered contacts and appropriate authorities in the event of an emergency.

[0222] "Applications installed on robots" are programs that are installed on robots to support the daily lives of elderly people.

[0223] "Voice input and voice output" refers to a technology in which elderly people give instructions or ask questions to a robot using their voice, and the robot responds using its voice.

[0224] "Natural language processing" is a technology that analyzes input language data and understands its meaning.

[0225] A "generative AI model" is a technology that uses artificial intelligence to automatically generate appropriate answers and advice.

[0226] In this embodiment of the invention, the system for supporting the lives of the elderly includes a user registration means, a consultation reception means, a response generation means, a follow-up means, and an emergency contact means. These means are realized through an application installed on a robot.

[0227] Program Processing Description

[0228] User registration method

[0229] The server displays a registration screen where the user enters information such as their name, contact details, address, and emergency contact information. The entered information is then stored in a database (e.g., MySQL) via a web server (e.g., Apache®).

[0230] Specific example: When an elderly person enters their name, phone number, and address and clicks the registration button, the entered information is saved to the database.

[0231] Consultation methods

[0232] When a user inputs their inquiry via voice, the robot's microphone captures the audio data, and Google® Cloud Speech-to-Text converts the speech to text. The robot then analyzes the inquiry using a natural language processing (NLP) engine (e.g., Google Cloud NLP).

[0233] Specific example: An elderly person complains that they "haven't been able to sleep lately," and a robot converts their voice into text and analyzes the content of their complaint.

[0234] Answer generation means

[0235] The server uses a generative AI model (e.g., OpenAI® GPT-4) to generate appropriate responses based on the analyzed consultation content. It then outputs these responses as speech using speech synthesis technology (e.g., Google Cloud Text-to-Speech) and communicates them to the user via the robot's speaker.

[0236] Specific example: When a server receives a request for help regarding "I can't sleep these days," it inputs a prompt into a generative AI model, and the generated response is output as speech using speech synthesis technology.

[0237] Example of a prompt:

[0238] A user has submitted the following inquiry: "I've been having trouble sleeping lately." Please generate an appropriate response to this inquiry, including possible solutions and advice.

[0239] Follow-up methods

[0240] The server stores a history of the consultation and uses a scheduler (e.g., Quartz Scheduler) to generate follow-up messages at appropriate times. These messages are sent via voice or text through a robot.

[0241] Specific example: A server sends a follow-up message to the elderly person via a robot a week later, asking, "Have you been able to sleep well since then?"

[0242] Emergency contact methods

[0243] The server detects emergency keywords from the entered consultation content and automatically sends notifications to registered emergency contacts and appropriate authorities using notification services such as Twilio. The robot also displays or voices messages to elderly people urging them to take emergency action.

[0244] Specific example: If an elderly person tells a robot, "My chest hurts and I can't breathe," the server will determine it's an emergency and automatically send a notification to the family and emergency authorities.

[0245] In this way, the system allows elderly people to easily communicate using voice and respond quickly in emergencies.

[0246] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0247] Step 1:

[0248] The user enters their name, contact information, address, and emergency contact information on the robot registration screen and clicks the register button.

[0249] Input: Name, contact information, address, and emergency contact information.

[0250] Output: User information is saved to the database.

[0251] Specific operation: The server saves the input information to a database (e.g., MySQL) via a web server (e.g., Apache). A message indicating that user information registration is complete is displayed on the screen.

[0252] Step 2:

[0253] The user inputs their inquiry details via voice.

[0254] Input: Audio data.

[0255] Output: The consultation content converted as text data.

[0256] Specific operation: The robot's microphone captures audio data, and Google Cloud Speech-to-Text is used to convert the audio to text. The server then receives the text data.

[0257] Step 3:

[0258] The server analyzes the content of the consultation it receives.

[0259] Input: Text data containing the consultation details.

[0260] Output: Analysis results.

[0261] Specific operation: The server uses an NLP engine (e.g., Google Cloud NLP) to analyze the consultation content and retrieve the analysis results.

[0262] Step 4:

[0263] The server generates an answer using an AI model based on the analysis results.

[0264] Input: Analysis results.

[0265] Output: Generated answer.

[0266] Specific operation: The server inputs prompt text to the generated AI model (e.g., OpenAI GPT-4) and retrieves the generated response.

[0267] Example of a prompt:

[0268] A user has submitted the following inquiry: "I've been having trouble sleeping lately." Please generate an appropriate response to this inquiry, including possible solutions and advice.

[0269] Step 5:

[0270] The server converts the generated response into speech data using speech synthesis technology, and outputs the response through the robot's speaker.

[0271] Input: Text data of the generated response.

[0272] Output: The response is in audio format.

[0273] Specific operation: The server uses Google Cloud Text-to-Speech to convert the generated response into audio data, which is then output as speech through the robot's speaker.

[0274] Step 6:

[0275] The server saves a history of the consultation and generates follow-up messages at the appropriate time.

[0276] Input: History of consultation details.

[0277] Output: Follow-up message.

[0278] Specific operation: The server uses a scheduler (e.g., Quartz Scheduler) to generate follow-up messages at the appropriate time and the robot sends the messages via voice or text.

[0279] Step 7:

[0280] The server detects emergency keywords in times of crisis and sends notifications to the appropriate authorities.

[0281] Input: Details of the urgent consultation.

[0282] Output: Notification message.

[0283] Specific operation: The server detects an emergency keyword and automatically sends messages to the registered contacts and appropriate authorities using a notification service such as Twilio. Also, the robot displays or audibly conveys a message prompting the elderly to take emergency measures.

[0284] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0285] The present invention provides a support system in which the elderly can live with peace of mind. In addition to a series of functions for receiving consultations from the elderly, generating appropriate answers, and performing follow-up and emergency contacts, it includes an emotion engine that recognizes the user's emotion.

[0286] User registration means

[0287] Terminal: The elderly access the system and display the user registration screen. Enter information such as name, contact information, address, and emergency contact, and send it.

[0288] Server: Receive the transmitted information and save it in the database.

[0289] Specific example: When Mr. Tanaka (user) enters his name, phone number, and address and clicks the registration button, the server saves the information.

[0290] Consultation reception means

[0291] Terminal: The user logs in and displays the consultation reception screen.

[0292] User: Enter the consultation content in text form.

[0293] Terminal: Send the entered consultation content to the server.

[0294] Server: Receives consultation content and analyzes it using a natural language processing (NLP) engine. It also uses an emotion engine to recognize the user's emotions from the input consultation content.

[0295] Specific example: Ms. Tanaka inputs "I haven't been able to sleep lately," and the server receives and analyzes this, while the emotion engine recognizes Ms. Tanaka's stress and anxiety.

[0296] Answer generation means

[0297] Server: Searches for appropriate answers from a database related to categories and sentiments.

[0298] Server: Generates appropriate responses using response generation algorithms as needed. Adjusts the response content based on the recognized user's sentiment.

[0299] Terminal: Displays the generated response to the user.

[0300] Specific example: The server retrieves information about "sleep disorders" from a database and displays a response on the terminal that is tailored to Tanaka's feelings, such as, "If you've been having trouble sleeping lately, it's important to avoid consuming caffeine before bed and to create a relaxing environment."

[0301] Follow-up methods

[0302] Server: Stores the consultation details and corresponding responses in a database.

[0303] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[0304] Server: Generates a follow-up message when the reminder time is reached.

[0305] Terminal: Send the generated follow-up message to the user.

[0306] Specific example: The server generates a follow-up message "Have you been sleeping well lately?" one week later and sends it to Mr. Tanaka's terminal.

[0307] Emergency contact means

[0308] User: When an emergency occurs, enter the emergency consultation content on the consultation reception screen.

[0309] Terminal: Send the emergency content to the server.

[0310] Server: Detect an emergency keyword based on the received content and further confirm the necessity of the emergency with the emotion engine.

[0311] Server: Activate an emergency alert and send notifications to the registered emergency contacts and appropriate authorities.

[0312] Terminal: Display an emergency instruction such as "I will call an ambulance immediately. I will also contact your family" to the user.

[0313] Specific example: When Mr. Tanaka enters "I have chest pain and can't breathe", the server determines this as an emergency and automatically notifies Mr. Tanaka's family and the emergency authorities. Also, a message prompting emergency measures will be displayed on Mr. Tanaka's terminal.

[0314] By these means, the anxiety and worries of the elderly can be quickly and appropriately resolved, and more personalized responses can be achieved through emotion recognition. With the comprehensive support and emotion-based responses provided by the system, a support system for the elderly to live with peace of mind is established.

[0315] The following describes the processing flow.

[0316] User registration means

[0317] Step 1:

[0318] Device: The user opens the system's website or app and displays the user registration screen.

[0319] Step 2:

[0320] User: Enter information such as name, contact information (phone number or email address), address, and emergency contact information.

[0321] Step 3:

[0322] Terminal: Checks the entered information and performs a simple check to ensure it is entered correctly.

[0323] Step 4:

[0324] Terminal: Sends information to the server.

[0325] Step 5:

[0326] Server: Receives the information and validates it to ensure there are no problems with its format or content.

[0327] Step 6:

[0328] Server: Saves information that passed validation to the database.

[0329] Consultation methods

[0330] Step 1:

[0331] Terminal: The user logs in and displays the consultation request screen.

[0332] Step 2:

[0333] User: Enter your inquiry in text format.

[0334] Step 3:

[0335] Terminal: Sends the entered consultation details to the server.

[0336] Step 4:

[0337] Server: Receives the consultation content and performs analysis using a natural language processing (NLP) engine.

[0338] Step 5:

[0339] Server: Based on the analysis results, classify the consultation content into the appropriate category.

[0340] Step 6:

[0341] Server: Sends the analyzed data to the emotion engine to recognize the user's emotions.

[0342] Answer generation means

[0343] Step 1:

[0344] Server: Searches for appropriate answers from a database related to categories and sentiments.

[0345] Step 2:

[0346] Server: If an answer is not automatically generated, run the answer generation algorithm to generate an appropriate answer.

[0347] Step 3:

[0348] Server: Adjusts the response based on the recognized user's emotions.

[0349] Step 4:

[0350] Terminal: Displays the generated response to the user.

[0351] Follow-up methods

[0352] Step 1:

[0353] Server: Stores the consultation details and corresponding responses in a database.

[0354] Step 2:

[0355] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[0356] Step 3:

[0357] Server: Generates a follow-up message when the reminder time is reached.

[0358] Step 4:

[0359] Terminal: Sends the generated follow-up message to the user.

[0360] Emergency contact methods

[0361] Step 1:

[0362] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[0363] Step 2:

[0364] Terminal: Sends urgent information to the server.

[0365] Step 3:

[0366] Server: Based on the received information, it detects urgent keywords and uses an emotion engine to further confirm the urgent need.

[0367] Step 4:

[0368] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[0369] Step 5:

[0370] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[0371] (Example 2)

[0372] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0373] The challenge lies in responding quickly and appropriately to the anxieties and worries that elderly people face in their daily lives, and in providing personalized support that takes their emotions into consideration. Furthermore, there is a need for a system that can respond quickly in the event of an emergency, but existing systems do not adequately meet these requirements.

[0374] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0375] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, an emotion recognition means, and a response adjustment means. This enables a rapid and appropriate response to the anxieties and worries of the elderly, and by considering the emotions of the elderly, it becomes possible to provide personalized support. Furthermore, a rapid response can be achieved in emergency situations.

[0376] "User registration means" refers to a means of providing an interface for elderly people to access the system and input and register necessary personal information.

[0377] A "consultation reception method" refers to a means of providing an interface for elderly people to log in to the system, input their consultation details in text format, and submit them.

[0378] A "response generation method" is a means for analyzing the received consultation content and generating an appropriate response.

[0379] "Follow-up methods" refer to means of generating and sending regular follow-up messages to elderly individuals.

[0380] "Emergency contact methods" refer to means of notifying registered contacts and appropriate authorities in the event of an emergency.

[0381] "Emotion recognition means" refers to methods for recognizing the emotions of elderly people based on the consultation content entered by the user.

[0382] "Response adjustment means" are methods for adjusting the content of responses based on the perceived emotions of elderly individuals.

[0383] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it also includes an emotion engine that recognizes the user's emotions.

[0384] User registration method

[0385] Terminal: The elderly person accesses the system and displays the user registration screen. Here, they enter and submit information such as their name, address, contact information, and emergency contact information.

[0386] Server: Receives the transmitted information and saves it to a database (e.g., MySQL).

[0387] Example: A user enters their name, phone number, and address on the registration screen and clicks the "Register" button. The server saves this information to the database.

[0388] Consultation methods

[0389] Terminal: The user logs in and displays the consultation request screen.

[0390] User: Enter your inquiry in text format.

[0391] Terminal: Sends the entered consultation details to the server.

[0392] Server: Receives the consultation content and analyzes it using a natural language processing (NLP) engine (e.g., spaCy, BERT). Furthermore, it uses an emotion engine (e.g., OpenAI's emotion recognition model) to recognize the user's emotions from the input consultation content.

[0393] Specific example: A user enters "I haven't been able to sleep lately," and the server receives and analyzes this, while also using an emotion engine to recognize the user's stress and anxiety.

[0394] Answer generation means

[0395] Server: Based on the category of the consultation content (e.g., sleep disorder) and the recognized emotion (e.g., anxiety), it searches the database for relevant data.

[0396] Server: Generates appropriate responses using a generation AI model (e.g., OpenAI's GPT-3®) as needed. Adjusts the response content considering the user's sentiment data.

[0397] Terminal: Displays the generated response to the user.

[0398] Specific example: The server retrieves information about "sleep disorders" from its database and displays the message, "If you've been having trouble sleeping lately, it's important to avoid consuming caffeine before bed and create a relaxing environment."

[0399] Follow-up methods

[0400] Server: Stores consultation details and responses in a database.

[0401] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[0402] Server: When the reminder time is reached, it automatically generates a follow-up message.

[0403] Terminal: Sends the generated follow-up message to the user.

[0404] Specific example: A server generates a message one week later asking, "Have you been able to sleep since then?" and sends it to the user's terminal.

[0405] Emergency contact methods

[0406] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[0407] Terminal: Sends urgent information to the server.

[0408] Server: Analyzes the received content and detects urgent keywords. Simultaneously, the emotion engine further confirms the urgency.

[0409] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities (e.g., emergency services).

[0410] Terminal: Displays the user with the message, "We will call an ambulance immediately. We will also contact your family."

[0411] For example, if a user enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify registered contacts and emergency authorities. A message prompting emergency action will also be displayed on the user's device.

[0412] Example of a prompt

[0413] "Please explain the steps to register for the senior support system."

[0414] "Please tell me how to analyze consultations regarding insomnia and provide appropriate advice."

[0415] "Please explain how to set up follow-up messages based on the content of the consultation."

[0416] "Please explain the procedures for responding in the event of an emergency."

[0417] As described above, this system quickly and appropriately resolves the anxieties and worries of the elderly, and enables more personalized responses through emotion recognition. Furthermore, it is designed to respond quickly even in emergencies.

[0418] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0419] Step 1:

[0420] Terminal: The user accesses the new registration screen. The user enters information such as name, address, phone number, and emergency contact information.

[0421] Input: User's (elderly) personal information.

[0422] Output: Data of the entered personal information.

[0423] Specific operation: When a user enters information into a form displayed in the browser and presses the "Register" button, the information is collected by the device.

[0424] Step 2:

[0425] Terminal: Sends the entered personal information to the server. Specifically, the data is sent as an HTTP request.

[0426] Input: Data of collected personal information.

[0427] Output: HTTP request to the server.

[0428] Specific operation: The terminal sends the form data to the server using the POST method.

[0429] Step 3:

[0430] Server: Stores received personal information in a database. This includes a process to verify data integrity and security.

[0431] Input: Personal information data sent to the server.

[0432] Output: Personal information stored in the database.

[0433] Specific operation: The server receives the data and saves it to the database using an SQL insert command.

[0434] Step 4:

[0435] Terminal: The user logs into the system and displays the consultation request screen.

[0436] Input: User login information.

[0437] Output: Consultation request screen.

[0438] Specific operation: When a user accesses the login screen in their browser, enters their ID and password, and clicks the "Login" button, the system performs authentication, and if successful, the consultation request screen is displayed.

[0439] Step 5:

[0440] User: Enter the consultation details in text format. Specifically, enter the text data of the consultation details.

[0441] Input: User's inquiry.

[0442] Output: Text data of the entered consultation content.

[0443] Specific action: The user enters their inquiry into a text box and presses the "Send" button.

[0444] Step 6:

[0445] Terminal: Sends the entered consultation details to the server. Specifically, the data is sent as an HTTP request.

[0446] Input: Text data of the entered consultation content.

[0447] Output: HTTP request to the server.

[0448] Specific operation: The terminal sends the form data to the server using the POST method.

[0449] Step 7:

[0450] Server: Analyzes received consultation content using a natural language processing (NLP) engine. It also uses an emotion engine to recognize the user's emotions from the input consultation content.

[0451] Input: Text data of the consultation content sent to the server.

[0452] Output: Analysis results and sentiment data.

[0453] Specific operation: Text analysis is performed using an NLP engine (e.g., spaCy, BERT), and then emotions are recognized using an emotion engine (e.g., OpenAI's emotion recognition model).

[0454] Step 8:

[0455] Server: Based on the category of the consultation content and the recognized emotions, it searches the database for relevant data. If necessary, it uses a generative AI model to generate an answer.

[0456] Input: Analysis results and sentiment data.

[0457] Output: Generated answer.

[0458] Specific operation: Execute database queries to retrieve relevant information. Generate new answers using a generated AI model (e.g., OpenAI's GPT-3) as needed.

[0459] Step 9:

[0460] Terminal: Displays the generated response to the user.

[0461] Input: Generated response.

[0462] Output: Display of the answer.

[0463] Specific operation: Send the response data to the terminal in HTML format and display it to the user.

[0464] Step 10:

[0465] Server: Stores the consultation content and the provided answers in a database. The stored content includes date and time, consultation content, answer content, recognized emotion data, etc.

[0466] Input: Consultation details, provided response, and sentiment data.

[0467] Output: Consultation history stored in the database.

[0468] Specific operation: The server receives the data and saves it to the database using an SQL insert command.

[0469] Step 11:

[0470] Server: Based on the saved data, set a follow-up reminder for one week later.

[0471] Input: Consultation history data.

[0472] Output: Reminder.

[0473] Specific operation: Set up reminder scheduling on the server and trigger the reminder at the follow-up date and time.

[0474] Step 12:

[0475] Server: When the reminder date and time arrive, it automatically generates a follow-up message.

[0476] Input: Reminder data.

[0477] Output: Follow-up message.

[0478] Specific operation: A scheduled reminder is triggered, and the message generation function generates a corresponding follow-up message.

[0479] Step 13:

[0480] Terminal: Sends the generated follow-up message to the user.

[0481] Input: Follow-up message.

[0482] Output: Notification of follow-up message.

[0483] Specific operation: Send a message using an HTTP request and display it in the browser.

[0484] Step 14:

[0485] User: When an emergency occurs, enter the details of your urgent request.

[0486] Input: Details of the urgent consultation.

[0487] Output: Text data of the entered emergency consultation details.

[0488] Specific action: The user enters their inquiry into the input form and presses the "Submit" button.

[0489] Step 15:

[0490] Terminal: Sends urgent information to the server.

[0491] Input: Text data of the emergency consultation details entered.

[0492] Output: HTTP request to the server.

[0493] Specific operation: The terminal sends the form data to the server using the POST method.

[0494] Step 16:

[0495] Server: Analyzes the received content and detects urgent keywords. Simultaneously, the emotion engine further confirms the urgency.

[0496] Input: Text data of the emergency consultation details.

[0497] Output: Emergency alert.

[0498] Specific actions: Analyze using an NLP engine and an emotion engine to determine urgency.

[0499] Step 17:

[0500] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[0501] Input: Emergency alert.

[0502] Output: Notification message.

[0503] Specific operation: Retrieve registered contact information and send notifications using the messaging API.

[0504] Step 18:

[0505] Terminal: Displays the user with the message, "We will call an ambulance immediately. We will also contact your family."

[0506] Input: Notification message.

[0507] Output: Displays emergency instructions.

[0508] Specific action: The notification message is received on the device and displayed in the browser.

[0509] (Application Example 2)

[0510] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0511] In support systems that enable elderly people to live with peace of mind, there is a need to provide more personalized responses through emotion recognition. In particular, the challenge lies in responding quickly and appropriately to anxieties and emergencies that arise in daily life, and providing appropriate support that is tailored to their emotions, even for basic needs such as meals.

[0512] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0513] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, an emotion recognition means, and an emotion adaptation means. This makes it possible to provide an advanced support system that allows elderly people to live with peace of mind and respond quickly to emergencies.

[0514] "User registration method" refers to a function that allows elderly people to access the system and enter and submit information such as their name, contact information, address, and emergency contact information.

[0515] The "consultation submission method" is a function that allows users to log in, enter their consultation details through the consultation screen, and send them to the system.

[0516] The "response generation means" is a function that analyzes the content of the consultation and generates an appropriate response based on that analysis.

[0517] The "follow-up method" is a function that sends periodic follow-up messages based on the content of the consultation and the history of the response.

[0518] "Emergency contact methods" refer to a function that allows users to notify registered contacts and appropriate authorities when they encounter an emergency.

[0519] "Emotion recognition means" refers to a function that detects emotions from the consultation content and comments entered by the user.

[0520] "Emotional adaptation measures" refer to functions that adjust the content of follow-up messages based on the recognized emotions of the user.

[0521] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it includes an emotion engine that recognizes the user's emotions. The specific form of this system is shown below.

[0522] User registration method

[0523] Users access the system using their smartphones, enter information such as their name, contact details, address, and emergency contact details, and send it to the server. The server stores the submitted information in a database. The software used typically includes a database management system (e.g., SQLite).

[0524] Consultation methods

[0525] The user logs in using their smartphone and enters their consultation request in text format on the consultation request screen. The entered consultation request is sent to the server, which analyzes the received content using a natural language processing engine (e.g., Spacy) and an emotion recognition engine (e.g., Google Cloud Natural Language API). Here, the user's emotions are recognized along with the consultation request.

[0526] Answer generation means

[0527] The server generates appropriate responses based on the analyzed consultation content and sentiment data. The generated responses are displayed on the smartphone. If necessary, it searches for relevant information in the database and further refines the responses using a generation AI model.

[0528] Follow-up methods

[0529] The server stores the consultation details and response records in a database and generates a follow-up message after a certain period of time has elapsed. This follow-up message is tailored based on the perceived emotions and sent to the user. Automated scheduling software (e.g., cron jobs) is used to set the reminders.

[0530] Emergency contact methods

[0531] If a user experiences an emergency, they enter the details of their urgent need using their smartphone. The server receives this information and uses an emotion recognition engine to confirm the urgency. If necessary, notifications are automatically sent to emergency contacts and the appropriate authorities. The software used here includes notification services (e.g., Twilio).

[0532] Specific example

[0533] The following is a specific example of a scenario in which elderly people use a food delivery system.

[0534] 1. Elderly individuals access the system using their smartphones and register their names, contact information, etc.

[0535] 2. When ordering breakfast, if you enter a message such as "I haven't had much of an appetite lately," the server will receive this message and perform emotion recognition.

[0536] 3. The server generates appropriate advice, such as "Choose easily digestible foods and consult a doctor if necessary," and displays it on the smartphone.

[0537] 4. One week later, a follow-up message will be automatically sent asking, "Has your appetite improved at all?"

[0538] Example of a prompt

[0539] "Please suggest how to care for an elderly person who has recently lost their appetite. The emotion recognition engine should detect a state of distress and provide appropriate advice. Also, please send a follow-up message one week later."

[0540] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0541] Step 1: User Registration

[0542] Users access the system using their smartphones and enter information such as their name, contact information, address, and emergency contact information. The entered information is sent to the server when the user presses the "Register" button. The server stores the received information in a database. The input data consists of personal information such as names, while the output data is user information recorded in the database. Specifically, the user enters data into input fields on the smartphone screen and then presses the "Register" button.

[0543] Step 2: Consultation

[0544] Users log in to the system using their smartphones and enter their consultation details in text format. The entered consultation details are sent to the server by pressing the "Send" button. The server receives these consultation details and analyzes them using a natural language processing engine and an emotion recognition engine. The input data is the text of the consultation details, and the output data consists of the analysis results and recognized emotion data. Specifically, the user enters their consultation details on their smartphone and presses the "Send" button.

[0545] Step 3: Generate Answer

[0546] The server generates appropriate responses based on the analyzed consultation content and sentiment data. It searches for relevant information in the database and adjusts the response using a generative AI model as needed. The input data is the analyzed consultation content and sentiment data, and the output data is the generated response text. Specifically, the server executes database queries and operates the generative AI model to generate responses.

[0547] Step 4: Display the answer

[0548] The generated response is sent from the server to the user's smartphone and displayed on the screen. The input data is the response text sent from the server, and the output data is the response displayed on the smartphone screen. Specifically, the smartphone app receives a notification from the server and displays the data on the screen.

[0549] Step 5: Setting up follow-up messages

[0550] The server stores the consultation details and their response records in a database and sets reminders to generate follow-up messages after a specified period has elapsed. The input data consists of the consultation details and their response records, while the output data consists of the set reminders. Specifically, the server uses automated scheduling software (e.g., cron jobs) to set the reminders.

[0551] Step 6: Send a follow-up message

[0552] When the reminder time arrives, the server generates a follow-up message and sends it to the user's smartphone. The input data is the reminder and its related information, as well as the set sentiment data, while the output data is the generated follow-up message. Specifically, the server automatically generates the follow-up message at the designated time and sends the message using a notification service.

[0553] Step 7: Emergency Contact

[0554] When a user encounters an emergency, they use their smartphone to input and send an urgent request. The server receives the information, uses an emotion recognition engine to confirm the urgency, and then notifies emergency contacts and the appropriate authorities. The input data is the text of the emergency request, and the output data is the notification message to emergency contacts and authorities. Specifically, an emergency response algorithm is executed, and an alert is quickly sent through the notification service.

[0555] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0556] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0557] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0558] [Second Embodiment]

[0559] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0560] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0561] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0562] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0563] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0564] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0565] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0566] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0567] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0568] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0569] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0570] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0571] This invention provides a support system that enables elderly people to live with peace of mind, and includes a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact.

[0572] User registration method

[0573] Terminal: The elderly person accesses the system and displays the user registration screen. They enter information such as their name, contact information, address, and emergency contact information, and then submit it.

[0574] Server: Receives the transmitted information and stores it in the database.

[0575] Specific example: When Mr. Tanaka (the user) enters his name, phone number, and address and clicks the register button, the server saves that information.

[0576] Consultation methods

[0577] Terminal: Displays a screen where the user can enter their consultation details. The user enters their consultation details and presses the send button.

[0578] Server: Receives the input consultation content and analyzes it using natural language processing.

[0579] Specific example: When Mr. Tanaka types "I can't sleep lately" and presses the send button, the server receives and analyzes it.

[0580] Answer generation means

[0581] Server: Based on the analysis of the consultation content, it searches the database for an appropriate answer or automatically generates an appropriate answer.

[0582] Terminal: Displays the generated response to the user.

[0583] Specific example: The server retrieves information about "sleep disorders" from its database and displays the following response on Tanaka's terminal: "If you've been having trouble sleeping recently, it's important to avoid consuming caffeine before bed and to create a relaxing environment."

[0584] Follow-up methods

[0585] Server: Saves a history of consultations and generates follow-up messages at appropriate times. It also sends messages to users periodically based on reminder settings.

[0586] Terminal: Displays a follow-up message to the user.

[0587] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?" and sends it to Mr. Tanaka's device.

[0588] Emergency contact methods

[0589] User: Enter the details of your urgent need for help. For example, enter something like, "My chest hurts."

[0590] Server: Detects emergency keywords, immediately triggers an emergency alert, and notifies registered emergency contacts and the appropriate authorities.

[0591] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[0592] For example, if Ms. Tanaka enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify Ms. Tanaka's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on Ms. Tanaka's device.

[0593] These measures can quickly and appropriately alleviate the anxieties and worries of the elderly, reducing the risk of dying alone. The comprehensive support provided by the system enables elderly people to live with peace of mind.

[0594] The following describes the processing flow.

[0595] User registration method

[0596] Step 1:

[0597] Device: The user opens the system's website or app and displays the user registration screen.

[0598] Step 2:

[0599] User: Enter your name, contact information (phone number or email address), address, emergency contact information, etc.

[0600] Step 3:

[0601] Terminal: Checks the entered information and performs a simple check to ensure it is entered correctly.

[0602] Step 4:

[0603] Terminal: Sends information to the server.

[0604] Step 5:

[0605] Server: Receives the information and validates it to ensure there are no problems with its format or content.

[0606] Step 6:

[0607] Server: Saves information that passed validation to the database.

[0608] Consultation methods

[0609] Step 1:

[0610] Terminal: The user logs in and displays the consultation request screen.

[0611] Step 2:

[0612] User: Enter your inquiry in text format.

[0613] Step 3:

[0614] Terminal: Sends the entered consultation details to the server.

[0615] Step 4:

[0616] Server: Receives the consultation content and performs analysis using a natural language processing (NLP) engine.

[0617] Step 5:

[0618] Server: Based on the analysis results, classify the consultation content into the appropriate category.

[0619] Answer generation means

[0620] Step 1:

[0621] Server: Searches for the appropriate answer from a database related to the category.

[0622] Step 2:

[0623] Server: If an adequate response is not available, it will use a response generation algorithm to generate an appropriate response.

[0624] Step 3:

[0625] Terminal: Displays the generated response to the user.

[0626] Follow-up methods

[0627] Step 1:

[0628] Server: Stores the consultation details and corresponding responses in a database.

[0629] Step 2:

[0630] Server: Sets follow-up reminders based on saved consultation history.

[0631] Step 3:

[0632] Server: Generates a follow-up message when the reminder time is reached.

[0633] Step 4:

[0634] Terminal: Sends the generated follow-up message to the user.

[0635] Emergency contact methods

[0636] Step 1:

[0637] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[0638] Step 2:

[0639] Terminal: Sends urgent information to the server.

[0640] Step 3:

[0641] Server: Based on the received information, it detects emergency keywords and determines the need for emergency response.

[0642] Step 4:

[0643] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[0644] Step 5:

[0645] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[0646] Based on the above procedures, a system will be established to receive consultations from the elderly, provide appropriate answers, and implement necessary follow-up and emergency response. This will create a comprehensive support system that enables the elderly to live with peace of mind.

[0647] (Example 1)

[0648] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0649] Conventional support systems for the elderly have often struggled to provide prompt and appropriate responses to the concerns of the elderly, and their emergency response capabilities have frequently been inadequate. In particular, there is a need for solutions to daily anxieties and worries, as well as prompt responses in emergencies, but systems that meet these needs are limited. This invention aims to solve these problems by providing a comprehensive support system that enables the elderly to live their daily lives with peace of mind.

[0650] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0651] In this invention, the server includes a user registration means for elderly people to register with the system, a consultation reception means for elderly people to input their consultation details, a response generation means for analyzing the consultation details and generating appropriate answers, a follow-up means for periodically sending follow-up messages to the elderly people, an emergency contact means for notifying registered contacts and appropriate authorities in the event of an emergency, and a natural language processing means for detecting emergencies by receiving and analyzing the consultation details and taking necessary measures. This enables a comprehensive support system that can not only respond quickly and appropriately to the consultation details of elderly people but also respond quickly to emergencies.

[0652] "User registration method" refers to a method by which elderly individuals can access the system, enter information such as their name, contact information, address, and emergency contact information, and send this information to the server.

[0653] A "consultation reception method" is a means of providing an interface for elderly people to input their consultation details and transmitting that input to a server.

[0654] "Answer generation means" refers to a means of analyzing the received consultation content and either searching for an appropriate answer from a database based on the analysis results, or automatically generating an answer using a generation AI model.

[0655] A "follow-up method" is a means of generating periodic follow-up messages based on the content of the consultation and the history of the response, and sending these messages to the user.

[0656] An "emergency contact method" is a system that analyzes the content of inquiries entered during an emergency and, when an emergency keyword is detected, promptly notifies registered emergency contacts and the appropriate authorities.

[0657] "Natural language processing means" refers to a method for analyzing received consultation content, understanding keywords and meaning within the text, classifying the consultation content, and taking appropriate action.

[0658] This invention provides a support system that enables elderly people to live with peace of mind, and it is equipped with various functions. Specific embodiments are described below.

[0659] This system primarily consists of three components: a server, a terminal, and a user. The following describes in detail how each component works together to provide support for the elderly.

[0660] User registration method

[0661] Terminal: When a user accesses the system, a user registration screen is displayed on the terminal. This screen is built using HTML, CSS, and JavaScript. On this screen, the user enters information such as their name, contact information, address, and emergency contact information, and then clicks the submit button.

[0662] Server: Receives information sent from the terminal and saves it to a database (MySQL or PostgreSQL) using a server-side program (e.g., Python or Java).

[0663] Specific example: When a user enters "Taro Yamada", phone number "090-1234-5678", address "Chiyoda-ku, Tokyo", and emergency contact "090-8765-4321", and presses the submit button, the server saves this information to the database.

[0664] Consultation methods

[0665] Terminal: When a user logs in, a screen is displayed where they can enter their inquiry details. A text input field and a submit button are displayed using HTML, CSS, and JavaScript.

[0666] User: Enter your question and press the submit button. For example, enter "I've been having trouble sleeping lately."

[0667] Server: Receives submitted consultation content and analyzes it using natural language processing libraries (e.g., spaCy or NLTK). This analysis extracts key keywords and themes.

[0668] Specific example: When a user types "I can't sleep lately" and presses the send button, the server receives the message and uses natural language processing to extract and analyze the keyword "can't sleep."

[0669] Answer generation means

[0670] Server: Based on the analysis results, the server either searches for the appropriate answer from the database or automatically generates an answer using a generative AI model (e.g., GPT-4). In doing so, the AI ​​model considers past data and question format to generate the most appropriate response.

[0671] Terminal: The generated response is sent to the user's terminal and displayed on the screen. It is displayed using HTML and JavaScript.

[0672] Specific example: The server retrieves information about "sleep disorders" from the database, generates a response such as "If you've been having trouble sleeping recently, it's important to avoid consuming caffeine before bed and create a relaxing environment," and displays it on the user's device.

[0673] Follow-up methods

[0674] Server: Stores consultation content and response history in a database and periodically generates follow-up messages.

[0675] Terminal: The generated follow-up message is sent to the user and displayed on the screen. It is displayed using HTML and JavaScript.

[0676] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?", sends it to the user's device, and the device displays the message.

[0677] Emergency contact methods

[0678] User: Enter the details of your urgent inquiry and press the send button. For example, enter "My chest hurts."

[0679] Server: If it detects an emergency keyword and determines that an emergency has occurred, it will notify registered emergency contacts and the appropriate authorities.

[0680] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[0681] For example, if a user enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify the user's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on the user's device.

[0682] Through the functions described above, the system of the present invention can quickly and appropriately alleviate the anxieties and worries of the elderly and respond quickly to emergencies. This enables comprehensive support for the elderly to live with peace of mind.

[0683] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0684] Step 1: Display the user registration screen.

[0685] Terminal: When a user accesses the system, the user registration screen is automatically displayed. The user registration screen is built using HTML, CSS, and JavaScript and includes input fields for name, contact information, address, emergency contact information, etc.

[0686] Input: None

[0687] Output: Display of user registration screen

[0688] Specific operation: The device displays a registration screen to the user via a browser and prompts them to enter user information.

[0689] Step 2: Enter and submit information

[0690] User: Enter the required information such as your name, contact information, address, and emergency contact information, then click the register button.

[0691] Input: Name, contact information, address, emergency contact information, etc.

[0692] Output: Entered user information

[0693] Specific operation: The user enters information into the displayed fields and presses the registration button to send the information to the device.

[0694] Step 3: Receiving and saving information

[0695] Server: Receives user information sent from the terminal and saves it to a database (MySQL or PostgreSQL) using a server-side program (e.g., Python or Java).

[0696] Input: Submitted user information

[0697] Output: User information stored in the database

[0698] Specific operation: The server analyzes the received information and saves it to the database in the appropriate format.

[0699] Step 4: Display of the consultation details input screen

[0700] Terminal: When a user logs in, a screen is displayed where they can enter their inquiry details. A text input field and a submit button are displayed using HTML, CSS, and JavaScript.

[0701] Input: None

[0702] Output: Display of the consultation input screen

[0703] Specific operation: The terminal displays an interface for the user to input their inquiry.

[0704] Step 5: Enter and submit your consultation details.

[0705] User: Enter your question and press the submit button. For example, enter "I've been having trouble sleeping lately."

[0706] Input: Consultation details

[0707] Output: Input consultation content

[0708] Specific operation: The user enters information into the consultation field and presses the send button to send the information to the device.

[0709] Step 6: Receiving and analyzing the consultation content

[0710] Server: Receives the submitted consultation content and analyzes it using a natural language processing library (e.g., spaCy or NLTK).

[0711] Input: Submitted consultation details

[0712] Output: Analysis results (main keywords, meaning, etc.)

[0713] Specific operation: The server analyzes the received consultation content using a natural language processing library and extracts important keywords and meaning.

[0714] Step 7: Generate and submit your response.

[0715] Server: Based on the analysis results, it generates the optimal answer using a generative AI model (e.g., GPT-4). It then sends the generated answer to the user's device.

[0716] Input: Analysis results (main keywords, meaning of sentences, etc.)

[0717] Output: Generated answer

[0718] Specific operation: The server uses a generative AI model to generate an appropriate response and sends it to the user's terminal.

[0719] Step 8: Display the generated response

[0720] Terminal: Displays the generated response received from the server to the user. It is displayed on the screen using HTML and JavaScript.

[0721] Input: Generated answer

[0722] Output: Display of responses to the user

[0723] Specific action: The terminal displays the received response and provides it to the user.

[0724] Step 9: Generate and send follow-up messages

[0725] Server: Stores consultation content and response history in a database, periodically generates follow-up messages, and sends them to the user.

[0726] Input: Consultation details and response history

[0727] Output: Follow-up message

[0728] Specific operation: The server generates a follow-up message based on the information stored in the database and sends it to the user.

[0729] Step 10: Display a follow-up message

[0730] Terminal: Displays received follow-up messages to the user. Displayed using HTML and JavaScript.

[0731] Input: Follow-up message

[0732] Output: Display of follow-up message

[0733] Specific action: The device displays the received follow-up message to the user.

[0734] Step 11: Enter and submit details of your emergency consultation.

[0735] User: Enter the details of your urgent inquiry and press the send button. For example, enter "My chest hurts."

[0736] Input: Emergency consultation details

[0737] Output: Sending of urgent consultation details

[0738] Specific action: The user enters information into the emergency field and presses the send button to send the information to the terminal.

[0739] Step 12: Detect and notify of urgent keywords

[0740] Server: If it detects an emergency keyword and determines that an emergency has occurred, it will notify registered emergency contacts and the appropriate authorities.

[0741] Input: Submitted emergency consultation details

[0742] Output: Emergency notification

[0743] Specific operation: The server analyzes the received emergency call content and, if it contains emergency keywords, notifies the emergency contact and the appropriate authorities.

[0744] Step 13: Displaying an emergency message

[0745] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[0746] Input: Emergency notification

[0747] Output: Display of emergency message

[0748] Specific action: The terminal receives an emergency notification from the server and displays the emergency message to the user.

[0749] (Application Example 1)

[0750] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0751] When elderly people live alone, they often face anxieties in daily life and difficulty dealing with emergencies. Therefore, support is needed to ensure that elderly people can live with peace of mind. However, conventional support systems have limited means for elderly people to initiate consultations or emergency contact, which can result in delays in appropriate responses. Furthermore, the analysis of consultation content and the generation of responses are not sufficiently automated, making it difficult to quickly alleviate the anxieties of elderly people.

[0752] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0753] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, and an application installed on a robot for supporting the lives of the elderly, wherein the robot performs voice input and voice output, and analyzes the consultation content and generates a response using a natural language processing and generative AI model. This enables the elderly to easily interact with the system by voice and to respond quickly in emergencies.

[0754] A "user registration method" is an interface that allows elderly people to register their information in the system.

[0755] A "consultation reception method" is an interface that allows elderly people to input the details of their consultation.

[0756] "Answer generation means" refers to a means for analyzing the input consultation content and generating an appropriate answer.

[0757] A "follow-up method" refers to a means of sending follow-up messages to elderly individuals on a regular basis.

[0758] "Emergency contact methods" refer to means of notifying registered contacts and appropriate authorities in the event of an emergency.

[0759] "Applications installed on robots" are programs that are installed on robots to support the daily lives of elderly people.

[0760] "Voice input and voice output" refers to a technology in which elderly people give instructions or ask questions to a robot using their voice, and the robot responds using its voice.

[0761] "Natural language processing" is a technology that analyzes input language data and understands its meaning.

[0762] A "generative AI model" is a technology that uses artificial intelligence to automatically generate appropriate answers and advice.

[0763] In this embodiment of the invention, the system for supporting the lives of the elderly includes user registration means, consultation reception means, response generation means, follow-up means, and emergency contact means. These means are realized through an application installed on a robot.

[0764] Program Processing Description

[0765] User registration method

[0766] The server displays a registration screen where the user enters information such as their name, contact details, address, and emergency contact information. The entered information is then stored in a database (e.g., MySQL) via a web server (e.g., Apache).

[0767] Specific example: When an elderly person enters their name, phone number, and address and clicks the registration button, the entered information is saved to the database.

[0768] Consultation methods

[0769] When a user inputs their inquiry via voice, the robot's microphone captures the audio data, and Google Cloud Speech-to-Text is used to convert the speech to text. The robot then analyzes the inquiry using a natural language processing (NLP) engine (e.g., Google Cloud NLP).

[0770] Specific example: An elderly person complains that they "haven't been able to sleep lately," and a robot converts their voice into text and analyzes the content of their complaint.

[0771] Answer generation means

[0772] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate appropriate responses based on the analyzed consultation content. It then outputs these responses as speech using speech synthesis technology (e.g., Google Cloud Text-to-Speech) and communicates them to the user via the robot's speaker.

[0773] Specific example: When a server receives a request for help regarding "I can't sleep these days," it inputs a prompt into a generative AI model, and the generated response is output as speech using speech synthesis technology.

[0774] Example of a prompt:

[0775] A user has submitted the following inquiry: "I've been having trouble sleeping lately." Please generate an appropriate response to this inquiry, including possible solutions and advice.

[0776] Follow-up methods

[0777] The server stores a history of the consultation and uses a scheduler (e.g., Quartz Scheduler) to generate follow-up messages at appropriate times. These messages are sent via voice or text through a robot.

[0778] Specific example: A server sends a follow-up message to the elderly person via a robot a week later, asking, "Have you been able to sleep well since then?"

[0779] Emergency contact methods

[0780] The server detects emergency keywords from the entered consultation content and automatically sends notifications to registered emergency contacts and appropriate authorities using notification services such as Twilio. The robot also displays or voices messages to elderly people urging them to take emergency action.

[0781] Specific example: If an elderly person tells a robot, "My chest hurts and I can't breathe," the server will determine it's an emergency and automatically send a notification to the family and emergency authorities.

[0782] In this way, the system allows elderly people to easily communicate using voice and respond quickly in emergencies.

[0783] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0784] Step 1:

[0785] The user enters their name, contact information, address, and emergency contact information on the robot registration screen and clicks the register button.

[0786] Input: Name, contact information, address, and emergency contact information.

[0787] Output: User information is saved to the database.

[0788] Specific operation: The server saves the input information to a database (e.g., MySQL) via a web server (e.g., Apache). A message indicating that user information registration is complete is displayed on the screen.

[0789] Step 2:

[0790] The user inputs their inquiry details via voice.

[0791] Input: Audio data.

[0792] Output: The consultation content converted as text data.

[0793] Specific operation: The robot's microphone captures audio data, and Google Cloud Speech-to-Text is used to convert the audio to text. The server then receives the text data.

[0794] Step 3:

[0795] The server analyzes the content of the consultation it receives.

[0796] Input: Text data containing the consultation details.

[0797] Output: Analysis results.

[0798] Specific operation: The server uses an NLP engine (e.g., Google Cloud NLP) to analyze the consultation content and retrieve the analysis results.

[0799] Step 4:

[0800] The server generates an answer using an AI model based on the analysis results.

[0801] Input: Analysis results.

[0802] Output: Generated answer.

[0803] Specific operation: The server inputs prompt text to the generated AI model (e.g., OpenAI GPT-4) and retrieves the generated response.

[0804] Example of a prompt:

[0805] A user has submitted the following inquiry: "I've been having trouble sleeping lately." Please generate an appropriate response to this inquiry, including possible solutions and advice.

[0806] Step 5:

[0807] The server converts the generated response into speech data using speech synthesis technology, and outputs the response through the robot's speaker.

[0808] Input: Text data of the generated response.

[0809] Output: The response is in audio format.

[0810] Specific operation: The server uses Google Cloud Text-to-Speech to convert the generated response into audio data, which is then output as speech through the robot's speaker.

[0811] Step 6:

[0812] The server saves a history of the consultation and generates follow-up messages at the appropriate time.

[0813] Input: History of consultation details.

[0814] Output: Follow-up message.

[0815] Specific operation: The server uses a scheduler (e.g., Quartz Scheduler) to generate follow-up messages at the appropriate time and the robot sends the messages via voice or text.

[0816] Step 7:

[0817] The server detects emergency keywords in times of crisis and sends notifications to the appropriate authorities.

[0818] Input: Details of the urgent consultation.

[0819] Output: Notification message.

[0820] Specific operation: The server detects emergency keywords and automatically sends messages to registered contacts and appropriate authorities using notification services such as Twilio. The robot also displays or voices messages to elderly people urging them to take emergency action.

[0821] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0822] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it also includes an emotion engine that recognizes the user's emotions.

[0823] User registration method

[0824] Terminal: The elderly person accesses the system and displays the user registration screen. They enter information such as their name, contact information, address, and emergency contact information, and then submit it.

[0825] Server: Receives the transmitted information and stores it in the database.

[0826] Specific example: When Mr. Tanaka (the user) enters his name, phone number, and address and clicks the register button, the server saves that information.

[0827] Consultation methods

[0828] Terminal: The user logs in and displays the consultation request screen.

[0829] User: Enter your inquiry in text format.

[0830] Terminal: Sends the entered consultation details to the server.

[0831] Server: Receives consultation content and analyzes it using a natural language processing (NLP) engine. It also uses an emotion engine to recognize the user's emotions from the input consultation content.

[0832] Specific example: Ms. Tanaka inputs "I haven't been able to sleep lately," and the server receives and analyzes this, while the emotion engine recognizes Ms. Tanaka's stress and anxiety.

[0833] Answer generation means

[0834] Server: Searches for appropriate answers from a database related to categories and sentiments.

[0835] Server: Generates appropriate responses using response generation algorithms as needed. Adjusts the response content based on the recognized user's sentiment.

[0836] Terminal: Displays the generated response to the user.

[0837] Specific example: The server retrieves information about "sleep disorders" from a database and displays a response on the terminal that is tailored to Tanaka's feelings, such as, "If you've been having trouble sleeping lately, it's important to avoid consuming caffeine before bed and to create a relaxing environment."

[0838] Follow-up methods

[0839] Server: Stores the consultation details and corresponding responses in a database.

[0840] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[0841] Server: Generates a follow-up message when the reminder time is reached.

[0842] Terminal: Sends the generated follow-up message to the user.

[0843] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?" and sends it to Mr. Tanaka's device.

[0844] Emergency contact methods

[0845] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[0846] Terminal: Sends urgent information to the server.

[0847] Server: Based on the received information, it detects urgent keywords and uses an emotion engine to further confirm the urgent need.

[0848] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[0849] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[0850] For example, if Ms. Tanaka enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify Ms. Tanaka's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on Ms. Tanaka's device.

[0851] These methods enable the rapid and appropriate resolution of anxieties and worries among the elderly, and allow for more personalized responses through emotional recognition. The comprehensive support and emotion-based responses provided by the system create a support system that allows the elderly to live with peace of mind.

[0852] The following describes the processing flow.

[0853] User registration method

[0854] Step 1:

[0855] Device: The user opens the system's website or app and displays the user registration screen.

[0856] Step 2:

[0857] User: Enter information such as name, contact information (phone number or email address), address, and emergency contact information.

[0858] Step 3:

[0859] Terminal: Checks the entered information and performs a simple check to ensure it is entered correctly.

[0860] Step 4:

[0861] Terminal: Sends information to the server.

[0862] Step 5:

[0863] Server: Receives the information and validates it to ensure there are no problems with its format or content.

[0864] Step 6:

[0865] Server: Saves information that passed validation to the database.

[0866] Consultation methods

[0867] Step 1:

[0868] Terminal: The user logs in and displays the consultation request screen.

[0869] Step 2:

[0870] User: Enter your inquiry in text format.

[0871] Step 3:

[0872] Terminal: Sends the entered consultation details to the server.

[0873] Step 4:

[0874] Server: Receives the consultation content and performs analysis using a natural language processing (NLP) engine.

[0875] Step 5:

[0876] Server: Based on the analysis results, classify the consultation content into the appropriate category.

[0877] Step 6:

[0878] Server: Sends the analyzed data to the emotion engine to recognize the user's emotions.

[0879] Answer generation means

[0880] Step 1:

[0881] Server: Searches for appropriate answers from a database related to categories and sentiments.

[0882] Step 2:

[0883] Server: If an answer is not automatically generated, run the answer generation algorithm to generate an appropriate answer.

[0884] Step 3:

[0885] Server: Adjusts the response based on the recognized user's emotions.

[0886] Step 4:

[0887] Terminal: Displays the generated response to the user.

[0888] Follow-up methods

[0889] Step 1:

[0890] Server: Stores the consultation details and corresponding responses in a database.

[0891] Step 2:

[0892] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[0893] Step 3:

[0894] Server: Generates a follow-up message when the reminder time is reached.

[0895] Step 4:

[0896] Terminal: Sends the generated follow-up message to the user.

[0897] Emergency contact methods

[0898] Step 1:

[0899] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[0900] Step 2:

[0901] Terminal: Sends urgent information to the server.

[0902] Step 3:

[0903] Server: Based on the received information, it detects urgent keywords and uses an emotion engine to further confirm the urgent need.

[0904] Step 4:

[0905] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[0906] Step 5:

[0907] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[0908] (Example 2)

[0909] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0910] The challenge lies in responding quickly and appropriately to the anxieties and worries that elderly people face in their daily lives, and in providing personalized support that takes their emotions into consideration. Furthermore, there is a need for a system that can respond quickly in the event of an emergency, but existing systems do not adequately meet these requirements.

[0911] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0912] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, an emotion recognition means, and a response adjustment means. This enables a rapid and appropriate response to the anxieties and worries of the elderly, and by considering the emotions of the elderly, it becomes possible to provide personalized support. Furthermore, a rapid response can be achieved in emergency situations.

[0913] "User registration means" refers to a means of providing an interface for elderly people to access the system and input and register necessary personal information.

[0914] A "consultation reception method" refers to a means of providing an interface for elderly people to log in to the system, input their consultation details in text format, and submit them.

[0915] A "response generation method" is a means for analyzing the received consultation content and generating an appropriate response.

[0916] "Follow-up methods" refer to means of generating and sending regular follow-up messages to elderly individuals.

[0917] "Emergency contact methods" refer to means of notifying registered contacts and appropriate authorities in the event of an emergency.

[0918] "Emotion recognition means" refers to methods for recognizing the emotions of elderly people based on the consultation content entered by the user.

[0919] "Response adjustment means" are methods for adjusting the content of responses based on the perceived emotions of elderly individuals.

[0920] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it also includes an emotion engine that recognizes the user's emotions.

[0921] User registration method

[0922] Terminal: The elderly person accesses the system and displays the user registration screen. Here, they enter and submit information such as their name, address, contact information, and emergency contact information.

[0923] Server: Receives the transmitted information and saves it to a database (e.g., MySQL).

[0924] Example: A user enters their name, phone number, and address on the registration screen and clicks the "Register" button. The server saves this information to the database.

[0925] Consultation methods

[0926] Terminal: The user logs in and displays the consultation request screen.

[0927] User: Enter your inquiry in text format.

[0928] Terminal: Sends the entered consultation details to the server.

[0929] Server: Receives the consultation content and analyzes it using a natural language processing (NLP) engine (e.g., spaCy, BERT). Furthermore, it uses an emotion engine (e.g., OpenAI's emotion recognition model) to recognize the user's emotions from the input consultation content.

[0930] Specific example: A user enters "I haven't been able to sleep lately," and the server receives and analyzes this, while also using an emotion engine to recognize the user's stress and anxiety.

[0931] Answer generation means

[0932] Server: Based on the category of the consultation content (e.g., sleep disorder) and the recognized emotion (e.g., anxiety), it searches the database for relevant data.

[0933] Server: Generates appropriate responses using a generation AI model (e.g., OpenAI's GPT-3) as needed. Adjusts the response content considering the user's sentiment data.

[0934] Terminal: Displays the generated response to the user.

[0935] Specific example: The server retrieves information about "sleep disorders" from its database and displays the message, "If you've been having trouble sleeping lately, it's important to avoid consuming caffeine before bed and create a relaxing environment."

[0936] Follow-up methods

[0937] Server: Stores consultation details and responses in a database.

[0938] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[0939] Server: When the reminder time is reached, it automatically generates a follow-up message.

[0940] Terminal: Sends the generated follow-up message to the user.

[0941] Specific example: A server generates a message one week later asking, "Have you been able to sleep since then?" and sends it to the user's terminal.

[0942] Emergency contact methods

[0943] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[0944] Terminal: Sends urgent information to the server.

[0945] Server: Analyzes the received content and detects urgent keywords. Simultaneously, the emotion engine further confirms the urgency.

[0946] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities (e.g., emergency services).

[0947] Terminal: Displays the user with the message, "We will call an ambulance immediately. We will also contact your family."

[0948] For example, if a user enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify registered contacts and emergency authorities. A message prompting emergency action will also be displayed on the user's device.

[0949] Example of a prompt

[0950] "Please explain the steps to register for the senior support system."

[0951] "Please tell me how to analyze consultations regarding insomnia and provide appropriate advice."

[0952] "Please explain how to set up follow-up messages based on the content of the consultation."

[0953] "Please explain the procedures for responding in the event of an emergency."

[0954] As described above, this system quickly and appropriately resolves the anxieties and worries of the elderly, and enables more personalized responses through emotion recognition. Furthermore, it is designed to respond quickly even in emergencies.

[0955] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0956] Step 1:

[0957] Terminal: The user accesses the new registration screen. The user enters information such as name, address, phone number, and emergency contact information.

[0958] Input: User's (elderly) personal information.

[0959] Output: Data of the entered personal information.

[0960] Specific operation: When a user enters information into a form displayed in the browser and presses the "Register" button, the information is collected by the device.

[0961] Step 2:

[0962] Terminal: Sends the entered personal information to the server. Specifically, the data is sent as an HTTP request.

[0963] Input: Data of collected personal information.

[0964] Output: HTTP request to the server.

[0965] Specific operation: The terminal sends the form data to the server using the POST method.

[0966] Step 3:

[0967] Server: Stores received personal information in a database. This includes a process to verify data integrity and security.

[0968] Input: Personal information data sent to the server.

[0969] Output: Personal information stored in the database.

[0970] Specific operation: The server receives the data and saves it to the database using an SQL insert command.

[0971] Step 4:

[0972] Terminal: The user logs into the system and displays the consultation request screen.

[0973] Input: User login information.

[0974] Output: Consultation request screen.

[0975] Specific operation: When a user accesses the login screen in their browser, enters their ID and password, and clicks the "Login" button, the system performs authentication, and if successful, the consultation request screen is displayed.

[0976] Step 5:

[0977] User: Enter the consultation details in text format. Specifically, enter the text data of the consultation details.

[0978] Input: User's inquiry.

[0979] Output: Text data of the entered consultation content.

[0980] Specific action: The user enters their inquiry into a text box and presses the "Send" button.

[0981] Step 6:

[0982] Terminal: Sends the entered consultation details to the server. Specifically, the data is sent as an HTTP request.

[0983] Input: Text data of the entered consultation content.

[0984] Output: HTTP request to the server.

[0985] Specific operation: The terminal sends the form data to the server using the POST method.

[0986] Step 7:

[0987] Server: Analyzes received consultation content using a natural language processing (NLP) engine. It also uses an emotion engine to recognize the user's emotions from the input consultation content.

[0988] Input: Text data of the consultation content sent to the server.

[0989] Output: Analysis results and sentiment data.

[0990] Specific operation: Text analysis is performed using an NLP engine (e.g., spaCy, BERT), and then emotions are recognized using an emotion engine (e.g., OpenAI's emotion recognition model).

[0991] Step 8:

[0992] Server: Based on the category of the consultation content and the recognized emotions, it searches the database for relevant data. If necessary, it uses a generative AI model to generate an answer.

[0993] Input: Analysis results and sentiment data.

[0994] Output: Generated answer.

[0995] Specific operation: Execute database queries to retrieve relevant information. Generate new answers using a generated AI model (e.g., OpenAI's GPT-3) as needed.

[0996] Step 9:

[0997] Terminal: Displays the generated response to the user.

[0998] Input: Generated response.

[0999] Output: Display of the answer.

[1000] Specific operation: Send the response data to the terminal in HTML format and display it to the user.

[1001] Step 10:

[1002] Server: Stores the consultation content and the provided answers in a database. The stored content includes date and time, consultation content, answer content, recognized emotion data, etc.

[1003] Input: Consultation details, provided response, and sentiment data.

[1004] Output: Consultation history stored in the database.

[1005] Specific operation: The server receives the data and saves it to the database using an SQL insert command.

[1006] Step 11:

[1007] Server: Based on the saved data, set a follow-up reminder for one week later.

[1008] Input: Consultation history data.

[1009] Output: Reminder.

[1010] Specific operation: Set up reminder scheduling on the server and trigger the reminder at the follow-up date and time.

[1011] Step 12:

[1012] Server: When the reminder date and time arrive, it automatically generates a follow-up message.

[1013] Input: Reminder data.

[1014] Output: Follow-up message.

[1015] Specific operation: A scheduled reminder is triggered, and the message generation function generates a corresponding follow-up message.

[1016] Step 13:

[1017] Terminal: Sends the generated follow-up message to the user.

[1018] Input: Follow-up message.

[1019] Output: Notification of follow-up message.

[1020] Specific operation: Send a message using an HTTP request and display it in the browser.

[1021] Step 14:

[1022] User: When an emergency occurs, enter the details of your urgent request.

[1023] Input: Details of the urgent consultation.

[1024] Output: Text data of the entered emergency consultation details.

[1025] Specific action: The user enters their inquiry into the input form and presses the "Submit" button.

[1026] Step 15:

[1027] Terminal: Sends urgent information to the server.

[1028] Input: Text data of the emergency consultation details entered.

[1029] Output: HTTP request to the server.

[1030] Specific operation: The terminal sends the form data to the server using the POST method.

[1031] Step 16:

[1032] Server: Analyzes the received content and detects urgent keywords. Simultaneously, the emotion engine further confirms the urgency.

[1033] Input: Text data of the emergency consultation details.

[1034] Output: Emergency alert.

[1035] Specific actions: Analyze using an NLP engine and an emotion engine to determine urgency.

[1036] Step 17:

[1037] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[1038] Input: Emergency alert.

[1039] Output: Notification message.

[1040] Specific operation: Retrieve registered contact information and send notifications using the messaging API.

[1041] Step 18:

[1042] Terminal: Displays the user with the message, "We will call an ambulance immediately. We will also contact your family."

[1043] Input: Notification message.

[1044] Output: Displays emergency instructions.

[1045] Specific action: The notification message is received on the device and displayed in the browser.

[1046] (Application Example 2)

[1047] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1048] In support systems that enable elderly people to live with peace of mind, there is a need to provide more personalized responses through emotion recognition. In particular, the challenge lies in responding quickly and appropriately to anxieties and emergencies that arise in daily life, and providing appropriate support that is tailored to their emotions, even for basic needs such as meals.

[1049] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1050] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, an emotion recognition means, and an emotion adaptation means. This makes it possible to provide an advanced support system that allows elderly people to live with peace of mind and respond quickly to emergencies.

[1051] "User registration method" refers to a function that allows elderly people to access the system and enter and submit information such as their name, contact information, address, and emergency contact information.

[1052] The "consultation submission method" is a function that allows users to log in, enter their consultation details through the consultation screen, and send them to the system.

[1053] The "response generation means" is a function that analyzes the content of the consultation and generates an appropriate response based on that analysis.

[1054] The "follow-up method" is a function that sends periodic follow-up messages based on the content of the consultation and the history of the response.

[1055] "Emergency contact methods" refer to a function that allows users to notify registered contacts and appropriate authorities when they encounter an emergency.

[1056] "Emotion recognition means" refers to a function that detects emotions from the consultation content and comments entered by the user.

[1057] "Emotional adaptation measures" refer to functions that adjust the content of follow-up messages based on the recognized emotions of the user.

[1058] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it includes an emotion engine that recognizes the user's emotions. The specific form of this system is shown below.

[1059] User registration method

[1060] Users access the system using their smartphones, enter information such as their name, contact details, address, and emergency contact details, and send it to the server. The server stores the submitted information in a database. The software used typically includes a database management system (e.g., SQLite).

[1061] Consultation methods

[1062] The user logs in using their smartphone and enters their consultation request in text format on the consultation request screen. The entered consultation request is sent to the server, which analyzes the received content using a natural language processing engine (e.g., Spacy) and an emotion recognition engine (e.g., Google Cloud Natural Language API). Here, the user's emotions are recognized along with the consultation request.

[1063] Answer generation means

[1064] The server generates appropriate responses based on the analyzed consultation content and sentiment data. The generated responses are displayed on the smartphone. If necessary, it searches for relevant information in the database and further refines the responses using a generation AI model.

[1065] Follow-up methods

[1066] The server stores the consultation details and response records in a database and generates a follow-up message after a certain period of time has elapsed. This follow-up message is tailored based on the perceived emotions and sent to the user. Automated scheduling software (e.g., cron jobs) is used to set the reminders.

[1067] Emergency contact methods

[1068] If a user experiences an emergency, they enter the details of their urgent need using their smartphone. The server receives this information and uses an emotion recognition engine to confirm the urgency. If necessary, notifications are automatically sent to emergency contacts and the appropriate authorities. The software used here includes notification services (e.g., Twilio).

[1069] Specific example

[1070] The following is a specific example of a scenario in which elderly people use a food delivery system.

[1071] 1. Elderly individuals access the system using their smartphones and register their names, contact information, etc.

[1072] 2. When ordering breakfast, if you enter a message such as "I haven't had much of an appetite lately," the server will receive this message and perform emotion recognition.

[1073] 3. The server generates appropriate advice, such as "Choose easily digestible foods and consult a doctor if necessary," and displays it on the smartphone.

[1074] 4. One week later, a follow-up message will be automatically sent asking, "Has your appetite improved at all?"

[1075] Example of a prompt

[1076] "Please suggest how to care for an elderly person who has recently lost their appetite. The emotion recognition engine should detect a state of distress and provide appropriate advice. Also, please send a follow-up message one week later."

[1077] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1078] Step 1: User Registration

[1079] Users access the system using their smartphones and enter information such as their name, contact information, address, and emergency contact information. The entered information is sent to the server when the user presses the "Register" button. The server stores the received information in a database. The input data consists of personal information such as names, while the output data is user information recorded in the database. Specifically, the user enters data into input fields on the smartphone screen and then presses the "Register" button.

[1080] Step 2: Consultation

[1081] Users log in to the system using their smartphones and enter their consultation details in text format. The entered consultation details are sent to the server by pressing the "Send" button. The server receives these consultation details and analyzes them using a natural language processing engine and an emotion recognition engine. The input data is the text of the consultation details, and the output data consists of the analysis results and recognized emotion data. Specifically, the user enters their consultation details on their smartphone and presses the "Send" button.

[1082] Step 3: Generate Answer

[1083] The server generates appropriate responses based on the analyzed consultation content and sentiment data. It searches for relevant information in the database and adjusts the response using a generative AI model as needed. The input data is the analyzed consultation content and sentiment data, and the output data is the generated response text. Specifically, the server executes database queries and operates the generative AI model to generate responses.

[1084] Step 4: Display the answer

[1085] The generated response is sent from the server to the user's smartphone and displayed on the screen. The input data is the response text sent from the server, and the output data is the response displayed on the smartphone screen. Specifically, the smartphone app receives a notification from the server and displays the data on the screen.

[1086] Step 5: Setting up follow-up messages

[1087] The server stores the consultation details and their response records in a database and sets reminders to generate follow-up messages after a specified period has elapsed. The input data consists of the consultation details and their response records, while the output data consists of the set reminders. Specifically, the server uses automated scheduling software (e.g., cron jobs) to set the reminders.

[1088] Step 6: Send a follow-up message

[1089] When the reminder time arrives, the server generates a follow-up message and sends it to the user's smartphone. The input data is the reminder and its related information, as well as the set sentiment data, while the output data is the generated follow-up message. Specifically, the server automatically generates the follow-up message at the designated time and sends the message using a notification service.

[1090] Step 7: Emergency Contact

[1091] When a user encounters an emergency, they use their smartphone to input and send an urgent request. The server receives the information, uses an emotion recognition engine to confirm the urgency, and then notifies emergency contacts and the appropriate authorities. The input data is the text of the emergency request, and the output data is the notification message to emergency contacts and authorities. Specifically, an emergency response algorithm is executed, and an alert is quickly sent through the notification service.

[1092] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1093] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1094] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1095] [Third Embodiment]

[1096] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1097] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1098] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1099] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1100] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1102] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1103] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1104] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1105] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1106] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1107] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1108] This invention provides a support system that enables elderly people to live with peace of mind, and includes a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact.

[1109] User registration method

[1110] Terminal: The elderly person accesses the system and displays the user registration screen. They enter information such as their name, contact information, address, and emergency contact information, and then submit it.

[1111] Server: Receives the transmitted information and stores it in the database.

[1112] Specific example: When Mr. Tanaka (the user) enters his name, phone number, and address and clicks the register button, the server saves that information.

[1113] Consultation methods

[1114] Terminal: Displays a screen where the user can enter their consultation details. The user enters their consultation details and presses the send button.

[1115] Server: Receives the input consultation content and analyzes it using natural language processing.

[1116] Specific example: When Mr. Tanaka types "I can't sleep lately" and presses the send button, the server receives and analyzes it.

[1117] Answer generation means

[1118] Server: Based on the analysis of the consultation content, it searches the database for an appropriate answer or automatically generates an appropriate answer.

[1119] Terminal: Displays the generated response to the user.

[1120] Specific example: The server retrieves information about "sleep disorders" from its database and displays the following response on Tanaka's terminal: "If you've been having trouble sleeping recently, it's important to avoid consuming caffeine before bed and to create a relaxing environment."

[1121] Follow-up methods

[1122] Server: Saves a history of consultations and generates follow-up messages at appropriate times. It also sends messages to users periodically based on reminder settings.

[1123] Terminal: Displays a follow-up message to the user.

[1124] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?" and sends it to Mr. Tanaka's device.

[1125] Emergency contact methods

[1126] User: Enter the details of your urgent need for help. For example, enter something like, "My chest hurts."

[1127] Server: Detects emergency keywords, immediately triggers an emergency alert, and notifies registered emergency contacts and the appropriate authorities.

[1128] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[1129] For example, if Ms. Tanaka enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify Ms. Tanaka's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on Ms. Tanaka's device.

[1130] These measures can quickly and appropriately alleviate the anxieties and worries of the elderly, reducing the risk of dying alone. The comprehensive support provided by the system enables elderly people to live with peace of mind.

[1131] The following describes the processing flow.

[1132] User registration method

[1133] Step 1:

[1134] Device: The user opens the system's website or app and displays the user registration screen.

[1135] Step 2:

[1136] User: Enter your name, contact information (phone number or email address), address, emergency contact information, etc.

[1137] Step 3:

[1138] Terminal: Checks the entered information and performs a simple check to ensure it is entered correctly.

[1139] Step 4:

[1140] Terminal: Sends information to the server.

[1141] Step 5:

[1142] Server: Receives the information and validates it to ensure there are no problems with its format or content.

[1143] Step 6:

[1144] Server: Saves information that passed validation to the database.

[1145] Consultation methods

[1146] Step 1:

[1147] Terminal: The user logs in and displays the consultation request screen.

[1148] Step 2:

[1149] User: Enter your inquiry in text format.

[1150] Step 3:

[1151] Terminal: Sends the entered consultation details to the server.

[1152] Step 4:

[1153] Server: Receives the consultation content and performs analysis using a natural language processing (NLP) engine.

[1154] Step 5:

[1155] Server: Based on the analysis results, classify the consultation content into the appropriate category.

[1156] Answer generation means

[1157] Step 1:

[1158] Server: Searches for the appropriate answer from a database related to the category.

[1159] Step 2:

[1160] Server: If an adequate response is not available, it will use a response generation algorithm to generate an appropriate response.

[1161] Step 3:

[1162] Terminal: Displays the generated response to the user.

[1163] Follow-up methods

[1164] Step 1:

[1165] Server: Stores the consultation details and corresponding responses in a database.

[1166] Step 2:

[1167] Server: Sets follow-up reminders based on saved consultation history.

[1168] Step 3:

[1169] Server: Generates a follow-up message when the reminder time is reached.

[1170] Step 4:

[1171] Terminal: Sends the generated follow-up message to the user.

[1172] Emergency contact methods

[1173] Step 1:

[1174] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[1175] Step 2:

[1176] Terminal: Sends urgent information to the server.

[1177] Step 3:

[1178] Server: Based on the received information, it detects emergency keywords and determines the need for emergency response.

[1179] Step 4:

[1180] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[1181] Step 5:

[1182] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[1183] Based on the above procedures, a system will be established to receive consultations from the elderly, provide appropriate answers, and implement necessary follow-up and emergency response. This will create a comprehensive support system that enables the elderly to live with peace of mind.

[1184] (Example 1)

[1185] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1186] Conventional support systems for the elderly have often struggled to provide prompt and appropriate responses to the concerns of the elderly, and their emergency response capabilities have frequently been inadequate. In particular, there is a need for solutions to daily anxieties and worries, as well as prompt responses in emergencies, but systems that meet these needs are limited. This invention aims to solve these problems by providing a comprehensive support system that enables the elderly to live their daily lives with peace of mind.

[1187] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1188] In this invention, the server includes a user registration means for elderly people to register with the system, a consultation reception means for elderly people to input their consultation details, a response generation means for analyzing the consultation details and generating appropriate answers, a follow-up means for periodically sending follow-up messages to the elderly people, an emergency contact means for notifying registered contacts and appropriate authorities in the event of an emergency, and a natural language processing means for detecting emergencies by receiving and analyzing the consultation details and taking necessary measures. This enables a comprehensive support system that can not only respond quickly and appropriately to the consultation details of elderly people but also respond quickly to emergencies.

[1189] "User registration method" refers to a method by which elderly individuals can access the system, enter information such as their name, contact information, address, and emergency contact information, and send this information to the server.

[1190] A "consultation reception method" is a means of providing an interface for elderly people to input their consultation details and transmitting that input to a server.

[1191] "Answer generation means" refers to a means of analyzing the received consultation content and either searching for an appropriate answer from a database based on the analysis results, or automatically generating an answer using a generation AI model.

[1192] A "follow-up method" is a means of generating periodic follow-up messages based on the content of the consultation and the history of the response, and sending these messages to the user.

[1193] An "emergency contact method" is a system that analyzes the content of inquiries entered during an emergency and, when an emergency keyword is detected, promptly notifies registered emergency contacts and the appropriate authorities.

[1194] "Natural language processing means" refers to a method for analyzing received consultation content, understanding keywords and meaning within the text, classifying the consultation content, and taking appropriate action.

[1195] This invention provides a support system that enables elderly people to live with peace of mind, and it is equipped with various functions. Specific embodiments are described below.

[1196] This system primarily consists of three components: a server, a terminal, and a user. The following describes in detail how each component works together to provide support for the elderly.

[1197] User registration method

[1198] Terminal: When a user accesses the system, a user registration screen is displayed on the terminal. This screen is built using HTML, CSS, and JavaScript. On this screen, the user enters information such as their name, contact information, address, and emergency contact information, and then clicks the submit button.

[1199] Server: Receives information sent from the terminal and saves it to a database (MySQL or PostgreSQL) using a server-side program (e.g., Python or Java).

[1200] Specific example: When a user enters "Taro Yamada", phone number "090-1234-5678", address "Chiyoda-ku, Tokyo", and emergency contact "090-8765-4321", and presses the submit button, the server saves this information to the database.

[1201] Consultation methods

[1202] Terminal: When a user logs in, a screen is displayed where they can enter their inquiry details. A text input field and a submit button are displayed using HTML, CSS, and JavaScript.

[1203] User: Enter your question and press the submit button. For example, enter "I've been having trouble sleeping lately."

[1204] Server: Receives submitted consultation content and analyzes it using natural language processing libraries (e.g., spaCy or NLTK). This analysis extracts key keywords and themes.

[1205] Specific example: When a user types "I can't sleep lately" and presses the send button, the server receives the message and uses natural language processing to extract and analyze the keyword "can't sleep."

[1206] Answer generation means

[1207] Server: Based on the analysis results, the server either searches for the appropriate answer from the database or automatically generates an answer using a generative AI model (e.g., GPT-4). In doing so, the AI ​​model considers past data and question format to generate the most appropriate response.

[1208] Terminal: The generated response is sent to the user's terminal and displayed on the screen. It is displayed using HTML and JavaScript.

[1209] Specific example: The server retrieves information about "sleep disorders" from the database, generates a response such as "If you've been having trouble sleeping recently, it's important to avoid consuming caffeine before bed and create a relaxing environment," and displays it on the user's device.

[1210] Follow-up methods

[1211] Server: Stores consultation content and response history in a database and periodically generates follow-up messages.

[1212] Terminal: The generated follow-up message is sent to the user and displayed on the screen. It is displayed using HTML and JavaScript.

[1213] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?", sends it to the user's device, and the device displays the message.

[1214] Emergency contact methods

[1215] User: Enter the details of your urgent inquiry and press the send button. For example, enter "My chest hurts."

[1216] Server: If it detects an emergency keyword and determines that an emergency has occurred, it will notify registered emergency contacts and the appropriate authorities.

[1217] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[1218] For example, if a user enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify the user's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on the user's device.

[1219] Through the functions described above, the system of the present invention can quickly and appropriately alleviate the anxieties and worries of the elderly and respond quickly to emergencies. This enables comprehensive support for the elderly to live with peace of mind.

[1220] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1221] Step 1: Display the user registration screen.

[1222] Terminal: When a user accesses the system, the user registration screen is automatically displayed. The user registration screen is built using HTML, CSS, and JavaScript and includes input fields for name, contact information, address, emergency contact information, etc.

[1223] Input: None

[1224] Output: Display of user registration screen

[1225] Specific operation: The device displays a registration screen to the user via a browser and prompts them to enter user information.

[1226] Step 2: Enter and submit information

[1227] User: Enter the required information such as your name, contact information, address, and emergency contact information, then click the register button.

[1228] Input: Name, contact information, address, emergency contact information, etc.

[1229] Output: Entered user information

[1230] Specific operation: The user enters information into the displayed fields and presses the registration button to send the information to the device.

[1231] Step 3: Receiving and saving information

[1232] Server: Receives user information sent from the terminal and saves it to a database (MySQL or PostgreSQL) using a server-side program (e.g., Python or Java).

[1233] Input: Submitted user information

[1234] Output: User information stored in the database

[1235] Specific operation: The server analyzes the received information and saves it to the database in the appropriate format.

[1236] Step 4: Display of the consultation details input screen

[1237] Terminal: When a user logs in, a screen is displayed where they can enter their inquiry details. A text input field and a submit button are displayed using HTML, CSS, and JavaScript.

[1238] Input: None

[1239] Output: Display of the consultation input screen

[1240] Specific operation: The terminal displays an interface for the user to input their inquiry.

[1241] Step 5: Enter and submit your consultation details.

[1242] User: Enter your question and press the submit button. For example, enter "I've been having trouble sleeping lately."

[1243] Input: Consultation details

[1244] Output: Input consultation content

[1245] Specific operation: The user enters information into the consultation field and presses the send button to send the information to the device.

[1246] Step 6: Receiving and analyzing the consultation content

[1247] Server: Receives the submitted consultation content and analyzes it using a natural language processing library (e.g., spaCy or NLTK).

[1248] Input: Submitted consultation details

[1249] Output: Analysis results (main keywords, meaning, etc.)

[1250] Specific operation: The server analyzes the received consultation content using a natural language processing library and extracts important keywords and meaning.

[1251] Step 7: Generate and submit your response.

[1252] Server: Based on the analysis results, it generates the optimal answer using a generative AI model (e.g., GPT-4). It then sends the generated answer to the user's device.

[1253] Input: Analysis results (main keywords, meaning of sentences, etc.)

[1254] Output: Generated answer

[1255] Specific operation: The server uses a generative AI model to generate an appropriate response and sends it to the user's terminal.

[1256] Step 8: Display the generated response

[1257] Terminal: Displays the generated response received from the server to the user. It is displayed on the screen using HTML and JavaScript.

[1258] Input: Generated answer

[1259] Output: Display of responses to the user

[1260] Specific action: The terminal displays the received response and provides it to the user.

[1261] Step 9: Generate and send follow-up messages

[1262] Server: Stores consultation content and response history in a database, periodically generates follow-up messages, and sends them to the user.

[1263] Input: Consultation details and response history

[1264] Output: Follow-up message

[1265] Specific operation: The server generates a follow-up message based on the information stored in the database and sends it to the user.

[1266] Step 10: Display a follow-up message

[1267] Terminal: Displays received follow-up messages to the user. Displayed using HTML and JavaScript.

[1268] Input: Follow-up message

[1269] Output: Display of follow-up message

[1270] Specific action: The device displays the received follow-up message to the user.

[1271] Step 11: Enter and submit details of your emergency consultation.

[1272] User: Enter the details of your urgent inquiry and press the send button. For example, enter "My chest hurts."

[1273] Input: Emergency consultation details

[1274] Output: Sending of urgent consultation details

[1275] Specific action: The user enters information into the emergency field and presses the send button to send the information to the terminal.

[1276] Step 12: Detect and notify of urgent keywords

[1277] Server: If it detects an emergency keyword and determines that an emergency has occurred, it will notify registered emergency contacts and the appropriate authorities.

[1278] Input: Submitted emergency consultation details

[1279] Output: Emergency notification

[1280] Specific operation: The server analyzes the received emergency call content and, if it contains emergency keywords, notifies the emergency contact and the appropriate authorities.

[1281] Step 13: Displaying an emergency message

[1282] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[1283] Input: Emergency notification

[1284] Output: Display of emergency message

[1285] Specific action: The terminal receives an emergency notification from the server and displays the emergency message to the user.

[1286] (Application Example 1)

[1287] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1288] When elderly people live alone, they often face anxieties in daily life and difficulty dealing with emergencies. Therefore, support is needed to ensure that elderly people can live with peace of mind. However, conventional support systems have limited means for elderly people to initiate consultations or emergency contact, which can result in delays in appropriate responses. Furthermore, the analysis of consultation content and the generation of responses are not sufficiently automated, making it difficult to quickly alleviate the anxieties of elderly people.

[1289] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1290] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, and an application installed on a robot for supporting the lives of the elderly, wherein the robot performs voice input and voice output, and analyzes the consultation content and generates a response using a natural language processing and generative AI model. This enables the elderly to easily interact with the system by voice and to respond quickly in emergencies.

[1291] A "user registration method" is an interface that allows elderly people to register their information in the system.

[1292] A "consultation reception method" is an interface that allows elderly people to input the details of their consultation.

[1293] "Answer generation means" refers to a means for analyzing the input consultation content and generating an appropriate answer.

[1294] A "follow-up method" refers to a means of sending follow-up messages to elderly individuals on a regular basis.

[1295] "Emergency contact methods" refer to means of notifying registered contacts and appropriate authorities in the event of an emergency.

[1296] "Applications installed on robots" are programs that are installed on robots to support the daily lives of elderly people.

[1297] "Voice input and voice output" refers to a technology in which elderly people give instructions or ask questions to a robot using their voice, and the robot responds using its voice.

[1298] "Natural language processing" is a technology that analyzes input language data and understands its meaning.

[1299] A "generative AI model" is a technology that uses artificial intelligence to automatically generate appropriate answers and advice.

[1300] In this embodiment of the invention, the system for supporting the lives of the elderly includes user registration means, consultation reception means, response generation means, follow-up means, and emergency contact means. These means are realized through an application installed on a robot.

[1301] Program Processing Description

[1302] User registration method

[1303] The server displays a registration screen where the user enters information such as their name, contact details, address, and emergency contact information. The entered information is then stored in a database (e.g., MySQL) via a web server (e.g., Apache).

[1304] Specific example: When an elderly person enters their name, phone number, and address and clicks the registration button, the entered information is saved to the database.

[1305] Consultation methods

[1306] When a user inputs their inquiry via voice, the robot's microphone captures the audio data, and Google Cloud Speech-to-Text is used to convert the speech to text. The robot then analyzes the inquiry using a natural language processing (NLP) engine (e.g., Google Cloud NLP).

[1307] Specific example: An elderly person complains that they "haven't been able to sleep lately," and a robot converts their voice into text and analyzes the content of their complaint.

[1308] Answer generation means

[1309] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate appropriate responses based on the analyzed consultation content. It then outputs these responses as speech using speech synthesis technology (e.g., Google Cloud Text-to-Speech) and communicates them to the user via the robot's speaker.

[1310] Specific example: When a server receives a request for help regarding "I can't sleep these days," it inputs a prompt into a generative AI model, and the generated response is output as speech using speech synthesis technology.

[1311] Example of a prompt:

[1312] A user has submitted the following inquiry: "I've been having trouble sleeping lately." Please generate an appropriate response to this inquiry, including possible solutions and advice.

[1313] Follow-up methods

[1314] The server stores a history of the consultation and uses a scheduler (e.g., Quartz Scheduler) to generate follow-up messages at appropriate times. These messages are sent via voice or text through a robot.

[1315] Specific example: A server sends a follow-up message to the elderly person via a robot a week later, asking, "Have you been able to sleep well since then?"

[1316] Emergency contact methods

[1317] The server detects emergency keywords from the entered consultation content and automatically sends notifications to registered emergency contacts and appropriate authorities using notification services such as Twilio. The robot also displays or voices messages to elderly people urging them to take emergency action.

[1318] Specific example: If an elderly person tells a robot, "My chest hurts and I can't breathe," the server will determine it's an emergency and automatically send a notification to the family and emergency authorities.

[1319] In this way, the system allows elderly people to easily communicate using voice and respond quickly in emergencies.

[1320] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1321] Step 1:

[1322] The user enters their name, contact information, address, and emergency contact information on the robot registration screen and clicks the register button.

[1323] Input: Name, contact information, address, and emergency contact information.

[1324] Output: User information is saved to the database.

[1325] Specific operation: The server saves the input information to a database (e.g., MySQL) via a web server (e.g., Apache). A message indicating that user information registration is complete is displayed on the screen.

[1326] Step 2:

[1327] The user inputs their inquiry details via voice.

[1328] Input: Audio data.

[1329] Output: The consultation content converted as text data.

[1330] Specific operation: The robot's microphone captures audio data, and Google Cloud Speech-to-Text is used to convert the audio to text. The server then receives the text data.

[1331] Step 3:

[1332] The server analyzes the content of the consultation it receives.

[1333] Input: Text data containing the consultation details.

[1334] Output: Analysis results.

[1335] Specific operation: The server uses an NLP engine (e.g., Google Cloud NLP) to analyze the consultation content and retrieve the analysis results.

[1336] Step 4:

[1337] The server generates an answer using an AI model based on the analysis results.

[1338] Input: Analysis results.

[1339] Output: Generated answer.

[1340] Specific operation: The server inputs prompt text to the generated AI model (e.g., OpenAI GPT-4) and retrieves the generated response.

[1341] Example of a prompt:

[1342] A user has submitted the following inquiry: "I've been having trouble sleeping lately." Please generate an appropriate response to this inquiry, including possible solutions and advice.

[1343] Step 5:

[1344] The server converts the generated response into speech data using speech synthesis technology, and outputs the response through the robot's speaker.

[1345] Input: Text data of the generated response.

[1346] Output: The response is in audio format.

[1347] Specific operation: The server uses Google Cloud Text-to-Speech to convert the generated response into audio data, which is then output as speech through the robot's speaker.

[1348] Step 6:

[1349] The server saves a history of the consultation and generates follow-up messages at the appropriate time.

[1350] Input: History of consultation details.

[1351] Output: Follow-up message.

[1352] Specific operation: The server uses a scheduler (e.g., Quartz Scheduler) to generate follow-up messages at the appropriate time and the robot sends the messages via voice or text.

[1353] Step 7:

[1354] The server detects emergency keywords in times of crisis and sends notifications to the appropriate authorities.

[1355] Input: Details of the urgent consultation.

[1356] Output: Notification message.

[1357] Specific operation: The server detects emergency keywords and automatically sends messages to registered contacts and appropriate authorities using notification services such as Twilio. The robot also displays or voices messages to elderly people urging them to take emergency action.

[1358] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1359] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it also includes an emotion engine that recognizes the user's emotions.

[1360] User registration method

[1361] Terminal: The elderly person accesses the system and displays the user registration screen. They enter information such as their name, contact information, address, and emergency contact information, and then submit it.

[1362] Server: Receives the transmitted information and stores it in the database.

[1363] Specific example: When Mr. Tanaka (the user) enters his name, phone number, and address and clicks the register button, the server saves that information.

[1364] Consultation methods

[1365] Terminal: The user logs in and displays the consultation request screen.

[1366] User: Enter your inquiry in text format.

[1367] Terminal: Sends the entered consultation details to the server.

[1368] Server: Receives consultation content and analyzes it using a natural language processing (NLP) engine. It also uses an emotion engine to recognize the user's emotions from the input consultation content.

[1369] Specific example: Ms. Tanaka inputs "I haven't been able to sleep lately," and the server receives and analyzes this, while the emotion engine recognizes Ms. Tanaka's stress and anxiety.

[1370] Answer generation means

[1371] Server: Searches for appropriate answers from a database related to categories and sentiments.

[1372] Server: Generates appropriate responses using response generation algorithms as needed. Adjusts the response content based on the recognized user's sentiment.

[1373] Terminal: Displays the generated response to the user.

[1374] Specific example: The server retrieves information about "sleep disorders" from a database and displays a response on the terminal that is tailored to Tanaka's feelings, such as, "If you've been having trouble sleeping lately, it's important to avoid consuming caffeine before bed and to create a relaxing environment."

[1375] Follow-up methods

[1376] Server: Stores the consultation details and corresponding responses in a database.

[1377] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[1378] Server: Generates a follow-up message when the reminder time is reached.

[1379] Terminal: Sends the generated follow-up message to the user.

[1380] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?" and sends it to Mr. Tanaka's device.

[1381] Emergency contact methods

[1382] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[1383] Terminal: Sends urgent information to the server.

[1384] Server: Based on the received information, it detects urgent keywords and uses an emotion engine to further confirm the urgent need.

[1385] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[1386] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[1387] For example, if Ms. Tanaka enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify Ms. Tanaka's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on Ms. Tanaka's device.

[1388] These methods enable the rapid and appropriate resolution of anxieties and worries among the elderly, and allow for more personalized responses through emotional recognition. The comprehensive support and emotion-based responses provided by the system create a support system that allows the elderly to live with peace of mind.

[1389] The following describes the processing flow.

[1390] User registration method

[1391] Step 1:

[1392] Device: The user opens the system's website or app and displays the user registration screen.

[1393] Step 2:

[1394] User: Enter information such as name, contact information (phone number or email address), address, and emergency contact information.

[1395] Step 3:

[1396] Terminal: Checks the entered information and performs a simple check to ensure it is entered correctly.

[1397] Step 4:

[1398] Terminal: Sends information to the server.

[1399] Step 5:

[1400] Server: Receives the information and validates it to ensure there are no problems with its format or content.

[1401] Step 6:

[1402] Server: Saves information that passed validation to the database.

[1403] Consultation methods

[1404] Step 1:

[1405] Terminal: The user logs in and displays the consultation request screen.

[1406] Step 2:

[1407] User: Enter your inquiry in text format.

[1408] Step 3:

[1409] Terminal: Sends the entered consultation details to the server.

[1410] Step 4:

[1411] Server: Receives the consultation content and performs analysis using a natural language processing (NLP) engine.

[1412] Step 5:

[1413] Server: Based on the analysis results, classify the consultation content into the appropriate category.

[1414] Step 6:

[1415] Server: Sends the analyzed data to the emotion engine to recognize the user's emotions.

[1416] Answer generation means

[1417] Step 1:

[1418] Server: Searches for appropriate answers from a database related to categories and sentiments.

[1419] Step 2:

[1420] Server: If an answer is not automatically generated, run the answer generation algorithm to generate an appropriate answer.

[1421] Step 3:

[1422] Server: Adjusts the response based on the recognized user's emotions.

[1423] Step 4:

[1424] Terminal: Displays the generated response to the user.

[1425] Follow-up methods

[1426] Step 1:

[1427] Server: Stores the consultation details and corresponding responses in a database.

[1428] Step 2:

[1429] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[1430] Step 3:

[1431] Server: Generates a follow-up message when the reminder time is reached.

[1432] Step 4:

[1433] Terminal: Sends the generated follow-up message to the user.

[1434] Emergency contact methods

[1435] Step 1:

[1436] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[1437] Step 2:

[1438] Terminal: Sends urgent information to the server.

[1439] Step 3:

[1440] Server: Based on the received information, it detects urgent keywords and uses an emotion engine to further confirm the urgent need.

[1441] Step 4:

[1442] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[1443] Step 5:

[1444] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[1445] (Example 2)

[1446] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1447] The challenge lies in responding quickly and appropriately to the anxieties and worries that elderly people face in their daily lives, and in providing personalized support that takes their emotions into consideration. Furthermore, there is a need for a system that can respond quickly in the event of an emergency, but existing systems do not adequately meet these requirements.

[1448] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1449] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, an emotion recognition means, and a response adjustment means. This enables a rapid and appropriate response to the anxieties and worries of the elderly, and by considering the emotions of the elderly, it becomes possible to provide personalized support. Furthermore, a rapid response can be achieved in emergency situations.

[1450] "User registration means" refers to a means of providing an interface for elderly people to access the system and input and register necessary personal information.

[1451] A "consultation reception method" refers to a means of providing an interface for elderly people to log in to the system, input their consultation details in text format, and submit them.

[1452] A "response generation method" is a means for analyzing the received consultation content and generating an appropriate response.

[1453] "Follow-up methods" refer to means of generating and sending regular follow-up messages to elderly individuals.

[1454] "Emergency contact methods" refer to means of notifying registered contacts and appropriate authorities in the event of an emergency.

[1455] "Emotion recognition means" refers to methods for recognizing the emotions of elderly people based on the consultation content entered by the user.

[1456] "Response adjustment means" are methods for adjusting the content of responses based on the perceived emotions of elderly individuals.

[1457] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it also includes an emotion engine that recognizes the user's emotions.

[1458] User registration method

[1459] Terminal: The elderly person accesses the system and displays the user registration screen. Here, they enter and submit information such as their name, address, contact information, and emergency contact information.

[1460] Server: Receives the transmitted information and saves it to a database (e.g., MySQL).

[1461] Example: A user enters their name, phone number, and address on the registration screen and clicks the "Register" button. The server saves this information to the database.

[1462] Consultation methods

[1463] Terminal: The user logs in and displays the consultation request screen.

[1464] User: Enter your inquiry in text format.

[1465] Terminal: Sends the entered consultation details to the server.

[1466] Server: Receives the consultation content and analyzes it using a natural language processing (NLP) engine (e.g., spaCy, BERT). Furthermore, it uses an emotion engine (e.g., OpenAI's emotion recognition model) to recognize the user's emotions from the input consultation content.

[1467] Specific example: A user enters "I haven't been able to sleep lately," and the server receives and analyzes this, while also using an emotion engine to recognize the user's stress and anxiety.

[1468] Answer generation means

[1469] Server: Based on the category of the consultation content (e.g., sleep disorder) and the recognized emotion (e.g., anxiety), it searches the database for relevant data.

[1470] Server: Generates appropriate responses using a generation AI model (e.g., OpenAI's GPT-3) as needed. Adjusts the response content considering the user's sentiment data.

[1471] Terminal: Displays the generated response to the user.

[1472] Specific example: The server retrieves information about "sleep disorders" from its database and displays the message, "If you've been having trouble sleeping lately, it's important to avoid consuming caffeine before bed and create a relaxing environment."

[1473] Follow-up methods

[1474] Server: Stores consultation details and responses in a database.

[1475] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[1476] Server: When the reminder time is reached, it automatically generates a follow-up message.

[1477] Terminal: Sends the generated follow-up message to the user.

[1478] Specific example: A server generates a message one week later asking, "Have you been able to sleep since then?" and sends it to the user's terminal.

[1479] Emergency contact methods

[1480] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[1481] Terminal: Sends urgent information to the server.

[1482] Server: Analyzes the received content and detects urgent keywords. Simultaneously, the emotion engine further confirms the urgency.

[1483] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities (e.g., emergency services).

[1484] Terminal: Displays the user with the message, "We will call an ambulance immediately. We will also contact your family."

[1485] For example, if a user enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify registered contacts and emergency authorities. A message prompting emergency action will also be displayed on the user's device.

[1486] Example of a prompt

[1487] "Please explain the steps to register for the senior support system."

[1488] "Please tell me how to analyze consultations regarding insomnia and provide appropriate advice."

[1489] "Please explain how to set up follow-up messages based on the content of the consultation."

[1490] "Please explain the procedures for responding in the event of an emergency."

[1491] As described above, this system quickly and appropriately resolves the anxieties and worries of the elderly, and enables more personalized responses through emotion recognition. Furthermore, it is designed to respond quickly even in emergencies.

[1492] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1493] Step 1:

[1494] Terminal: The user accesses the new registration screen. The user enters information such as name, address, phone number, and emergency contact information.

[1495] Input: User's (elderly) personal information.

[1496] Output: Data of the entered personal information.

[1497] Specific operation: When a user enters information into a form displayed in the browser and presses the "Register" button, the information is collected by the device.

[1498] Step 2:

[1499] Terminal: Sends the entered personal information to the server. Specifically, the data is sent as an HTTP request.

[1500] Input: Data of collected personal information.

[1501] Output: HTTP request to the server.

[1502] Specific operation: The terminal sends the form data to the server using the POST method.

[1503] Step 3:

[1504] Server: Stores received personal information in a database. This includes a process to verify data integrity and security.

[1505] Input: Personal information data sent to the server.

[1506] Output: Personal information stored in the database.

[1507] Specific operation: The server receives the data and saves it to the database using an SQL insert command.

[1508] Step 4:

[1509] Terminal: The user logs into the system and displays the consultation request screen.

[1510] Input: User login information.

[1511] Output: Consultation request screen.

[1512] Specific operation: When a user accesses the login screen in their browser, enters their ID and password, and clicks the "Login" button, the system performs authentication, and if successful, the consultation request screen is displayed.

[1513] Step 5:

[1514] User: Enter the consultation details in text format. Specifically, enter the text data of the consultation details.

[1515] Input: User's inquiry.

[1516] Output: Text data of the entered consultation content.

[1517] Specific action: The user enters their inquiry into a text box and presses the "Send" button.

[1518] Step 6:

[1519] Terminal: Sends the entered consultation details to the server. Specifically, the data is sent as an HTTP request.

[1520] Input: Text data of the entered consultation content.

[1521] Output: HTTP request to the server.

[1522] Specific operation: The terminal sends the form data to the server using the POST method.

[1523] Step 7:

[1524] Server: Analyzes received consultation content using a natural language processing (NLP) engine. It also uses an emotion engine to recognize the user's emotions from the input consultation content.

[1525] Input: Text data of the consultation content sent to the server.

[1526] Output: Analysis results and sentiment data.

[1527] Specific operation: Text analysis is performed using an NLP engine (e.g., spaCy, BERT), and then emotions are recognized using an emotion engine (e.g., OpenAI's emotion recognition model).

[1528] Step 8:

[1529] Server: Based on the category of the consultation content and the recognized emotions, it searches the database for relevant data. If necessary, it uses a generative AI model to generate an answer.

[1530] Input: Analysis results and sentiment data.

[1531] Output: Generated answer.

[1532] Specific operation: Execute database queries to retrieve relevant information. Generate new answers using a generated AI model (e.g., OpenAI's GPT-3) as needed.

[1533] Step 9:

[1534] Terminal: Displays the generated response to the user.

[1535] Input: Generated response.

[1536] Output: Display of the answer.

[1537] Specific operation: Send the response data to the terminal in HTML format and display it to the user.

[1538] Step 10:

[1539] Server: Stores the consultation content and the provided answers in a database. The stored content includes date and time, consultation content, answer content, recognized emotion data, etc.

[1540] Input: Consultation details, provided response, and sentiment data.

[1541] Output: Consultation history stored in the database.

[1542] Specific operation: The server receives the data and saves it to the database using an SQL insert command.

[1543] Step 11:

[1544] Server: Based on the saved data, set a follow-up reminder for one week later.

[1545] Input: Consultation history data.

[1546] Output: Reminder.

[1547] Specific operation: Set up reminder scheduling on the server and trigger the reminder at the follow-up date and time.

[1548] Step 12:

[1549] Server: When the reminder date and time arrive, it automatically generates a follow-up message.

[1550] Input: Reminder data.

[1551] Output: Follow-up message.

[1552] Specific operation: A scheduled reminder is triggered, and the message generation function generates a corresponding follow-up message.

[1553] Step 13:

[1554] Terminal: Sends the generated follow-up message to the user.

[1555] Input: Follow-up message.

[1556] Output: Notification of follow-up message.

[1557] Specific operation: Send a message using an HTTP request and display it in the browser.

[1558] Step 14:

[1559] User: When an emergency occurs, enter the details of your urgent request.

[1560] Input: Details of the urgent consultation.

[1561] Output: Text data of the entered emergency consultation details.

[1562] Specific action: The user enters their inquiry into the input form and presses the "Submit" button.

[1563] Step 15:

[1564] Terminal: Sends urgent information to the server.

[1565] Input: Text data of the emergency consultation details entered.

[1566] Output: HTTP request to the server.

[1567] Specific operation: The terminal sends the form data to the server using the POST method.

[1568] Step 16:

[1569] Server: Analyzes the received content and detects urgent keywords. Simultaneously, the emotion engine further confirms the urgency.

[1570] Input: Text data of the emergency consultation details.

[1571] Output: Emergency alert.

[1572] Specific actions: Analyze using an NLP engine and an emotion engine to determine urgency.

[1573] Step 17:

[1574] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[1575] Input: Emergency alert.

[1576] Output: Notification message.

[1577] Specific operation: Retrieve registered contact information and send notifications using the messaging API.

[1578] Step 18:

[1579] Terminal: Displays the user with the message, "We will call an ambulance immediately. We will also contact your family."

[1580] Input: Notification message.

[1581] Output: Displays emergency instructions.

[1582] Specific action: The notification message is received on the device and displayed in the browser.

[1583] (Application Example 2)

[1584] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1585] In support systems that enable elderly people to live with peace of mind, there is a need to provide more personalized responses through emotion recognition. In particular, the challenge lies in responding quickly and appropriately to anxieties and emergencies that arise in daily life, and providing appropriate support that is tailored to their emotions, even for basic needs such as meals.

[1586] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1587] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, an emotion recognition means, and an emotion adaptation means. This makes it possible to provide an advanced support system that allows elderly people to live with peace of mind and respond quickly to emergencies.

[1588] "User registration method" refers to a function that allows elderly people to access the system and enter and submit information such as their name, contact information, address, and emergency contact information.

[1589] The "consultation submission method" is a function that allows users to log in, enter their consultation details through the consultation screen, and send them to the system.

[1590] The "response generation means" is a function that analyzes the content of the consultation and generates an appropriate response based on that analysis.

[1591] The "follow-up method" is a function that sends periodic follow-up messages based on the content of the consultation and the history of the response.

[1592] "Emergency contact methods" refer to a function that allows users to notify registered contacts and appropriate authorities when they encounter an emergency.

[1593] "Emotion recognition means" refers to a function that detects emotions from the consultation content and comments entered by the user.

[1594] "Emotional adaptation measures" refer to functions that adjust the content of follow-up messages based on the recognized emotions of the user.

[1595] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it includes an emotion engine that recognizes the user's emotions. The specific form of this system is shown below.

[1596] User registration method

[1597] Users access the system using their smartphones, enter information such as their name, contact details, address, and emergency contact details, and send it to the server. The server stores the submitted information in a database. The software used typically includes a database management system (e.g., SQLite).

[1598] Consultation methods

[1599] The user logs in using their smartphone and enters their consultation request in text format on the consultation request screen. The entered consultation request is sent to the server, which analyzes the received content using a natural language processing engine (e.g., Spacy) and an emotion recognition engine (e.g., Google Cloud Natural Language API). Here, the user's emotions are recognized along with the consultation request.

[1600] Answer generation means

[1601] The server generates appropriate responses based on the analyzed consultation content and sentiment data. The generated responses are displayed on the smartphone. If necessary, it searches for relevant information in the database and further refines the responses using a generation AI model.

[1602] Follow-up methods

[1603] The server stores the consultation details and response records in a database and generates a follow-up message after a certain period of time has elapsed. This follow-up message is tailored based on the perceived emotions and sent to the user. Automated scheduling software (e.g., cron jobs) is used to set the reminders.

[1604] Emergency contact methods

[1605] If a user experiences an emergency, they enter the details of their urgent need using their smartphone. The server receives this information and uses an emotion recognition engine to confirm the urgency. If necessary, notifications are automatically sent to emergency contacts and the appropriate authorities. The software used here includes notification services (e.g., Twilio).

[1606] Specific example

[1607] The following is a specific example of a scenario in which elderly people use a food delivery system.

[1608] 1. Elderly individuals access the system using their smartphones and register their names, contact information, etc.

[1609] 2. When ordering breakfast, if you enter a message such as "I haven't had much of an appetite lately," the server will receive this message and perform emotion recognition.

[1610] 3. The server generates appropriate advice, such as "Choose easily digestible foods and consult a doctor if necessary," and displays it on the smartphone.

[1611] 4. One week later, a follow-up message will be automatically sent asking, "Has your appetite improved at all?"

[1612] Example of a prompt

[1613] "Please suggest how to care for an elderly person who has recently lost their appetite. The emotion recognition engine should detect a state of distress and provide appropriate advice. Also, please send a follow-up message one week later."

[1614] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1615] Step 1: User Registration

[1616] Users access the system using their smartphones and enter information such as their name, contact information, address, and emergency contact information. The entered information is sent to the server when the user presses the "Register" button. The server stores the received information in a database. The input data consists of personal information such as names, while the output data is user information recorded in the database. Specifically, the user enters data into input fields on the smartphone screen and then presses the "Register" button.

[1617] Step 2: Consultation

[1618] Users log in to the system using their smartphones and enter their consultation details in text format. The entered consultation details are sent to the server by pressing the "Send" button. The server receives these consultation details and analyzes them using a natural language processing engine and an emotion recognition engine. The input data is the text of the consultation details, and the output data consists of the analysis results and recognized emotion data. Specifically, the user enters their consultation details on their smartphone and presses the "Send" button.

[1619] Step 3: Generate Answer

[1620] The server generates appropriate responses based on the analyzed consultation content and sentiment data. It searches for relevant information in the database and adjusts the response using a generative AI model as needed. The input data is the analyzed consultation content and sentiment data, and the output data is the generated response text. Specifically, the server executes database queries and operates the generative AI model to generate responses.

[1621] Step 4: Display the answer

[1622] The generated response is sent from the server to the user's smartphone and displayed on the screen. The input data is the response text sent from the server, and the output data is the response displayed on the smartphone screen. Specifically, the smartphone app receives a notification from the server and displays the data on the screen.

[1623] Step 5: Setting up follow-up messages

[1624] The server stores the consultation details and their corresponding records in a database and sets reminders to generate follow-up messages after a specified period has elapsed. The input data consists of the consultation details and their corresponding records, while the output data consists of the set reminders. Specifically, the server uses automated scheduling software (e.g., cron jobs) to set the reminders.

[1625] Step 6: Send a follow-up message

[1626] When the reminder time arrives, the server generates a follow-up message and sends it to the user's smartphone. The input data is the reminder and its related information, as well as the set sentiment data, while the output data is the generated follow-up message. Specifically, the server automatically generates the follow-up message at the designated time and sends the message using a notification service.

[1627] Step 7: Emergency Contact

[1628] When a user encounters an emergency, they use their smartphone to input and send an urgent request. The server receives the information, uses an emotion recognition engine to confirm the urgency, and then notifies emergency contacts and the appropriate authorities. The input data is the text of the emergency request, and the output data is the notification message to emergency contacts and authorities. Specifically, an emergency response algorithm is executed, and an alert is quickly sent through the notification service.

[1629] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1630] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1631] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1632] [Fourth Embodiment]

[1633] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1634] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1635] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1636] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1637] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1638] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1639] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1640] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1641] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1642] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1643] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1644] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1645] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1646] This invention provides a support system that enables elderly people to live with peace of mind, and includes a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact.

[1647] User registration method

[1648] Terminal: The elderly person accesses the system and displays the user registration screen. They enter information such as their name, contact information, address, and emergency contact information, and then submit it.

[1649] Server: Receives the transmitted information and stores it in the database.

[1650] Specific example: When Mr. Tanaka (the user) enters his name, phone number, and address and clicks the register button, the server saves that information.

[1651] Consultation methods

[1652] Terminal: Displays a screen where the user can enter their consultation details. The user enters their consultation details and presses the send button.

[1653] Server: Receives the input consultation content and analyzes it using natural language processing.

[1654] Specific example: When Mr. Tanaka types "I can't sleep lately" and presses the send button, the server receives and analyzes it.

[1655] Answer generation means

[1656] Server: Based on the analysis of the consultation content, it searches the database for an appropriate answer or automatically generates an appropriate answer.

[1657] Terminal: Displays the generated response to the user.

[1658] Specific example: The server retrieves information about "sleep disorders" from its database and displays the following response on Tanaka's terminal: "If you've been having trouble sleeping recently, it's important to avoid consuming caffeine before bed and to create a relaxing environment."

[1659] Follow-up methods

[1660] Server: Saves a history of consultations and generates follow-up messages at appropriate times. It also sends messages to users periodically based on reminder settings.

[1661] Terminal: Displays a follow-up message to the user.

[1662] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?" and sends it to Mr. Tanaka's device.

[1663] Emergency contact methods

[1664] User: Enter the details of your urgent need for help. For example, enter something like, "My chest hurts."

[1665] Server: Detects emergency keywords, immediately triggers an emergency alert, and notifies registered emergency contacts and the appropriate authorities.

[1666] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[1667] For example, if Ms. Tanaka enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify Ms. Tanaka's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on Ms. Tanaka's device.

[1668] These measures can quickly and appropriately alleviate the anxieties and worries of the elderly, reducing the risk of dying alone. The comprehensive support provided by the system enables elderly people to live with peace of mind.

[1669] The following describes the processing flow.

[1670] User registration method

[1671] Step 1:

[1672] Device: The user opens the system's website or app and displays the user registration screen.

[1673] Step 2:

[1674] User: Enter your name, contact information (phone number or email address), address, emergency contact information, etc.

[1675] Step 3:

[1676] Terminal: Checks the entered information and performs a simple check to ensure it is entered correctly.

[1677] Step 4:

[1678] Terminal: Sends information to the server.

[1679] Step 5:

[1680] Server: Receives the information and validates it to ensure there are no problems with its format or content.

[1681] Step 6:

[1682] Server: Saves information that passed validation to the database.

[1683] Consultation methods

[1684] Step 1:

[1685] Terminal: The user logs in and displays the consultation request screen.

[1686] Step 2:

[1687] User: Enter your inquiry in text format.

[1688] Step 3:

[1689] Terminal: Sends the entered consultation details to the server.

[1690] Step 4:

[1691] Server: Receives the consultation content and performs analysis using a natural language processing (NLP) engine.

[1692] Step 5:

[1693] Server: Based on the analysis results, classify the consultation content into the appropriate category.

[1694] Answer generation means

[1695] Step 1:

[1696] Server: Searches for the appropriate answer from a database related to the category.

[1697] Step 2:

[1698] Server: If an adequate response is not available, it will use a response generation algorithm to generate an appropriate response.

[1699] Step 3:

[1700] Terminal: Displays the generated response to the user.

[1701] Follow-up methods

[1702] Step 1:

[1703] Server: Stores the consultation details and corresponding responses in a database.

[1704] Step 2:

[1705] Server: Sets follow-up reminders based on saved consultation history.

[1706] Step 3:

[1707] Server: Generates a follow-up message when the reminder time is reached.

[1708] Step 4:

[1709] Terminal: Sends the generated follow-up message to the user.

[1710] Emergency contact methods

[1711] Step 1:

[1712] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[1713] Step 2:

[1714] Terminal: Sends urgent information to the server.

[1715] Step 3:

[1716] Server: Based on the received information, it detects emergency keywords and determines the need for emergency response.

[1717] Step 4:

[1718] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[1719] Step 5:

[1720] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[1721] Based on the above procedures, a system will be established to receive consultations from the elderly, provide appropriate answers, and implement necessary follow-up and emergency response. This will create a comprehensive support system that enables the elderly to live with peace of mind.

[1722] (Example 1)

[1723] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1724] Conventional support systems for the elderly have often struggled to provide prompt and appropriate responses to the concerns of the elderly, and their emergency response capabilities have frequently been inadequate. In particular, there is a need for solutions to daily anxieties and worries, as well as prompt responses in emergencies, but systems that meet these needs are limited. This invention aims to solve these problems by providing a comprehensive support system that enables the elderly to live their daily lives with peace of mind.

[1725] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1726] In this invention, the server includes a user registration means for elderly people to register with the system, a consultation reception means for elderly people to input their consultation details, a response generation means for analyzing the consultation details and generating appropriate answers, a follow-up means for periodically sending follow-up messages to the elderly people, an emergency contact means for notifying registered contacts and appropriate authorities in the event of an emergency, and a natural language processing means for detecting emergencies by receiving and analyzing the consultation details and taking necessary measures. This enables a comprehensive support system that can not only respond quickly and appropriately to the consultation details of elderly people but also respond quickly to emergencies.

[1727] "User registration method" refers to a method by which elderly individuals can access the system, enter information such as their name, contact information, address, and emergency contact information, and send this information to the server.

[1728] A "consultation reception method" is a means of providing an interface for elderly people to input their consultation details and transmitting that input to a server.

[1729] "Answer generation means" refers to a means of analyzing the received consultation content and either searching for an appropriate answer from a database based on the analysis results, or automatically generating an answer using a generation AI model.

[1730] A "follow-up method" is a means of generating periodic follow-up messages based on the content of the consultation and the history of the response, and sending these messages to the user.

[1731] An "emergency contact method" is a system that analyzes the content of inquiries entered during an emergency and, when an emergency keyword is detected, promptly notifies registered emergency contacts and the appropriate authorities.

[1732] "Natural language processing means" refers to a method for analyzing received consultation content, understanding keywords and meaning within the text, classifying the consultation content, and taking appropriate action.

[1733] This invention provides a support system that enables elderly people to live with peace of mind, and it is equipped with various functions. Specific embodiments are described below.

[1734] This system primarily consists of three components: a server, a terminal, and a user. The following describes in detail how each component works together to provide support for the elderly.

[1735] User registration method

[1736] Terminal: When a user accesses the system, a user registration screen is displayed on the terminal. This screen is built using HTML, CSS, and JavaScript. On this screen, the user enters information such as their name, contact information, address, and emergency contact information, and then clicks the submit button.

[1737] Server: Receives information sent from the terminal and saves it to a database (MySQL or PostgreSQL) using a server-side program (e.g., Python or Java).

[1738] Specific example: When a user enters "Taro Yamada", phone number "090-1234-5678", address "Chiyoda-ku, Tokyo", and emergency contact "090-8765-4321", and presses the submit button, the server saves this information to the database.

[1739] Consultation methods

[1740] Terminal: When a user logs in, a screen is displayed where they can enter their inquiry details. A text input field and a submit button are displayed using HTML, CSS, and JavaScript.

[1741] User: Enter your question and press the submit button. For example, enter "I've been having trouble sleeping lately."

[1742] Server: Receives submitted consultation content and analyzes it using natural language processing libraries (e.g., spaCy or NLTK). This analysis extracts key keywords and themes.

[1743] Specific example: When a user types "I can't sleep lately" and presses the send button, the server receives the message and uses natural language processing to extract and analyze the keyword "can't sleep."

[1744] Answer generation means

[1745] Server: Based on the analysis results, the server either searches for the appropriate answer from the database or automatically generates an answer using a generative AI model (e.g., GPT-4). In doing so, the AI ​​model considers past data and question format to generate the most appropriate response.

[1746] Terminal: The generated response is sent to the user's terminal and displayed on the screen. It is displayed using HTML and JavaScript.

[1747] Specific example: The server retrieves information about "sleep disorders" from the database, generates a response such as "If you've been having trouble sleeping recently, it's important to avoid consuming caffeine before bed and create a relaxing environment," and displays it on the user's device.

[1748] Follow-up methods

[1749] Server: Stores consultation content and response history in a database and periodically generates follow-up messages.

[1750] Terminal: The generated follow-up message is sent to the user and displayed on the screen. It is displayed using HTML and JavaScript.

[1751] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?", sends it to the user's device, and the device displays the message.

[1752] Emergency contact methods

[1753] User: Enter the details of your urgent inquiry and press the send button. For example, enter "My chest hurts."

[1754] Server: If it detects an emergency keyword and determines that an emergency has occurred, it will notify registered emergency contacts and the appropriate authorities.

[1755] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[1756] For example, if a user enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify the user's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on the user's device.

[1757] Through the functions described above, the system of the present invention can quickly and appropriately alleviate the anxieties and worries of the elderly and respond quickly to emergencies. This enables comprehensive support for the elderly to live with peace of mind.

[1758] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1759] Step 1: Display the user registration screen.

[1760] Terminal: When a user accesses the system, the user registration screen is automatically displayed. The user registration screen is built using HTML, CSS, and JavaScript and includes input fields for name, contact information, address, emergency contact information, etc.

[1761] Input: None

[1762] Output: Display of user registration screen

[1763] Specific operation: The device displays a registration screen to the user via a browser and prompts them to enter user information.

[1764] Step 2: Enter and submit information

[1765] User: Enter the required information such as your name, contact information, address, and emergency contact information, then click the register button.

[1766] Input: Name, contact information, address, emergency contact information, etc.

[1767] Output: Entered user information

[1768] Specific operation: The user enters information into the displayed fields and presses the registration button to send the information to the device.

[1769] Step 3: Receiving and saving information

[1770] Server: Receives user information sent from the terminal and saves it to a database (MySQL or PostgreSQL) using a server-side program (e.g., Python or Java).

[1771] Input: Submitted user information

[1772] Output: User information stored in the database

[1773] Specific operation: The server analyzes the received information and saves it to the database in the appropriate format.

[1774] Step 4: Display of the consultation details input screen

[1775] Terminal: When a user logs in, a screen is displayed where they can enter their inquiry details. A text input field and a submit button are displayed using HTML, CSS, and JavaScript.

[1776] Input: None

[1777] Output: Display of the consultation input screen

[1778] Specific operation: The terminal displays an interface for the user to input their inquiry.

[1779] Step 5: Enter and submit your consultation details.

[1780] User: Enter your question and press the submit button. For example, enter "I've been having trouble sleeping lately."

[1781] Input: Consultation details

[1782] Output: Input consultation content

[1783] Specific operation: The user enters information into the consultation field and presses the send button to send the information to the device.

[1784] Step 6: Receiving and analyzing the consultation content

[1785] Server: Receives the submitted consultation content and analyzes it using a natural language processing library (e.g., spaCy or NLTK).

[1786] Input: Submitted consultation details

[1787] Output: Analysis results (main keywords, meaning, etc.)

[1788] Specific operation: The server analyzes the received consultation content using a natural language processing library and extracts important keywords and meaning.

[1789] Step 7: Generate and submit your response.

[1790] Server: Based on the analysis results, it generates the optimal answer using a generative AI model (e.g., GPT-4). It then sends the generated answer to the user's device.

[1791] Input: Analysis results (main keywords, meaning of sentences, etc.)

[1792] Output: Generated answer

[1793] Specific operation: The server uses a generative AI model to generate an appropriate response and sends it to the user's terminal.

[1794] Step 8: Display the generated response

[1795] Terminal: Displays the generated response received from the server to the user. It is displayed on the screen using HTML and JavaScript.

[1796] Input: Generated answer

[1797] Output: Display of responses to the user

[1798] Specific action: The terminal displays the received response and provides it to the user.

[1799] Step 9: Generate and send follow-up messages

[1800] Server: Stores consultation content and response history in a database, periodically generates follow-up messages, and sends them to the user.

[1801] Input: Consultation details and response history

[1802] Output: Follow-up message

[1803] Specific operation: The server generates a follow-up message based on the information stored in the database and sends it to the user.

[1804] Step 10: Display a follow-up message

[1805] Terminal: Displays received follow-up messages to the user. Displayed using HTML and JavaScript.

[1806] Input: Follow-up message

[1807] Output: Display of follow-up message

[1808] Specific action: The device displays the received follow-up message to the user.

[1809] Step 11: Enter and submit details of your emergency consultation.

[1810] User: Enter the details of your urgent inquiry and press the send button. For example, enter "My chest hurts."

[1811] Input: Emergency consultation details

[1812] Output: Sending of urgent consultation details

[1813] Specific action: The user enters information into the emergency field and presses the send button to send the information to the terminal.

[1814] Step 12: Detect and notify of urgent keywords

[1815] Server: If it detects an emergency keyword and determines that an emergency has occurred, it will notify registered emergency contacts and the appropriate authorities.

[1816] Input: Submitted emergency consultation details

[1817] Output: Emergency notification

[1818] Specific operation: The server analyzes the received emergency call content and, if it contains emergency keywords, notifies the emergency contact and the appropriate authorities.

[1819] Step 13: Displaying an emergency message

[1820] Terminal: Displays an emergency message to the user such as, "We will call an ambulance immediately. We will also contact your family."

[1821] Input: Emergency notification

[1822] Output: Display of emergency message

[1823] Specific action: The terminal receives an emergency notification from the server and displays the emergency message to the user.

[1824] (Application Example 1)

[1825] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1826] When elderly people live alone, they often face anxieties in daily life and difficulty dealing with emergencies. Therefore, support is needed to ensure that elderly people can live with peace of mind. However, conventional support systems have limited means for elderly people to initiate consultations or emergency contact, which can result in delays in appropriate responses. Furthermore, the analysis of consultation content and the generation of responses are not sufficiently automated, making it difficult to quickly alleviate the anxieties of elderly people.

[1827] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1828] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, and an application installed on a robot for supporting the lives of the elderly, wherein the robot performs voice input and voice output, and analyzes the consultation content and generates a response using a natural language processing and generative AI model. This enables the elderly to easily interact with the system by voice and to respond quickly in emergencies.

[1829] A "user registration method" is an interface that allows elderly people to register their information in the system.

[1830] A "consultation reception method" is an interface that allows elderly people to input the details of their consultation.

[1831] "Answer generation means" refers to a means for analyzing the input consultation content and generating an appropriate answer.

[1832] A "follow-up method" refers to a means of sending follow-up messages to elderly individuals on a regular basis.

[1833] "Emergency contact methods" refer to means of notifying registered contacts and appropriate authorities in the event of an emergency.

[1834] "Applications installed on robots" are programs that are installed on robots to support the daily lives of elderly people.

[1835] "Voice input and voice output" refers to a technology in which elderly people give instructions or ask questions to a robot using their voice, and the robot responds using its voice.

[1836] "Natural language processing" is a technology that analyzes input language data and understands its meaning.

[1837] A "generative AI model" is a technology that uses artificial intelligence to automatically generate appropriate answers and advice.

[1838] In this embodiment of the invention, the system for supporting the lives of the elderly includes user registration means, consultation reception means, response generation means, follow-up means, and emergency contact means. These means are realized through an application installed on a robot.

[1839] Program Processing Description

[1840] User registration method

[1841] The server displays a registration screen where the user enters information such as their name, contact details, address, and emergency contact information. The entered information is then stored in a database (e.g., MySQL) via a web server (e.g., Apache).

[1842] Specific example: When an elderly person enters their name, phone number, and address and clicks the registration button, the entered information is saved to the database.

[1843] Consultation methods

[1844] When a user inputs their inquiry via voice, the robot's microphone captures the audio data, and Google Cloud Speech-to-Text is used to convert the speech to text. The robot then analyzes the inquiry using a natural language processing (NLP) engine (e.g., Google Cloud NLP).

[1845] Specific example: An elderly person complains that they "haven't been able to sleep lately," and a robot converts their voice into text and analyzes the content of their complaint.

[1846] Answer generation means

[1847] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate appropriate responses based on the analyzed consultation content. It then outputs these responses as speech using speech synthesis technology (e.g., Google Cloud Text-to-Speech) and communicates them to the user via the robot's speaker.

[1848] Specific example: When a server receives a request for help regarding "I can't sleep these days," it inputs a prompt into a generative AI model, and the generated response is output as speech using speech synthesis technology.

[1849] Example of a prompt:

[1850] A user has submitted the following inquiry: "I've been having trouble sleeping lately." Please generate an appropriate response to this inquiry, including possible solutions and advice.

[1851] Follow-up methods

[1852] The server stores a history of the consultation and uses a scheduler (e.g., Quartz Scheduler) to generate follow-up messages at appropriate times. These messages are sent via voice or text through a robot.

[1853] Specific example: A server sends a follow-up message to the elderly person via a robot a week later, asking, "Have you been able to sleep well since then?"

[1854] Emergency contact methods

[1855] The server detects emergency keywords from the entered consultation content and automatically sends notifications to registered emergency contacts and appropriate authorities using notification services such as Twilio. The robot also displays or voices messages to elderly people urging them to take emergency action.

[1856] Specific example: If an elderly person tells a robot, "My chest hurts and I can't breathe," the server will determine it's an emergency and automatically send a notification to the family and emergency authorities.

[1857] In this way, the system allows elderly people to easily communicate using voice and respond quickly in emergencies.

[1858] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1859] Step 1:

[1860] The user enters their name, contact information, address, and emergency contact information on the robot registration screen and clicks the register button.

[1861] Input: Name, contact information, address, and emergency contact information.

[1862] Output: User information is saved to the database.

[1863] Specific operation: The server saves the input information to a database (e.g., MySQL) via a web server (e.g., Apache). A message indicating that user information registration is complete is displayed on the screen.

[1864] Step 2:

[1865] The user inputs their inquiry details via voice.

[1866] Input: Audio data.

[1867] Output: The consultation content converted as text data.

[1868] Specific operation: The robot's microphone captures audio data, and Google Cloud Speech-to-Text is used to convert the audio to text. The server then receives the text data.

[1869] Step 3:

[1870] The server analyzes the content of the consultation it receives.

[1871] Input: Text data containing the consultation details.

[1872] Output: Analysis results.

[1873] Specific operation: The server uses an NLP engine (e.g., Google Cloud NLP) to analyze the consultation content and retrieve the analysis results.

[1874] Step 4:

[1875] The server generates an answer using an AI model based on the analysis results.

[1876] Input: Analysis results.

[1877] Output: Generated answer.

[1878] Specific operation: The server inputs prompt text to the generated AI model (e.g., OpenAI GPT-4) and retrieves the generated response.

[1879] Example of a prompt:

[1880] A user has submitted the following inquiry: "I've been having trouble sleeping lately." Please generate an appropriate response to this inquiry, including possible solutions and advice.

[1881] Step 5:

[1882] The server converts the generated response into speech data using speech synthesis technology, and outputs the response through the robot's speaker.

[1883] Input: Text data of the generated response.

[1884] Output: The response is in audio format.

[1885] Specific operation: The server uses Google Cloud Text-to-Speech to convert the generated response into audio data, which is then output as speech through the robot's speaker.

[1886] Step 6:

[1887] The server saves a history of the consultation and generates follow-up messages at the appropriate time.

[1888] Input: History of consultation details.

[1889] Output: Follow-up message.

[1890] Specific operation: The server uses a scheduler (e.g., Quartz Scheduler) to generate follow-up messages at the appropriate time and the robot sends the messages via voice or text.

[1891] Step 7:

[1892] The server detects emergency keywords in times of crisis and sends notifications to the appropriate authorities.

[1893] Input: Details of the urgent consultation.

[1894] Output: Notification message.

[1895] Specific operation: The server detects emergency keywords and automatically sends messages to registered contacts and appropriate authorities using notification services such as Twilio. The robot also displays or voices messages to elderly people urging them to take emergency action.

[1896] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1897] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it also includes an emotion engine that recognizes the user's emotions.

[1898] User registration method

[1899] Terminal: The elderly person accesses the system and displays the user registration screen. They enter information such as their name, contact information, address, and emergency contact information, and then submit it.

[1900] Server: Receives the transmitted information and stores it in the database.

[1901] Specific example: When Mr. Tanaka (the user) enters his name, phone number, and address and clicks the register button, the server saves that information.

[1902] Consultation methods

[1903] Terminal: The user logs in and displays the consultation request screen.

[1904] User: Enter your inquiry in text format.

[1905] Terminal: Sends the entered consultation details to the server.

[1906] Server: Receives consultation content and analyzes it using a natural language processing (NLP) engine. It also uses an emotion engine to recognize the user's emotions from the input consultation content.

[1907] Specific example: Ms. Tanaka inputs "I haven't been able to sleep lately," and the server receives and analyzes this, while the emotion engine recognizes Ms. Tanaka's stress and anxiety.

[1908] Answer generation means

[1909] Server: Searches for appropriate answers from a database related to categories and sentiments.

[1910] Server: Generates appropriate responses using response generation algorithms as needed. Adjusts the response content based on the recognized user's sentiment.

[1911] Terminal: Displays the generated response to the user.

[1912] Specific example: The server retrieves information about "sleep disorders" from a database and displays a response on the terminal that is tailored to Tanaka's feelings, such as, "If you've been having trouble sleeping lately, it's important to avoid consuming caffeine before bed and to create a relaxing environment."

[1913] Follow-up methods

[1914] Server: Stores the consultation details and corresponding responses in a database.

[1915] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[1916] Server: Generates a follow-up message when the reminder time is reached.

[1917] Terminal: Sends the generated follow-up message to the user.

[1918] Specific example: A server generates a follow-up message a week later asking, "Have you been able to sleep well since then?" and sends it to Mr. Tanaka's device.

[1919] Emergency contact methods

[1920] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[1921] Terminal: Sends urgent information to the server.

[1922] Server: Based on the received information, it detects urgent keywords and uses an emotion engine to further confirm the urgent need.

[1923] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[1924] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[1925] For example, if Ms. Tanaka enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify Ms. Tanaka's family and emergency authorities. Additionally, a message prompting emergency action will be displayed on Ms. Tanaka's device.

[1926] These methods enable the rapid and appropriate resolution of anxieties and worries among the elderly, and allow for more personalized responses through emotional recognition. The comprehensive support and emotion-based responses provided by the system create a support system that allows the elderly to live with peace of mind.

[1927] The following describes the processing flow.

[1928] User registration method

[1929] Step 1:

[1930] Device: The user opens the system's website or app and displays the user registration screen.

[1931] Step 2:

[1932] User: Enter information such as name, contact information (phone number or email address), address, and emergency contact information.

[1933] Step 3:

[1934] Terminal: Checks the entered information and performs a simple check to ensure it is entered correctly.

[1935] Step 4:

[1936] Terminal: Sends information to the server.

[1937] Step 5:

[1938] Server: Receives the information and validates it to ensure there are no problems with its format or content.

[1939] Step 6:

[1940] Server: Saves information that passed validation to the database.

[1941] Consultation methods

[1942] Step 1:

[1943] Terminal: The user logs in and displays the consultation request screen.

[1944] Step 2:

[1945] User: Enter your inquiry in text format.

[1946] Step 3:

[1947] Terminal: Sends the entered consultation details to the server.

[1948] Step 4:

[1949] Server: Receives the consultation content and performs analysis using a natural language processing (NLP) engine.

[1950] Step 5:

[1951] Server: Based on the analysis results, classify the consultation content into the appropriate category.

[1952] Step 6:

[1953] Server: Sends the analyzed data to the emotion engine to recognize the user's emotions.

[1954] Answer generation means

[1955] Step 1:

[1956] Server: Searches for appropriate answers from a database related to categories and sentiments.

[1957] Step 2:

[1958] Server: If an answer is not automatically generated, run the answer generation algorithm to generate an appropriate answer.

[1959] Step 3:

[1960] Server: Adjusts the response based on the recognized user's emotions.

[1961] Step 4:

[1962] Terminal: Displays the generated response to the user.

[1963] Follow-up methods

[1964] Step 1:

[1965] Server: Stores the consultation details and corresponding responses in a database.

[1966] Step 2:

[1967] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[1968] Step 3:

[1969] Server: Generates a follow-up message when the reminder time is reached.

[1970] Step 4:

[1971] Terminal: Sends the generated follow-up message to the user.

[1972] Emergency contact methods

[1973] Step 1:

[1974] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[1975] Step 2:

[1976] Terminal: Sends urgent information to the server.

[1977] Step 3:

[1978] Server: Based on the received information, it detects urgent keywords and uses an emotion engine to further confirm the urgent need.

[1979] Step 4:

[1980] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[1981] Step 5:

[1982] Terminal: Displays emergency instructions to the user, such as "We will call an ambulance immediately. We will also contact your family."

[1983] (Example 2)

[1984] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1985] The challenge lies in responding quickly and appropriately to the anxieties and worries that elderly people face in their daily lives, and in providing personalized support that takes their emotions into consideration. Furthermore, there is a need for a system that can respond quickly in the event of an emergency, but existing systems do not adequately meet these requirements.

[1986] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1987] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, an emotion recognition means, and a response adjustment means. This enables a rapid and appropriate response to the anxieties and worries of the elderly, and by considering the emotions of the elderly, it becomes possible to provide personalized support. Furthermore, a rapid response can be achieved in emergency situations.

[1988] "User registration means" refers to a means of providing an interface for elderly people to access the system and input and register necessary personal information.

[1989] A "consultation reception method" refers to a means of providing an interface for elderly people to log in to the system, input their consultation details in text format, and submit them.

[1990] A "response generation method" is a means for analyzing the received consultation content and generating an appropriate response.

[1991] "Follow-up methods" refer to means of generating and sending regular follow-up messages to elderly individuals.

[1992] "Emergency contact methods" refer to means of notifying registered contacts and appropriate authorities in the event of an emergency.

[1993] "Emotion recognition means" refers to methods for recognizing the emotions of elderly people based on the consultation content entered by the user.

[1994] "Response adjustment means" are methods for adjusting the content of responses based on the perceived emotions of elderly individuals.

[1995] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it also includes an emotion engine that recognizes the user's emotions.

[1996] User registration method

[1997] Terminal: The elderly person accesses the system and displays the user registration screen. Here, they enter and submit information such as their name, address, contact information, and emergency contact information.

[1998] Server: Receives the transmitted information and saves it to a database (e.g., MySQL).

[1999] Example: A user enters their name, phone number, and address on the registration screen and clicks the "Register" button. The server saves this information to the database.

[2000] Consultation methods

[2001] Terminal: The user logs in and displays the consultation request screen.

[2002] User: Enter your inquiry in text format.

[2003] Terminal: Sends the entered consultation details to the server.

[2004] Server: Receives the consultation content and analyzes it using a natural language processing (NLP) engine (e.g., spaCy, BERT). Furthermore, it uses an emotion engine (e.g., OpenAI's emotion recognition model) to recognize the user's emotions from the input consultation content.

[2005] Specific example: A user enters "I haven't been able to sleep lately," and the server receives and analyzes this, while also using an emotion engine to recognize the user's stress and anxiety.

[2006] Answer generation means

[2007] Server: Based on the category of the consultation content (e.g., sleep disorder) and the recognized emotion (e.g., anxiety), it searches the database for relevant data.

[2008] Server: Generates appropriate responses using a generation AI model (e.g., OpenAI's GPT-3) as needed. Adjusts the response content considering the user's sentiment data.

[2009] Terminal: Displays the generated response to the user.

[2010] Specific example: The server retrieves information about "sleep disorders" from its database and displays the message, "If you've been having trouble sleeping lately, it's important to avoid consuming caffeine before bed and create a relaxing environment."

[2011] Follow-up methods

[2012] Server: Stores consultation details and responses in a database.

[2013] Server: Sets follow-up reminders based on saved consultation history and recognized emotion data.

[2014] Server: When the reminder time is reached, it automatically generates a follow-up message.

[2015] Terminal: Sends the generated follow-up message to the user.

[2016] Specific example: A server generates a message one week later asking, "Have you been able to sleep since then?" and sends it to the user's terminal.

[2017] Emergency contact methods

[2018] User: When an emergency occurs, enter the details of the emergency into the consultation request screen.

[2019] Terminal: Sends urgent information to the server.

[2020] Server: Analyzes the received content and detects urgent keywords. Simultaneously, the emotion engine further confirms the urgency.

[2021] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities (e.g., emergency services).

[2022] Terminal: Displays the user with the message, "We will call an ambulance immediately. We will also contact your family."

[2023] For example, if a user enters "My chest hurts and I can't breathe," the server will recognize this as an emergency and automatically notify registered contacts and emergency authorities. A message prompting emergency action will also be displayed on the user's device.

[2024] Example of a prompt

[2025] "Please explain the steps to register for the senior support system."

[2026] "Please tell me how to analyze consultations regarding insomnia and provide appropriate advice."

[2027] "Please explain how to set up follow-up messages based on the content of the consultation."

[2028] "Please explain the procedures for responding in the event of an emergency."

[2029] As described above, this system quickly and appropriately resolves the anxieties and worries of the elderly, and enables more personalized responses through emotion recognition. Furthermore, it is designed to respond quickly even in emergencies.

[2030] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2031] Step 1:

[2032] Terminal: The user accesses the new registration screen. The user enters information such as name, address, phone number, and emergency contact information.

[2033] Input: User's (elderly) personal information.

[2034] Output: Data of the entered personal information.

[2035] Specific operation: When a user enters information into a form displayed in the browser and presses the "Register" button, the information is collected by the device.

[2036] Step 2:

[2037] Terminal: Sends the entered personal information to the server. Specifically, the data is sent as an HTTP request.

[2038] Input: Data of collected personal information.

[2039] Output: HTTP request to the server.

[2040] Specific operation: The terminal sends the form data to the server using the POST method.

[2041] Step 3:

[2042] Server: Stores received personal information in a database. This includes a process to verify data integrity and security.

[2043] Input: Personal information data sent to the server.

[2044] Output: Personal information stored in the database.

[2045] Specific operation: The server receives the data and saves it to the database using an SQL insert command.

[2046] Step 4:

[2047] Terminal: The user logs into the system and displays the consultation request screen.

[2048] Input: User login information.

[2049] Output: Consultation request screen.

[2050] Specific operation: When a user accesses the login screen in their browser, enters their ID and password, and clicks the "Login" button, the system performs authentication, and if successful, the consultation request screen is displayed.

[2051] Step 5:

[2052] User: Enter the consultation details in text format. Specifically, enter the text data of the consultation details.

[2053] Input: User's inquiry.

[2054] Output: Text data of the entered consultation content.

[2055] Specific action: The user enters their inquiry into a text box and presses the "Send" button.

[2056] Step 6:

[2057] Terminal: Sends the entered consultation details to the server. Specifically, the data is sent as an HTTP request.

[2058] Input: Text data of the entered consultation content.

[2059] Output: HTTP request to the server.

[2060] Specific operation: The terminal sends the form data to the server using the POST method.

[2061] Step 7:

[2062] Server: Analyzes received consultation content using a natural language processing (NLP) engine. It also uses an emotion engine to recognize the user's emotions from the input consultation content.

[2063] Input: Text data of the consultation content sent to the server.

[2064] Output: Analysis results and sentiment data.

[2065] Specific operation: Text analysis is performed using an NLP engine (e.g., spaCy, BERT), and then emotions are recognized using an emotion engine (e.g., OpenAI's emotion recognition model).

[2066] Step 8:

[2067] Server: Based on the category of the consultation content and the recognized emotions, it searches the database for relevant data. If necessary, it uses a generative AI model to generate an answer.

[2068] Input: Analysis results and sentiment data.

[2069] Output: Generated answer.

[2070] Specific operation: Execute database queries to retrieve relevant information. Generate new answers using a generated AI model (e.g., OpenAI's GPT-3) as needed.

[2071] Step 9:

[2072] Terminal: Displays the generated response to the user.

[2073] Input: Generated response.

[2074] Output: Display of the answer.

[2075] Specific operation: Send the response data to the terminal in HTML format and display it to the user.

[2076] Step 10:

[2077] Server: Stores the consultation content and the provided answers in a database. The stored content includes date and time, consultation content, answer content, recognized emotion data, etc.

[2078] Input: Consultation details, provided response, and sentiment data.

[2079] Output: Consultation history stored in the database.

[2080] Specific operation: The server receives the data and saves it to the database using an SQL insert command.

[2081] Step 11:

[2082] Server: Based on the saved data, set a follow-up reminder for one week later.

[2083] Input: Consultation history data.

[2084] Output: Reminder.

[2085] Specific operation: Set up reminder scheduling on the server and trigger the reminder at the follow-up date and time.

[2086] Step 12:

[2087] Server: When the reminder date and time arrive, it automatically generates a follow-up message.

[2088] Input: Reminder data.

[2089] Output: Follow-up message.

[2090] Specific operation: A scheduled reminder is triggered, and the message generation function generates a corresponding follow-up message.

[2091] Step 13:

[2092] Terminal: Sends the generated follow-up message to the user.

[2093] Input: Follow-up message.

[2094] Output: Notification of follow-up message.

[2095] Specific operation: Send a message using an HTTP request and display it in the browser.

[2096] Step 14:

[2097] User: When an emergency occurs, enter the details of your urgent request.

[2098] Input: Details of the urgent consultation.

[2099] Output: Text data of the entered emergency consultation details.

[2100] Specific action: The user enters their inquiry into the input form and presses the "Submit" button.

[2101] Step 15:

[2102] Terminal: Sends urgent information to the server.

[2103] Input: Text data of the emergency consultation details entered.

[2104] Output: HTTP request to the server.

[2105] Specific operation: The terminal sends the form data to the server using the POST method.

[2106] Step 16:

[2107] Server: Analyzes the received content and detects urgent keywords. Simultaneously, the emotion engine further confirms the urgency.

[2108] Input: Text data of the emergency consultation details.

[2109] Output: Emergency alert.

[2110] Specific actions: Analyze using an NLP engine and an emotion engine to determine urgency.

[2111] Step 17:

[2112] Server: Activates an emergency alert and sends notifications to registered emergency contacts and the appropriate authorities.

[2113] Input: Emergency alert.

[2114] Output: Notification message.

[2115] Specific operation: Retrieve registered contact information and send notifications using the messaging API.

[2116] Step 18:

[2117] Terminal: Displays the user with the message, "We will call an ambulance immediately. We will also contact your family."

[2118] Input: Notification message.

[2119] Output: Displays emergency instructions.

[2120] Specific action: The notification message is received on the device and displayed in the browser.

[2121] (Application Example 2)

[2122] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2123] In support systems that enable elderly people to live with peace of mind, there is a need to provide more personalized responses through emotion recognition. In particular, the challenge lies in responding quickly and appropriately to anxieties and emergencies that arise in daily life, and providing appropriate support that is tailored to their emotions, even for basic needs such as meals.

[2124] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[2125] In this invention, the server includes a user registration means, a consultation reception means, a response generation means, a follow-up means, an emergency contact means, an emotion recognition means, and an emotion adaptation means. This makes it possible to provide an advanced support system that allows elderly people to live with peace of mind and respond quickly to emergencies.

[2126] "User registration method" refers to a function that allows elderly people to access the system and enter and submit information such as their name, contact information, address, and emergency contact information.

[2127] The "consultation submission method" is a function that allows users to log in, enter their consultation details through the consultation screen, and send them to the system.

[2128] The "response generation means" is a function that analyzes the content of the consultation and generates an appropriate response based on that analysis.

[2129] The "follow-up method" is a function that sends periodic follow-up messages based on the content of the consultation and the history of the response.

[2130] "Emergency contact methods" refer to a function that allows users to notify registered contacts and appropriate authorities when they encounter an emergency.

[2131] "Emotion recognition means" refers to a function that detects emotions from the consultation content and comments entered by the user.

[2132] "Emotional adaptation measures" refer to functions that adjust the content of follow-up messages based on the recognized emotions of the user.

[2133] This invention provides a support system that enables elderly people to live with peace of mind. In addition to a series of functions such as receiving consultations from the elderly, generating appropriate responses, and providing follow-up and emergency contact, it includes an emotion engine that recognizes the user's emotions. The specific form of this system is shown below.

[2134] User registration method

[2135] Users access the system using their smartphones, enter information such as their name, contact details, address, and emergency contact details, and send it to the server. The server stores the submitted information in a database. The software used typically includes a database management system (e.g., SQLite).

[2136] Consultation methods

[2137] The user logs in using their smartphone and enters their consultation request in text format on the consultation request screen. The entered consultation request is sent to the server, which analyzes the received content using a natural language processing engine (e.g., Spacy) and an emotion recognition engine (e.g., Google Cloud Natural Language API). Here, the user's emotions are recognized along with the consultation request.

[2138] Answer generation means

[2139] The server generates appropriate responses based on the analyzed consultation content and sentiment data. The generated responses are displayed on the smartphone. If necessary, it searches for relevant information in the database and further refines the responses using a generation AI model.

[2140] Follow-up methods

[2141] The server stores the consultation details and response records in a database and generates a follow-up message after a certain period of time has elapsed. This follow-up message is tailored based on the perceived emotions and sent to the user. Automated scheduling software (e.g., cron jobs) is used to set the reminders.

[2142] Emergency contact methods

[2143] If a user experiences an emergency, they enter the details of their urgent need using their smartphone. The server receives this information and uses an emotion recognition engine to confirm the urgency. If necessary, notifications are automatically sent to emergency contacts and the appropriate authorities. The software used here includes notification services (e.g., Twilio).

[2144] Specific example

[2145] The following is a specific example of a scenario in which elderly people use a food delivery system.

[2146] 1. Elderly individuals access the system using their smartphones and register their names, contact information, etc.

[2147] 2. When ordering breakfast, if you enter a message such as "I haven't had much of an appetite lately," the server will receive this message and perform emotion recognition.

[2148] 3. The server generates appropriate advice, such as "Choose easily digestible foods and consult a doctor if necessary," and displays it on the smartphone.

[2149] 4. One week later, a follow-up message will be automatically sent asking, "Has your appetite improved at all?"

[2150] Example of a prompt

[2151] "Please suggest how to care for an elderly person who has recently lost their appetite. The emotion recognition engine should detect a state of distress and provide appropriate advice. Also, please send a follow-up message one week later."

[2152] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2153] Step 1: User Registration

[2154] Users access the system using their smartphones and enter information such as their name, contact information, address, and emergency contact information. The entered information is sent to the server when the user presses the "Register" button. The server stores the received information in a database. The input data consists of personal information such as names, while the output data is user information recorded in the database. Specifically, the user enters data into input fields on the smartphone screen and then presses the "Register" button.

[2155] Step 2: Consultation

[2156] Users log in to the system using their smartphones and enter their consultation details in text format. The entered consultation details are sent to the server by pressing the "Send" button. The server receives these consultation details and analyzes them using a natural language processing engine and an emotion recognition engine. The input data is the text of the consultation details, and the output data consists of the analysis results and recognized emotion data. Specifically, the user enters their consultation details on their smartphone and presses the "Send" button.

[2157] Step 3: Generate Answer

[2158] The server generates appropriate responses based on the analyzed consultation content and sentiment data. It searches for relevant information in the database and adjusts the response using a generative AI model as needed. The input data is the analyzed consultation content and sentiment data, and the output data is the generated response text. Specifically, the server executes database queries and operates the generative AI model to generate responses.

[2159] Step 4: Display the answer

[2160] The generated response is sent from the server to the user's smartphone and displayed on the screen. The input data is the response text sent from the server, and the output data is the response displayed on the smartphone screen. Specifically, the smartphone app receives a notification from the server and displays the data on the screen.

[2161] Step 5: Setting up follow-up messages

[2162] The server stores the consultation details and their corresponding records in a database and sets reminders to generate follow-up messages after a specified period has elapsed. The input data consists of the consultation details and their corresponding records, while the output data consists of the set reminders. Specifically, the server uses automated scheduling software (e.g., cron jobs) to set the reminders.

[2163] Step 6: Send a follow-up message

[2164] When the reminder time arrives, the server generates a follow-up message and sends it to the user's smartphone. The input data is the reminder and its related information, as well as the set sentiment data, while the output data is the generated follow-up message. Specifically, the server automatically generates the follow-up message at the designated time and sends the message using a notification service.

[2165] Step 7: Emergency Contact

[2166] When a user encounters an emergency, they use their smartphone to input and send an urgent request. The server receives the information, uses an emotion recognition engine to confirm the urgency, and then notifies emergency contacts and the appropriate authorities. The input data is the text of the emergency request, and the output data is the notification message to emergency contacts and authorities. Specifically, an emergency response algorithm is executed, and an alert is quickly sent through the notification service.

[2167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2168] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2169] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2171] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[2172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[2173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[2174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[2175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[2176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[2177] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[2178] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[2179] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[2180] 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.

[2181] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[2182] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[2183] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[2184] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[2185] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[2186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[2187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[2188] The following is further disclosed regarding the embodiments described above.

[2189] (Claim 1)

[2190] A user registration method for elderly people to register in the system,

[2191] A means for receiving consultations for the elderly person to input the details of the consultation,

[2192] A response generation means that analyzes the aforementioned consultation content and generates an appropriate response,

[2193] A follow-up means for sending follow-up messages to the aforementioned elderly person on a regular basis,

[2194] In an emergency, an emergency contact system is in place to notify registered contacts and the appropriate authorities.

[2195] A system that includes this.

[2196] (Claim 2)

[2197] The system according to claim 1, further comprising an analytical means for classifying the content of the consultation into categories.

[2198] (Claim 3)

[2199] The system according to claim 1, wherein the follow-up means includes means for setting a reminder based on the consultation record.

[2200] "Example 1"

[2201] (Claim 1)

[2202] A means for elderly people to register as users in the system,

[2203] A means for receiving consultations for the elderly person to input the details of the consultation,

[2204] A response generation means that analyzes the aforementioned consultation content and generates an appropriate response,

[2205] A follow-up means for sending follow-up messages to the aforementioned elderly person on a regular basis,

[2206] In an emergency, an emergency contact system is in place to notify registered contacts and the appropriate authorities.

[2207] A natural language processing means that detects an emergency by receiving and analyzing the aforementioned consultation content and takes necessary measures,

[2208] A system that includes this.

[2209] (Claim 2)

[2210] The system according to claim 1, further comprising an analytical means for classifying the content of the consultation into categories.

[2211] (Claim 3)

[2212] The system according to claim 1, wherein the follow-up means includes means for setting a reminder based on the consultation record.

[2213] "Application Example 1"

[2214] (Claim 1)

[2215] A user registration method for elderly people to register in the system,

[2216] A means for receiving consultations for the elderly person to input the details of the consultation,

[2217] A response generation means that analyzes the aforementioned consultation content and generates an appropriate response,

[2218] A follow-up means for sending follow-up messages to the aforementioned elderly person on a regular basis,

[2219] In an emergency, an emergency contact system is in place to notify registered contacts and the appropriate authorities.

[2220] An application installed on a robot designed to support the lives of the elderly, wherein the robot performs voice input and output, and uses natural language processing and generative AI models to analyze the content of the consultation and generate a response.

[2221] A system that includes this.

[2222] (Claim 2)

[2223] The system according to claim 1, further comprising an analytical means for classifying the content of the consultation into categories.

[2224] (Claim 3)

[2225] The system according to claim 1, wherein the follow-up means includes means for setting a reminder based on the consultation record.

[2226] "Example 2 of combining an emotion engine"

[2227] (Claim 1)

[2228] A user registration method for elderly people to register in the system,

[2229] A means for receiving consultations for the elderly person to input the details of the consultation,

[2230] A response generation means that analyzes the aforementioned consultation content and generates an appropriate response,

[2231] A follow-up means for sending follow-up messages to the aforementioned elderly person on a regular basis,

[2232] In an emergency, an emergency contact system is in place to notify registered contacts and the appropriate authorities.

[2233] A means of recognizing the emotions of elderly people based on the content of the consultation,

[2234] A response adjustment means that adjusts the content of the response based on the emotions of the elderly person,

[2235] A system that includes this.

[2236] (Claim 2)

[2237] The system according to claim 1, further comprising an analytical means for classifying the content of the consultation into categories.

[2238] (Claim 3)

[2239] The system according to claim 1, wherein the follow-up means includes means for setting a reminder based on the consultation record.

[2240] "Application example 2 when combining with an emotional engine"

[2241] (Claim 1)

[2242] A user registration method for elderly people to register in the system,

[2243] A means for receiving consultations for the elderly person to input the details of the consultation,

[2244] A response generation means that analyzes the aforementioned consultation content and generates an appropriate response,

[2245] A follow-up means for sending follow-up messages to the aforementioned elderly person on a regular basis,

[2246] Emergency contact means that will notify registered contacts and appropriate authorities in the event of an emergency,

[2247] An emotion recognition means for recognizing comments and emotions included in the aforementioned consultation content,

[2248] An emotion adaptation means that adjusts follow-up messages based on the recognized emotions,

[2249] A system that includes this.

[2250] (Claim 2)

[2251] The system according to claim 1, wherein the emotion recognition means includes an analysis means that detects emotions from the content of a consultation using a generative AI model.

[2252] (Claim 3)

[2253] The system according to claim 1, wherein the follow-up means includes means for setting reminders based on consultation records and detected emotions. [Explanation of Symbols]

[2254] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A user registration method for elderly people to register in the system, A means for receiving consultations for the elderly person to input the details of the consultation, A response generation means that analyzes the aforementioned consultation content and generates an appropriate response, A follow-up means for sending follow-up messages to the aforementioned elderly person on a regular basis, In an emergency, an emergency contact system is in place to notify registered contacts and the appropriate authorities. A system that includes this.

2. The system according to claim 1, further comprising an analytical means for classifying the content of the consultation into categories.

3. The system according to claim 1, wherein the follow-up means includes means for setting a reminder based on the consultation record.

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