System

Abstract

A system is provided.SOLUTION: A system comprising: speech recognition means; generation and AI means; document generation means; means for collecting speech input from a user and converting the speech input into text by the speech recognition means; means for generating a response based on the text by using the generation and AI means; means for providing the generated response to the user; and means for collecting necessary information based on the response and generating a document by using the document generation means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Please propose the "problem the invention aims to solve" and "means for solving the problem" in your book using the following format.

[0005] The present invention aims to solve the problem of communication regarding end-of-life planning becoming more difficult among family members living far apart due to the trend toward nuclear families and an increasing elderly population. In particular, gathering and organizing important information, such as inheritance from parents and desired funeral arrangements, requires specialized knowledge and face-to-face communication. However, busy daily lives and distance often make face-to-face communication difficult. Under these circumstances, there is a need for a support tool that allows smooth end-of-life planning even without specialized knowledge. [Means for solving the problem]

[0006] The present invention solves the above-mentioned problems by providing a system that includes a speech recognition means, a generation AI means, and a document generation means. Specifically, the system includes a means for collecting speech input from a user and converting it into text data using the speech recognition means. It also includes a means for generating a response based on this text data using the generation AI means and providing that response to the user. Necessary information can be collected through a dialogue between the user and the generation AI, and documents necessary for end-of-life planning can be automatically generated using the document generation means. This system allows users to easily prepare for end-of-life planning without specialized knowledge.

[0007] A "voice recognition means" is a means for capturing a user's voice input and converting the voice into text data.

[0008] "Generative AI means" refers to means that use natural language processing technology to generate appropriate responses based on collected text data.

[0009] The "document generation means" is a means for automatically generating documents necessary for the user's end-of-life planning based on the collected information.

[0010] "Voice input from the user" refers to the act of the user verbally communicating their requests and questions regarding end-of-life planning to the system.

[0011] "Text data" is character information of voice input converted by a voice recognition means.

[0012] "Means for generating responses" refers to a function that uses generative AI means to generate appropriate answers or next questions from text data.

[0013] "System" refers to a comprehensive support tool that includes speech recognition means, generation AI means, and document generation means. [Brief explanation of the drawings]

[0014] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention is implemented by a system including a speech recognition unit, a generation AI unit, and a document generation unit. This system operates as follows.

[0036] System initialization

[0037] The server creates Recognizer and Microphone objects for speech recognition, instantiates a generative AI model, and a document generator, and the system is now ready to capture voice input, generate appropriate responses, and finally create the required documents.

[0038] Capturing voice input

[0039] The user speaks to the system about their end-of-life wishes (e.g., "I want to prepare for my inheritance"). The device uses a microphone to capture the user's voice and converts the voice data into text data via the Google speech recognition service.

[0040] AI-powered response generation

[0041] The server sends the text data obtained by speech recognition to the AI ​​generator, which generates an appropriate response. For example, this response might be in the form of "Please tell us how you would like to distribute your inheritance." The terminal provides the generated response to the user and prompts for further input.

[0042] Collection of necessary information

[0043] The user responds to questions posed by the AI ​​by voice, such as "I want to distribute the property to my eldest son and the cash to my second son." The device again captures this as voice data, and the server sends the text data to the AI, which then generates further questions and provides them to the user. This process is repeated until all the necessary information is collected.

[0044] Creation of end-of-life documents

[0045] Once all the necessary information has been collected, the server uses a document generation tool to generate end-of-life documents (e.g., wills, funeral plans) based on the collected data. The terminal presents the generated documents to the user, which can be provided as digital files or paper-based.

[0046] Specific examples

[0047] Example 1: Preparing for inheritance

[0048] 1. The user says, "I want to prepare for inheritance."

[0049] 2. The device captures this audio, and the server converts it into text data through a speech recognition service.

[0050] 3. The server uses its generative AI means to generate a response such as "Tell us which inheritances you would like to distribute and how."

[0051] 4. The user responds by voice, "I want to distribute the real estate to my eldest son and the cash to my second son."

[0052] 5. The server continues to collect information, and once all necessary information is gathered, it uses the document generation means to generate a document for the inheritance.

[0053] 6. The terminal presents this document to the user, who then provides final confirmation.

[0054] In this way, the present invention is a system that utilizes voice recognition and generative AI to enable users to easily prepare for end-of-life planning even without specialized knowledge.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] The server initializes the system. Specifically, it creates Recognizer and Microphone objects for speech recognition, and creates instances of the generative AI model and document generation means.

[0058] Step 2:

[0059] The user speaks to the system about their end-of-life requests (e.g., "I want to prepare for my inheritance").

[0060] Step 3:

[0061] The terminal captures the user's voice input through a microphone and obtains it as voice data.

[0062] Step 4:

[0063] The terminal transmits the captured voice data to a voice recognition service via the Internet, and converts the voice data into text data.

[0064] Step 5:

[0065] The server sends the text data to a generative AI means that generates an appropriate response based on the input, for example, formulating a question such as "Please tell us how you would like to distribute your inheritance."

[0066] Step 6:

[0067] The terminal presents the user with the response generated by the generative AI means, and the user is provided with a question by voice or text.

[0068] Step 7:

[0069] The user responds to the generated question by voice with additional information (e.g., "I want to distribute the real estate to my eldest son and the cash to my second son").

[0070] Step 8:

[0071] The terminal again captures the user's response through the microphone and obtains it as voice data.

[0072] Step 9:

[0073] The terminal transmits the captured voice data to a voice recognition service and converts it back into text data.

[0074] Step 10:

[0075] The server continues to collect necessary information using the acquired text data, for example, by generating the next question using a generative AI means and presenting it to the user via the terminal.

[0076] Step 11:

[0077] The device repeats this process, continuing to capture voice and generate AI-generated responses until all the necessary information is collected.

[0078] Step 12:

[0079] The server uses a document generation means to generate documents necessary for end-of-life preparations (such as a will or funeral plan) based on the collected user data.

[0080] Step 13:

[0081] The terminal presents the generated document to the user, which can be provided as a digital file or on paper.

[0082] Step 14:

[0083] The user finally reviews and completes the presented document.

[0084] Example 1

[0085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] Conventional end-of-life preparations have been problematic in that general users without specialized knowledge have difficulty creating complex documents and gathering information, resulting in significant stress and effort. In particular, there are no systems that utilize voice input to efficiently and interactively collect necessary information and generate accurate and appropriate documents based on that information. This makes it difficult for users to prepare for end-of-life planning on their own, and in many cases requires the assistance of an expert. The present invention aims to provide a system that solves these problems.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0088] In this invention, the server includes a speech recognition means, a generation AI means, a document generation means, a means for collecting speech input from a user and converting it into text data using the speech recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, a means for equipping the terminal with a microphone for capturing speech data, a means for initializing the speech recognition means and generating an instance of the generation AI model, and a means for receiving and displaying information from the user through a user interface. This makes it possible for a user to collect necessary information for easy and efficient end-of-life preparations through speech input and automatically generate accurate documents based on that information.

[0089] "Speech recognition means" refers to technical means for converting a user's voice input into text data.

[0090] "Generative AI means" refers to artificial intelligence technical means for generating appropriate responses or questions for users based on acquired text data.

[0091] The "document generation means" is a technical means for automatically creating a document in a specified format based on collected information.

[0092] "Means for collecting voice input from a user and converting it into text data by voice recognition means" refers to a technical means for capturing a user's voice using a voice input device such as a microphone and converting it into text data.

[0093] "Means for generating a response based on text data using generative AI means" refers to technical means that utilizes a generative AI model to create an appropriate response to a user from text data.

[0094] "Means for providing the generated response to the user" refers to technical means for providing the response generated by the generation AI means to the user by means of screen display, audio output, etc.

[0095] "Means for collecting necessary information based on responses and generating documents using document generation means" refers to technical means for collecting information based on user responses and using this information to generate necessary documents using document generation means.

[0096] "Means for providing a terminal with a microphone for capturing voice data" refers to technical means for providing a microphone in a terminal for receiving voice input from a user and recording the voice data.

[0097] "Means for initializing speech recognition means and generating an instance of a generative AI model" refers to technical means for performing the initial settings required to start the operation of speech recognition technology and generative AI technology and generating an instance of each.

[0098] "Means for receiving and displaying information from a user through a user interface" refers to technical means for providing an interface for a user to input information and for displaying the input.

[0099] The present invention is a system that includes a voice recognition unit, a generation AI unit, and a document generation unit, which allows users to easily prepare for end-of-life planning. This system operates as follows.

[0100] Initialization

[0101] The server creates Recognizer and Microphone objects for speech recognition, instantiates a generative AI model, and prepares LaTeX and Word templates for document generation. This initialization process ensures that the server is ready to capture voice input, generate appropriate responses, and ultimately create the required documents.

[0102] Capturing voice input

[0103] The user speaks to the system about their wishes regarding end-of-life planning. The device uses a microphone to capture the user's voice, and converts the voice data into text data using a voice recognition service via the Internet. For example, the user might say, "I want to prepare for my inheritance."

[0104] AI-powered response generation

[0105] The server sends the converted text data to the AI ​​generator, which generates an appropriate response, such as "Please tell us how you would like to distribute your inheritance." The terminal provides the generated response to the user and prompts for further input.

[0106] Collection of necessary information

[0107] The user responds to questions posed by the AI ​​by voice, such as "I want to distribute the property to my eldest son and the cash to my second son." The device again captures this as voice data, and the server sends the text data to the AI ​​generator, which generates further questions and provides them to the user. This process is repeated until all the necessary information is collected.

[0108] Creation of end-of-life documents

[0109] Once all the necessary information has been collected, the server uses a document generation tool to generate end-of-life documents (such as a will or funeral plan) based on the collected data. The generated document is saved in a format such as PDF and displayed to the user on the device. The user can then review the document and make any necessary corrections. The final document can be saved as a digital file or printed on paper.

[0110] Specific examples

[0111] Example 1: Preparing for inheritance

[0112] 1. The user says, "I want to prepare for inheritance."

[0113] 2. The device captures this audio, and the server converts it into text data using Google speech recognition services.

[0114] 3. The server uses its generative AI means to generate a response such as "Tell us which inheritances you would like to distribute and how."

[0115] 4. The user responds by voice, "I want to distribute the real estate to my eldest son and the cash to my second son."

[0116] 5. The server continues to collect information, and once all necessary information is gathered, it uses the document generation means to generate a document for the inheritance.

[0117] 6. The terminal presents this document to the user, who then provides final confirmation.

[0118] In this way, the present invention is a system that utilizes voice recognition and generative AI to enable users to easily prepare for end-of-life planning even without specialized knowledge.

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] The server initializes the system. Specifically, it creates the Recognizer and Microphone objects of the speech recognition library and creates an instance of the generative AI model. It also prepares LaTeX and Word templates as a means of document generation. The input here is the system startup signal, and the output is the instance of each initialized object.

[0122] Step 2:

[0123] The user speaks their wishes regarding end-of-life planning into the device. For example, they might say, "I want to prepare for my inheritance." This voice data becomes the input.

[0124] Step 3:

[0125] The device uses a microphone to capture the user's voice, and then sends the voice data to a voice recognition service via the Internet to convert it into text data. The input here is voice data, and the output is text data. For example, the text data obtained is "I would like to prepare for my inheritance."

[0126] Step 4:

[0127] The server sends the text data obtained by speech recognition to the generative AI model, which generates an appropriate response based on the input text. The generative AI model outputs a generated response, such as a prompt, "Please tell us how you would like to distribute your inheritance."

[0128] Step 5:

[0129] The device displays or speaks the response received from the generative AI model to the user. Here, the response text returned from the server is received as input, and the output is presented to the user.

[0130] Step 6:

[0131] The user answers questions posed by the AI ​​by voice. For example, they might say, "I want to distribute the real estate to my eldest son and the cash to my second son." The user's voice response becomes the input.

[0132] Step 7:

[0133] The device again uses a microphone to capture the user's voice and converts the voice data into text data using a voice recognition service. The input here is voice data, and the output is text data. For example, the text data obtained is "I want to distribute the real estate to my eldest son and the cash to my second son."

[0134] Step 8:

[0135] The server sends the text data back to the generative AI model to generate a further question. For example, a question like "What is the address of the property?" is generated. The input here is the user's answer text data, and the output is the next question text.

[0136] Step 9:

[0137] The server and terminal repeat this process until all necessary information is collected. Finally, based on the collected information, the server uses a document generation means to generate end-of-life documents, such as a will or funeral plan. The input is the collected information text data, and the output is the generated end-of-life documents.

[0138] Step 10:

[0139] The terminal presents the generated document to the user, who then performs final confirmation. The user can check the document content and make corrections as necessary. The input is the generated document data, and the output is the final document confirmed by the user.

[0140] In this way, by clearly indicating the inputs and outputs at each processing step and explaining the specific operations, the overall processing flow of the system can be understood.

[0141] (Application example 1)

[0142] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0143] In modern society, personal end-of-life planning and security management are important issues. However, it is difficult for users without specialized knowledge to perform these tasks efficiently and safely. In particular, emergency contact and password management, as well as automatic emergency contact functions, are necessary to reduce the burden on users and improve safety, and there is a demand for methods to easily implement these functions.

[0144] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0145] In this invention, the server includes a voice recognition means, a generation AI means, a document generation means, a means for collecting voice input from a user and converting it into text data by the voice recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, a means for registering and managing the user's emergency contact information and password information by voice, and a means for automatically sending a notification to designated contacts in the event of an emergency. This enables users to efficiently and safely prepare for end-of-life planning and manage security without having specialized knowledge.

[0146] A "voice recognition means" is a device or software that has the function of capturing a user's voice input and converting the voice data into text data.

[0147] A "generative AI means" is a means that uses an artificial intelligence model to generate appropriate responses or questions based on text data.

[0148] "Document generation means" refers to a device or software that has the function of automatically generating the necessary documents based on collected information.

[0149] The "means for collecting voice input from a user and converting it into text data by a voice recognition means" refers to a means for executing a process of collecting a user's voice and converting the voice into a text format by voice recognition.

[0150] "Means for generating responses based on text data using generative AI means" refers to means for executing a process that uses a generative AI model to generate appropriate responses or questions based on text data obtained through speech recognition.

[0151] The "means for providing a generated response to a user" is a means for executing a process for presenting a response created by the generation AI means to a user.

[0152] "Means for collecting necessary information based on the response and generating a document using a document generation means" refers to means for executing a process of collecting additional information from the user in accordance with the response provided by the generation AI means and automatically generating a necessary document based on that information.

[0153] The "means for registering and managing a user's emergency contact and password information by voice" refers to a means for executing a process in which a user inputs emergency contact and password information by voice, and registers and manages this information in a database.

[0154] The "means for automatically sending a notification to a designated contact in an emergency" is a means having a function for automatically sending a notification to a user-designated contact in the event of a specific emergency.

[0155] The present invention is implemented by a speech recognition means, a generation AI means, a document generation means, and a speech-based information collection and management system. This system efficiently recognizes information input by a user through speech, collects and manages necessary information, and generates documents based on the information.

[0156] System Configuration

[0157] The system uses the following major hardware and software:

[0158] Hardware: smartphones, head-mounted displays, smart glasses, microphones, servers

[0159] Software: Google speech recognition services, generative AI models (e.g., OpenAI GPT), databases for data storage (e.g., Firebase)

[0160] Program processing overview

[0161] 1. Capture voice input

[0162] The server captures the user's voice input via devices such as smartphones and head-mounted displays, and uses the Google speech recognition service to convert the captured voice data into text data.

[0163] 2. Response generation using generative AI

[0164] The server sends the text data obtained by speech recognition to a generative AI model, which generates appropriate responses and follow-up questions. The generated responses are presented to the user, prompting them for further speech input.

[0165] 3. Information collection and management

[0166] The user answers questions posed by the AI ​​by voice, which collects important information such as emergency contacts and passwords. This information is then stored and managed in a database. Settings are also made to automatically send notifications to designated contacts in the event of an emergency.

[0167] 4. Document Generation

[0168] After all the necessary information has been collected, the server uses a document generation tool to automatically generate end-of-life and security-related documents, which are then presented to the user in digital or paper form.

[0169] Specific examples

[0170] Example 1: Registering an emergency contact

[0171] User: "My wife's phone number is 090-xxxx-xxxx."

[0172] Voice data is captured through the smartphone's microphone and converted into text data using Google's voice recognition service.

[0173] The server uses a generative AI model to generate a response based on the text data, and returns a response to the user in the form of, for example, "Your phone number has been registered."

[0174] Emergency contact information is stored in the database.

[0175] Example 2: Managing Passwords

[0176] User: "My Amazon password is abc123"

[0177] Voice data is captured and converted into text data using the Google speech recognition service.

[0178] The server uses the generative AI model to generate a response such as "Your password has been registered" and presents it to the user.

[0179] Passwords are stored securely in a database.

[0180] Example 3: Automatic contact in an emergency

[0181] In the system settings, the user says, "In case of an emergency, please contact my son's cell phone."

[0182] Once all necessary information has been collected, designated contacts will be automatically notified in the event of an emergency.

[0183] Prompt Sentence Examples

[0184] Check the security status

[0185] Please register your emergency contact information.

[0186] "Would you like to update your password?"

[0187] In this way, the present invention utilizes voice recognition and generative AI, enabling users to prepare for end-of-life planning and manage personal information efficiently and safely, even without specialized knowledge.

[0188] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0189] Step 1:

[0190] The server captures the user's voice input. The microphone on the smartphone or head-mounted display is used to collect the voice data the user speaks. The data is sent to the Google speech recognition service. The input is the user's voice, and the output is text data.

[0191] Step 2:

[0192] The server converts the received voice data into text data using a voice recognition service. The server uses the Google voice recognition service as the voice recognition method and converts the received voice data into text data. The input is voice data and the output is text data.

[0193] Step 3:

[0194] The server sends the text data to a generative AI model, which uses the model (e.g., OpenAI GPT) to generate appropriate responses or questions based on the speech-recognized text data. The input is the text data, and the output is the generated response or question.

[0195] Step 4:

[0196] The server provides the generated response to the user. The generated response is presented to the user through the screen or speaker of a smartphone or head-mounted display. The input is the response from the generative AI model, and the output is what is presented to the user.

[0197] Step 5:

[0198] The user answers questions posed by the AI ​​by voice. For example, they provide information such as "My Amazon password is abc123." The input is the user's voice input in response to the questions posed by the AI, and the output is the user's voice data.

[0199] Step 6:

[0200] The server again captures the voice input and converts it to text data. As in steps 1 and 2, the voice data is stored in temporary storage and sent to the Google speech recognition service for conversion to text data. The input is voice data and the output is text data.

[0201] Step 7:

[0202] The server uses generation AI to collect any further required information and save it in a database. If more information is needed based on the user's response data, additional questions are generated and additional information is collected from the user. This process is repeated until all the necessary information has been collected and the data is saved in the database. The input is text data from the user, and the output is saved in the database.

[0203] Step 8:

[0204] The server generates a document using the document generation means. Based on all the collected information, it generates the necessary document (e.g., end-of-life document or security information document). The input is the information stored in the database, and the output is the generated document.

[0205] Step 9:

[0206] The server presents the generated document to the user, who then reviews the document and makes any necessary corrections. The input is the generated document, and the output is the presentation and review to the user.

[0207] Step 10:

[0208] The system automatically sends notifications to designated contacts in the event of an emergency. Based on the user's settings, the system automatically sends emails and messages to designated contacts in the event of an emergency. The input is the configured emergency contact information, and the output is the automatically sent notification.

[0209] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0210] The present invention is a system that provides more personalized end-of-life planning support by adding an emotion recognition engine to recognize the user's emotional state and adjusting responses based on that. This system performs the following operations.

[0211] System initialization

[0212] The server initializes the system by instantiating a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine, which enables capturing voice input, recognizing emotions, generating appropriate responses, and creating documents.

[0213] Voice input capture and emotion recognition

[0214] The user speaks to the system about their end-of-life wishes (e.g., "I want to prepare for my inheritance"). The device uses a microphone to capture this voice. At the same time, an emotion recognition engine analyzes the user's emotional state from their voice in real time.

[0215] Converting voice data and sending emotion data

[0216] The device sends the captured voice data to a voice recognition service, where it is converted into text data. The emotion recognition engine also analyzes the data and sends it to the server.

[0217] AI-powered response generation

[0218] The server sends the text data and emotional data to the AI ​​generator, which generates a response based on the user's emotional state. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the AI ​​generator will respond in a gentle tone, "How would you like to distribute it?"

[0219] Collection of necessary information

[0220] The user responds to the generated response with additional information by voice, which the device captures again and converts into text data via a speech recognition service. At the same time, an emotion recognition engine analyzes the user's emotional state and sends that data to the server.

[0221] Creation of end-of-life documents

[0222] After all the necessary information has been collected, the server uses a document generation means to generate end-of-life documents (such as a will or funeral plan) based on the collected data.

[0223] Present and verify documents

[0224] The terminal presents the generated document to the user, either as a digital file or on paper, for final review.

[0225] Specific examples

[0226] Example 1: Preparing for inheritance

[0227] 1. The user says, "I want to prepare for inheritance."

[0228] 2. The device captures the voice and an emotion recognition engine analyzes the user's emotions. In this case, if the user expresses anxiety, that data is also captured.

[0229] 3. The device converts the voice data into text data and sends it to the server along with the emotion data.

[0230] 4. The server uses generative AI methods to generate a gentle response based on the user's feelings of anxiety, such as "Don't worry. Just tell us how you would like to distribute your inheritance."

[0231] 5. The user responds, "I would like to distribute the real estate to my eldest son and the cash to my second son."

[0232] 6. The device captures the voice again, and the emotion recognition engine analyzes the user's emotions. The voice data is converted into text and sent to the server along with the emotion data.

[0233] 7. This process is repeated until all necessary information is collected.

[0234] 8. The server finally generates a document based on the collected data and sentiment.

[0235] 9. The terminal presents the generated document to the user, who reviews the proposed document and suggests corrections if necessary.

[0236] In this way, the present invention is a system that uses an emotion recognition engine to provide responses that take into consideration the user's emotions, thereby enabling smoother end-of-life support with less psychological burden.

[0237] The processing flow will be explained below.

[0238] Step 1:

[0239] The server initializes the system. Specifically, it creates instances of the Recognizer and Microphone objects for speech recognition, the generative AI model, the document generation means, and the emotion recognition engine.

[0240] Step 2:

[0241] The user speaks a request regarding end-of-life planning, such as "I want to prepare for inheritance."

[0242] Step 3:

[0243] The terminal captures the user's voice input through a microphone and obtains it as voice data.

[0244] Step 4:

[0245] The device sends the captured voice data to an emotion recognition engine to analyze the user's emotional state in real time.

[0246] Step 5:

[0247] The terminal transmits the voice data to a voice recognition service via the Internet, and converts the voice data into text data.

[0248] Step 6:

[0249] The server sends the converted text data and the emotional state data obtained from the emotion recognition engine to the generation AI means, which then generates an appropriate response based on the user's emotions. For example, if the user is feeling anxious, it generates a gentle response such as, "Don't worry. Please tell us how you would like to distribute your inheritance."

[0250] Step 7:

[0251] The device provides the user with a response generated by the generative AI means, either by voice or text.

[0252] Step 8:

[0253] The user answers the generated question by voice with specific details (e.g., "I want to distribute the real estate to my eldest son and the cash to my second son").

[0254] Step 9:

[0255] The device captures the user's voice again, and the emotion recognition engine analyzes the user's emotional state and captures it as data.

[0256] Step 10:

[0257] The terminal converts the voice data into text data and transmits it to the server together with the acquired emotion data.

[0258] Step 11:

[0259] The server then uses the generative AI means again based on the received text data and emotion data to generate the next question, or proceeds to the next processing step if no additional information is needed.

[0260] Step 12:

[0261] The device repeats this process until all the necessary information is collected, continually capturing input and emotions from the user and generating responses.

[0262] Step 13:

[0263] The server uses a document generation means to generate documents necessary for end-of-life planning (such as a will or funeral plan) based on all collected user data and emotion data.

[0264] Step 14:

[0265] The terminal presents the generated document to the user, either as a digital file or on paper, for final review.

[0266] Step 15:

[0267] The user checks the generated document and, if necessary, indicates corrections by voice.

[0268] This allows the system of the present invention to efficiently support end-of-life preparations while taking into consideration the user's feelings.

[0269] Example 2

[0270] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0271] Conventional end-of-life planning support systems have had the problem of being difficult to respond to users' emotions, which can lead to a heavy psychological burden. In particular, when it comes to the delicate topic of end-of-life planning, it is important to respond to the emotions of users, but conventional technologies have not been able to fully achieve this.

[0272] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a voice recognition means, a generation AI means, a document generation means, a means for collecting voice input from the user and converting it into text data by the voice recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, an emotion recognition means for analyzing emotions from the user's voice in real time, and a means for the generation AI means to generate a response according to the user's emotional state based on the emotion data acquired by the emotion recognition means. This makes it possible to provide a response that takes the user's emotions into consideration, reduce psychological burden, and enable smooth end-of-life support.

[0273] 1. "Speech recognition means" means a device or method capable of converting speech input from a user into text data.

[0274] 2. "Generative AI means" refers to technology that generates appropriate responses based on the user's text data.

[0275] 3. "Document generation means" means a device or method that has the function of generating documents based on collected information.

[0276] 4. "Emotion recognition means" is a technology that analyzes emotions from the user's voice in real time.

[0277] 5. A "means for collecting voice input" is a device or method capable of capturing a user's speech.

[0278] 6. "Means for converting into text data" refers to a function for converting captured audio data into text data.

[0279] 7. "Means for generating responses" refers to a function that generates responses that meet the user's needs based on text data and emotional data.

[0280] 8. "Means for providing a response" means the functionality for communicating the generated response to the user.

[0281] 9. "Means for gathering required information" refers to the functionality for obtaining additional information from the user based on the response generated.

[0282] 10. "Means for generating a response based on emotional data" refers to a function that uses emotional data acquired by the emotion recognition means to generate a response according to the user's emotional state.

[0283] This invention is a system that provides more personalized support for end-of-life planning by adding an emotion recognition engine to recognize the user's emotional state and adjusting responses based on that. This system uses the following hardware and software.

[0284] Hardware and software used

[0285] Recognizer object: Used to recognize the user's voice input and functions as a voice recognition means.

[0286] Microphone object: A device for capturing user speech and serves as a means of collecting audio input.

[0287] Generative AI model: A technology that generates appropriate responses based on the user's text data.

[0288] Document generation means: Has the function of generating end-of-life documents based on collected information.

[0289] Emotion recognition engine: A technology that analyzes emotions from the user's voice in real time, and functions as an emotion recognition means.

[0290] System Operation

[0291] 1. During system initialization, the server instantiates a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine, enabling it to capture voice input, recognize emotions, generate appropriate responses, and create documents.

[0292] Specific examples

[0293] Example 1: Preparing for inheritance

[0294] 1. The user says, "I want to prepare for my inheritance."

[0295] 2. The device uses a Microphone object to capture the user's voice. At the same time, the emotion recognition engine analyzes the user's voice in real time to determine emotions. In this case, if the user expresses anxiety, the device also captures that data.

[0296] 3. The device sends the captured voice data to the voice recognition service, which converts it into text data. The converted text data and the emotion data analyzed by the emotion recognition engine are then sent to the server.

[0297] 4. The server uses a generative AI model to generate a response based on the text and emotion data. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the server generates a gentle response such as, "Don't worry. How would you like to distribute your inheritance?"

[0298] 5. The user responds, "I want to distribute the real estate to my eldest son and the cash to my second son."

[0299] 6. The device captures the voice again, and the emotion recognition engine analyzes the user's emotions. The voice data is converted back into text and sent to the server along with the emotion data.

[0300] 7. This process is repeated until all necessary information has been collected, at which point the server uses the document generation means to generate a final-life document based on the collected data.

[0301] 8. The terminal presents the generated document to the user, who can review it and suggest corrections if necessary.

[0302] Prompt Sentence Examples

[0303] "I want to prepare for inheritance."

[0304] "How do you want your inheritance distributed?"

[0305] "I want to distribute the real estate to my eldest son and the cash to my second son."

[0306] In this way, the present invention is a system that uses an emotion recognition engine to provide responses that take into consideration the user's emotions, thereby enabling smoother end-of-life support with less psychological burden.

[0307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0308] Step 1: Initialize the system

[0309] The server creates instances of a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine. This enables voice input capture, emotion recognition, appropriate response generation, and document creation. Specifically, it creates the following various objects and instances:

[0310] Recognizer: An object for recognizing audio data

[0311] Microphone: An object for capturing sound

[0312] Generative AI models: Models that create responses based on user text input

[0313] Document generation means: An instance that generates a document based on collected data

[0314] Emotion recognition engine: An engine that analyzes emotions from voice

[0315] Step 2: Capturing voice input and recognizing emotions

[0316] The user says, "I want to prepare for my inheritance." The device uses the Microphone object to capture this voice. At the same time, the emotion recognition engine works simultaneously to analyze emotional data from the voice in real time. Specifically, the device captures the voice input and sends the voice data to the emotion recognition engine to obtain emotional data. The input here is the user's voice data, and the output is text data and emotional data.

[0317] Step 3: Converting voice data and sending emotion data

[0318] The device sends the captured voice data to a voice recognition service, which converts it into text data. At the same time, it also sends the emotional data acquired by the emotion recognition engine to the server. As a result, the server receives the user's intentions in text format and is able to understand their emotional state as well. Specifically, the device sends the voice data, and then sends the returned text data and the emotional data generated by the emotion recognition engine to the server all at once. The input is voice data, and the output is text data and emotional data.

[0319] Step 4: AI-generated responses

[0320] The server inputs the received text data and emotional data into the generative AI model and generates a response that takes the user's emotional state into account. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the generative AI model will create a gentle response such as, "Don't worry. How would you like to distribute your inheritance?" Specifically, the generative AI model processes the input text data and emotional data and generates an appropriate response text. The input is text data and emotional data, and the output is response text.

[0321] Step 5: Gather required information

[0322] The user provides further information based on the generated response. For example, the user might reply, "I want to distribute the property to my eldest son and the cash to my second son." The device captures this again and converts it into text data through a voice recognition service. At the same time, the emotion recognition engine analyzes the user's emotions again and sends the emotion data to the server. Specifically, the device captures the voice again, converts it into text data through a voice recognition service, and sends it to the server together with the emotion data. The input is voice data, and the output is text data and emotion data.

[0323] Step 6: Generate end-of-life documents

[0324] After all necessary information is collected, the server uses a document generation means to generate end-of-life documents based on the collected data. Examples include wills and funeral plans. Specifically, the server integrates the collected text data and emotion data to automatically generate a document that reflects the user's intentions and emotions. The input is the aggregated text data and emotion data, and the output is the generated document.

[0325] Step 7: Present and review documents

[0326] The terminal presents the generated document to the user, who can review the generated document and suggest corrections if necessary. Specifically, the terminal presents the generated document to the user and accepts feedback on it. The input is the generated document, and the output is feedback for the user's review or corrections.

[0327] (Application example 2)

[0328] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0329] It is difficult to properly grasp a customer's emotional state and provide personalized customer service in real time. Especially in brick-and-mortar stores, it is important for staff to instantly understand a customer's emotions and respond appropriately, but this is difficult to do. Conventional systems lack emotion recognition capabilities, making it impossible to respond in a way that takes the user's emotions into consideration.

[0330] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0331] In this invention, the server includes a voice recognition means, an emotion recognition means, a generation AI means, and a document generation means, which makes it possible to collect voice input from a user, convert it into text data by the voice recognition means, analyze the user's emotional state by the emotion recognition means, generate a response based on the text data and the emotion data by the generation AI means, provide the generated response to the user, collect necessary information based on the response, and generate a document by the document generation means.

[0332] 1. "Speech recognition means" means a technology for converting voice data into text data.

[0333] 2. "Generative AI methods" are artificial intelligence technologies that generate appropriate responses or content based on text data or other input data.

[0334] 3. "Document generation means" refers to technology for automatically creating documents based on collected data.

[0335] 4. "Emotion recognition means" refers to technology that analyzes input data such as voice and facial expressions to identify the user's emotional state.

[0336] 5. "Means for collecting voice input from a user and converting it into text data through speech recognition means" refers to technology that performs a process of recording a user's speech and converting it into text information.

[0337] 6. "Means for generating responses based on text data and emotional data using generative AI means" refers to technology that creates appropriate replies or instructions based on input text and emotional information.

[0338] 7. "Means for providing a generated response to a user" means any technology that displays or conveys a generated text or audio message to a user.

[0339] 8. "Means for collecting necessary information based on responses and generating a document using a document generation means" refers to a technology that executes a process of collecting additional information based on the generated responses and creating a final document based on that information.

[0340] This invention is a system that provides personalized services by analyzing the user's emotional state in real time using an emotion recognition engine to enhance customer service in brick-and-mortar stores. The following elements are used to implement this system:

[0341] Hardware used

[0342] Smartphone (iOS / Android)

[0343] Smart glasses (general name example: AR glasses)

[0344] Head-mounted display (general name example: AR headset)

[0345] Software used

[0346] Speech recognition service (general name example: Cloud speech recognition API)

[0347] Emotion recognition engine (general name example: Cloud Emotion Analysis API)

[0348] Generative AI model (general name example: generative model)

[0349] Program processing explanation

[0350] Voice input and emotional state capture

[0351] Users (customers) speak to staff in stores to ask questions about products or services. The microphones and cameras built into the devices (smartphones, smart glasses, head-mounted displays) capture the customer's voice and facial expressions in real time.

[0352] Analysis of speech and emotion data

[0353] The device sends the captured voice data to a cloud speech recognition API and converts it into text data. At the same time, an emotion recognition engine analyzes the captured voice and facial expression data to detect the user's emotional state. This allows the device to grasp the customer's emotional state, such as stress or joy, in real time.

[0354] Response Generation

[0355] The device then sends the converted text data and emotion data to a server. The server, equipped with a generative model, uses this data to generate an appropriate response based on the user's emotional state. For example, if the user is feeling stressed, the server generates a response such as, "We have some products here that can help relieve stress. Would you like me to show you some?"

[0356] Providing a response

[0357] The generated response is provided to the user through the terminal and displayed in real time on the staff's smart glasses or head-mounted display, allowing the staff to respond to the user accordingly.

[0358] Specific examples

[0359] Situation

[0360] A customer approaches a store staff member and asks a product question. The customer appears stressed.

[0361] Program operation example

[0362] 1. Voice Capture: A customer says, "I'm looking for a product that will help me relieve stress."

[0363] 2. Emotion analysis: An emotion recognition engine detects stress from customer voices.

[0364] 3. Voice transcription: The cloud speech recognition API converts the voice into text data such as "I'm looking for a product that will help relieve stress."

[0365] 4. Response generation: The generative model generates a response such as, "We have some products that can help relieve stress. Would you like to know more?"

[0366] 5. Response presentation: The response is displayed on the staff member's smart glasses, and the staff member responds to the customer in the same words.

[0367] Prompt Sentence Examples

[0368] Customer utterance: 'I'm looking for a product that will help me relieve stress.' The customer's emotional state indicates stress. Generate an appropriate response.

[0369] In this way, the system uses an emotion recognition engine and generative AI model to understand the customer's emotional state in real time and provide personalized responses, thereby improving customer satisfaction.

[0370] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0371] Step 1:

[0372] A customer speaks to a staff member in a physical store (input: customer speech). A microphone built into the device (smartphone, smart glasses, head-mounted display) captures the customer's voice in real time (output: voice data).

[0373] Step 2:

[0374] The device sends the captured voice data to the cloud, where it is converted into text data using a cloud voice recognition API (input: voice data, output: text data). This converts the voice content into text data.

[0375] Step 3:

[0376] At the same time, the device's built-in camera captures the customer's facial expressions and sends the data to an emotion recognition engine for analysis (input: facial expression data, output: emotion data), which determines the customer's current emotional state (e.g., joy, stress, excitement, etc.).

[0377] Step 4:

[0378] The device sends the converted text data and analyzed emotion data to the server (input: text data, emotion data, output: data sent to the server). The server receives this data and proceeds to the next analysis step.

[0379] Step 5:

[0380] The server uses a generative AI model to generate an appropriate response based on the received text data and emotional data (input: text data, emotional data, output: response data). For example, if a customer is feeling stressed, a response such as "We have products that will help relieve stress" will be generated.

[0381] Step 6:

[0382] The generated response data is sent to the terminal (input: response data, output: response data sent to the terminal). The terminal provides the generated response to the user in the form of a display or voice guidance.

[0383] Step 7:

[0384] The terminal displays the generated response on the staff member's smart glasses or head-mounted display (input: response data, output: visual display provided to staff member). The staff member then responds appropriately to the customer based on the displayed response. This enables personalized service that is in line with the customer's emotional state.

[0385] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0386] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0387] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0388] [Second embodiment]

[0389] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0390] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0391] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0392] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0393] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0394] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0395] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0396] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0397] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0398] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0399] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0400] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0401] The present invention is implemented by a system including a speech recognition unit, a generation AI unit, and a document generation unit. This system operates as follows.

[0402] System initialization

[0403] The server creates Recognizer and Microphone objects for speech recognition, instantiates a generative AI model, and a document generator, and the system is now ready to capture voice input, generate appropriate responses, and finally create the required documents.

[0404] Capturing voice input

[0405] The user speaks to the system about their end-of-life wishes (e.g., "I want to prepare for my inheritance"). The device uses a microphone to capture the user's voice and converts the voice data into text data via the Google speech recognition service.

[0406] AI-powered response generation

[0407] The server sends the text data obtained by speech recognition to the AI ​​generator, which generates an appropriate response. For example, this response might be in the form of "Please tell us how you would like to distribute your inheritance." The terminal provides the generated response to the user and prompts for further input.

[0408] Collection of necessary information

[0409] The user responds to questions posed by the AI ​​by voice, such as "I want to distribute the property to my eldest son and the cash to my second son." The device again captures this as voice data, and the server sends the text data to the AI, which then generates further questions and provides them to the user. This process is repeated until all the necessary information is collected.

[0410] Creation of end-of-life documents

[0411] Once all the necessary information has been collected, the server uses a document generation tool to generate end-of-life documents (e.g., wills, funeral plans) based on the collected data. The terminal presents the generated documents to the user, which can be provided as digital files or paper-based.

[0412] Specific examples

[0413] Example 1: Preparing for inheritance

[0414] 1. The user says, "I want to prepare for inheritance."

[0415] 2. The device captures this audio, and the server converts it into text data through a speech recognition service.

[0416] 3. The server uses its generative AI means to generate a response such as "Tell us which inheritances you would like to distribute and how."

[0417] 4. The user responds by voice, "I want to distribute the real estate to my eldest son and the cash to my second son."

[0418] 5. The server continues to collect information, and once all necessary information is gathered, it uses the document generation means to generate a document for the inheritance.

[0419] 6. The terminal presents this document to the user, who then provides final confirmation.

[0420] In this way, the present invention is a system that utilizes voice recognition and generative AI to enable users to easily prepare for end-of-life planning even without specialized knowledge.

[0421] The processing flow will be explained below.

[0422] Step 1:

[0423] The server initializes the system. Specifically, it creates Recognizer and Microphone objects for speech recognition, and creates instances of the generative AI model and document generation means.

[0424] Step 2:

[0425] The user speaks to the system about their end-of-life requests (e.g., "I want to prepare for my inheritance").

[0426] Step 3:

[0427] The terminal captures the user's voice input through a microphone and obtains it as voice data.

[0428] Step 4:

[0429] The terminal transmits the captured voice data to a voice recognition service via the Internet, and converts the voice data into text data.

[0430] Step 5:

[0431] The server sends the text data to a generative AI means that generates an appropriate response based on the input, for example, formulating a question such as "Please tell us how you would like to distribute your inheritance."

[0432] Step 6:

[0433] The terminal presents the user with the response generated by the generative AI means, and the user is provided with a question by voice or text.

[0434] Step 7:

[0435] The user responds to the generated question by voice with additional information (e.g., "I want to distribute the real estate to my eldest son and the cash to my second son").

[0436] Step 8:

[0437] The terminal again captures the user's response through the microphone and obtains it as voice data.

[0438] Step 9:

[0439] The terminal transmits the captured voice data to a voice recognition service and converts it back into text data.

[0440] Step 10:

[0441] The server continues to collect necessary information using the acquired text data, for example, by generating the next question using a generative AI means and presenting it to the user via the terminal.

[0442] Step 11:

[0443] The device repeats this process, continuing to capture voice and generate AI-generated responses until all the necessary information is collected.

[0444] Step 12:

[0445] The server uses a document generation means to generate documents necessary for end-of-life preparations (such as a will or funeral plan) based on the collected user data.

[0446] Step 13:

[0447] The terminal presents the generated document to the user, which can be provided as a digital file or on paper.

[0448] Step 14:

[0449] The user finally reviews and completes the presented document.

[0450] Example 1

[0451] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0452] Conventional end-of-life preparations have been problematic in that general users without specialized knowledge have difficulty creating complex documents and gathering information, resulting in significant stress and effort. In particular, there are no systems that utilize voice input to efficiently and interactively collect necessary information and generate accurate and appropriate documents based on that information. This makes it difficult for users to prepare for end-of-life planning on their own, and in many cases requires the assistance of an expert. The present invention aims to provide a system that solves these problems.

[0453] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0454] In this invention, the server includes a speech recognition means, a generation AI means, a document generation means, a means for collecting speech input from a user and converting it into text data using the speech recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, a means for equipping the terminal with a microphone for capturing speech data, a means for initializing the speech recognition means and generating an instance of the generation AI model, and a means for receiving and displaying information from the user through a user interface. This makes it possible for a user to collect necessary information for easy and efficient end-of-life preparations through speech input and automatically generate accurate documents based on that information.

[0455] "Speech recognition means" refers to technical means for converting a user's voice input into text data.

[0456] "Generative AI means" refers to artificial intelligence technical means for generating appropriate responses or questions for users based on acquired text data.

[0457] The "document generation means" is a technical means for automatically creating a document in a specified format based on collected information.

[0458] "Means for collecting voice input from a user and converting it into text data by voice recognition means" refers to a technical means for capturing a user's voice using a voice input device such as a microphone and converting it into text data.

[0459] "Means for generating a response based on text data using generative AI means" refers to technical means that utilizes a generative AI model to create an appropriate response to a user from text data.

[0460] "Means for providing the generated response to the user" refers to technical means for providing the response generated by the generation AI means to the user by means of screen display, audio output, etc.

[0461] "Means for collecting necessary information based on responses and generating documents using document generation means" refers to technical means for collecting information based on user responses and using this information to generate necessary documents using document generation means.

[0462] "Means for providing a terminal with a microphone for capturing voice data" refers to technical means for providing a microphone in a terminal for receiving voice input from a user and recording the voice data.

[0463] "Means for initializing speech recognition means and generating an instance of a generative AI model" refers to technical means for performing the initial settings required to start the operation of speech recognition technology and generative AI technology and generating an instance of each.

[0464] "Means for receiving and displaying information from a user through a user interface" refers to technical means for providing an interface for a user to input information and for displaying the input.

[0465] The present invention is a system that includes a voice recognition unit, a generation AI unit, and a document generation unit, which allows users to easily prepare for end-of-life planning. This system operates as follows.

[0466] Initialization

[0467] The server creates Recognizer and Microphone objects for speech recognition, instantiates a generative AI model, and prepares LaTeX and Word templates for document generation. This initialization process ensures that the server is ready to capture voice input, generate appropriate responses, and ultimately create the required documents.

[0468] Capturing voice input

[0469] The user speaks to the system about their wishes regarding end-of-life planning. The device uses a microphone to capture the user's voice, and converts the voice data into text data using a voice recognition service via the Internet. For example, the user might say, "I want to prepare for my inheritance."

[0470] AI-powered response generation

[0471] The server sends the converted text data to the AI ​​generator, which generates an appropriate response, such as "Please tell us how you would like to distribute your inheritance." The terminal provides the generated response to the user and prompts for further input.

[0472] Collection of necessary information

[0473] The user responds to questions posed by the AI ​​by voice, such as "I want to distribute the property to my eldest son and the cash to my second son." The device again captures this as voice data, and the server sends the text data to the AI ​​generator, which generates further questions and provides them to the user. This process is repeated until all the necessary information is collected.

[0474] Creation of end-of-life documents

[0475] Once all the necessary information has been collected, the server uses a document generation tool to generate end-of-life documents (such as a will or funeral plan) based on the collected data. The generated document is saved in a format such as PDF and displayed to the user on the device. The user can then review the document and make any necessary corrections. The final document can be saved as a digital file or printed on paper.

[0476] Specific examples

[0477] Example 1: Preparing for inheritance

[0478] 1. The user says, "I want to prepare for inheritance."

[0479] 2. The device captures this audio, and the server converts it into text data using Google speech recognition services.

[0480] 3. The server uses its generative AI means to generate a response such as "Tell us which inheritances you would like to distribute and how."

[0481] 4. The user responds by voice, "I want to distribute the real estate to my eldest son and the cash to my second son."

[0482] 5. The server continues to collect information, and once all necessary information is gathered, it uses the document generation means to generate a document for the inheritance.

[0483] 6. The terminal presents this document to the user, who then provides final confirmation.

[0484] In this way, the present invention is a system that utilizes voice recognition and generative AI to enable users to easily prepare for end-of-life planning even without specialized knowledge.

[0485] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0486] Step 1:

[0487] The server initializes the system. Specifically, it creates the Recognizer and Microphone objects of the speech recognition library and creates an instance of the generative AI model. It also prepares LaTeX and Word templates as a means of document generation. The input here is the system startup signal, and the output is the instance of each initialized object.

[0488] Step 2:

[0489] The user speaks their wishes regarding end-of-life planning into the device. For example, they might say, "I want to prepare for my inheritance." This voice data becomes the input.

[0490] Step 3:

[0491] The device uses a microphone to capture the user's voice, and then sends the voice data to a voice recognition service via the Internet to convert it into text data. The input here is voice data, and the output is text data. For example, the text data obtained is "I would like to prepare for my inheritance."

[0492] Step 4:

[0493] The server sends the text data obtained by speech recognition to the generative AI model, which generates an appropriate response based on the input text. The generative AI model outputs a generated response, such as a prompt, "Please tell us how you would like to distribute your inheritance."

[0494] Step 5:

[0495] The device displays or speaks the response received from the generative AI model to the user. Here, the response text returned from the server is received as input, and the output is presented to the user.

[0496] Step 6:

[0497] The user answers questions posed by the AI ​​by voice. For example, they might say, "I want to distribute the real estate to my eldest son and the cash to my second son." The user's voice response becomes the input.

[0498] Step 7:

[0499] The device again uses a microphone to capture the user's voice and converts the voice data into text data using a voice recognition service. The input here is voice data, and the output is text data. For example, the text data obtained is "I want to distribute the real estate to my eldest son and the cash to my second son."

[0500] Step 8:

[0501] The server sends the text data back to the generative AI model to generate a further question. For example, a question like "What is the address of the property?" is generated. The input here is the user's answer text data, and the output is the next question text.

[0502] Step 9:

[0503] The server and terminal repeat this process until all necessary information is collected. Finally, based on the collected information, the server uses a document generation means to generate end-of-life documents, such as a will or funeral plan. The input is the collected information text data, and the output is the generated end-of-life documents.

[0504] Step 10:

[0505] The terminal presents the generated document to the user, who then performs final confirmation. The user can check the document content and make corrections as necessary. The input is the generated document data, and the output is the final document confirmed by the user.

[0506] In this way, by clearly indicating the inputs and outputs at each processing step and explaining the specific operations, the overall processing flow of the system can be understood.

[0507] (Application example 1)

[0508] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0509] In modern society, personal end-of-life planning and security management are important issues. However, it is difficult for users without specialized knowledge to perform these tasks efficiently and safely. In particular, emergency contact and password management, as well as automatic emergency contact functions, are necessary to reduce the burden on users and improve safety, and there is a demand for methods to easily implement these functions.

[0510] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0511] In this invention, the server includes a voice recognition means, a generation AI means, a document generation means, a means for collecting voice input from a user and converting it into text data by the voice recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, a means for registering and managing the user's emergency contact information and password information by voice, and a means for automatically sending a notification to designated contacts in the event of an emergency. This enables users to efficiently and safely prepare for end-of-life planning and manage security without having specialized knowledge.

[0512] A "voice recognition means" is a device or software that has the function of capturing a user's voice input and converting the voice data into text data.

[0513] A "generative AI means" is a means that uses an artificial intelligence model to generate appropriate responses or questions based on text data.

[0514] "Document generation means" refers to a device or software that has the function of automatically generating the necessary documents based on collected information.

[0515] The "means for collecting voice input from a user and converting it into text data by a voice recognition means" refers to a means for executing a process of collecting a user's voice and converting the voice into a text format by voice recognition.

[0516] "Means for generating responses based on text data using generative AI means" refers to means for executing a process that uses a generative AI model to generate appropriate responses or questions based on text data obtained through speech recognition.

[0517] The "means for providing a generated response to a user" is a means for executing a process for presenting a response created by the generation AI means to a user.

[0518] "Means for collecting necessary information based on the response and generating a document using a document generation means" refers to means for executing a process of collecting additional information from the user in accordance with the response provided by the generation AI means and automatically generating a necessary document based on that information.

[0519] The "means for registering and managing a user's emergency contact and password information by voice" refers to a means for executing a process in which a user inputs emergency contact and password information by voice, and registers and manages this information in a database.

[0520] The "means for automatically sending a notification to a designated contact in an emergency" is a means having a function for automatically sending a notification to a user-designated contact in the event of a specific emergency.

[0521] The present invention is implemented by a speech recognition means, a generation AI means, a document generation means, and a speech-based information collection and management system. This system efficiently recognizes information input by a user through speech, collects and manages necessary information, and generates documents based on the information.

[0522] System Configuration

[0523] The system uses the following major hardware and software:

[0524] Hardware: smartphones, head-mounted displays, smart glasses, microphones, servers

[0525] Software: Google speech recognition services, generative AI models (e.g., OpenAI GPT), databases for data storage (e.g., Firebase)

[0526] Program processing overview

[0527] 1. Capture voice input

[0528] The server captures the user's voice input via devices such as smartphones and head-mounted displays, and uses the Google speech recognition service to convert the captured voice data into text data.

[0529] 2. Response generation using generative AI

[0530] The server sends the text data obtained by speech recognition to a generative AI model, which generates appropriate responses and follow-up questions. The generated responses are presented to the user, prompting them for further speech input.

[0531] 3. Information collection and management

[0532] The user answers questions posed by the AI ​​by voice, which collects important information such as emergency contacts and passwords. This information is then stored and managed in a database. Settings are also made to automatically send notifications to designated contacts in the event of an emergency.

[0533] 4. Document Generation

[0534] After all the necessary information has been collected, the server uses a document generation tool to automatically generate end-of-life and security-related documents, which are then presented to the user in digital or paper form.

[0535] Specific examples

[0536] Example 1: Registering an emergency contact

[0537] User: "My wife's phone number is 090-xxxx-xxxx."

[0538] Voice data is captured through the smartphone's microphone and converted into text data using Google's voice recognition service.

[0539] The server uses a generative AI model to generate a response based on the text data, and returns a response to the user in the form of, for example, "Your phone number has been registered."

[0540] Emergency contact information is stored in the database.

[0541] Example 2: Managing Passwords

[0542] User: "My Amazon password is abc123"

[0543] Voice data is captured and converted into text data using the Google speech recognition service.

[0544] The server uses the generative AI model to generate a response such as "Your password has been registered" and presents it to the user.

[0545] Passwords are stored securely in a database.

[0546] Example 3: Automatic contact in an emergency

[0547] In the system settings, the user says, "In case of an emergency, please contact my son's cell phone."

[0548] Once all necessary information has been collected, designated contacts will be automatically notified in the event of an emergency.

[0549] Prompt Sentence Examples

[0550] Check the security status

[0551] Please register your emergency contact information.

[0552] "Would you like to update your password?"

[0553] In this way, the present invention utilizes voice recognition and generative AI, enabling users to prepare for end-of-life planning and manage personal information efficiently and safely, even without specialized knowledge.

[0554] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0555] Step 1:

[0556] The server captures the user's voice input. The microphone on the smartphone or head-mounted display is used to collect the voice data the user speaks. The data is sent to the Google speech recognition service. The input is the user's voice, and the output is text data.

[0557] Step 2:

[0558] The server converts the received voice data into text data using a voice recognition service. The server uses the Google voice recognition service as the voice recognition method and converts the received voice data into text data. The input is voice data and the output is text data.

[0559] Step 3:

[0560] The server sends the text data to a generative AI model, which uses the model (e.g., OpenAI GPT) to generate appropriate responses or questions based on the speech-recognized text data. The input is the text data, and the output is the generated response or question.

[0561] Step 4:

[0562] The server provides the generated response to the user. The generated response is presented to the user through the screen or speaker of a smartphone or head-mounted display. The input is the response from the generative AI model, and the output is what is presented to the user.

[0563] Step 5:

[0564] The user answers questions posed by the AI ​​by voice. For example, they provide information such as "My Amazon password is abc123." The input is the user's voice input in response to the questions posed by the AI, and the output is the user's voice data.

[0565] Step 6:

[0566] The server again captures the voice input and converts it to text data. As in steps 1 and 2, the voice data is stored in temporary storage and sent to the Google speech recognition service for conversion to text data. The input is voice data and the output is text data.

[0567] Step 7:

[0568] The server uses generation AI to collect any further required information and save it in a database. If more information is needed based on the user's response data, additional questions are generated and additional information is collected from the user. This process is repeated until all the necessary information has been collected and the data is saved in the database. The input is text data from the user, and the output is saved in the database.

[0569] Step 8:

[0570] The server generates a document using the document generation means. Based on all the collected information, it generates the necessary document (e.g., end-of-life document or security information document). The input is the information stored in the database, and the output is the generated document.

[0571] Step 9:

[0572] The server presents the generated document to the user, who then reviews the document and makes any necessary corrections. The input is the generated document, and the output is the presentation and review to the user.

[0573] Step 10:

[0574] The system automatically sends notifications to designated contacts in the event of an emergency. Based on the user's settings, the system automatically sends emails and messages to designated contacts in the event of an emergency. The input is the configured emergency contact information, and the output is the automatically sent notification.

[0575] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0576] The present invention is a system that provides more personalized end-of-life planning support by adding an emotion recognition engine to recognize the user's emotional state and adjusting responses based on that. This system performs the following operations.

[0577] System initialization

[0578] The server initializes the system by instantiating a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine, which enables capturing voice input, recognizing emotions, generating appropriate responses, and creating documents.

[0579] Voice input capture and emotion recognition

[0580] The user speaks to the system about their end-of-life wishes (e.g., "I want to prepare for my inheritance"). The device uses a microphone to capture this voice. At the same time, an emotion recognition engine analyzes the user's emotional state from their voice in real time.

[0581] Converting voice data and sending emotion data

[0582] The device sends the captured voice data to a voice recognition service, where it is converted into text data. The emotion recognition engine also analyzes the data and sends it to the server.

[0583] AI-powered response generation

[0584] The server sends the text data and emotional data to the AI ​​generator, which generates a response based on the user's emotional state. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the AI ​​generator will respond in a gentle tone, "How would you like to distribute it?"

[0585] Collection of necessary information

[0586] The user responds to the generated response with additional information by voice, which the device captures again and converts into text data via a speech recognition service. At the same time, an emotion recognition engine analyzes the user's emotional state and sends that data to the server.

[0587] Creation of end-of-life documents

[0588] After all the necessary information has been collected, the server uses a document generation means to generate end-of-life documents (such as a will or funeral plan) based on the collected data.

[0589] Present and verify documents

[0590] The terminal presents the generated document to the user, either as a digital file or on paper, for final review.

[0591] Specific examples

[0592] Example 1: Preparing for inheritance

[0593] 1. The user says, "I want to prepare for inheritance."

[0594] 2. The device captures the voice and an emotion recognition engine analyzes the user's emotions. In this case, if the user expresses anxiety, that data is also captured.

[0595] 3. The device converts the voice data into text data and sends it to the server along with the emotion data.

[0596] 4. The server uses generative AI methods to generate a gentle response based on the user's feelings of anxiety, such as "Don't worry. Just tell us how you would like to distribute your inheritance."

[0597] 5. The user responds, "I would like to distribute the real estate to my eldest son and the cash to my second son."

[0598] 6. The device captures the voice again, and the emotion recognition engine analyzes the user's emotions. The voice data is converted into text and sent to the server along with the emotion data.

[0599] 7. This process is repeated until all necessary information is collected.

[0600] 8. The server finally generates a document based on the collected data and sentiment.

[0601] 9. The terminal presents the generated document to the user, who reviews the proposed document and suggests corrections if necessary.

[0602] In this way, the present invention is a system that uses an emotion recognition engine to provide responses that take into consideration the user's emotions, thereby enabling smoother end-of-life support with less psychological burden.

[0603] The processing flow will be explained below.

[0604] Step 1:

[0605] The server initializes the system. Specifically, it creates instances of the Recognizer and Microphone objects for speech recognition, the generative AI model, the document generation means, and the emotion recognition engine.

[0606] Step 2:

[0607] The user speaks a request regarding end-of-life planning, such as "I want to prepare for inheritance."

[0608] Step 3:

[0609] The terminal captures the user's voice input through a microphone and obtains it as voice data.

[0610] Step 4:

[0611] The device sends the captured voice data to an emotion recognition engine to analyze the user's emotional state in real time.

[0612] Step 5:

[0613] The terminal transmits the voice data to a voice recognition service via the Internet, and converts the voice data into text data.

[0614] Step 6:

[0615] The server sends the converted text data and the emotional state data obtained from the emotion recognition engine to the generation AI means, which then generates an appropriate response based on the user's emotions. For example, if the user is feeling anxious, it generates a gentle response such as, "Don't worry. Please tell us how you would like to distribute your inheritance."

[0616] Step 7:

[0617] The device provides the user with a response generated by the generative AI means, either by voice or text.

[0618] Step 8:

[0619] The user answers the generated question by voice with specific details (e.g., "I want to distribute the real estate to my eldest son and the cash to my second son").

[0620] Step 9:

[0621] The device captures the user's voice again, and the emotion recognition engine analyzes the user's emotional state and captures it as data.

[0622] Step 10:

[0623] The terminal converts the voice data into text data and transmits it to the server together with the acquired emotion data.

[0624] Step 11:

[0625] The server then uses the generative AI means again based on the received text data and emotion data to generate the next question, or proceeds to the next processing step if no additional information is needed.

[0626] Step 12:

[0627] The device repeats this process until all the necessary information is collected, continually capturing input and emotions from the user and generating responses.

[0628] Step 13:

[0629] The server uses a document generation means to generate documents necessary for end-of-life planning (such as a will or funeral plan) based on all collected user data and emotion data.

[0630] Step 14:

[0631] The terminal presents the generated document to the user, either as a digital file or on paper, for final review.

[0632] Step 15:

[0633] The user checks the generated document and, if necessary, indicates corrections by voice.

[0634] This allows the system of the present invention to efficiently support end-of-life preparations while taking into consideration the user's feelings.

[0635] Example 2

[0636] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0637] Conventional end-of-life planning support systems have had the problem of being difficult to respond to users' emotions, which can lead to a heavy psychological burden. In particular, when it comes to the delicate topic of end-of-life planning, it is important to respond to the emotions of users, but conventional technologies have not been able to fully achieve this.

[0638] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a voice recognition means, a generation AI means, a document generation means, a means for collecting voice input from the user and converting it into text data by the voice recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, an emotion recognition means for analyzing emotions from the user's voice in real time, and a means for the generation AI means to generate a response according to the user's emotional state based on the emotion data acquired by the emotion recognition means. This makes it possible to provide a response that takes the user's emotions into consideration, reduce psychological burden, and enable smooth end-of-life support.

[0639] 1. "Speech recognition means" means a device or method capable of converting speech input from a user into text data.

[0640] 2. "Generative AI means" refers to technology that generates appropriate responses based on the user's text data.

[0641] 3. "Document generation means" means a device or method that has the function of generating documents based on collected information.

[0642] 4. "Emotion recognition means" is a technology that analyzes emotions from the user's voice in real time.

[0643] 5. A "means for collecting voice input" is a device or method capable of capturing a user's speech.

[0644] 6. "Means for converting into text data" refers to a function for converting captured audio data into text data.

[0645] 7. "Means for generating responses" refers to a function that generates responses that meet the user's needs based on text data and emotional data.

[0646] 8. "Means for providing a response" means the functionality for communicating the generated response to the user.

[0647] 9. "Means for gathering required information" refers to the functionality for obtaining additional information from the user based on the response generated.

[0648] 10. "Means for generating a response based on emotional data" refers to a function that uses emotional data acquired by the emotion recognition means to generate a response according to the user's emotional state.

[0649] This invention is a system that provides more personalized support for end-of-life planning by adding an emotion recognition engine to recognize the user's emotional state and adjusting responses based on that. This system uses the following hardware and software.

[0650] Hardware and software used

[0651] Recognizer object: Used to recognize the user's voice input and functions as a voice recognition means.

[0652] Microphone object: A device for capturing user speech and serves as a means of collecting audio input.

[0653] Generative AI model: A technology that generates appropriate responses based on the user's text data.

[0654] Document generation means: Has the function of generating end-of-life documents based on collected information.

[0655] Emotion recognition engine: A technology that analyzes emotions from the user's voice in real time, and functions as an emotion recognition means.

[0656] System Operation

[0657] 1. During system initialization, the server instantiates a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine, enabling it to capture voice input, recognize emotions, generate appropriate responses, and create documents.

[0658] Specific examples

[0659] Example 1: Preparing for inheritance

[0660] 1. The user says, "I want to prepare for my inheritance."

[0661] 2. The device uses a Microphone object to capture the user's voice. At the same time, the emotion recognition engine analyzes the user's voice in real time to determine emotions. In this case, if the user expresses anxiety, the device also captures that data.

[0662] 3. The device sends the captured voice data to the voice recognition service, which converts it into text data. The converted text data and the emotion data analyzed by the emotion recognition engine are then sent to the server.

[0663] 4. The server uses a generative AI model to generate a response based on the text and emotion data. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the server generates a gentle response such as, "Don't worry. How would you like to distribute your inheritance?"

[0664] 5. The user responds, "I want to distribute the real estate to my eldest son and the cash to my second son."

[0665] 6. The device captures the voice again, and the emotion recognition engine analyzes the user's emotions. The voice data is converted back into text and sent to the server along with the emotion data.

[0666] 7. This process is repeated until all necessary information has been collected, at which point the server uses the document generation means to generate a final-life document based on the collected data.

[0667] 8. The terminal presents the generated document to the user, who can review it and suggest corrections if necessary.

[0668] Prompt Sentence Examples

[0669] "I want to prepare for inheritance."

[0670] "How do you want your inheritance distributed?"

[0671] "I want to distribute the real estate to my eldest son and the cash to my second son."

[0672] In this way, the present invention is a system that uses an emotion recognition engine to provide responses that take into consideration the user's emotions, thereby enabling smoother end-of-life support with less psychological burden.

[0673] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0674] Step 1: Initialize the system

[0675] The server creates instances of a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine. This enables voice input capture, emotion recognition, appropriate response generation, and document creation. Specifically, it creates the following various objects and instances:

[0676] Recognizer: An object for recognizing audio data

[0677] Microphone: An object for capturing sound

[0678] Generative AI models: Models that create responses based on user text input

[0679] Document generation means: An instance that generates a document based on collected data

[0680] Emotion recognition engine: An engine that analyzes emotions from voice

[0681] Step 2: Capturing voice input and recognizing emotions

[0682] The user says, "I want to prepare for my inheritance." The device uses the Microphone object to capture this voice. At the same time, the emotion recognition engine works simultaneously to analyze emotional data from the voice in real time. Specifically, the device captures the voice input and sends the voice data to the emotion recognition engine to obtain emotional data. The input here is the user's voice data, and the output is text data and emotional data.

[0683] Step 3: Converting voice data and sending emotion data

[0684] The device sends the captured voice data to a voice recognition service, which converts it into text data. At the same time, it also sends the emotional data acquired by the emotion recognition engine to the server. As a result, the server receives the user's intentions in text format and is able to understand their emotional state as well. Specifically, the device sends the voice data, and then sends the returned text data and the emotional data generated by the emotion recognition engine to the server all at once. The input is voice data, and the output is text data and emotional data.

[0685] Step 4: AI-generated responses

[0686] The server inputs the received text data and emotional data into the generative AI model and generates a response that takes the user's emotional state into account. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the generative AI model will create a gentle response such as, "Don't worry. How would you like to distribute your inheritance?" Specifically, the generative AI model processes the input text data and emotional data and generates an appropriate response text. The input is text data and emotional data, and the output is response text.

[0687] Step 5: Gather required information

[0688] The user provides further information based on the generated response. For example, the user might reply, "I want to distribute the property to my eldest son and the cash to my second son." The device captures this again and converts it into text data through a voice recognition service. At the same time, the emotion recognition engine analyzes the user's emotions again and sends the emotion data to the server. Specifically, the device captures the voice again, converts it into text data through a voice recognition service, and sends it to the server together with the emotion data. The input is voice data, and the output is text data and emotion data.

[0689] Step 6: Generate end-of-life documents

[0690] After all necessary information is collected, the server uses a document generation means to generate end-of-life documents based on the collected data. Examples include wills and funeral plans. Specifically, the server integrates the collected text data and emotion data to automatically generate a document that reflects the user's intentions and emotions. The input is the aggregated text data and emotion data, and the output is the generated document.

[0691] Step 7: Present and review documents

[0692] The terminal presents the generated document to the user, who can review the generated document and suggest corrections if necessary. Specifically, the terminal presents the generated document to the user and accepts feedback on it. The input is the generated document, and the output is feedback for the user's review or corrections.

[0693] (Application example 2)

[0694] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0695] It is difficult to properly grasp a customer's emotional state and provide personalized customer service in real time. Especially in brick-and-mortar stores, it is important for staff to instantly understand a customer's emotions and respond appropriately, but this is difficult to do. Conventional systems lack emotion recognition capabilities, making it impossible to respond in a way that takes the user's emotions into consideration.

[0696] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0697] In this invention, the server includes a voice recognition means, an emotion recognition means, a generation AI means, and a document generation means, which makes it possible to collect voice input from a user, convert it into text data by the voice recognition means, analyze the user's emotional state by the emotion recognition means, generate a response based on the text data and the emotion data by the generation AI means, provide the generated response to the user, collect necessary information based on the response, and generate a document by the document generation means.

[0698] 1. "Speech recognition means" means a technology for converting voice data into text data.

[0699] 2. "Generative AI methods" are artificial intelligence technologies that generate appropriate responses or content based on text data or other input data.

[0700] 3. "Document generation means" refers to technology for automatically creating documents based on collected data.

[0701] 4. "Emotion recognition means" refers to technology that analyzes input data such as voice and facial expressions to identify the user's emotional state.

[0702] 5. "Means for collecting voice input from a user and converting it into text data through speech recognition means" refers to technology that performs a process of recording a user's speech and converting it into text information.

[0703] 6. "Means for generating responses based on text data and emotional data using generative AI means" refers to technology that creates appropriate replies or instructions based on input text and emotional information.

[0704] 7. "Means for providing a generated response to a user" means any technology that displays or conveys a generated text or audio message to a user.

[0705] 8. "Means for collecting necessary information based on responses and generating a document using a document generation means" refers to a technology that executes a process of collecting additional information based on the generated responses and creating a final document based on that information.

[0706] This invention is a system that provides personalized services by analyzing the user's emotional state in real time using an emotion recognition engine to enhance customer service in brick-and-mortar stores. The following elements are used to implement this system:

[0707] Hardware used

[0708] Smartphone (iOS / Android)

[0709] Smart glasses (general name example: AR glasses)

[0710] Head-mounted display (general name example: AR headset)

[0711] Software used

[0712] Speech recognition service (general name example: Cloud speech recognition API)

[0713] Emotion recognition engine (general name example: Cloud Emotion Analysis API)

[0714] Generative AI model (general name example: generative model)

[0715] Program processing explanation

[0716] Voice input and emotional state capture

[0717] Users (customers) speak to staff in stores to ask questions about products or services. The microphones and cameras built into the devices (smartphones, smart glasses, head-mounted displays) capture the customer's voice and facial expressions in real time.

[0718] Analysis of speech and emotion data

[0719] The device sends the captured voice data to a cloud speech recognition API and converts it into text data. At the same time, an emotion recognition engine analyzes the captured voice and facial expression data to detect the user's emotional state. This allows the device to grasp the customer's emotional state, such as stress or joy, in real time.

[0720] Response Generation

[0721] The device then sends the converted text data and emotion data to a server. The server, equipped with a generative model, uses this data to generate an appropriate response based on the user's emotional state. For example, if the user is feeling stressed, the server generates a response such as, "We have some products here that can help relieve stress. Would you like me to show you some?"

[0722] Providing a response

[0723] The generated response is provided to the user through the terminal and displayed in real time on the staff's smart glasses or head-mounted display, allowing the staff to respond to the user accordingly.

[0724] Specific examples

[0725] Situation

[0726] A customer approaches a store staff member and asks a product question. The customer appears stressed.

[0727] Program operation example

[0728] 1. Voice Capture: A customer says, "I'm looking for a product that will help me relieve stress."

[0729] 2. Emotion analysis: An emotion recognition engine detects stress from customer voices.

[0730] 3. Voice transcription: The cloud speech recognition API converts the voice into text data such as "I'm looking for a product that will help relieve stress."

[0731] 4. Response generation: The generative model generates a response such as, "We have some products that can help relieve stress. Would you like to know more?"

[0732] 5. Response presentation: The response is displayed on the staff member's smart glasses, and the staff member responds to the customer in the same words.

[0733] Prompt Sentence Examples

[0734] Customer utterance: 'I'm looking for a product that will help me relieve stress.' The customer's emotional state indicates stress. Generate an appropriate response.

[0735] In this way, the system uses an emotion recognition engine and generative AI model to understand the customer's emotional state in real time and provide personalized responses, thereby improving customer satisfaction.

[0736] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0737] Step 1:

[0738] A customer speaks to a staff member in a physical store (input: customer speech). A microphone built into the device (smartphone, smart glasses, head-mounted display) captures the customer's voice in real time (output: voice data).

[0739] Step 2:

[0740] The device sends the captured voice data to the cloud, where it is converted into text data using a cloud voice recognition API (input: voice data, output: text data). This converts the voice content into text data.

[0741] Step 3:

[0742] At the same time, the device's built-in camera captures the customer's facial expressions and sends the data to an emotion recognition engine for analysis (input: facial expression data, output: emotion data), which determines the customer's current emotional state (e.g., joy, stress, excitement, etc.).

[0743] Step 4:

[0744] The device sends the converted text data and analyzed emotion data to the server (input: text data, emotion data, output: data sent to the server). The server receives this data and proceeds to the next analysis step.

[0745] Step 5:

[0746] The server uses a generative AI model to generate an appropriate response based on the received text data and emotional data (input: text data, emotional data, output: response data). For example, if a customer is feeling stressed, a response such as "We have products that will help relieve stress" will be generated.

[0747] Step 6:

[0748] The generated response data is sent to the terminal (input: response data, output: response data sent to the terminal). The terminal provides the generated response to the user in the form of a display or voice guidance.

[0749] Step 7:

[0750] The terminal displays the generated response on the staff member's smart glasses or head-mounted display (input: response data, output: visual display provided to staff member). The staff member then responds appropriately to the customer based on the displayed response. This enables personalized service that is in line with the customer's emotional state.

[0751] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0752] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0753] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0754] [Third embodiment]

[0755] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0756] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0757] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0758] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0759] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0760] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0761] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0762] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0763] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0764] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0765] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0766] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0767] The present invention is implemented by a system including a speech recognition unit, a generation AI unit, and a document generation unit. This system operates as follows.

[0768] System initialization

[0769] The server creates Recognizer and Microphone objects for speech recognition, instantiates a generative AI model, and a document generator, and the system is now ready to capture voice input, generate appropriate responses, and finally create the required documents.

[0770] Capturing voice input

[0771] The user speaks to the system about their end-of-life wishes (e.g., "I want to prepare for my inheritance"). The device uses a microphone to capture the user's voice and converts the voice data into text data via the Google speech recognition service.

[0772] AI-powered response generation

[0773] The server sends the text data obtained by speech recognition to the AI ​​generator, which generates an appropriate response. For example, this response might be in the form of "Please tell us how you would like to distribute your inheritance." The terminal provides the generated response to the user and prompts for further input.

[0774] Collection of necessary information

[0775] The user responds to questions posed by the AI ​​by voice, such as "I want to distribute the property to my eldest son and the cash to my second son." The device again captures this as voice data, and the server sends the text data to the AI, which then generates further questions and provides them to the user. This process is repeated until all the necessary information is collected.

[0776] Creation of end-of-life documents

[0777] Once all the necessary information has been collected, the server uses a document generation tool to generate end-of-life documents (e.g., wills, funeral plans) based on the collected data. The terminal presents the generated documents to the user, which can be provided as digital files or paper-based.

[0778] Specific examples

[0779] Example 1: Preparing for inheritance

[0780] 1. The user says, "I want to prepare for inheritance."

[0781] 2. The device captures this audio, and the server converts it into text data through a speech recognition service.

[0782] 3. The server uses its generative AI means to generate a response such as "Tell us which inheritances you would like to distribute and how."

[0783] 4. The user responds by voice, "I want to distribute the real estate to my eldest son and the cash to my second son."

[0784] 5. The server continues to collect information, and once all necessary information is gathered, it uses the document generation means to generate a document for the inheritance.

[0785] 6. The terminal presents this document to the user, who then provides final confirmation.

[0786] In this way, the present invention is a system that utilizes voice recognition and generative AI to enable users to easily prepare for end-of-life planning even without specialized knowledge.

[0787] The processing flow will be explained below.

[0788] Step 1:

[0789] The server initializes the system. Specifically, it creates Recognizer and Microphone objects for speech recognition, and creates instances of the generative AI model and document generation means.

[0790] Step 2:

[0791] The user speaks to the system about their end-of-life requests (e.g., "I want to prepare for my inheritance").

[0792] Step 3:

[0793] The terminal captures the user's voice input through a microphone and obtains it as voice data.

[0794] Step 4:

[0795] The terminal transmits the captured voice data to a voice recognition service via the Internet, and converts the voice data into text data.

[0796] Step 5:

[0797] The server sends the text data to a generative AI means that generates an appropriate response based on the input, for example, formulating a question such as "Please tell us how you would like to distribute your inheritance."

[0798] Step 6:

[0799] The terminal presents the user with the response generated by the generative AI means, and the user is provided with a question by voice or text.

[0800] Step 7:

[0801] The user responds to the generated question by voice with additional information (e.g., "I want to distribute the real estate to my eldest son and the cash to my second son").

[0802] Step 8:

[0803] The terminal again captures the user's response through the microphone and obtains it as voice data.

[0804] Step 9:

[0805] The terminal transmits the captured voice data to a voice recognition service and converts it back into text data.

[0806] Step 10:

[0807] The server continues to collect necessary information using the acquired text data, for example, by generating the next question using a generative AI means and presenting it to the user via the terminal.

[0808] Step 11:

[0809] The device repeats this process, continuing to capture voice and generate AI-generated responses until all the necessary information is collected.

[0810] Step 12:

[0811] The server uses a document generation means to generate documents necessary for end-of-life preparations (such as a will or funeral plan) based on the collected user data.

[0812] Step 13:

[0813] The terminal presents the generated document to the user, which can be provided as a digital file or on paper.

[0814] Step 14:

[0815] The user finally reviews and completes the presented document.

[0816] Example 1

[0817] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0818] Conventional end-of-life preparations have been problematic in that general users without specialized knowledge have difficulty creating complex documents and gathering information, resulting in significant stress and effort. In particular, there are no systems that utilize voice input to efficiently and interactively collect necessary information and generate accurate and appropriate documents based on that information. This makes it difficult for users to prepare for end-of-life planning on their own, and in many cases requires the assistance of an expert. The present invention aims to provide a system that solves these problems.

[0819] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0820] In this invention, the server includes a speech recognition means, a generation AI means, a document generation means, a means for collecting speech input from a user and converting it into text data using the speech recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, a means for equipping the terminal with a microphone for capturing speech data, a means for initializing the speech recognition means and generating an instance of the generation AI model, and a means for receiving and displaying information from the user through a user interface. This makes it possible for a user to collect necessary information for easy and efficient end-of-life preparations through speech input and automatically generate accurate documents based on that information.

[0821] "Speech recognition means" refers to technical means for converting a user's voice input into text data.

[0822] "Generative AI means" refers to artificial intelligence technical means for generating appropriate responses or questions for users based on acquired text data.

[0823] The "document generation means" is a technical means for automatically creating a document in a specified format based on collected information.

[0824] "Means for collecting voice input from a user and converting it into text data by voice recognition means" refers to a technical means for capturing a user's voice using a voice input device such as a microphone and converting it into text data.

[0825] "Means for generating a response based on text data using generative AI means" refers to technical means that utilizes a generative AI model to create an appropriate response to a user from text data.

[0826] "Means for providing the generated response to the user" refers to technical means for providing the response generated by the generation AI means to the user by means of screen display, audio output, etc.

[0827] "Means for collecting necessary information based on responses and generating documents using document generation means" refers to technical means for collecting information based on user responses and using this information to generate necessary documents using document generation means.

[0828] "Means for providing a terminal with a microphone for capturing voice data" refers to technical means for providing a microphone in a terminal for receiving voice input from a user and recording the voice data.

[0829] "Means for initializing speech recognition means and generating an instance of a generative AI model" refers to technical means for performing the initial settings required to start the operation of speech recognition technology and generative AI technology and generating an instance of each.

[0830] "Means for receiving and displaying information from a user through a user interface" refers to technical means for providing an interface for a user to input information and for displaying the input.

[0831] The present invention is a system that includes a voice recognition unit, a generation AI unit, and a document generation unit, which allows users to easily prepare for end-of-life planning. This system operates as follows.

[0832] Initialization

[0833] The server creates Recognizer and Microphone objects for speech recognition, instantiates a generative AI model, and prepares LaTeX and Word templates for document generation. This initialization process ensures that the server is ready to capture voice input, generate appropriate responses, and ultimately create the required documents.

[0834] Capturing voice input

[0835] The user speaks to the system about their wishes regarding end-of-life planning. The device uses a microphone to capture the user's voice, and converts the voice data into text data using a voice recognition service via the Internet. For example, the user might say, "I want to prepare for my inheritance."

[0836] AI-powered response generation

[0837] The server sends the converted text data to the AI ​​generator, which generates an appropriate response, such as "Please tell us how you would like to distribute your inheritance." The terminal provides the generated response to the user and prompts for further input.

[0838] Collection of necessary information

[0839] The user responds to questions posed by the AI ​​by voice, such as "I want to distribute the property to my eldest son and the cash to my second son." The device again captures this as voice data, and the server sends the text data to the AI ​​generator, which generates further questions and provides them to the user. This process is repeated until all the necessary information is collected.

[0840] Creation of end-of-life documents

[0841] Once all the necessary information has been collected, the server uses a document generation tool to generate end-of-life documents (such as a will or funeral plan) based on the collected data. The generated document is saved in a format such as PDF and displayed to the user on the device. The user can then review the document and make any necessary corrections. The final document can be saved as a digital file or printed on paper.

[0842] Specific examples

[0843] Example 1: Preparing for inheritance

[0844] 1. The user says, "I want to prepare for inheritance."

[0845] 2. The device captures this audio, and the server converts it into text data using Google speech recognition services.

[0846] 3. The server uses its generative AI means to generate a response such as "Tell us which inheritances you would like to distribute and how."

[0847] 4. The user responds by voice, "I want to distribute the real estate to my eldest son and the cash to my second son."

[0848] 5. The server continues to collect information, and once all necessary information is gathered, it uses the document generation means to generate a document for the inheritance.

[0849] 6. The terminal presents this document to the user, who then provides final confirmation.

[0850] In this way, the present invention is a system that utilizes voice recognition and generative AI to enable users to easily prepare for end-of-life planning even without specialized knowledge.

[0851] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0852] Step 1:

[0853] The server initializes the system. Specifically, it creates the Recognizer and Microphone objects of the speech recognition library and creates an instance of the generative AI model. It also prepares LaTeX and Word templates as a means of document generation. The input here is the system startup signal, and the output is the instance of each initialized object.

[0854] Step 2:

[0855] The user speaks their wishes regarding end-of-life planning into the device. For example, they might say, "I want to prepare for my inheritance." This voice data becomes the input.

[0856] Step 3:

[0857] The device uses a microphone to capture the user's voice, and then sends the voice data to a voice recognition service via the Internet to convert it into text data. The input here is voice data, and the output is text data. For example, the text data obtained is "I would like to prepare for my inheritance."

[0858] Step 4:

[0859] The server sends the text data obtained by speech recognition to the generative AI model, which generates an appropriate response based on the input text. The generative AI model outputs a generated response, such as a prompt, "Please tell us how you would like to distribute your inheritance."

[0860] Step 5:

[0861] The device displays or speaks the response received from the generative AI model to the user. Here, the response text returned from the server is received as input, and the output is presented to the user.

[0862] Step 6:

[0863] The user answers questions posed by the AI ​​by voice. For example, they might say, "I want to distribute the real estate to my eldest son and the cash to my second son." The user's voice response becomes the input.

[0864] Step 7:

[0865] The device again uses a microphone to capture the user's voice and converts the voice data into text data using a voice recognition service. The input here is voice data, and the output is text data. For example, the text data obtained is "I want to distribute the real estate to my eldest son and the cash to my second son."

[0866] Step 8:

[0867] The server sends the text data back to the generative AI model to generate a further question. For example, a question like "What is the address of the property?" is generated. The input here is the user's answer text data, and the output is the next question text.

[0868] Step 9:

[0869] The server and terminal repeat this process until all necessary information is collected. Finally, based on the collected information, the server uses a document generation means to generate end-of-life documents, such as a will or funeral plan. The input is the collected information text data, and the output is the generated end-of-life documents.

[0870] Step 10:

[0871] The terminal presents the generated document to the user, who then performs final confirmation. The user can check the document content and make corrections as necessary. The input is the generated document data, and the output is the final document confirmed by the user.

[0872] In this way, by clearly indicating the inputs and outputs at each processing step and explaining the specific operations, the overall processing flow of the system can be understood.

[0873] (Application example 1)

[0874] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0875] In modern society, personal end-of-life planning and security management are important issues. However, it is difficult for users without specialized knowledge to perform these tasks efficiently and safely. In particular, emergency contact and password management, as well as automatic emergency contact functions, are necessary to reduce the burden on users and improve safety, and there is a demand for methods to easily implement these functions.

[0876] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0877] In this invention, the server includes a voice recognition means, a generation AI means, a document generation means, a means for collecting voice input from a user and converting it into text data by the voice recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, a means for registering and managing the user's emergency contact information and password information by voice, and a means for automatically sending a notification to designated contacts in the event of an emergency. This enables users to efficiently and safely prepare for end-of-life planning and manage security without having specialized knowledge.

[0878] A "voice recognition means" is a device or software that has the function of capturing a user's voice input and converting the voice data into text data.

[0879] A "generative AI means" is a means that uses an artificial intelligence model to generate appropriate responses or questions based on text data.

[0880] "Document generation means" refers to a device or software that has the function of automatically generating the necessary documents based on collected information.

[0881] The "means for collecting voice input from a user and converting it into text data by a voice recognition means" refers to a means for executing a process of collecting a user's voice and converting the voice into a text format by voice recognition.

[0882] "Means for generating responses based on text data using generative AI means" refers to means for executing a process that uses a generative AI model to generate appropriate responses or questions based on text data obtained through speech recognition.

[0883] The "means for providing a generated response to a user" is a means for executing a process for presenting a response created by the generation AI means to a user.

[0884] "Means for collecting necessary information based on the response and generating a document using a document generation means" refers to means for executing a process of collecting additional information from the user in accordance with the response provided by the generation AI means and automatically generating a necessary document based on that information.

[0885] The "means for registering and managing a user's emergency contact and password information by voice" refers to a means for executing a process in which a user inputs emergency contact and password information by voice, and registers and manages this information in a database.

[0886] The "means for automatically sending a notification to a designated contact in an emergency" is a means having a function for automatically sending a notification to a user-designated contact in the event of a specific emergency.

[0887] The present invention is implemented by a speech recognition means, a generation AI means, a document generation means, and a speech-based information collection and management system. This system efficiently recognizes information input by a user through speech, collects and manages necessary information, and generates documents based on the information.

[0888] System Configuration

[0889] The system uses the following major hardware and software:

[0890] Hardware: smartphones, head-mounted displays, smart glasses, microphones, servers

[0891] Software: Google speech recognition services, generative AI models (e.g., OpenAI GPT), databases for data storage (e.g., Firebase)

[0892] Program processing overview

[0893] 1. Capture voice input

[0894] The server captures the user's voice input via devices such as smartphones and head-mounted displays, and uses the Google speech recognition service to convert the captured voice data into text data.

[0895] 2. Response generation using generative AI

[0896] The server sends the text data obtained by speech recognition to a generative AI model, which generates appropriate responses and follow-up questions. The generated responses are presented to the user, prompting them for further speech input.

[0897] 3. Information collection and management

[0898] The user answers questions posed by the AI ​​by voice, which collects important information such as emergency contacts and passwords. This information is then stored and managed in a database. Settings are also made to automatically send notifications to designated contacts in the event of an emergency.

[0899] 4. Document Generation

[0900] After all the necessary information has been collected, the server uses a document generation tool to automatically generate end-of-life and security-related documents, which are then presented to the user in digital or paper form.

[0901] Specific examples

[0902] Example 1: Registering an emergency contact

[0903] User: "My wife's phone number is 090-xxxx-xxxx."

[0904] Voice data is captured through the smartphone's microphone and converted into text data using Google's voice recognition service.

[0905] The server uses a generative AI model to generate a response based on the text data, and returns a response to the user in the form of, for example, "Your phone number has been registered."

[0906] Emergency contact information is stored in the database.

[0907] Example 2: Managing Passwords

[0908] User: "My Amazon password is abc123"

[0909] Voice data is captured and converted into text data using the Google speech recognition service.

[0910] The server uses the generative AI model to generate a response such as "Your password has been registered" and presents it to the user.

[0911] Passwords are stored securely in a database.

[0912] Example 3: Automatic contact in an emergency

[0913] In the system settings, the user says, "In case of an emergency, please contact my son's cell phone."

[0914] Once all necessary information has been collected, designated contacts will be automatically notified in the event of an emergency.

[0915] Prompt Sentence Examples

[0916] Check the security status

[0917] Please register your emergency contact information.

[0918] "Would you like to update your password?"

[0919] In this way, the present invention utilizes voice recognition and generative AI, enabling users to prepare for end-of-life planning and manage personal information efficiently and safely, even without specialized knowledge.

[0920] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0921] Step 1:

[0922] The server captures the user's voice input. The microphone on the smartphone or head-mounted display is used to collect the voice data the user speaks. The data is sent to the Google speech recognition service. The input is the user's voice, and the output is text data.

[0923] Step 2:

[0924] The server converts the received voice data into text data using a voice recognition service. The server uses the Google voice recognition service as the voice recognition method and converts the received voice data into text data. The input is voice data and the output is text data.

[0925] Step 3:

[0926] The server sends the text data to a generative AI model, which uses the model (e.g., OpenAI GPT) to generate appropriate responses or questions based on the speech-recognized text data. The input is the text data, and the output is the generated response or question.

[0927] Step 4:

[0928] The server provides the generated response to the user. The generated response is presented to the user through the screen or speaker of a smartphone or head-mounted display. The input is the response from the generative AI model, and the output is what is presented to the user.

[0929] Step 5:

[0930] The user answers questions posed by the AI ​​by voice. For example, they provide information such as "My Amazon password is abc123." The input is the user's voice input in response to the questions posed by the AI, and the output is the user's voice data.

[0931] Step 6:

[0932] The server again captures the voice input and converts it to text data. As in steps 1 and 2, the voice data is stored in temporary storage and sent to the Google speech recognition service for conversion to text data. The input is voice data and the output is text data.

[0933] Step 7:

[0934] The server uses generation AI to collect any further required information and save it in a database. If more information is needed based on the user's response data, additional questions are generated and additional information is collected from the user. This process is repeated until all the necessary information has been collected and the data is saved in the database. The input is text data from the user, and the output is saved in the database.

[0935] Step 8:

[0936] The server generates a document using the document generation means. Based on all the collected information, it generates the necessary document (e.g., end-of-life document or security information document). The input is the information stored in the database, and the output is the generated document.

[0937] Step 9:

[0938] The server presents the generated document to the user, who then reviews the document and makes any necessary corrections. The input is the generated document, and the output is the presentation and review to the user.

[0939] Step 10:

[0940] The system automatically sends notifications to designated contacts in the event of an emergency. Based on the user's settings, the system automatically sends emails and messages to designated contacts in the event of an emergency. The input is the configured emergency contact information, and the output is the automatically sent notification.

[0941] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0942] The present invention is a system that provides more personalized end-of-life planning support by adding an emotion recognition engine to recognize the user's emotional state and adjusting responses based on that. This system performs the following operations.

[0943] System initialization

[0944] The server initializes the system by instantiating a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine, which enables capturing voice input, recognizing emotions, generating appropriate responses, and creating documents.

[0945] Voice input capture and emotion recognition

[0946] The user speaks to the system about their end-of-life wishes (e.g., "I want to prepare for my inheritance"). The device uses a microphone to capture this voice. At the same time, an emotion recognition engine analyzes the user's emotional state from their voice in real time.

[0947] Converting voice data and sending emotion data

[0948] The device sends the captured voice data to a voice recognition service, where it is converted into text data. The emotion recognition engine also analyzes the data and sends it to the server.

[0949] AI-powered response generation

[0950] The server sends the text data and emotional data to the AI ​​generator, which generates a response based on the user's emotional state. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the AI ​​generator will respond in a gentle tone, "How would you like to distribute it?"

[0951] Collection of necessary information

[0952] The user responds to the generated response with additional information by voice, which the device captures again and converts into text data via a speech recognition service. At the same time, an emotion recognition engine analyzes the user's emotional state and sends that data to the server.

[0953] Creation of end-of-life documents

[0954] After all the necessary information has been collected, the server uses a document generation means to generate end-of-life documents (such as a will or funeral plan) based on the collected data.

[0955] Present and verify documents

[0956] The terminal presents the generated document to the user, either as a digital file or on paper, for final review.

[0957] Specific examples

[0958] Example 1: Preparing for inheritance

[0959] 1. The user says, "I want to prepare for inheritance."

[0960] 2. The device captures the voice and an emotion recognition engine analyzes the user's emotions. In this case, if the user expresses anxiety, that data is also captured.

[0961] 3. The device converts the voice data into text data and sends it to the server along with the emotion data.

[0962] 4. The server uses generative AI methods to generate a gentle response based on the user's feelings of anxiety, such as "Don't worry. Just tell us how you would like to distribute your inheritance."

[0963] 5. The user responds, "I would like to distribute the real estate to my eldest son and the cash to my second son."

[0964] 6. The device captures the voice again, and the emotion recognition engine analyzes the user's emotions. The voice data is converted into text and sent to the server along with the emotion data.

[0965] 7. This process is repeated until all necessary information is collected.

[0966] 8. The server finally generates a document based on the collected data and sentiment.

[0967] 9. The terminal presents the generated document to the user, who reviews the proposed document and suggests corrections if necessary.

[0968] In this way, the present invention is a system that uses an emotion recognition engine to provide responses that take into consideration the user's emotions, thereby enabling smoother end-of-life support with less psychological burden.

[0969] The processing flow will be explained below.

[0970] Step 1:

[0971] The server initializes the system. Specifically, it creates instances of the Recognizer and Microphone objects for speech recognition, the generative AI model, the document generation means, and the emotion recognition engine.

[0972] Step 2:

[0973] The user speaks a request regarding end-of-life planning, such as "I want to prepare for inheritance."

[0974] Step 3:

[0975] The terminal captures the user's voice input through a microphone and obtains it as voice data.

[0976] Step 4:

[0977] The device sends the captured voice data to an emotion recognition engine to analyze the user's emotional state in real time.

[0978] Step 5:

[0979] The terminal transmits the voice data to a voice recognition service via the Internet, and converts the voice data into text data.

[0980] Step 6:

[0981] The server sends the converted text data and the emotional state data obtained from the emotion recognition engine to the generation AI means, which then generates an appropriate response based on the user's emotions. For example, if the user is feeling anxious, it generates a gentle response such as, "Don't worry. Please tell us how you would like to distribute your inheritance."

[0982] Step 7:

[0983] The device provides the user with a response generated by the generative AI means, either by voice or text.

[0984] Step 8:

[0985] The user answers the generated question by voice with specific details (e.g., "I want to distribute the real estate to my eldest son and the cash to my second son").

[0986] Step 9:

[0987] The device captures the user's voice again, and the emotion recognition engine analyzes the user's emotional state and captures it as data.

[0988] Step 10:

[0989] The terminal converts the voice data into text data and transmits it to the server together with the acquired emotion data.

[0990] Step 11:

[0991] The server then uses the generative AI means again based on the received text data and emotion data to generate the next question, or proceeds to the next processing step if no additional information is needed.

[0992] Step 12:

[0993] The device repeats this process until all the necessary information is collected, continually capturing input and emotions from the user and generating responses.

[0994] Step 13:

[0995] The server uses a document generation means to generate documents necessary for end-of-life planning (such as a will or funeral plan) based on all collected user data and emotion data.

[0996] Step 14:

[0997] The terminal presents the generated document to the user, either as a digital file or on paper, for final review.

[0998] Step 15:

[0999] The user checks the generated document and, if necessary, indicates corrections by voice.

[1000] This allows the system of the present invention to efficiently support end-of-life preparations while taking into consideration the user's feelings.

[1001] Example 2

[1002] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1003] Conventional end-of-life planning support systems have had the problem of being difficult to respond to users' emotions, which can lead to a heavy psychological burden. In particular, when it comes to the delicate topic of end-of-life planning, it is important to respond to the emotions of users, but conventional technologies have not been able to fully achieve this.

[1004] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a voice recognition means, a generation AI means, a document generation means, a means for collecting voice input from the user and converting it into text data by the voice recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, an emotion recognition means for analyzing emotions from the user's voice in real time, and a means for the generation AI means to generate a response according to the user's emotional state based on the emotion data acquired by the emotion recognition means. This makes it possible to provide a response that takes the user's emotions into consideration, reduce psychological burden, and enable smooth end-of-life support.

[1005] 1. "Speech recognition means" means a device or method capable of converting speech input from a user into text data.

[1006] 2. "Generative AI means" refers to technology that generates appropriate responses based on the user's text data.

[1007] 3. "Document generation means" means a device or method that has the function of generating documents based on collected information.

[1008] 4. "Emotion recognition means" is a technology that analyzes emotions from the user's voice in real time.

[1009] 5. A "means for collecting voice input" is a device or method capable of capturing a user's speech.

[1010] 6. "Means for converting into text data" refers to a function for converting captured audio data into text data.

[1011] 7. "Means for generating responses" refers to a function that generates responses that meet the user's needs based on text data and emotional data.

[1012] 8. "Means for providing a response" means the functionality for communicating the generated response to the user.

[1013] 9. "Means for gathering required information" refers to the functionality for obtaining additional information from the user based on the response generated.

[1014] 10. "Means for generating a response based on emotional data" refers to a function that uses emotional data acquired by the emotion recognition means to generate a response according to the user's emotional state.

[1015] This invention is a system that provides more personalized support for end-of-life planning by adding an emotion recognition engine to recognize the user's emotional state and adjusting responses based on that. This system uses the following hardware and software.

[1016] Hardware and software used

[1017] Recognizer object: Used to recognize the user's voice input and functions as a voice recognition means.

[1018] Microphone object: A device for capturing user speech and serves as a means of collecting audio input.

[1019] Generative AI model: A technology that generates appropriate responses based on the user's text data.

[1020] Document generation means: Has the function of generating end-of-life documents based on collected information.

[1021] Emotion recognition engine: A technology that analyzes emotions from the user's voice in real time, and functions as an emotion recognition means.

[1022] System Operation

[1023] 1. During system initialization, the server instantiates a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine, enabling it to capture voice input, recognize emotions, generate appropriate responses, and create documents.

[1024] Specific examples

[1025] Example 1: Preparing for inheritance

[1026] 1. The user says, "I want to prepare for my inheritance."

[1027] 2. The device uses a Microphone object to capture the user's voice. At the same time, the emotion recognition engine analyzes the user's voice in real time to determine emotions. In this case, if the user expresses anxiety, the device also captures that data.

[1028] 3. The device sends the captured voice data to the voice recognition service, which converts it into text data. The converted text data and the emotion data analyzed by the emotion recognition engine are then sent to the server.

[1029] 4. The server uses a generative AI model to generate a response based on the text and emotion data. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the server generates a gentle response such as, "Don't worry. How would you like to distribute your inheritance?"

[1030] 5. The user responds, "I want to distribute the real estate to my eldest son and the cash to my second son."

[1031] 6. The device captures the voice again, and the emotion recognition engine analyzes the user's emotions. The voice data is converted back into text and sent to the server along with the emotion data.

[1032] 7. This process is repeated until all necessary information has been collected, at which point the server uses the document generation means to generate a final-life document based on the collected data.

[1033] 8. The terminal presents the generated document to the user, who can review it and suggest corrections if necessary.

[1034] Prompt Sentence Examples

[1035] "I want to prepare for inheritance."

[1036] "How do you want your inheritance distributed?"

[1037] "I want to distribute the real estate to my eldest son and the cash to my second son."

[1038] In this way, the present invention is a system that uses an emotion recognition engine to provide responses that take into consideration the user's emotions, thereby enabling smoother end-of-life support with less psychological burden.

[1039] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1040] Step 1: Initialize the system

[1041] The server creates instances of a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine. This enables voice input capture, emotion recognition, appropriate response generation, and document creation. Specifically, it creates the following various objects and instances:

[1042] Recognizer: An object for recognizing audio data

[1043] Microphone: An object for capturing sound

[1044] Generative AI models: Models that create responses based on user text input

[1045] Document generation means: An instance that generates a document based on collected data

[1046] Emotion recognition engine: An engine that analyzes emotions from voice

[1047] Step 2: Capturing voice input and recognizing emotions

[1048] The user says, "I want to prepare for my inheritance." The device uses the Microphone object to capture this voice. At the same time, the emotion recognition engine works simultaneously to analyze emotional data from the voice in real time. Specifically, the device captures the voice input and sends the voice data to the emotion recognition engine to obtain emotional data. The input here is the user's voice data, and the output is text data and emotional data.

[1049] Step 3: Converting voice data and sending emotion data

[1050] The device sends the captured voice data to a voice recognition service, which converts it into text data. At the same time, it also sends the emotional data acquired by the emotion recognition engine to the server. As a result, the server receives the user's intentions in text format and is able to understand their emotional state as well. Specifically, the device sends the voice data, and then sends the returned text data and the emotional data generated by the emotion recognition engine to the server all at once. The input is voice data, and the output is text data and emotional data.

[1051] Step 4: AI-generated responses

[1052] The server inputs the received text data and emotional data into the generative AI model and generates a response that takes the user's emotional state into account. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the generative AI model will create a gentle response such as, "Don't worry. How would you like to distribute your inheritance?" Specifically, the generative AI model processes the input text data and emotional data and generates an appropriate response text. The input is text data and emotional data, and the output is response text.

[1053] Step 5: Gather required information

[1054] The user provides further information based on the generated response. For example, the user might reply, "I want to distribute the property to my eldest son and the cash to my second son." The device captures this again and converts it into text data through a voice recognition service. At the same time, the emotion recognition engine analyzes the user's emotions again and sends the emotion data to the server. Specifically, the device captures the voice again, converts it into text data through a voice recognition service, and sends it to the server together with the emotion data. The input is voice data, and the output is text data and emotion data.

[1055] Step 6: Generate end-of-life documents

[1056] After all necessary information is collected, the server uses a document generation means to generate end-of-life documents based on the collected data. Examples include wills and funeral plans. Specifically, the server integrates the collected text data and emotion data to automatically generate a document that reflects the user's intentions and emotions. The input is the aggregated text data and emotion data, and the output is the generated document.

[1057] Step 7: Present and review documents

[1058] The terminal presents the generated document to the user, who can review the generated document and suggest corrections if necessary. Specifically, the terminal presents the generated document to the user and accepts feedback on it. The input is the generated document, and the output is feedback for the user's review or corrections.

[1059] (Application example 2)

[1060] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1061] It is difficult to properly grasp a customer's emotional state and provide personalized customer service in real time. Especially in brick-and-mortar stores, it is important for staff to instantly understand a customer's emotions and respond appropriately, but this is difficult to do. Conventional systems lack emotion recognition capabilities, making it impossible to respond in a way that takes the user's emotions into consideration.

[1062] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1063] In this invention, the server includes a voice recognition means, an emotion recognition means, a generation AI means, and a document generation means, which makes it possible to collect voice input from a user, convert it into text data by the voice recognition means, analyze the user's emotional state by the emotion recognition means, generate a response based on the text data and the emotion data by the generation AI means, provide the generated response to the user, collect necessary information based on the response, and generate a document by the document generation means.

[1064] 1. "Speech recognition means" means a technology for converting voice data into text data.

[1065] 2. "Generative AI methods" are artificial intelligence technologies that generate appropriate responses or content based on text data or other input data.

[1066] 3. "Document generation means" refers to technology for automatically creating documents based on collected data.

[1067] 4. "Emotion recognition means" refers to technology that analyzes input data such as voice and facial expressions to identify the user's emotional state.

[1068] 5. "Means for collecting voice input from a user and converting it into text data through speech recognition means" refers to technology that performs a process of recording a user's speech and converting it into text information.

[1069] 6. "Means for generating responses based on text data and emotional data using generative AI means" refers to technology that creates appropriate replies or instructions based on input text and emotional information.

[1070] 7. "Means for providing a generated response to a user" means any technology that displays or conveys a generated text or audio message to a user.

[1071] 8. "Means for collecting necessary information based on responses and generating a document using a document generation means" refers to a technology that executes a process of collecting additional information based on the generated responses and creating a final document based on that information.

[1072] This invention is a system that provides personalized services by analyzing the user's emotional state in real time using an emotion recognition engine to enhance customer service in brick-and-mortar stores. The following elements are used to implement this system:

[1073] Hardware used

[1074] Smartphone (iOS / Android)

[1075] Smart glasses (general name example: AR glasses)

[1076] Head-mounted display (general name example: AR headset)

[1077] Software used

[1078] Speech recognition service (general name example: Cloud speech recognition API)

[1079] Emotion recognition engine (general name example: Cloud Emotion Analysis API)

[1080] Generative AI model (general name example: generative model)

[1081] Program processing explanation

[1082] Voice input and emotional state capture

[1083] Users (customers) speak to staff in stores to ask questions about products or services. The microphones and cameras built into the devices (smartphones, smart glasses, head-mounted displays) capture the customer's voice and facial expressions in real time.

[1084] Analysis of speech and emotion data

[1085] The device sends the captured voice data to a cloud speech recognition API and converts it into text data. At the same time, an emotion recognition engine analyzes the captured voice and facial expression data to detect the user's emotional state. This allows the device to grasp the customer's emotional state, such as stress or joy, in real time.

[1086] Response Generation

[1087] The device then sends the converted text data and emotion data to a server. The server, equipped with a generative model, uses this data to generate an appropriate response based on the user's emotional state. For example, if the user is feeling stressed, the server generates a response such as, "We have some products here that can help relieve stress. Would you like me to show you some?"

[1088] Providing a response

[1089] The generated response is provided to the user through the terminal and displayed in real time on the staff's smart glasses or head-mounted display, allowing the staff to respond to the user accordingly.

[1090] Specific examples

[1091] Situation

[1092] A customer approaches a store staff member and asks a product question. The customer appears stressed.

[1093] Program operation example

[1094] 1. Voice Capture: A customer says, "I'm looking for a product that will help me relieve stress."

[1095] 2. Emotion analysis: An emotion recognition engine detects stress from customer voices.

[1096] 3. Voice transcription: The cloud speech recognition API converts the voice into text data such as "I'm looking for a product that will help relieve stress."

[1097] 4. Response generation: The generative model generates a response such as, "We have some products that can help relieve stress. Would you like to know more?"

[1098] 5. Response presentation: The response is displayed on the staff member's smart glasses, and the staff member responds to the customer in the same words.

[1099] Prompt Sentence Examples

[1100] Customer utterance: 'I'm looking for a product that will help me relieve stress.' The customer's emotional state indicates stress. Generate an appropriate response.

[1101] In this way, the system uses an emotion recognition engine and generative AI model to understand the customer's emotional state in real time and provide personalized responses, thereby improving customer satisfaction.

[1102] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1103] Step 1:

[1104] A customer speaks to a staff member in a physical store (input: customer speech). A microphone built into the device (smartphone, smart glasses, head-mounted display) captures the customer's voice in real time (output: voice data).

[1105] Step 2:

[1106] The device sends the captured voice data to the cloud, where it is converted into text data using a cloud voice recognition API (input: voice data, output: text data). This converts the voice content into text data.

[1107] Step 3:

[1108] At the same time, the device's built-in camera captures the customer's facial expressions and sends the data to an emotion recognition engine for analysis (input: facial expression data, output: emotion data), which determines the customer's current emotional state (e.g., joy, stress, excitement, etc.).

[1109] Step 4:

[1110] The device sends the converted text data and analyzed emotion data to the server (input: text data, emotion data, output: data sent to the server). The server receives this data and proceeds to the next analysis step.

[1111] Step 5:

[1112] The server uses a generative AI model to generate an appropriate response based on the received text data and emotional data (input: text data, emotional data, output: response data). For example, if a customer is feeling stressed, a response such as "We have products that will help relieve stress" will be generated.

[1113] Step 6:

[1114] The generated response data is sent to the terminal (input: response data, output: response data sent to the terminal). The terminal provides the generated response to the user in the form of a display or voice guidance.

[1115] Step 7:

[1116] The terminal displays the generated response on the staff member's smart glasses or head-mounted display (input: response data, output: visual display provided to staff member). The staff member then responds appropriately to the customer based on the displayed response. This enables personalized service that is in line with the customer's emotional state.

[1117] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1119] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1120] [Fourth embodiment]

[1121] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1122] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1124] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1125] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1128] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1129] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1130] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1132] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1133] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1134] The present invention is implemented by a system including a speech recognition unit, a generation AI unit, and a document generation unit. This system operates as follows.

[1135] System initialization

[1136] The server creates Recognizer and Microphone objects for speech recognition, instantiates a generative AI model, and a document generator, and the system is now ready to capture voice input, generate appropriate responses, and finally create the required documents.

[1137] Capturing voice input

[1138] The user speaks to the system about their end-of-life wishes (e.g., "I want to prepare for my inheritance"). The device uses a microphone to capture the user's voice and converts the voice data into text data via the Google speech recognition service.

[1139] AI-powered response generation

[1140] The server sends the text data obtained by speech recognition to the AI ​​generator, which generates an appropriate response. For example, this response might be in the form of "Please tell us how you would like to distribute your inheritance." The terminal provides the generated response to the user and prompts for further input.

[1141] Collection of necessary information

[1142] The user responds to questions posed by the AI ​​by voice, such as "I want to distribute the property to my eldest son and the cash to my second son." The device again captures this as voice data, and the server sends the text data to the AI, which then generates further questions and provides them to the user. This process is repeated until all the necessary information is collected.

[1143] Creation of end-of-life documents

[1144] Once all the necessary information has been collected, the server uses a document generation tool to generate end-of-life documents (e.g., wills, funeral plans) based on the collected data. The terminal presents the generated documents to the user, which can be provided as digital files or paper-based.

[1145] Specific examples

[1146] Example 1: Preparing for inheritance

[1147] 1. The user says, "I want to prepare for inheritance."

[1148] 2. The device captures this audio, and the server converts it into text data through a speech recognition service.

[1149] 3. The server uses its generative AI means to generate a response such as "Tell us which inheritances you would like to distribute and how."

[1150] 4. The user responds by voice, "I want to distribute the real estate to my eldest son and the cash to my second son."

[1151] 5. The server continues to collect information, and once all necessary information is gathered, it uses the document generation means to generate a document for the inheritance.

[1152] 6. The terminal presents this document to the user, who then provides final confirmation.

[1153] In this way, the present invention is a system that utilizes voice recognition and generative AI to enable users to easily prepare for end-of-life planning even without specialized knowledge.

[1154] The processing flow will be explained below.

[1155] Step 1:

[1156] The server initializes the system. Specifically, it creates Recognizer and Microphone objects for speech recognition, and creates instances of the generative AI model and document generation means.

[1157] Step 2:

[1158] The user speaks to the system about their end-of-life requests (e.g., "I want to prepare for my inheritance").

[1159] Step 3:

[1160] The terminal captures the user's voice input through a microphone and obtains it as voice data.

[1161] Step 4:

[1162] The terminal transmits the captured voice data to a voice recognition service via the Internet, and converts the voice data into text data.

[1163] Step 5:

[1164] The server sends the text data to a generative AI means that generates an appropriate response based on the input, for example, formulating a question such as "Please tell us how you would like to distribute your inheritance."

[1165] Step 6:

[1166] The terminal presents the user with the response generated by the generative AI means, and the user is provided with a question by voice or text.

[1167] Step 7:

[1168] The user responds to the generated question by voice with additional information (e.g., "I want to distribute the real estate to my eldest son and the cash to my second son").

[1169] Step 8:

[1170] The terminal again captures the user's response through the microphone and obtains it as voice data.

[1171] Step 9:

[1172] The terminal transmits the captured voice data to a voice recognition service and converts it back into text data.

[1173] Step 10:

[1174] The server continues to collect necessary information using the acquired text data, for example, by generating the next question using a generative AI means and presenting it to the user via the terminal.

[1175] Step 11:

[1176] The device repeats this process, continuing to capture voice and generate AI-generated responses until all the necessary information is collected.

[1177] Step 12:

[1178] The server uses a document generation means to generate documents necessary for end-of-life preparations (such as a will or funeral plan) based on the collected user data.

[1179] Step 13:

[1180] The terminal presents the generated document to the user, which can be provided as a digital file or on paper.

[1181] Step 14:

[1182] The user finally reviews and completes the presented document.

[1183] Example 1

[1184] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1185] Conventional end-of-life preparations have been problematic in that general users without specialized knowledge have difficulty creating complex documents and gathering information, resulting in significant stress and effort. In particular, there are no systems that utilize voice input to efficiently and interactively collect necessary information and generate accurate and appropriate documents based on that information. This makes it difficult for users to prepare for end-of-life planning on their own, and in many cases requires the assistance of an expert. The present invention aims to provide a system that solves these problems.

[1186] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1187] In this invention, the server includes a speech recognition means, a generation AI means, a document generation means, a means for collecting speech input from a user and converting it into text data using the speech recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, a means for equipping the terminal with a microphone for capturing speech data, a means for initializing the speech recognition means and generating an instance of the generation AI model, and a means for receiving and displaying information from the user through a user interface. This makes it possible for a user to collect necessary information for easy and efficient end-of-life preparations through speech input and automatically generate accurate documents based on that information.

[1188] "Speech recognition means" refers to technical means for converting a user's voice input into text data.

[1189] "Generative AI means" refers to artificial intelligence technical means for generating appropriate responses or questions for users based on acquired text data.

[1190] The "document generation means" is a technical means for automatically creating a document in a specified format based on collected information.

[1191] "Means for collecting voice input from a user and converting it into text data by voice recognition means" refers to a technical means for capturing a user's voice using a voice input device such as a microphone and converting it into text data.

[1192] "Means for generating a response based on text data using generative AI means" refers to technical means that utilizes a generative AI model to create an appropriate response to a user from text data.

[1193] "Means for providing the generated response to the user" refers to technical means for providing the response generated by the generation AI means to the user by means of screen display, audio output, etc.

[1194] "Means for collecting necessary information based on responses and generating documents using document generation means" refers to technical means for collecting information based on user responses and using this information to generate necessary documents using document generation means.

[1195] "Means for providing a terminal with a microphone for capturing voice data" refers to technical means for providing a microphone in a terminal for receiving voice input from a user and recording the voice data.

[1196] "Means for initializing speech recognition means and generating an instance of a generative AI model" refers to technical means for performing the initial settings required to start the operation of speech recognition technology and generative AI technology and generating an instance of each.

[1197] "Means for receiving and displaying information from a user through a user interface" refers to technical means for providing an interface for a user to input information and for displaying the input.

[1198] The present invention is a system that includes a voice recognition unit, a generation AI unit, and a document generation unit, which allows users to easily prepare for end-of-life planning. This system operates as follows.

[1199] Initialization

[1200] The server creates Recognizer and Microphone objects for speech recognition, instantiates a generative AI model, and prepares LaTeX and Word templates for document generation. This initialization process ensures that the server is ready to capture voice input, generate appropriate responses, and ultimately create the required documents.

[1201] Capturing voice input

[1202] The user speaks to the system about their wishes regarding end-of-life planning. The device uses a microphone to capture the user's voice, and converts the voice data into text data using a voice recognition service via the Internet. For example, the user might say, "I want to prepare for my inheritance."

[1203] AI-powered response generation

[1204] The server sends the converted text data to the AI ​​generator, which generates an appropriate response, such as "Please tell us how you would like to distribute your inheritance." The terminal provides the generated response to the user and prompts for further input.

[1205] Collection of necessary information

[1206] The user responds to questions posed by the AI ​​by voice, such as "I want to distribute the property to my eldest son and the cash to my second son." The device again captures this as voice data, and the server sends the text data to the AI ​​generator, which generates further questions and provides them to the user. This process is repeated until all the necessary information is collected.

[1207] Creation of end-of-life documents

[1208] Once all the necessary information has been collected, the server uses a document generation tool to generate end-of-life documents (such as a will or funeral plan) based on the collected data. The generated document is saved in a format such as PDF and displayed to the user on the device. The user can then review the document and make any necessary corrections. The final document can be saved as a digital file or printed on paper.

[1209] Specific examples

[1210] Example 1: Preparing for inheritance

[1211] 1. The user says, "I want to prepare for inheritance."

[1212] 2. The device captures this audio, and the server converts it into text data using Google speech recognition services.

[1213] 3. The server uses its generative AI means to generate a response such as "Tell us which inheritances you would like to distribute and how."

[1214] 4. The user responds by voice, "I want to distribute the real estate to my eldest son and the cash to my second son."

[1215] 5. The server continues to collect information, and once all necessary information is gathered, it uses the document generation means to generate a document for the inheritance.

[1216] 6. The terminal presents this document to the user, who then provides final confirmation.

[1217] In this way, the present invention is a system that utilizes voice recognition and generative AI to enable users to easily prepare for end-of-life planning even without specialized knowledge.

[1218] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1219] Step 1:

[1220] The server initializes the system. Specifically, it creates the Recognizer and Microphone objects of the speech recognition library and creates an instance of the generative AI model. It also prepares LaTeX and Word templates as a means of document generation. The input here is the system startup signal, and the output is the instance of each initialized object.

[1221] Step 2:

[1222] The user speaks their wishes regarding end-of-life planning into the device. For example, they might say, "I want to prepare for my inheritance." This voice data becomes the input.

[1223] Step 3:

[1224] The device uses a microphone to capture the user's voice, and then sends the voice data to a voice recognition service via the Internet to convert it into text data. The input here is voice data, and the output is text data. For example, the text data obtained is "I would like to prepare for my inheritance."

[1225] Step 4:

[1226] The server sends the text data obtained by speech recognition to the generative AI model, which generates an appropriate response based on the input text. The generative AI model outputs a generated response, such as a prompt, "Please tell us how you would like to distribute your inheritance."

[1227] Step 5:

[1228] The device displays or speaks the response received from the generative AI model to the user. Here, the response text returned from the server is received as input, and the output is presented to the user.

[1229] Step 6:

[1230] The user answers questions posed by the AI ​​by voice. For example, they might say, "I want to distribute the real estate to my eldest son and the cash to my second son." The user's voice response becomes the input.

[1231] Step 7:

[1232] The device again uses a microphone to capture the user's voice and converts the voice data into text data using a voice recognition service. The input here is voice data, and the output is text data. For example, the text data obtained is "I want to distribute the real estate to my eldest son and the cash to my second son."

[1233] Step 8:

[1234] The server sends the text data back to the generative AI model to generate a further question. For example, a question like "What is the address of the property?" is generated. The input here is the user's answer text data, and the output is the next question text.

[1235] Step 9:

[1236] The server and terminal repeat this process until all necessary information is collected. Finally, based on the collected information, the server uses a document generation means to generate end-of-life documents, such as a will or funeral plan. The input is the collected information text data, and the output is the generated end-of-life documents.

[1237] Step 10:

[1238] The terminal presents the generated document to the user, who then performs final confirmation. The user can check the document content and make corrections as necessary. The input is the generated document data, and the output is the final document confirmed by the user.

[1239] In this way, by clearly indicating the inputs and outputs at each processing step and explaining the specific operations, the overall processing flow of the system can be understood.

[1240] (Application example 1)

[1241] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1242] In modern society, personal end-of-life planning and security management are important issues. However, it is difficult for users without specialized knowledge to perform these tasks efficiently and safely. In particular, emergency contact and password management, as well as automatic emergency contact functions, are necessary to reduce the burden on users and improve safety, and there is a demand for methods to easily implement these functions.

[1243] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1244] In this invention, the server includes a voice recognition means, a generation AI means, a document generation means, a means for collecting voice input from a user and converting it into text data by the voice recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, a means for registering and managing the user's emergency contact information and password information by voice, and a means for automatically sending a notification to designated contacts in the event of an emergency. This enables users to efficiently and safely prepare for end-of-life planning and manage security without having specialized knowledge.

[1245] A "voice recognition means" is a device or software that has the function of capturing a user's voice input and converting the voice data into text data.

[1246] A "generative AI means" is a means that uses an artificial intelligence model to generate appropriate responses or questions based on text data.

[1247] "Document generation means" refers to a device or software that has the function of automatically generating the necessary documents based on collected information.

[1248] The "means for collecting voice input from a user and converting it into text data by a voice recognition means" refers to a means for executing a process of collecting a user's voice and converting the voice into a text format by voice recognition.

[1249] "Means for generating responses based on text data using generative AI means" refers to means for executing a process that uses a generative AI model to generate appropriate responses or questions based on text data obtained through speech recognition.

[1250] The "means for providing a generated response to a user" is a means for executing a process for presenting a response created by the generation AI means to a user.

[1251] "Means for collecting necessary information based on the response and generating a document using a document generation means" refers to means for executing a process of collecting additional information from the user in accordance with the response provided by the generation AI means and automatically generating a necessary document based on that information.

[1252] The "means for registering and managing a user's emergency contact and password information by voice" refers to a means for executing a process in which a user inputs emergency contact and password information by voice, and registers and manages this information in a database.

[1253] The "means for automatically sending a notification to a designated contact in an emergency" is a means having a function for automatically sending a notification to a user-designated contact in the event of a specific emergency.

[1254] The present invention is implemented by a speech recognition means, a generation AI means, a document generation means, and a speech-based information collection and management system. This system efficiently recognizes information input by a user through speech, collects and manages necessary information, and generates documents based on the information.

[1255] System Configuration

[1256] The system uses the following major hardware and software:

[1257] Hardware: smartphones, head-mounted displays, smart glasses, microphones, servers

[1258] Software: Google speech recognition services, generative AI models (e.g., OpenAI GPT), databases for data storage (e.g., Firebase)

[1259] Program processing overview

[1260] 1. Capture voice input

[1261] The server captures the user's voice input via devices such as smartphones and head-mounted displays, and uses the Google speech recognition service to convert the captured voice data into text data.

[1262] 2. Response generation using generative AI

[1263] The server sends the text data obtained by speech recognition to a generative AI model, which generates appropriate responses and follow-up questions. The generated responses are presented to the user, prompting them for further speech input.

[1264] 3. Information collection and management

[1265] The user answers questions posed by the AI ​​by voice, which collects important information such as emergency contacts and passwords. This information is then stored and managed in a database. Settings are also made to automatically send notifications to designated contacts in the event of an emergency.

[1266] 4. Document Generation

[1267] After all the necessary information has been collected, the server uses a document generation tool to automatically generate end-of-life and security-related documents, which are then presented to the user in digital or paper form.

[1268] Specific examples

[1269] Example 1: Registering an emergency contact

[1270] User: "My wife's phone number is 090-xxxx-xxxx."

[1271] Voice data is captured through the smartphone's microphone and converted into text data using Google's voice recognition service.

[1272] The server uses a generative AI model to generate a response based on the text data, and returns a response to the user in the form of, for example, "Your phone number has been registered."

[1273] Emergency contact information is stored in the database.

[1274] Example 2: Managing Passwords

[1275] User: "My Amazon password is abc123"

[1276] Voice data is captured and converted into text data using the Google speech recognition service.

[1277] The server uses the generative AI model to generate a response such as "Your password has been registered" and presents it to the user.

[1278] Passwords are stored securely in a database.

[1279] Example 3: Automatic contact in an emergency

[1280] In the system settings, the user says, "In case of an emergency, please contact my son's cell phone."

[1281] Once all necessary information has been collected, designated contacts will be automatically notified in the event of an emergency.

[1282] Prompt Sentence Examples

[1283] Check the security status

[1284] Please register your emergency contact information.

[1285] "Would you like to update your password?"

[1286] In this way, the present invention utilizes voice recognition and generative AI, enabling users to prepare for end-of-life planning and manage personal information efficiently and safely, even without specialized knowledge.

[1287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1288] Step 1:

[1289] The server captures the user's voice input. The microphone on the smartphone or head-mounted display is used to collect the voice data the user speaks. The data is sent to the Google speech recognition service. The input is the user's voice, and the output is text data.

[1290] Step 2:

[1291] The server converts the received voice data into text data using a voice recognition service. The server uses the Google voice recognition service as the voice recognition method and converts the received voice data into text data. The input is voice data and the output is text data.

[1292] Step 3:

[1293] The server sends the text data to a generative AI model, which uses the model (e.g., OpenAI GPT) to generate appropriate responses or questions based on the speech-recognized text data. The input is the text data, and the output is the generated response or question.

[1294] Step 4:

[1295] The server provides the generated response to the user. The generated response is presented to the user through the screen or speaker of a smartphone or head-mounted display. The input is the response from the generative AI model, and the output is what is presented to the user.

[1296] Step 5:

[1297] The user answers questions posed by the AI ​​by voice. For example, they provide information such as "My Amazon password is abc123." The input is the user's voice input in response to the questions posed by the AI, and the output is the user's voice data.

[1298] Step 6:

[1299] The server again captures the voice input and converts it to text data. As in steps 1 and 2, the voice data is stored in temporary storage and sent to the Google speech recognition service for conversion to text data. The input is voice data and the output is text data.

[1300] Step 7:

[1301] The server uses generation AI to collect any further required information and save it in a database. If more information is needed based on the user's response data, additional questions are generated and additional information is collected from the user. This process is repeated until all the necessary information has been collected and the data is saved in the database. The input is text data from the user, and the output is saved in the database.

[1302] Step 8:

[1303] The server generates a document using the document generation means. Based on all the collected information, it generates the necessary document (e.g., end-of-life document or security information document). The input is the information stored in the database, and the output is the generated document.

[1304] Step 9:

[1305] The server presents the generated document to the user, who then reviews the document and makes any necessary corrections. The input is the generated document, and the output is the presentation and review to the user.

[1306] Step 10:

[1307] The system automatically sends notifications to designated contacts in the event of an emergency. Based on the user's settings, the system automatically sends emails and messages to designated contacts in the event of an emergency. The input is the configured emergency contact information, and the output is the automatically sent notification.

[1308] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1309] The present invention is a system that provides more personalized end-of-life planning support by adding an emotion recognition engine to recognize the user's emotional state and adjusting responses based on that. This system performs the following operations.

[1310] System initialization

[1311] The server initializes the system by instantiating a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine, which enables capturing voice input, recognizing emotions, generating appropriate responses, and creating documents.

[1312] Voice input capture and emotion recognition

[1313] The user speaks to the system about their end-of-life wishes (e.g., "I want to prepare for my inheritance"). The device uses a microphone to capture this voice. At the same time, an emotion recognition engine analyzes the user's emotional state from their voice in real time.

[1314] Converting voice data and sending emotion data

[1315] The device sends the captured voice data to a voice recognition service, where it is converted into text data. The emotion recognition engine also analyzes the data and sends it to the server.

[1316] AI-powered response generation

[1317] The server sends the text data and emotional data to the AI ​​generator, which generates a response based on the user's emotional state. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the AI ​​generator will respond in a gentle tone, "How would you like to distribute it?"

[1318] Collection of necessary information

[1319] The user responds to the generated response with additional information by voice, which the device captures again and converts into text data via a speech recognition service. At the same time, an emotion recognition engine analyzes the user's emotional state and sends that data to the server.

[1320] Creation of end-of-life documents

[1321] After all the necessary information has been collected, the server uses a document generation means to generate end-of-life documents (such as a will or funeral plan) based on the collected data.

[1322] Present and verify documents

[1323] The terminal presents the generated document to the user, either as a digital file or on paper, for final review.

[1324] Specific examples

[1325] Example 1: Preparing for inheritance

[1326] 1. The user says, "I want to prepare for inheritance."

[1327] 2. The device captures the voice and an emotion recognition engine analyzes the user's emotions. In this case, if the user expresses anxiety, that data is also captured.

[1328] 3. The device converts the voice data into text data and sends it to the server along with the emotion data.

[1329] 4. The server uses generative AI methods to generate a gentle response based on the user's feelings of anxiety, such as "Don't worry. Just tell us how you would like to distribute your inheritance."

[1330] 5. The user responds, "I would like to distribute the real estate to my eldest son and the cash to my second son."

[1331] 6. The device captures the voice again, and the emotion recognition engine analyzes the user's emotions. The voice data is converted into text and sent to the server along with the emotion data.

[1332] 7. This process is repeated until all necessary information is collected.

[1333] 8. The server finally generates a document based on the collected data and sentiment.

[1334] 9. The terminal presents the generated document to the user, who reviews the proposed document and suggests corrections if necessary.

[1335] In this way, the present invention is a system that uses an emotion recognition engine to provide responses that take into consideration the user's emotions, thereby enabling smoother end-of-life support with less psychological burden.

[1336] The processing flow will be explained below.

[1337] Step 1:

[1338] The server initializes the system. Specifically, it creates instances of the Recognizer and Microphone objects for speech recognition, the generative AI model, the document generation means, and the emotion recognition engine.

[1339] Step 2:

[1340] The user speaks a request regarding end-of-life planning, such as "I want to prepare for inheritance."

[1341] Step 3:

[1342] The terminal captures the user's voice input through a microphone and obtains it as voice data.

[1343] Step 4:

[1344] The device sends the captured voice data to an emotion recognition engine to analyze the user's emotional state in real time.

[1345] Step 5:

[1346] The terminal transmits the voice data to a voice recognition service via the Internet, and converts the voice data into text data.

[1347] Step 6:

[1348] The server sends the converted text data and the emotional state data obtained from the emotion recognition engine to the generation AI means, which then generates an appropriate response based on the user's emotions. For example, if the user is feeling anxious, it generates a gentle response such as, "Don't worry. Please tell us how you would like to distribute your inheritance."

[1349] Step 7:

[1350] The device provides the user with a response generated by the generative AI means, either by voice or text.

[1351] Step 8:

[1352] The user answers the generated question by voice with specific details (e.g., "I want to distribute the real estate to my eldest son and the cash to my second son").

[1353] Step 9:

[1354] The device captures the user's voice again, and the emotion recognition engine analyzes the user's emotional state and captures it as data.

[1355] Step 10:

[1356] The terminal converts the voice data into text data and transmits it to the server together with the acquired emotion data.

[1357] Step 11:

[1358] The server then uses the generative AI means again based on the received text data and emotion data to generate the next question, or proceeds to the next processing step if no additional information is needed.

[1359] Step 12:

[1360] The device repeats this process until all the necessary information is collected, continually capturing input and emotions from the user and generating responses.

[1361] Step 13:

[1362] The server uses a document generation means to generate documents necessary for end-of-life planning (such as a will or funeral plan) based on all collected user data and emotion data.

[1363] Step 14:

[1364] The terminal presents the generated document to the user, either as a digital file or on paper, for final review.

[1365] Step 15:

[1366] The user checks the generated document and, if necessary, indicates corrections by voice.

[1367] This allows the system of the present invention to efficiently support end-of-life preparations while taking into consideration the user's feelings.

[1368] Example 2

[1369] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1370] Conventional end-of-life planning support systems have had the problem of being difficult to respond to users' emotions, which can lead to a heavy psychological burden. In particular, when it comes to the delicate topic of end-of-life planning, it is important to respond to the emotions of users, but conventional technologies have not been able to fully achieve this.

[1371] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a voice recognition means, a generation AI means, a document generation means, a means for collecting voice input from the user and converting it into text data by the voice recognition means, a means for generating a response based on the text data using the generation AI means, a means for providing the generated response to the user, a means for collecting necessary information based on the response and generating a document using the document generation means, an emotion recognition means for analyzing emotions from the user's voice in real time, and a means for the generation AI means to generate a response according to the user's emotional state based on the emotion data acquired by the emotion recognition means. This makes it possible to provide a response that takes the user's emotions into consideration, reduce psychological burden, and enable smooth end-of-life support.

[1372] 1. "Speech recognition means" means a device or method capable of converting speech input from a user into text data.

[1373] 2. "Generative AI means" refers to technology that generates appropriate responses based on the user's text data.

[1374] 3. "Document generation means" means a device or method that has the function of generating documents based on collected information.

[1375] 4. "Emotion recognition means" is a technology that analyzes emotions from the user's voice in real time.

[1376] 5. A "means for collecting voice input" is a device or method capable of capturing a user's speech.

[1377] 6. "Means for converting into text data" refers to a function for converting captured audio data into text data.

[1378] 7. "Means for generating responses" refers to a function that generates responses that meet the user's needs based on text data and emotional data.

[1379] 8. "Means for providing a response" means the functionality for communicating the generated response to the user.

[1380] 9. "Means for gathering required information" refers to the functionality for obtaining additional information from the user based on the response generated.

[1381] 10. "Means for generating a response based on emotional data" refers to a function that uses emotional data acquired by the emotion recognition means to generate a response according to the user's emotional state.

[1382] This invention is a system that provides more personalized support for end-of-life planning by adding an emotion recognition engine to recognize the user's emotional state and adjusting responses based on that. This system uses the following hardware and software.

[1383] Hardware and software used

[1384] Recognizer object: Used to recognize the user's voice input and functions as a voice recognition means.

[1385] Microphone object: A device for capturing user speech and serves as a means of collecting audio input.

[1386] Generative AI model: A technology that generates appropriate responses based on the user's text data.

[1387] Document generation means: Has the function of generating end-of-life documents based on collected information.

[1388] Emotion recognition engine: A technology that analyzes emotions from the user's voice in real time, and functions as an emotion recognition means.

[1389] System Operation

[1390] 1. During system initialization, the server instantiates a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine, enabling it to capture voice input, recognize emotions, generate appropriate responses, and create documents.

[1391] Specific examples

[1392] Example 1: Preparing for inheritance

[1393] 1. The user says, "I want to prepare for my inheritance."

[1394] 2. The device uses a Microphone object to capture the user's voice. At the same time, the emotion recognition engine analyzes the user's voice in real time to determine emotions. In this case, if the user expresses anxiety, the device also captures that data.

[1395] 3. The device sends the captured voice data to the voice recognition service, which converts it into text data. The converted text data and the emotion data analyzed by the emotion recognition engine are then sent to the server.

[1396] 4. The server uses a generative AI model to generate a response based on the text and emotion data. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the server generates a gentle response such as, "Don't worry. How would you like to distribute your inheritance?"

[1397] 5. The user responds, "I want to distribute the real estate to my eldest son and the cash to my second son."

[1398] 6. The device captures the voice again, and the emotion recognition engine analyzes the user's emotions. The voice data is converted back into text and sent to the server along with the emotion data.

[1399] 7. This process is repeated until all necessary information has been collected, at which point the server uses the document generation means to generate a final-life document based on the collected data.

[1400] 8. The terminal presents the generated document to the user, who can review it and suggest corrections if necessary.

[1401] Prompt Sentence Examples

[1402] "I want to prepare for inheritance."

[1403] "How do you want your inheritance distributed?"

[1404] "I want to distribute the real estate to my eldest son and the cash to my second son."

[1405] In this way, the present invention is a system that uses an emotion recognition engine to provide responses that take into consideration the user's emotions, thereby enabling smoother end-of-life support with less psychological burden.

[1406] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1407] Step 1: Initialize the system

[1408] The server creates instances of a Recognizer, a Microphone object, a generative AI model, a document generator, and an emotion recognition engine. This enables voice input capture, emotion recognition, appropriate response generation, and document creation. Specifically, it creates the following various objects and instances:

[1409] Recognizer: An object for recognizing audio data

[1410] Microphone: An object for capturing sound

[1411] Generative AI models: Models that create responses based on user text input

[1412] Document generation means: An instance that generates a document based on collected data

[1413] Emotion recognition engine: An engine that analyzes emotions from voice

[1414] Step 2: Capturing voice input and recognizing emotions

[1415] The user says, "I want to prepare for my inheritance." The device uses the Microphone object to capture this voice. At the same time, the emotion recognition engine works simultaneously to analyze emotional data from the voice in real time. Specifically, the device captures the voice input and sends the voice data to the emotion recognition engine to obtain emotional data. The input here is the user's voice data, and the output is text data and emotional data.

[1416] Step 3: Converting voice data and sending emotion data

[1417] The device sends the captured voice data to a voice recognition service, which converts it into text data. At the same time, it also sends the emotional data acquired by the emotion recognition engine to the server. As a result, the server receives the user's intentions in text format and is able to understand their emotional state as well. Specifically, the device sends the voice data, and then sends the returned text data and the emotional data generated by the emotion recognition engine to the server all at once. The input is voice data, and the output is text data and emotional data.

[1418] Step 4: AI-generated responses

[1419] The server inputs the received text data and emotional data into the generative AI model and generates a response that takes the user's emotional state into account. For example, if the user says in a sad voice, "I want to prepare for my inheritance," the generative AI model will create a gentle response such as, "Don't worry. How would you like to distribute your inheritance?" Specifically, the generative AI model processes the input text data and emotional data and generates an appropriate response text. The input is text data and emotional data, and the output is response text.

[1420] Step 5: Gather required information

[1421] The user provides further information based on the generated response. For example, the user might reply, "I want to distribute the property to my eldest son and the cash to my second son." The device captures this again and converts it into text data through a voice recognition service. At the same time, the emotion recognition engine analyzes the user's emotions again and sends the emotion data to the server. Specifically, the device captures the voice again, converts it into text data through a voice recognition service, and sends it to the server together with the emotion data. The input is voice data, and the output is text data and emotion data.

[1422] Step 6: Generate end-of-life documents

[1423] After all necessary information is collected, the server uses a document generation means to generate end-of-life documents based on the collected data. Examples include wills and funeral plans. Specifically, the server integrates the collected text data and emotion data to automatically generate a document that reflects the user's intentions and emotions. The input is the aggregated text data and emotion data, and the output is the generated document.

[1424] Step 7: Present and review documents

[1425] The terminal presents the generated document to the user, who can review the generated document and suggest corrections if necessary. Specifically, the terminal presents the generated document to the user and accepts feedback on it. The input is the generated document, and the output is feedback for the user's review or corrections.

[1426] (Application example 2)

[1427] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1428] It is difficult to properly grasp a customer's emotional state and provide personalized customer service in real time. Especially in brick-and-mortar stores, it is important for staff to instantly understand a customer's emotions and respond appropriately, but this is difficult to do. Conventional systems lack emotion recognition capabilities, making it impossible to respond in a way that takes the user's emotions into consideration.

[1429] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1430] In this invention, the server includes a voice recognition means, an emotion recognition means, a generation AI means, and a document generation means, which makes it possible to collect voice input from a user, convert it into text data by the voice recognition means, analyze the user's emotional state by the emotion recognition means, generate a response based on the text data and the emotion data by the generation AI means, provide the generated response to the user, collect necessary information based on the response, and generate a document by the document generation means.

[1431] 1. "Speech recognition means" means a technology for converting voice data into text data.

[1432] 2. "Generative AI methods" are artificial intelligence technologies that generate appropriate responses or content based on text data or other input data.

[1433] 3. "Document generation means" refers to technology for automatically creating documents based on collected data.

[1434] 4. "Emotion recognition means" refers to technology that analyzes input data such as voice and facial expressions to identify the user's emotional state.

[1435] 5. "Means for collecting voice input from a user and converting it into text data through speech recognition means" refers to technology that performs a process of recording a user's speech and converting it into text information.

[1436] 6. "Means for generating responses based on text data and emotional data using generative AI means" refers to technology that creates appropriate replies or instructions based on input text and emotional information.

[1437] 7. "Means for providing a generated response to a user" means any technology that displays or conveys a generated text or audio message to a user.

[1438] 8. "Means for collecting necessary information based on responses and generating a document using a document generation means" refers to a technology that executes a process of collecting additional information based on the generated responses and creating a final document based on that information.

[1439] This invention is a system that provides personalized services by analyzing the user's emotional state in real time using an emotion recognition engine to enhance customer service in brick-and-mortar stores. The following elements are used to implement this system:

[1440] Hardware used

[1441] Smartphone (iOS / Android)

[1442] Smart glasses (general name example: AR glasses)

[1443] Head-mounted display (general name example: AR headset)

[1444] Software used

[1445] Speech recognition service (general name example: Cloud speech recognition API)

[1446] Emotion recognition engine (general name example: Cloud Emotion Analysis API)

[1447] Generative AI model (general name example: generative model)

[1448] Program processing explanation

[1449] Voice input and emotional state capture

[1450] Users (customers) speak to staff in stores to ask questions about products or services. The microphones and cameras built into the devices (smartphones, smart glasses, head-mounted displays) capture the customer's voice and facial expressions in real time.

[1451] Analysis of speech and emotion data

[1452] The device sends the captured voice data to a cloud speech recognition API and converts it into text data. At the same time, an emotion recognition engine analyzes the captured voice and facial expression data to detect the user's emotional state. This allows the device to grasp the customer's emotional state, such as stress or joy, in real time.

[1453] Response Generation

[1454] The device then sends the converted text data and emotion data to a server. The server, equipped with a generative model, uses this data to generate an appropriate response based on the user's emotional state. For example, if the user is feeling stressed, the server generates a response such as, "We have some products here that can help relieve stress. Would you like me to show you some?"

[1455] Providing a response

[1456] The generated response is provided to the user through the terminal and displayed in real time on the staff's smart glasses or head-mounted display, allowing the staff to respond to the user accordingly.

[1457] Specific examples

[1458] Situation

[1459] A customer approaches a store staff member and asks a product question. The customer appears stressed.

[1460] Program operation example

[1461] 1. Voice Capture: A customer says, "I'm looking for a product that will help me relieve stress."

[1462] 2. Emotion analysis: An emotion recognition engine detects stress from customer voices.

[1463] 3. Voice transcription: The cloud speech recognition API converts the voice into text data such as "I'm looking for a product that will help relieve stress."

[1464] 4. Response generation: The generative model generates a response such as, "We have some products that can help relieve stress. Would you like to know more?"

[1465] 5. Response presentation: The response is displayed on the staff member's smart glasses, and the staff member responds to the customer in the same words.

[1466] Prompt Sentence Examples

[1467] Customer utterance: 'I'm looking for a product that will help me relieve stress.' The customer's emotional state indicates stress. Generate an appropriate response.

[1468] In this way, the system uses an emotion recognition engine and generative AI model to understand the customer's emotional state in real time and provide personalized responses, thereby improving customer satisfaction.

[1469] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1470] Step 1:

[1471] A customer speaks to a staff member in a physical store (input: customer speech). A microphone built into the device (smartphone, smart glasses, head-mounted display) captures the customer's voice in real time (output: voice data).

[1472] Step 2:

[1473] The device sends the captured voice data to the cloud, where it is converted into text data using a cloud voice recognition API (input: voice data, output: text data). This converts the voice content into text data.

[1474] Step 3:

[1475] At the same time, the device's built-in camera captures the customer's facial expressions and sends the data to an emotion recognition engine for analysis (input: facial expression data, output: emotion data), which determines the customer's current emotional state (e.g., joy, stress, excitement, etc.).

[1476] Step 4:

[1477] The device sends the converted text data and analyzed emotion data to the server (input: text data, emotion data, output: data sent to the server). The server receives this data and proceeds to the next analysis step.

[1478] Step 5:

[1479] The server uses a generative AI model to generate an appropriate response based on the received text data and emotional data (input: text data, emotional data, output: response data). For example, if a customer is feeling stressed, a response such as "We have products that will help relieve stress" will be generated.

[1480] Step 6:

[1481] The generated response data is sent to the terminal (input: response data, output: response data sent to the terminal). The terminal provides the generated response to the user in the form of a display or voice guidance.

[1482] Step 7:

[1483] The terminal displays the generated response on the staff member's smart glasses or head-mounted display (input: response data, output: visual display provided to staff member). The staff member then responds appropriately to the customer based on the displayed response. This enables personalized service that is in line with the customer's emotional state.

[1484] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1485] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1486] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1487] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1488] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1489] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1490] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1491] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1492] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1493] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1494] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1495] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1496] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1497] 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.

[1498] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1499] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1500] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1501] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1502] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1503] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1504] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1505] The following is further disclosed regarding the above embodiment.

[1506] (Claim 1)

[1507] a speech recognition means;

[1508] Generative AI means;

[1509] a document generation means;

[1510] means for collecting speech input from a user and converting it into text data by speech recognition means;

[1511] means for generating a response based on the text data using a generative AI means;

[1512] means for providing the generated response to a user;

[1513] The system includes a means for collecting necessary information based on the responses and generating a document using a document generation means.

[1514] (Claim 2)

[1515] 2. The system of claim 1, wherein the speech recognition means is connected to a speech recognition service via the Internet.

[1516] (Claim 3)

[1517] The system described in claim 1, characterized in that the generating AI means automatically asks questions and collects information necessary for the user's end-of-life planning in an interactive format.

[1518] "Example 1"

[1519] (Claim 1)

[1520] a speech recognition means;

[1521] Generative AI means;

[1522] a document generation means;

[1523] means for collecting speech input from a user and converting it into text data by speech recognition means;

[1524] means for generating a response based on the text data using a generative AI means;

[1525] means for providing the generated response to a user;

[1526] a means for collecting necessary information based on the response and generating a document using a document generation means;

[1527] means for providing the terminal with a microphone for capturing audio data;

[1528] A means for initializing the speech recognition means and generating an instance of the generative AI model;

[1529] means for receiving and displaying information from a user through a user interface;

[1530] A system including:

[1531] (Claim 2)

[1532] 2. The system of claim 1, wherein the speech recognition means is connected to a speech recognition service via the Internet.

[1533] (Claim 3)

[1534] The system of claim 1, wherein the generating AI means automatically asks questions and interactively collects information necessary for the user's end-of-life plan.

[1535] "Application Example 1"

[1536] (Claim 1)

[1537] a speech recognition means;

[1538] Generative AI means;

[1539] a document generation means;

[1540] means for collecting speech input from a user and converting it into text data by speech recognition means;

[1541] means for generating a response based on the text data using a generative AI means;

[1542] means for providing the generated response to a user;

[1543] a means for collecting necessary information based on the response and generating a document using a document generation means;

[1544] A means for registering and managing the user's emergency contact and password information by voice;

[1545] A system that includes a means for automatically sending notifications to designated contacts in the event of an emergency.

[1546] (Claim 2)

[1547] 2. The system of claim 1, wherein the speech recognition means is connected to a speech recognition service via the Internet.

[1548] (Claim 3)

[1549] The system described in claim 1, characterized in that the generation AI means automatically asks questions and collects information necessary for the user's end-of-life preparations and information necessary for emergency response in an interactive format.

[1550] "Example 2: Combining Emotion Engines"

[1551] (Claim 1)

[1552] a speech recognition means;

[1553] Generative AI means;

[1554] a document generation means;

[1555] means for collecting speech input from a user and converting it into text data by speech recognition means;

[1556] means for generating a response based on the text data using a generative AI means;

[1557] means for providing the generated response to a user;

[1558] a means for collecting necessary information based on the response and generating a document using a document generation means;

[1559] an emotion recognition means for analyzing emotions from a user's voice in real time;

[1560] A system including means for generating a response according to the emotional state of a user, based on emotional data acquired by an emotion recognition means, using an AI generation means.

[1561] (Claim 2)

[1562] 2. The system of claim 1, wherein the speech recognition means is connected to a speech recognition service via the Internet.

[1563] (Claim 3)

[1564] The system described in claim 1, characterized in that the generating AI means automatically asks questions and collects information necessary for the user's end-of-life planning in an interactive format.

[1565] "Application example 2 when combining emotion engines"

[1566] (Claim 1)

[1567] a speech recognition means;

[1568] Generative AI means;

[1569] a document generation means;

[1570] An emotion recognition means;

[1571] means for collecting speech input from a user and converting it into text data by speech recognition means;

[1572] means for analyzing the emotional state of a user using emotion recognition means;

[1573] means for generating a response based on the text data and the emotion data using a generative AI means;

[1574] means for providing the generated response to a user;

[1575] The system includes a means for collecting necessary information based on the responses and generating a document using a document generation means.

[1576] (Claim 2)

[1577] 2. The system of claim 1, wherein the speech recognition means is connected to a speech recognition service via the Internet.

[1578] (Claim 3)

[1579] The system according to claim 1, characterized in that the generating AI means generates a customized response according to the user's emotional state and collects necessary information in an interactive manner. [Explanation of symbols]

[1580] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a speech recognition means; Generative AI means; a document generation means; means for collecting speech input from a user and converting it into text data by speech recognition means; means for generating a response based on the text data using a generative AI means; means for providing the generated response to a user; The system includes a means for collecting necessary information based on the responses and generating a document using a document generation means.

2. 2. The system of claim 1, wherein the speech recognition means is connected to a speech recognition service via the Internet.

3. The system described in claim 1, characterized in that the generation AI means automatically asks questions and collects information necessary for the user's end-of-life planning in an interactive format.

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