System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting text messages of a user; means for analyzing the collected text messages and modeling style and values of the user; means for constructing a chat bot using a generative AI that generates a response of the user based on the analyzed text messages; and means for providing the constructed chat bot to a person designated by the user.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] The problem this invention aims to solve is the lack of means for users to process their emotional needs and continue dialogue with their surviving family members after the death of the deceased. Conventional end-of-life services focus on financial procedures and information organization, and provide very limited emotional support. In particular, there is a need for a means for users to continue sharing the values ​​and memories of the deceased after their death. [Means for solving the problem]

[0005] The present invention provides a system that collects and analyzes user text messages to model the user's writing style and values. Specifically, the system includes a means for analyzing the collected text messages and modeling the user's writing style and values, and a means for constructing a chatbot using a generative AI that generates user responses based on the analyzed data. The system also includes a means for providing the chatbot to a party designated by the user.

[0006] Furthermore, a means is provided to provide conversation history with specific interlocutors, and by analyzing this, the quality of responses can be improved by learning the relationship and unique response patterns of each interlocutor. Additionally, a means is provided to manage the timing of providing the chatbot to designated individuals after the user's death, and by providing the chatbot at the appropriate time, the user's values ​​and memories can be shared with the bereaved. In this way, the system supports the user in sorting out the emotional aspects of end-of-life planning and provides a new means of conveying memories of the deceased to the bereaved.

[0007] "User text message" means text information entered and sent by a User through electronic means of communication.

[0008] A "collection method" is a program or hardware that records and stores text messages sent by users.

[0009] "Means for analysis" refers to algorithms or programs used to extract specific information from collected text messages and analyze them for patterns and characteristics.

[0010] "Modeling means" refers to a mechanism for generating a data model based on analyzed data to understand and express the user's writing style and values.

[0011] "Generative AI" is a technology that uses artificial intelligence to automatically generate user responses from collected and analyzed data.

[0012] A "chatbot" is a program that can interact with users and uses artificial intelligence to generate responses in natural language.

[0013] The "means of construction" refers to the programs and methods used to design and develop chatbots based on user characteristics.

[0014] The "means of provision" refers to the system or process for delivering the generated chatbot in a usable state to the person specified by the user.

[0015] "Conversation history" is a record of past text messages between a user and a particular conversation partner.

[0016] A "relationship" is a social or emotional connection that exists between a user and a particular interlocutor.

[0017] A "response pattern" is a set of responses given to a particular topic or question in a dialogue.

[0018] A "timing management tool" is a system or program that adjusts the launch timing of a chatbot based on specific events or conditions. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The system of the present invention supports users' end-of-life planning and serves as a means to maintain communication with their surviving family members after the user's death. The system collects and analyzes the user's text messages to model the user's writing style and values. It then uses generative AI to build a chatbot with the user's characteristics and provides the chatbot to a contact specified by the user.

[0041] To implement the present invention, several major components are required, including:

[0042] 1. Programs that collect users' text messages

[0043] A user uses a messaging application such as LINE from their device (e.g., a smartphone or PC) to send a text message to the server.

[0044] The server collects messages sent by users and stores them in a database.

[0045] To give a concrete example, when a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database.

[0046] 2. A program that analyzes collected text messages and models the user's writing style and values.

[0047] The text messages collected by the server are analyzed using natural language processing technology.

[0048] Based on the analysis results, the server identifies the user's writing style (e.g., polite language or casual expressions) and values ​​(e.g., valuing family, liking adventure, etc.) and models them.

[0049] As a concrete example, a server analyzes the message "I went to the movies with a friend today" and models the user as being sociable and a movie lover.

[0050] 3. Program to build chatbots using generative AI

[0051] The server uses the analyzed data to feed it back into an AI model that generates a chatbot with the user's characteristics.

[0052] The generated chatbot will generate natural responses based on the user's writing style and values.

[0053] For example, if the chatbot is asked, "How was your day today?", it will respond by mimicking the user's writing style, saying, "Today I went to the movies with a friend."

[0054] 4. A program that provides the generated chatbot to the person specified by the user

[0055] After the user's death, the server provides the chatbot to the surviving family at a pre-specified time.

[0056] The chatbot will be set up so that bereaved family members can interact with it through the LINE app.

[0057] For example, if a user had previously requested, "When I die, please provide this chatbot to my daughter," the server would provide the chatbot to the daughter at the specified time as soon as it confirmed the user's death.

[0058] With these components, the system of the present invention allows the user to reflect their own values ​​and writing style in the chatbot during their end-of-life planning, and maintain an emotional connection with their surviving family members even after their death. This provides a valuable means for both the user and their surviving family members to process their emotions and remember the deceased.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user sends a message saying "Start creating a chatbot" using the LINE application installed on their device (e.g., a smartphone).

[0062] Step 2:

[0063] The server receives the message sent by the user, records the user's LINE ID in the database, and replies to the user, asking "Please tell us your name and date of birth."

[0064] Step 3:

[0065] The user uses a terminal to enter "Yamada Taro, January 1, 1980" and sends it back to the server.

[0066] Step 4:

[0067] The server receives the user's name and date of birth and stores them in a database. The server then notifies the user that the settings are complete and that regular messages will be sent.

[0068] Step 5:

[0069] Every day at 7pm, the server sends a message to the user's device via LINE asking, "How was your day?"

[0070] Step 6:

[0071] The user uses the device to reply with a message saying, "I went to the movies with a friend today."

[0072] Step 7:

[0073] The server receives the user's replies and stores them in a database. The server then analyzes the collected messages using natural language processing technology to determine the user's writing style and values.

[0074] Step 8:

[0075] Based on the analysis results, the server models the user's writing style and values ​​and stores this information in a database.

[0076] Step 9:

[0077] The user uses the device to set up the device to send specific LINE chat conversation history to the server as "conversations with family" or similar.

[0078] Step 10:

[0079] The server receives the conversation history with a specific partner, stores it in a database, and analyzes the relationship and unique response patterns of each partner.

[0080] Step 11:

[0081] Based on the analyzed data, the server uses a generative AI model to generate a chatbot that reflects the user's characteristics. The generated chatbot then generates natural responses based on the user's writing style and values.

[0082] Step 12:

[0083] The user or agent notifies the server of the user's death.

[0084] Step 13:

[0085] The server receives the user's death notification and notifies pre-designated family members via LINE that "you have a message from the deceased."

[0086] Step 14:

[0087] The bereaved family member uses the device to send a message to the chatbot asking, "Dad, what do you think?"

[0088] Step 15:

[0089] The server inputs the message from the bereaved family into the chatbot and sends the generated response (e.g., "As a father, the most important thing is that you are happy") to the bereaved family.

[0090] Example 1

[0091] 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."

[0092] In modern society, people frequently communicate digitally, but there is a problem that there is a lack of ways to convey precious memories and values ​​to bereaved families after a user passes away.In addition, there is a problem that the emotional connection with bereaved families is severed because there is no communication method that reflects the user's writing style and values ​​when they were alive.

[0093] 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.

[0094] In this invention, the server includes a means for collecting the user's text messages, a means for analyzing the collected text messages and modeling the user's writing style and values, and a means for constructing a chatbot using a generative AI that generates responses to the user based on the analyzed data. This makes it possible to continue to emotionally connect with the bereaved family even after the user has passed away through a chatbot that inherits the user's writing style and values.

[0095] "User" means an individual or legal entity that uses the System.

[0096] "Text message" refers to textual information sent by a user using a device.

[0097] "Collection" refers to the process by which the server obtains and stores text messages sent from the device.

[0098] "Analysis" is the process by which the server uses natural language processing technology to analyze the content of collected text messages and extract important information.

[0099] "Writing style" refers to the language and style of expression used by a User in their text messages.

[0100] "Values" refer to the beliefs and principles of behavior held by users.

[0101] "Modeling" is the process of abstracting the user's writing style and values ​​from the analyzed data and expressing them as a data model.

[0102] "Generative AI" refers to artificial intelligence that generates new text based on the user's characteristics.

[0103] A "chatbot" is a program that converses with a user by recreating their writing style and values.

[0104] "Terminal" refers to an electronic device used by a user (e.g., a smartphone or PC).

[0105] A "server" is a computer system that collects, analyzes, and stores text messages and generates chatbots.

[0106] A "prompt sentence" is an input sentence given to a generation AI, and is the text that the AI ​​uses to generate a response based on those instructions.

[0107] "LINE API" refers to an interface that allows external use of the functions of the messaging application LINE.

[0108] "Natural language processing technology" refers to technology that automatically analyzes text data and understands and processes human language.

[0109] The system of the present invention supports users in their end-of-life planning and serves as a means for maintaining communication with their surviving family members after their death. The system collects and analyzes the user's text messages to model the user's writing style and values. It then uses generative AI to build a chatbot with the user's characteristics and provides the chatbot to a contact specified by the user.

[0110] Hardware and software used

[0111] Server: A computer system for collecting, analyzing, and storing messages, and generating chatbots.

[0112] Device: The electronic device used by the user (e.g. smartphone, computer).

[0113] Messaging applications: LINE, etc.

[0114] Natural language processing technologies: Google Cloud NLP API, IBM Watson, etc.

[0115] Generative AI model: OpenAI GPT-4.

[0116] LINE API: An interface for using LINE functions externally.

[0117] Program processing

[0118] 1. Text message collection

[0119] A user sends a text message using a messaging application such as LINE on their device. The server receives this message via the LINE API and stores it in a database.

[0120] Example: When a user sends a message saying "I went to the movies with a friend today," the server receives this message and automatically stores it in a database.

[0121] 2. Text Message Analysis and Modeling

[0122] The server analyzes the collected text messages using natural language processing technology, and based on the analysis results, identifies the user's writing style and values ​​and creates a model of them.

[0123] Example: A server parses the message "I went to the movies with a friend today" and models the user as sociable and movie-loving.

[0124] 3. Chatbot Creation

[0125] Based on the analyzed data, the server provides feedback on the user's characteristics to the generation AI (GPT-4), which then generates natural responses based on the user's writing style and values.

[0126] Example: When asked, "How was your day?", a chatbot responds in the user's style, "Today I went to the movies with a friend."

[0127] Example prompt: "If a user sends, 'I went to the movies with a friend today,' model their writing style and values ​​around this."

[0128] "Generate a chatbot with your writing style and have it respond to the question, 'How was your day?'"

[0129] 4. Providing chatbots

[0130] Before dying, the user specifies to the server who the chatbot should be provided to.

[0131] After a user's death, the server provides the chatbot to the family at a specified time. The family can then use the LINE API to interact with the chatbot through the LINE app.

[0132] Example: A user requests, "Please provide this chatbot to my daughter after I die." After confirming the user's death, the server provides the chatbot to the daughter's LINE account, and the chatbot speaks to the daughter, saying, "Let's talk about memories of Dad."

[0133] This system allows users to maintain an emotional connection with their families even after their death through a chatbot that inherits the user's values ​​and writing style, providing a valuable tool for both the user and their family to process their emotions and remember the deceased.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] User-submitted messages

[0137] A user uses a device (smartphone or computer), launches a messaging application such as LINE, and types and sends a text message.

[0138] Input: A text message typed by the user (e.g., "I went to the movies with a friend today").

[0139] Output: The text message sent.

[0140] Specific operation: The user types a message in the LINE app such as "Today I went to the movies with a friend" and presses the send button.

[0141] Step 2:

[0142] Server receives and stores messages

[0143] The server uses the LINE API to receive messages sent from the user's device and save them in a database.

[0144] Input: A text message sent from the user's device.

[0145] Output: The text message stored in the database.

[0146] Specific operation: The server receives a message through the LINE API and automatically saves the message "I went to the movies with a friend today" in a database.

[0147] Step 3:

[0148] Message parsing by the server

[0149] The server analyzes the stored text messages using natural language processing technology (e.g., Google Cloud NLP API or IBM Watson).

[0150] Input: A text message stored in the database.

[0151] Output: Parsed message style and value data.

[0152] Specific operation: The server analyzes the message "I went to the movies with a friend today" using natural language processing technology and identifies that the user is sociable and loves movies.

[0153] Step 4:

[0154] Analysis data modeling

[0155] The server models the user's writing style and values ​​based on the data analyzed.

[0156] Input: Parsed message stylistic and value data.

[0157] Output: A model that reflects the user's writing style and values.

[0158] Specific operation: The server generates a user model with the characteristics of being "sociable" and "movie lover."

[0159] Step 5:

[0160] Chatbot generation by the server

[0161] The server feeds user characteristics into a generative AI model (e.g., OpenAI GPT-4) to build a chatbot that generates natural responses based on the user's writing style and values.

[0162] Input: Model data that reflects the user's writing style and values.

[0163] Output: A chatbot with the user's writing style and values.

[0164] Specific operation: The server inputs the feature data "sociable" and "movie lover" into GPT-4, and generates a chatbot that reproduces the user's writing style. When the chatbot is asked "How was your day?", it responds "Today, I went to the movies with a friend."

[0165] Step 6:

[0166] Chatbot provision settings

[0167] Before dying, the user specifies to the server who the chatbot should be provided to.

[0168] Input: User-specified information (e.g., contact information for recipient, timing of provision).

[0169] Output: Provided configuration data.

[0170] Specific operation: The user enters information into the server specifying, "Please provide this chatbot to my daughter after I die."

[0171] Step 7:

[0172] Providing chatbots

[0173] After a user's death, the server provides the chatbot to the surviving family at a specified time. Using the LINE API, the server sends permission to use the chatbot to the surviving family's LINE account.

[0174] Input: Provided configuration data, user death confirmation information.

[0175] Output: A chatbot provided to the family's LINE account.

[0176] Specific operation: After the server confirms the user's death, it provides a chatbot to the daughter's LINE account and asks her to "tell us about memories of your father."

[0177] (Application example 1)

[0178] 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."

[0179] It is difficult to provide a realistic interaction experience in a specific virtual environment or with customers, while inheriting the various values ​​and interaction styles that users developed during their lifetime.In addition, there is a lack of systems that can maintain emotional connections with users after their death and reproduce interactions in specific environments such as virtual stores.

[0180] 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.

[0181] In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages and modeling the user's writing style and values, means for constructing a chatbot using a generation AI that generates responses for the user based on the analyzed data, means for providing the constructed chatbot to a party designated by the user, means for the user to reproduce a specific response style, and means for the generated chatbot to respond in a virtual environment. This makes it possible to provide a realistic conversation experience in a designated virtual environment or in conversations with customers even after the user's death.

[0182] "User's text message" means a text message sent by a User using an information and communication terminal, including personal conversations, business correspondence, social interactions, etc.

[0183] The "means for collecting text messages" refers to a program or device for storing text messages sent from an information communication terminal in a database such as a server.

[0184] "Means for analyzing text messages and modeling a user's writing style and values" refers to an algorithm that uses natural language processing technology to extract, understand, and model a user's writing style, speech patterns, and the values ​​they value based on collected text messages.

[0185] A "means for building a chatbot using generative AI" is a program or system that feeds analyzed user data back into generative AI to create a chatbot that reproduces the user's writing style and values.

[0186] "Means for providing chatbots to people designated by users" refers to a system or service that makes the constructed chatbot available at the appropriate time to people designated by the user during their lifetime.

[0187] "Means for users to recreate a specific response style" refers to an algorithm that recreates the way a user would respond to a specific situation or person based on collected text messages.

[0188] "Means for responding in a virtual environment" refers to a program that enables the generated chatbot to respond in an online environment such as virtual reality or a virtual store while reproducing the user's writing style and response style.

[0189] To implement this invention, the following main components are required: a system that collects users' text messages, analyzes them to model their writing style and values, uses generative AI to build a chatbot, and responds in a virtual environment.

[0190] How to collect user text messages

[0191] The server collects text messages sent from the user's device (e.g., smartphone or PC). During this collection process, the server receives text messages using messaging applications (e.g., LINE or WhatsApp) and stores them in a database. For example, if a user sends a message saying, "Today, I went to the movies with a friend," the server receives this message and stores it in a database.

[0192] Text message analysis and modeling

[0193] The server analyzes the text messages stored in the database using natural language processing technology. Specifically, it uses NLP (natural language processing) libraries (such as spaCy and BERT) to extract and model the user's writing style and values. For example, the server analyzes the message "Today I went to the movies with a friend" and models the user as sociable and a movie lover.

[0194] Building a chatbot using generative AI

[0195] The server feeds user data obtained through natural language processing back to the generation AI, building a chatbot that reflects the user's unique writing style and values. The generation AI uses OpenAI's GPT-3 and other technologies. This allows the chatbot to respond in the user's writing style when asked, for example, "How was your day?", with, for example, "I went to the movies with a friend today."

[0196] Providing chatbots and responses in a virtual environment

[0197] The constructed chatbot is provided to a person specified by the user. The server can also provide the chatbot at a specified time after the user's death. Furthermore, the constructed chatbot can interact with the user in a virtual environment (for example, a VR shopping mall or online customer support), recreating the user's response style.

[0198] Specific examples

[0199] For example, text messages from store staff members' previous customer service interactions can be collected and analyzed to model their writing style and response style. The generated chatbot can respond to customers based on the prompt, "You are Staff A. We strive to provide our customers with the highest quality products. Customer: Hello, is the item in stock?"

[0200] Through the above components and processes, the system of this invention can inherit the user's values ​​and writing style from their previous life and provide a realistic conversational experience even in a specific virtual environment.

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1:

[0203] The server collects text messages sent from the user's device (e.g., smartphone or PC).

[0204] Input: A text message sent by a user through a messaging app.

[0205] Output: The collected text messages are stored in the server's database. The specific operation is to receive the text messages and store them in the database along with the user ID.

[0206] Step 2:

[0207] The server analyzes the text messages stored in the database using natural language processing techniques.

[0208] Input: The user's text message stored in the database.

[0209] Output: Data that models the user's writing style and values ​​as a result of the analysis. Specifically, it uses NLP libraries (e.g., spaCy, BERT) to extract writing style, keywords, sentiment, etc., and statistically models them.

[0210] Step 3:

[0211] Based on the analysis results, the server uses generative AI to build a chatbot that reflects the user's writing style and values.

[0212] Input: Data that models the user's writing style and values.

[0213] Output: A chatbot that reflects the user's writing style and values. The specific operation is to feed the analysis results into a generative AI model (e.g., OpenAI GPT-3) to train and adjust the chatbot so that it can have natural conversations.

[0214] Step 4:

[0215] The server configures the constructed chatbot so that it can respond in a virtual environment.

[0216] Input: The chatbot you built.

[0217] Output: Configuration data that enables the chatbot to operate in a virtual environment. The specific operation is to connect and place the chatbot in a virtual environment (e.g., VR shopping mall, online customer support) and enable it to respond through an appropriate interface.

[0218] Step 5:

[0219] The server provides the chatbot to the user after the user's death or to a specified person.

[0220] Input: Information about the recipient and timing of donation designated by the user during their lifetime.

[0221] Output: A chatbot provided to the specified person. The specific operation is to notify the specified person of the chatbot and make it available when the user's death is confirmed.

[0222] Step 6:

[0223] The server ensures that the chatbot responds in a virtual environment while replicating a particular interaction style.

[0224] Input: Model data for reproducing the user's response style.

[0225] Output: Natural and consistent responses in the virtual environment. The specific behavior is that the generated chatbot will conduct a dialogue based on a pre-modeled response style.

[0226] 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.

[0227] The system of the present invention supports users in planning for their end of life and provides a means for maintaining communication with their surviving family members after their death. The system collects and analyzes the user's text messages to model the user's writing style, values, and even emotions. Furthermore, it uses generative AI to build a chatbot that reflects the user's characteristics and emotions, and provides the chatbot to a contact specified by the user.

[0228] To implement the present invention, the following major components are required, including:

[0229] 1. Programs that collect users' text messages

[0230] A user uses a messaging application such as LINE from their device (e.g., a smartphone or PC) to send a text message to the server.

[0231] The server collects messages sent by users and stores them in a database.

[0232] To give a concrete example, when a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database.

[0233] 2. A program that analyzes collected text messages and models the user's writing style and values.

[0234] The text messages collected by the server are analyzed using natural language processing technology.

[0235] Based on the analysis results, the server identifies the user's writing style (e.g., polite language or casual expressions) and values ​​(e.g., valuing family, liking adventure, etc.) and models them.

[0236] As a concrete example, a server analyzes the message "I went to the movies with a friend today" and models the user as being sociable and a movie lover.

[0237] 3. Emotion engine that recognizes emotions from collected text messages

[0238] The server uses an emotion engine to analyze emotions (e.g., joy, sadness, anger, etc.) from messages sent by users.

[0239] The server stores the analyzed emotional information in a database and uses it in the next step.

[0240] For example, the server may recognize from a message such as "I went to the movies with a friend today" that the user is feeling "joy."

[0241] 4. Program for building chatbots using generative AI

[0242] The server uses the analyzed data and emotional information to feed back into an AI model to generate a chatbot that reflects the user's characteristics and emotions.

[0243] The resulting chatbot will generate natural responses based on the user's writing style, values, and even emotions.

[0244] For example, if a chatbot is asked, "How was your day today?", it will respond by mimicking the user's writing style and emotions, saying, "Today I'm going to the movies with a friend. I'm looking forward to it!"

[0245] 5. A program that provides the generated chatbot to the person specified by the user

[0246] After the user's death, the server provides the chatbot to the surviving family at a pre-specified time.

[0247] The chatbot will be set up so that bereaved family members can interact with it through the LINE app.

[0248] For example, if a user had previously requested, "When I die, please provide this chatbot to my daughter," the server would provide the chatbot to the daughter at the specified time as soon as it confirmed the user's death.

[0249] The above components allow the system of the present invention to reflect the user's own values, writing style, and even emotions in the chatbot throughout the end-of-life planning process. This allows the user to maintain an emotional connection and dialogue with their family members even after their death. This provides a valuable means for both the user and the family members to process their emotions and remember the deceased.

[0250] The processing flow will be explained below.

[0251] Step 1:

[0252] The user sends a message saying "Start creating a chatbot" using the LINE application installed on their device (e.g., a smartphone).

[0253] Step 2:

[0254] The server receives this message, records the user's LINE ID in the database, and replies to the user, "Please tell us your name and date of birth."

[0255] Step 3:

[0256] The user uses a terminal to enter "Yamada Taro, January 1, 1980" and sends it back to the server.

[0257] Step 4:

[0258] The server receives the user's name and date of birth and stores them in a database. The server then notifies the user that the settings are complete and that regular messages will be sent.

[0259] Step 5:

[0260] Every day at 7pm, the server sends a message to the user's device via LINE asking, "How was your day?"

[0261] Step 6:

[0262] The user uses the device to reply with a message saying, "I went to the movies with a friend today."

[0263] Step 7:

[0264] The server receives the user's replies and stores them in a database. The server then analyzes the collected messages using natural language processing technology to determine the user's writing style and values.

[0265] Step 8:

[0266] Based on the analysis results, the server models the user's writing style and values ​​and stores this information in a database.

[0267] Step 9:

[0268] The server analyzes the text message sent by the user using an emotion engine to recognize the user's emotions (e.g., joy, sadness, anger, etc.).

[0269] Step 10:

[0270] The server stores the emotional information obtained from the emotion engine in a database. The analyzed emotional information is also reflected in the modeling.

[0271] Step 11:

[0272] The user uses the device to set up the device to send specific LINE chat conversation history to the server as "conversations with family" or similar.

[0273] Step 12:

[0274] The server receives the conversation history with a specific partner, stores it in a database, and analyzes the relationship and unique response patterns of each partner.

[0275] Step 13:

[0276] The server uses a generative AI model to generate a chatbot that reflects the user's characteristics and emotions based on the analyzed data and emotional information. The generated chatbot then generates natural responses based on the user's writing style, values, and emotions.

[0277] Step 14:

[0278] The user or agent notifies the server of the user's death.

[0279] Step 15:

[0280] The server receives the user's death notification and notifies pre-designated family members via LINE that "you have a message from the deceased."

[0281] Step 16:

[0282] Family members can use the device to send messages to the chatbot, such as "Dad, how are you feeling today?"

[0283] Step 17:

[0284] The server inputs the message from the bereaved family into the chatbot and sends the generated response (e.g., "I feel a little lonely today, but as long as you're happy, that's all that matters") to the bereaved family.

[0285] Example 2

[0286] 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."

[0287] This invention relates to a system that generates a chatbot that reflects a user's writing style, values, and emotions based on text messages sent by the user while the user was alive, allowing the user to maintain an emotional connection with their surviving family members even after the user's death. Such a system allows the surviving family members to easily process their emotions by continuing to communicate with the user, providing a valuable means of remembering the deceased. However, existing systems face the challenge of effectively generating a chatbot that accurately reflects the user's writing style, values, and emotions.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0289] In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages using natural language processing technology and modeling the user's writing style and values, means for recognizing emotions from the analyzed data, means for training a generative AI model based on the recognized emotions and the modeled writing style and values, means for constructing a chatbot that generates user responses using the generative AI model, and means for providing the constructed chatbot to a party designated by the user. This makes it possible to generate a chatbot that reproduces the user's characteristics and emotions and to maintain an emotional connection with the user's surviving family members after the user's death.

[0290] "User" refers to the person who uses the System and sends text messages.

[0291] "Text Message" refers to any written communication sent by a User through a Device.

[0292] "Means for collection" refers to the function or device for capturing text messages sent by users onto a server.

[0293] "Natural language processing technology" refers to computational methods and programming techniques for analyzing, understanding, and generating human language.

[0294] "Writing style" refers to the linguistic expression style used when a user sends a text message, including, for example, polite language or casual expressions.

[0295] "Values" refer to the beliefs and ways of thinking that users consider important, such as a love of family or a love of adventure.

[0296] "Means for analyzing" refers to a function or device for analyzing text messages and modeling writing styles and values.

[0297] "Emotion" refers to the state of mind expressed by a user in a text message, including, for example, joy, sadness, anger, etc.

[0298] "Means for recognizing emotions" refers to a function or device for analyzing and identifying a user's emotions from collected text messages.

[0299] A "generative AI model" refers to a machine learning model that automatically generates responses that reflect the user's writing style, values, and emotions.

[0300] "Training means" refers to the functionality or equipment that allows a generative AI model to learn based on the characteristics and emotions of the user.

[0301] A "chatbot" is a conversational agent that uses generative AI models to replicate a user's writing style, values, and emotions.

[0302] "Means for providing" refers to a function or device for delivering the constructed chatbot to a specified party in a usable state.

[0303] "Means for managing timing" refers to a function or device for managing the appropriate timing for providing the chatbot to a designated person after a user's death.

[0304] The system of the present invention supports users in planning their end of life and provides a means for maintaining communication with their surviving family members even after the user's death. The system collects and analyzes the user's text messages to model the user's writing style, values, and even emotions. Furthermore, it uses a generative AI model to build a chatbot that reflects the user's characteristics and emotions, and provides the chatbot to a contact specified by the user.

[0305] Hardware and Software Configuration

[0306] Server: The central processing unit of the system, responsible for collecting, analyzing, and modeling user text messages, training generative AI models, and building and serving chatbots.

[0307] Device: This refers to the smartphone or computer used by the user to send text messages.

[0308] Database: A data storage for saving collected text messages, analyzed data, and emotional information, using, for example, MySQL.

[0309] Natural language processing tools: Software used to analyze text messages, such as MeCab and NLTK.

[0310] Generative AI models: Machine learning models used to generate responses that reflect a user's writing style, values, and emotions, such as GPT-4.

[0311] Specific examples of program processing

[0312] 1. Text message collection

[0313] The user sends a message from their device through the LINE app saying, "Today I went to the movies with a friend."

[0314] The server receives this message using the LINE app API and stores it in a database (MySQL) in text format.

[0315] 2. Text message analysis

[0316] The message data collected by the server is analyzed using a morphological analysis tool (MeCab).

[0317] The server extracts the user's writing style (polite language, casual expressions) and values ​​(family-oriented, adventurous) from the analysis results and models them.

[0318] 3. Emotional Recognition

[0319] The server uses a natural language processing library (NLTK) to analyze the sentiment from the message.

[0320] For example, the server recognizes the emotion "joy" from the message "I went to the movies with a friend today" and stores the emotion information in a database.

[0321] 4. Building a Chatbot

[0322] The server feeds back the analyzed data (writing style, values, emotional information) to the generative AI model (GPT-4).

[0323] The server uses a generative AI model to generate a chatbot that reflects the user's writing style and emotions.

[0324] 5. Providing chatbots

[0325] Once the server confirms the user's death, it will provide the chatbot to the bereaved family at a pre-specified time.

[0326] The chatbot will be set up so that bereaved family members can smoothly communicate with it through the LINE app.

[0327] Examples of prompt statements

[0328] Prompts: Analyzing the user's writing style and values

[0329] "Parse the following message and model the user's writing style and values: 'Today I went to the movies with a friend. It was a great time.'"

[0330] Prompts: Chatbot response generation

[0331] "If a user is asked, 'How was your day?' generate a response based on the following text: 'User is a movie lover and sociable. Emotion: Joy'"

[0332] This system makes it possible to generate chatbots that replicate the characteristics and emotions of users, and to maintain an emotional connection with their surviving family members after the user's death.

[0333] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0334] Step 1: Collect text messages

[0335] 1. A user sends a text message using a messaging application such as LINE from their device (smartphone or PC).

[0336] Input: User's text message

[0337] Output: Text message sent to the server

[0338] Specific operation: The user sends a message using the LINE app saying, "Today I went to the movies with a friend."

[0339] 2. The server receives this message using the LINE app API.

[0340] Input: Text message from the LINE app

[0341] Output: Messages stored on the server

[0342] Specific operation: The server receives the message through the LINE API and saves it as text.

[0343] 3. The server stores the received message in a database.

[0344] Input: Received message

[0345] Output: Messages stored in the database

[0346] Specific operation: The server stores the received message in, for example, a MySQL database.

[0347] Step 2: Parsing the text messages

[0348] 1. The message data collected by the server is analyzed using a morphological analysis tool (MeCab).

[0349] Input: Message stored in the database

[0350] Output: Parsed message data

[0351] Specific operation: The server uses MeCab to split the message "Today I went to the movies with a friend" into words.

[0352] 2. The server extracts and models the user's writing style and values ​​from the analysis results.

[0353] Input: Parsed message data

[0354] Output: Modeled writing style and values

[0355] Specific operation: Based on the analysis results, the server models that the user writes politely and has values ​​that value family.

[0356] Step 3: Recognize emotions

[0357] 1. The server uses a natural language processing library (NLTK) to analyze the sentiment from the message.

[0358] Input: Parsed message data

[0359] Output: Recognized emotion data

[0360] Specific operation: The server uses NLTK to recognize the emotion "joy" from the message "Today I went to the movies with a friend."

[0361] 2. The server stores the analyzed emotional information in a database.

[0362] Input: Recognized emotion data

[0363] Output: Emotion data stored in a database

[0364] Specific operation: The server stores the recognized emotion data of "joy" in the database.

[0365] Step 4: Build your chatbot

[0366] 1. The server feeds back the analyzed data (writing style, values, emotional information) to the generative AI model (GPT-4).

[0367] Input: Analyzed data (style, values, sentiment)

[0368] Output: Data fed back to the AI ​​model

[0369] Specific operation: The server inputs the following data into GPT-4: "User loves movies and is sociable. Emotion: joy."

[0370] 2. The server uses the generative AI model to generate a chatbot that reflects the user's characteristics and emotions.

[0371] Input: Feedback data

[0372] Output: The generated chatbot

[0373] How it works: The server uses GPT-4 to generate a chatbot that replicates the user's writing style and emotions.

[0374] Step 5: Serve the chatbot

[0375] 1. Once the server confirms the user's death, it will provide the chatbot to the surviving family at a pre-specified time.

[0376] Input: User death confirmation and specified timing

[0377] Output: Chatbot provided to the family

[0378] Specific operation: When the server confirms the user's death, it sends a chatbot access link to the surviving family member's LINE account at the specified time.

[0379] 2. Set up the chatbot so that bereaved family members can smoothly interact with it through the LINE app.

[0380] Input: LINE account of the bereaved family

[0381] Output: A chatbot that can interact with bereaved families

[0382] Specific operation: The bereaved family member uses the LINE app and clicks on the sent link to start a conversation with the chatbot.

[0383] (Application example 2)

[0384] 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."

[0385] The problem that this invention aims to solve is not only to enable continued dialogue with surviving family members after a user's death, but also to provide a means to ensure the user's safety while they are still alive. Current chatbot systems have difficulty automatically providing responses and safety measures tailored to the user in specific situations. Therefore, there is a need for a system that can generate a chatbot that responds appropriately to the user's writing style and values, and further analyzes information related to emotions and safety to provide appropriate responses.

[0386] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages and modeling the user's writing style and values, means for constructing a chatbot using a generation AI that generates user responses based on the analyzed data, means for providing the constructed chatbot to a party specified by the user, means for analyzing the user's emotions and storing the emotion information, and means for notifying a security service when the generated chatbot detects an abnormality or danger. This enables appropriate responses and measures that take into consideration the user's emotions and safety not only after the user's death but also while the user is still alive.

[0387] A "user's text message" is text or character string data sent by a user via a communication terminal.

[0388] A "collection method" is a system or program designed to store a user's text messages on a server or database.

[0389] "Means for analysis" refers to a system or program that uses natural language processing technology to analyze collected text messages and extract the user's writing style and values.

[0390] A "style and value modeling method" is a method or algorithm that identifies a user's specific style and values ​​from the analyzed text data.

[0391] "Generative AI" is a system that uses artificial intelligence technology to generate responses based on the user's writing style and values.

[0392] A "chatbot" is a program or application designed to interact with users.

[0393] "Means for providing to designated parties" refers to the method or process by which the generated chatbot is made available to parties designated in advance by the user.

[0394] "Means for analyzing emotions" refers to algorithms or systems that read and analyze emotions from users' text messages.

[0395] The "means for storing emotional information" refers to a method or device for storing analyzed emotional data of a user in a database or the like.

[0396] "Means for notifying when an abnormality or danger is detected" refers to a system or process for alerting or notifying security services when the chatbot detects suspicious signs in a user's message.

[0397] This system collects and analyzes users' text messages, builds a chatbot that models their writing style and values, and provides the built chatbot to a person specified by the user. It also has the ability to analyze the user's emotions and notify a security service if it detects an abnormality or danger.

[0398] The system includes the following major components:

[0399] 1. How to collect user text messages

[0400] Users use messaging applications on their smartphones, PCs, or other devices to send text messages to a server, which then collects the messages and stores them in a database.

[0401] 2. A means of analyzing collected text messages and modeling users' writing style and values

[0402] The server analyzes the collected text messages using natural language processing techniques, such as using Hugging Face's Transformers library to identify and model the user's writing style (e.g., polite or casual) and values.

[0403] 3. A means of analyzing user emotions and storing emotional information

[0404] The server uses an emotion engine to analyze the emotions (e.g., joy, sadness, anger, etc.) from the message sent by the user. This emotion information is stored in a database and used in the next step.

[0405] 4. A means to build chatbots using generative AI that generates user responses based on analyzed data

[0406] The server uses the analyzed data and emotional information to feed back into an AI model that generates a chatbot that reflects the user's characteristics and emotions. The generated chatbot then generates natural responses based on the user's writing style, values, and emotions.

[0407] 5. A means to provide the created chatbot to the person specified by the user

[0408] The server provides the chatbot at the time specified by the user. For example, if the user requests that the chatbot be provided to their family after their death, the server will provide the chatbot according to that request.

[0409] 6. A method for notifying security services when the generated chatbot detects an abnormality or danger.

[0410] If the chatbot detects any suspicious signs in the user's message, it will alert or notify the security service, for example, by sending a warning email using an SMTP server.

[0411] Examples:

[0412] When a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database. An analysis program then analyzes the message and models that the user is sociable and a movie lover. An emotion engine also detects "joy" and stores this information. A generative AI model uses this data to generate a chatbot that reflects the user's unique writing style and emotions. After the user's death, this chatbot is provided to the specified recipient. If a user sends a message such as "I feel like there's a suspicious person in my house," the chatbot will detect this as a danger signal and automatically notify security services.

[0413] Example prompt sentence:

[0414] Perform sentiment analysis on the following user messages and generate appropriate responses and security warnings where necessary:

[0415] Message: "I'm feeling a bit down today."

[0416] Message: "I feel like there's someone suspicious in my house."

[0417] This allows communication to continue while taking into consideration the user's feelings and safety, both during and after the user's death.

[0418] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0419] Step 1:

[0420] A user sends a text message using a messaging application on their smartphone or computer. The server receives this message and stores it in a database. The input is the user's text message, and the output is the message data stored in the database. Specifically, the server receives the message content and stores it as a record in the database along with a timestamp.

[0421] Step 2:

[0422] The server analyzes the collected text messages and uses natural language processing techniques to identify the user's writing style and values. The input is the text message stored in the database, and the output is the analysis results that indicate the user's writing style and values. Specifically, the server analyzes the messages using Hugging Face's Transformers library and stores the resulting writing style and values ​​data in a separate database.

[0423] Step 3:

[0424] The server uses an emotion engine to detect user emotions from collected text messages. The input is the text message to be analyzed, and the output is data indicating the user's emotions. Specifically, the server runs the emotion detection algorithm and stores the emotion information in a database.

[0425] Step 4:

[0426] Based on the analyzed data and emotional information, the server uses generative AI to generate a chatbot that reproduces the user's writing style and emotions. The input is the analyzed data and emotional information, and the output is a chatbot dedicated to the user. Specifically, the server feeds the data back into the AI ​​model, training and generating a chatbot that reflects the user's writing style and emotions.

[0427] Step 5:

[0428] The server provides the generated chatbot to the person specified by the user. The input is the generated chatbot and the user's specified information, and the output is the chatbot provided to the specified person. In concrete terms, the server provides the chatbot to the specified person in the form of a link, making it accessible.

[0429] Step 6:

[0430] If the server detects an abnormality or danger through the generated chatbot, it notifies the security service. The input is a text message indicating the abnormality or danger, and the output is warning information sent to the security service. Specifically, as soon as the server detects a suspicious message, it sends a warning email to the security service via the SMTP server.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] [Second embodiment]

[0435] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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."

[0447] The system of the present invention supports users' end-of-life planning and serves as a means to maintain communication with their surviving family members after the user's death. The system collects and analyzes the user's text messages to model the user's writing style and values. It then uses generative AI to build a chatbot with the user's characteristics and provides the chatbot to a contact specified by the user.

[0448] To implement the present invention, several major components are required, including:

[0449] 1. Programs that collect users' text messages

[0450] A user uses a messaging application such as LINE from their device (e.g., a smartphone or PC) to send a text message to the server.

[0451] The server collects messages sent by users and stores them in a database.

[0452] To give a concrete example, when a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database.

[0453] 2. A program that analyzes collected text messages and models the user's writing style and values.

[0454] The text messages collected by the server are analyzed using natural language processing technology.

[0455] Based on the analysis results, the server identifies the user's writing style (e.g., polite language or casual expressions) and values ​​(e.g., valuing family, liking adventure, etc.) and models them.

[0456] As a concrete example, a server analyzes the message "I went to the movies with a friend today" and models the user as being sociable and a movie lover.

[0457] 3. Program to build chatbots using generative AI

[0458] The server uses the analyzed data to feed it back into an AI model that generates a chatbot with the user's characteristics.

[0459] The generated chatbot will generate natural responses based on the user's writing style and values.

[0460] For example, if the chatbot is asked, "How was your day today?", it will respond by mimicking the user's writing style, saying, "Today I went to the movies with a friend."

[0461] 4. A program that provides the generated chatbot to the person specified by the user

[0462] After the user's death, the server provides the chatbot to the surviving family at a pre-specified time.

[0463] The chatbot will be set up so that bereaved family members can interact with it through the LINE app.

[0464] For example, if a user had previously requested, "When I die, please provide this chatbot to my daughter," the server would provide the chatbot to the daughter at the specified time as soon as it confirmed the user's death.

[0465] With these components, the system of the present invention allows the user to reflect their own values ​​and writing style in the chatbot during their end-of-life planning, and maintain an emotional connection with their surviving family members even after their death. This provides a valuable means for both the user and their surviving family members to process their emotions and remember the deceased.

[0466] The processing flow will be explained below.

[0467] Step 1:

[0468] The user sends a message saying "Start creating a chatbot" using the LINE application installed on their device (e.g., a smartphone).

[0469] Step 2:

[0470] The server receives the message sent by the user, records the user's LINE ID in the database, and replies to the user, asking "Please tell us your name and date of birth."

[0471] Step 3:

[0472] The user uses a terminal to enter "Yamada Taro, January 1, 1980" and sends it back to the server.

[0473] Step 4:

[0474] The server receives the user's name and date of birth and stores them in a database. The server then notifies the user that the settings are complete and that regular messages will be sent.

[0475] Step 5:

[0476] Every day at 7pm, the server sends a message to the user's device via LINE asking, "How was your day?"

[0477] Step 6:

[0478] The user uses the device to reply with a message saying, "I went to the movies with a friend today."

[0479] Step 7:

[0480] The server receives the user's replies and stores them in a database. The server then analyzes the collected messages using natural language processing technology to determine the user's writing style and values.

[0481] Step 8:

[0482] Based on the analysis results, the server models the user's writing style and values ​​and stores this information in a database.

[0483] Step 9:

[0484] The user uses the device to set up the device to send specific LINE chat conversation history to the server as "conversations with family" or similar.

[0485] Step 10:

[0486] The server receives the conversation history with a specific partner, stores it in a database, and analyzes the relationship and unique response patterns of each partner.

[0487] Step 11:

[0488] Based on the analyzed data, the server uses a generative AI model to generate a chatbot that reflects the user's characteristics. The generated chatbot then generates natural responses based on the user's writing style and values.

[0489] Step 12:

[0490] The user or agent notifies the server of the user's death.

[0491] Step 13:

[0492] The server receives the user's death notification and notifies pre-designated family members via LINE that "you have a message from the deceased."

[0493] Step 14:

[0494] The bereaved family member uses the device to send a message to the chatbot asking, "Dad, what do you think?"

[0495] Step 15:

[0496] The server inputs the message from the bereaved family into the chatbot and sends the generated response (e.g., "As a father, the most important thing is that you are happy") to the bereaved family.

[0497] Example 1

[0498] 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."

[0499] In modern society, people frequently communicate digitally, but there is a problem that there is a lack of ways to convey precious memories and values ​​to bereaved families after a user passes away.In addition, there is a problem that the emotional connection with bereaved families is severed because there is no communication method that reflects the user's writing style and values ​​when they were alive.

[0500] 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.

[0501] In this invention, the server includes a means for collecting the user's text messages, a means for analyzing the collected text messages and modeling the user's writing style and values, and a means for constructing a chatbot using a generative AI that generates responses to the user based on the analyzed data. This makes it possible to continue to emotionally connect with the bereaved family even after the user has passed away through a chatbot that inherits the user's writing style and values.

[0502] "User" means an individual or legal entity that uses the System.

[0503] "Text message" refers to textual information sent by a user using a device.

[0504] "Collection" refers to the process by which the server obtains and stores text messages sent from the device.

[0505] "Analysis" is the process by which the server uses natural language processing technology to analyze the content of collected text messages and extract important information.

[0506] "Writing style" refers to the language and style of expression used by a User in their text messages.

[0507] "Values" refer to the beliefs and principles of behavior held by users.

[0508] "Modeling" is the process of abstracting the user's writing style and values ​​from the analyzed data and expressing them as a data model.

[0509] "Generative AI" refers to artificial intelligence that generates new text based on the user's characteristics.

[0510] A "chatbot" is a program that converses with a user by recreating their writing style and values.

[0511] "Terminal" refers to an electronic device used by a user (e.g., a smartphone or PC).

[0512] A "server" is a computer system that collects, analyzes, and stores text messages and generates chatbots.

[0513] A "prompt sentence" is an input sentence given to a generation AI, and is the text that the AI ​​uses to generate a response based on those instructions.

[0514] "LINE API" refers to an interface that allows external use of the functions of the messaging application LINE.

[0515] "Natural language processing technology" refers to technology that automatically analyzes text data and understands and processes human language.

[0516] The system of the present invention supports users in their end-of-life planning and serves as a means for maintaining communication with their surviving family members after their death. The system collects and analyzes the user's text messages to model the user's writing style and values. It then uses generative AI to build a chatbot with the user's characteristics and provides the chatbot to a contact specified by the user.

[0517] Hardware and software used

[0518] Server: A computer system for collecting, analyzing, and storing messages, and generating chatbots.

[0519] Device: The electronic device used by the user (e.g. smartphone, computer).

[0520] Messaging applications: LINE, etc.

[0521] Natural language processing technologies: Google Cloud NLP API, IBM Watson, etc.

[0522] Generative AI model: OpenAI GPT-4.

[0523] LINE API: An interface for using LINE functions externally.

[0524] Program processing

[0525] 1. Text message collection

[0526] A user sends a text message using a messaging application such as LINE on their device. The server receives this message via the LINE API and stores it in a database.

[0527] Example: When a user sends a message saying "I went to the movies with a friend today," the server receives this message and automatically stores it in a database.

[0528] 2. Text Message Analysis and Modeling

[0529] The server analyzes the collected text messages using natural language processing technology, and based on the analysis results, identifies the user's writing style and values ​​and creates a model of them.

[0530] Example: A server parses the message "I went to the movies with a friend today" and models the user as sociable and movie-loving.

[0531] 3. Chatbot Creation

[0532] Based on the analyzed data, the server provides feedback on the user's characteristics to the generation AI (GPT-4), which then generates natural responses based on the user's writing style and values.

[0533] Example: When asked, "How was your day?", a chatbot responds in the user's style, "Today I went to the movies with a friend."

[0534] Example prompt: "If a user sends, 'I went to the movies with a friend today,' model their writing style and values ​​around this."

[0535] "Generate a chatbot with your writing style and have it respond to the question, 'How was your day?'"

[0536] 4. Providing chatbots

[0537] Before dying, the user specifies to the server who the chatbot should be provided to.

[0538] After a user's death, the server provides the chatbot to the family at a specified time. The family can then use the LINE API to interact with the chatbot through the LINE app.

[0539] Example: A user requests, "Please provide this chatbot to my daughter after I die." After confirming the user's death, the server provides the chatbot to the daughter's LINE account, and the chatbot speaks to the daughter, saying, "Let's talk about memories of Dad."

[0540] This system allows users to maintain an emotional connection with their families even after their death through a chatbot that inherits the user's values ​​and writing style, providing a valuable tool for both the user and their family to process their emotions and remember the deceased.

[0541] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0542] Step 1:

[0543] User-submitted messages

[0544] A user uses a device (smartphone or computer), launches a messaging application such as LINE, and types and sends a text message.

[0545] Input: A text message typed by the user (e.g., "I went to the movies with a friend today").

[0546] Output: The text message sent.

[0547] Specific operation: The user types a message in the LINE app such as "Today I went to the movies with a friend" and presses the send button.

[0548] Step 2:

[0549] Server receives and stores messages

[0550] The server uses the LINE API to receive messages sent from the user's device and save them in a database.

[0551] Input: A text message sent from the user's device.

[0552] Output: The text message stored in the database.

[0553] Specific operation: The server receives a message through the LINE API and automatically saves the message "I went to the movies with a friend today" in a database.

[0554] Step 3:

[0555] Message parsing by the server

[0556] The server analyzes the stored text messages using natural language processing technology (e.g., Google Cloud NLP API or IBM Watson).

[0557] Input: A text message stored in the database.

[0558] Output: Parsed message style and value data.

[0559] Specific operation: The server analyzes the message "I went to the movies with a friend today" using natural language processing technology and identifies that the user is sociable and loves movies.

[0560] Step 4:

[0561] Analysis data modeling

[0562] The server models the user's writing style and values ​​based on the data analyzed.

[0563] Input: Parsed message stylistic and value data.

[0564] Output: A model that reflects the user's writing style and values.

[0565] Specific operation: The server generates a user model with the characteristics of being "sociable" and "movie lover."

[0566] Step 5:

[0567] Chatbot generation by the server

[0568] The server feeds user characteristics into a generative AI model (e.g., OpenAI GPT-4) to build a chatbot that generates natural responses based on the user's writing style and values.

[0569] Input: Model data that reflects the user's writing style and values.

[0570] Output: A chatbot with the user's writing style and values.

[0571] Specific operation: The server inputs the feature data "sociable" and "movie lover" into GPT-4, and generates a chatbot that reproduces the user's writing style. When the chatbot is asked "How was your day?", it responds "Today, I went to the movies with a friend."

[0572] Step 6:

[0573] Chatbot provision settings

[0574] Before dying, the user specifies to the server who the chatbot should be provided to.

[0575] Input: User-specified information (e.g., contact information for recipient, timing of provision).

[0576] Output: Provided configuration data.

[0577] Specific operation: The user enters information into the server specifying, "Please provide this chatbot to my daughter after I die."

[0578] Step 7:

[0579] Providing chatbots

[0580] After a user's death, the server provides the chatbot to the surviving family at a specified time. Using the LINE API, the server sends permission to use the chatbot to the surviving family's LINE account.

[0581] Input: Provided configuration data, user death confirmation information.

[0582] Output: A chatbot provided to the family's LINE account.

[0583] Specific operation: After the server confirms the user's death, it provides a chatbot to the daughter's LINE account and asks her to "tell us about memories of your father."

[0584] (Application example 1)

[0585] 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."

[0586] It is difficult to provide a realistic interaction experience in a specific virtual environment or with customers, while inheriting the various values ​​and interaction styles that users developed during their lifetime.In addition, there is a lack of systems that can maintain emotional connections with users after their death and reproduce interactions in specific environments such as virtual stores.

[0587] 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.

[0588] In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages and modeling the user's writing style and values, means for constructing a chatbot using a generation AI that generates responses for the user based on the analyzed data, means for providing the constructed chatbot to a party designated by the user, means for the user to reproduce a specific response style, and means for the generated chatbot to respond in a virtual environment. This makes it possible to provide a realistic conversation experience in a designated virtual environment or in conversations with customers even after the user's death.

[0589] "User's text message" means a text message sent by a User using an information and communication terminal, including personal conversations, business correspondence, social interactions, etc.

[0590] The "means for collecting text messages" refers to a program or device for storing text messages sent from an information communication terminal in a database such as a server.

[0591] "Means for analyzing text messages and modeling a user's writing style and values" refers to an algorithm that uses natural language processing technology to extract, understand, and model a user's writing style, speech patterns, and the values ​​they value based on collected text messages.

[0592] A "means for building a chatbot using generative AI" is a program or system that feeds analyzed user data back into generative AI to create a chatbot that reproduces the user's writing style and values.

[0593] "Means for providing chatbots to people designated by users" refers to a system or service that makes the constructed chatbot available at the appropriate time to people designated by the user during their lifetime.

[0594] "Means for users to recreate a specific response style" refers to an algorithm that recreates the way a user would respond to a specific situation or person based on collected text messages.

[0595] "Means for responding in a virtual environment" refers to a program that enables the generated chatbot to respond in an online environment such as virtual reality or a virtual store while reproducing the user's writing style and response style.

[0596] To implement this invention, the following main components are required: a system that collects users' text messages, analyzes them to model their writing style and values, uses generative AI to build a chatbot, and responds in a virtual environment.

[0597] How to collect user text messages

[0598] The server collects text messages sent from the user's device (e.g., smartphone or PC). During this collection process, the server receives text messages using messaging applications (e.g., LINE or WhatsApp) and stores them in a database. For example, if a user sends a message saying, "Today, I went to the movies with a friend," the server receives this message and stores it in a database.

[0599] Text message analysis and modeling

[0600] The server analyzes the text messages stored in the database using natural language processing technology. Specifically, it uses NLP (natural language processing) libraries (such as spaCy and BERT) to extract and model the user's writing style and values. For example, the server analyzes the message "Today I went to the movies with a friend" and models the user as sociable and a movie lover.

[0601] Building a chatbot using generative AI

[0602] The server feeds user data obtained through natural language processing back to the generation AI, building a chatbot that reflects the user's unique writing style and values. The generation AI uses OpenAI's GPT-3 and other technologies. This allows the chatbot to respond in the user's writing style when asked, for example, "How was your day?", with, for example, "I went to the movies with a friend today."

[0603] Providing chatbots and responses in a virtual environment

[0604] The constructed chatbot is provided to a person specified by the user. The server can also provide the chatbot at a specified time after the user's death. Furthermore, the constructed chatbot can interact with the user in a virtual environment (for example, a VR shopping mall or online customer support), recreating the user's response style.

[0605] Specific examples

[0606] For example, text messages from store staff members' previous customer service interactions can be collected and analyzed to model their writing style and response style. The generated chatbot can respond to customers based on the prompt, "You are Staff A. We strive to provide our customers with the highest quality products. Customer: Hello, is the item in stock?"

[0607] Through the above components and processes, the system of this invention can inherit the user's values ​​and writing style from their previous life and provide a realistic conversational experience even in a specific virtual environment.

[0608] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0609] Step 1:

[0610] The server collects text messages sent from the user's device (e.g., smartphone or PC).

[0611] Input: A text message sent by a user through a messaging app.

[0612] Output: The collected text messages are stored in the server's database. The specific operation is to receive the text messages and store them in the database along with the user ID.

[0613] Step 2:

[0614] The server analyzes the text messages stored in the database using natural language processing techniques.

[0615] Input: The user's text message stored in the database.

[0616] Output: Data that models the user's writing style and values ​​as a result of the analysis. Specifically, it uses NLP libraries (e.g., spaCy, BERT) to extract writing style, keywords, sentiment, etc., and statistically models them.

[0617] Step 3:

[0618] Based on the analysis results, the server uses generative AI to build a chatbot that reflects the user's writing style and values.

[0619] Input: Data that models the user's writing style and values.

[0620] Output: A chatbot that reflects the user's writing style and values. The specific operation is to feed the analysis results into a generative AI model (e.g., OpenAI GPT-3) to train and adjust the chatbot so that it can have natural conversations.

[0621] Step 4:

[0622] The server configures the constructed chatbot so that it can respond in a virtual environment.

[0623] Input: The chatbot you built.

[0624] Output: Configuration data that enables the chatbot to operate in a virtual environment. The specific operation is to connect and place the chatbot in a virtual environment (e.g., VR shopping mall, online customer support) and enable it to respond through an appropriate interface.

[0625] Step 5:

[0626] The server provides the chatbot to the user after the user's death or to a specified person.

[0627] Input: Information about the recipient and timing of donation designated by the user during their lifetime.

[0628] Output: A chatbot provided to the specified person. The specific operation is to notify the specified person of the chatbot and make it available when the user's death is confirmed.

[0629] Step 6:

[0630] The server ensures that the chatbot responds in a virtual environment while replicating a particular interaction style.

[0631] Input: Model data for reproducing the user's response style.

[0632] Output: Natural and consistent responses in the virtual environment. The specific behavior is that the generated chatbot will conduct a dialogue based on a pre-modeled response style.

[0633] 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.

[0634] The system of the present invention supports users in planning for their end of life and provides a means for maintaining communication with their surviving family members after their death. The system collects and analyzes the user's text messages to model the user's writing style, values, and even emotions. Furthermore, it uses generative AI to build a chatbot that reflects the user's characteristics and emotions, and provides the chatbot to a contact specified by the user.

[0635] To implement the present invention, the following major components are required, including:

[0636] 1. Programs that collect users' text messages

[0637] A user uses a messaging application such as LINE from their device (e.g., a smartphone or PC) to send a text message to the server.

[0638] The server collects messages sent by users and stores them in a database.

[0639] To give a concrete example, when a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database.

[0640] 2. A program that analyzes collected text messages and models the user's writing style and values.

[0641] The text messages collected by the server are analyzed using natural language processing technology.

[0642] Based on the analysis results, the server identifies the user's writing style (e.g., polite language or casual expressions) and values ​​(e.g., valuing family, liking adventure, etc.) and models them.

[0643] As a concrete example, a server analyzes the message "I went to the movies with a friend today" and models the user as being sociable and a movie lover.

[0644] 3. Emotion engine that recognizes emotions from collected text messages

[0645] The server uses an emotion engine to analyze emotions (e.g., joy, sadness, anger, etc.) from messages sent by users.

[0646] The server stores the analyzed emotional information in a database and uses it in the next step.

[0647] For example, the server may recognize from a message such as "I went to the movies with a friend today" that the user is feeling "joy."

[0648] 4. Program for building chatbots using generative AI

[0649] The server uses the analyzed data and emotional information to feed back into an AI model to generate a chatbot that reflects the user's characteristics and emotions.

[0650] The resulting chatbot will generate natural responses based on the user's writing style, values, and even emotions.

[0651] For example, if a chatbot is asked, "How was your day today?", it will respond by mimicking the user's writing style and emotions, saying, "Today I'm going to the movies with a friend. I'm looking forward to it!"

[0652] 5. A program that provides the generated chatbot to the person specified by the user

[0653] After the user's death, the server provides the chatbot to the surviving family at a pre-specified time.

[0654] The chatbot will be set up so that bereaved family members can interact with it through the LINE app.

[0655] For example, if a user had previously requested, "When I die, please provide this chatbot to my daughter," the server would provide the chatbot to the daughter at the specified time as soon as it confirmed the user's death.

[0656] The above components allow the system of the present invention to reflect the user's own values, writing style, and even emotions in the chatbot throughout the end-of-life planning process. This allows the user to maintain an emotional connection and dialogue with their family members even after their death. This provides a valuable means for both the user and the family members to process their emotions and remember the deceased.

[0657] The processing flow will be explained below.

[0658] Step 1:

[0659] The user sends a message saying "Start creating a chatbot" using the LINE application installed on their device (e.g., a smartphone).

[0660] Step 2:

[0661] The server receives this message, records the user's LINE ID in the database, and replies to the user, "Please tell us your name and date of birth."

[0662] Step 3:

[0663] The user uses a terminal to enter "Yamada Taro, January 1, 1980" and sends it back to the server.

[0664] Step 4:

[0665] The server receives the user's name and date of birth and stores them in a database. The server then notifies the user that the settings are complete and that regular messages will be sent.

[0666] Step 5:

[0667] Every day at 7pm, the server sends a message to the user's device via LINE asking, "How was your day?"

[0668] Step 6:

[0669] The user uses the device to reply with a message saying, "I went to the movies with a friend today."

[0670] Step 7:

[0671] The server receives the user's replies and stores them in a database. The server then analyzes the collected messages using natural language processing technology to determine the user's writing style and values.

[0672] Step 8:

[0673] Based on the analysis results, the server models the user's writing style and values ​​and stores this information in a database.

[0674] Step 9:

[0675] The server analyzes the text message sent by the user using an emotion engine to recognize the user's emotions (e.g., joy, sadness, anger, etc.).

[0676] Step 10:

[0677] The server stores the emotional information obtained from the emotion engine in a database. The analyzed emotional information is also reflected in the modeling.

[0678] Step 11:

[0679] The user uses the device to set up the device to send specific LINE chat conversation history to the server as "conversations with family" or similar.

[0680] Step 12:

[0681] The server receives the conversation history with a specific partner, stores it in a database, and analyzes the relationship and unique response patterns of each partner.

[0682] Step 13:

[0683] The server uses a generative AI model to generate a chatbot that reflects the user's characteristics and emotions based on the analyzed data and emotional information. The generated chatbot then generates natural responses based on the user's writing style, values, and emotions.

[0684] Step 14:

[0685] The user or agent notifies the server of the user's death.

[0686] Step 15:

[0687] The server receives the user's death notification and notifies pre-designated family members via LINE that "you have a message from the deceased."

[0688] Step 16:

[0689] Family members can use the device to send messages to the chatbot, such as "Dad, how are you feeling today?"

[0690] Step 17:

[0691] The server inputs the message from the bereaved family into the chatbot and sends the generated response (e.g., "I feel a little lonely today, but as long as you're happy, that's all that matters") to the bereaved family.

[0692] Example 2

[0693] 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."

[0694] This invention relates to a system that generates a chatbot that reflects a user's writing style, values, and emotions based on text messages sent by the user while the user was alive, allowing the user to maintain an emotional connection with their surviving family members even after the user's death. Such a system allows the surviving family members to easily process their emotions by continuing to communicate with the user, providing a valuable means of remembering the deceased. However, existing systems face the challenge of effectively generating a chatbot that accurately reflects the user's writing style, values, and emotions.

[0695] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0696] In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages using natural language processing technology and modeling the user's writing style and values, means for recognizing emotions from the analyzed data, means for training a generative AI model based on the recognized emotions and the modeled writing style and values, means for constructing a chatbot that generates user responses using the generative AI model, and means for providing the constructed chatbot to a party designated by the user. This makes it possible to generate a chatbot that reproduces the user's characteristics and emotions and to maintain an emotional connection with the user's surviving family members after the user's death.

[0697] "User" refers to the person who uses the System and sends text messages.

[0698] "Text Message" refers to any written communication sent by a User through a Device.

[0699] "Means for collection" refers to the function or device for capturing text messages sent by users onto a server.

[0700] "Natural language processing technology" refers to computational methods and programming techniques for analyzing, understanding, and generating human language.

[0701] "Writing style" refers to the linguistic expression style used when a user sends a text message, including, for example, polite language or casual expressions.

[0702] "Values" refer to the beliefs and ways of thinking that users consider important, such as a love of family or a love of adventure.

[0703] "Means for analyzing" refers to a function or device for analyzing text messages and modeling writing styles and values.

[0704] "Emotion" refers to the state of mind expressed by a user in a text message, including, for example, joy, sadness, anger, etc.

[0705] "Means for recognizing emotions" refers to a function or device for analyzing and identifying a user's emotions from collected text messages.

[0706] A "generative AI model" refers to a machine learning model that automatically generates responses that reflect the user's writing style, values, and emotions.

[0707] "Training means" refers to the functionality or equipment that allows a generative AI model to learn based on the characteristics and emotions of the user.

[0708] A "chatbot" is a conversational agent that uses generative AI models to replicate a user's writing style, values, and emotions.

[0709] "Means for providing" refers to a function or device for delivering the constructed chatbot to a specified party in a usable state.

[0710] "Means for managing timing" refers to a function or device for managing the appropriate timing for providing the chatbot to a designated person after a user's death.

[0711] The system of the present invention supports users in planning their end of life and provides a means for maintaining communication with their surviving family members even after the user's death. The system collects and analyzes the user's text messages to model the user's writing style, values, and even emotions. Furthermore, it uses a generative AI model to build a chatbot that reflects the user's characteristics and emotions, and provides the chatbot to a contact specified by the user.

[0712] Hardware and Software Configuration

[0713] Server: The central processing unit of the system, responsible for collecting, analyzing, and modeling user text messages, training generative AI models, and building and serving chatbots.

[0714] Device: This refers to the smartphone or computer used by the user to send text messages.

[0715] Database: A data storage for saving collected text messages, analyzed data, and emotional information, using, for example, MySQL.

[0716] Natural language processing tools: Software used to analyze text messages, such as MeCab and NLTK.

[0717] Generative AI models: Machine learning models used to generate responses that reflect a user's writing style, values, and emotions, such as GPT-4.

[0718] Specific examples of program processing

[0719] 1. Text message collection

[0720] The user sends a message from their device through the LINE app saying, "Today I went to the movies with a friend."

[0721] The server receives this message using the LINE app API and stores it in a database (MySQL) in text format.

[0722] 2. Text message analysis

[0723] The message data collected by the server is analyzed using a morphological analysis tool (MeCab).

[0724] The server extracts the user's writing style (polite language, casual expressions) and values ​​(family-oriented, adventurous) from the analysis results and models them.

[0725] 3. Emotional Recognition

[0726] The server uses a natural language processing library (NLTK) to analyze the sentiment from the message.

[0727] For example, the server recognizes the emotion "joy" from the message "I went to the movies with a friend today" and stores the emotion information in a database.

[0728] 4. Building a Chatbot

[0729] The server feeds back the analyzed data (writing style, values, emotional information) to the generative AI model (GPT-4).

[0730] The server uses a generative AI model to generate a chatbot that reflects the user's writing style and emotions.

[0731] 5. Providing chatbots

[0732] Once the server confirms the user's death, it will provide the chatbot to the bereaved family at a pre-specified time.

[0733] The chatbot will be set up so that bereaved family members can smoothly communicate with it through the LINE app.

[0734] Examples of prompt statements

[0735] Prompts: Analyzing the user's writing style and values

[0736] "Parse the following message and model the user's writing style and values: 'Today I went to the movies with a friend. It was a great time.'"

[0737] Prompts: Chatbot response generation

[0738] "If a user is asked, 'How was your day?' generate a response based on the following text: 'User is a movie lover and sociable. Emotion: Joy'"

[0739] This system makes it possible to generate chatbots that replicate the characteristics and emotions of users, and to maintain an emotional connection with their surviving family members after the user's death.

[0740] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0741] Step 1: Collect text messages

[0742] 1. A user sends a text message using a messaging application such as LINE from their device (smartphone or PC).

[0743] Input: User's text message

[0744] Output: Text message sent to the server

[0745] Specific operation: The user sends a message using the LINE app saying, "Today I went to the movies with a friend."

[0746] 2. The server receives this message using the LINE app API.

[0747] Input: Text message from the LINE app

[0748] Output: Messages stored on the server

[0749] Specific operation: The server receives the message through the LINE API and saves it as text.

[0750] 3. The server stores the received message in a database.

[0751] Input: Received message

[0752] Output: Messages stored in the database

[0753] Specific operation: The server stores the received message in, for example, a MySQL database.

[0754] Step 2: Parsing the text messages

[0755] 1. The message data collected by the server is analyzed using a morphological analysis tool (MeCab).

[0756] Input: Message stored in the database

[0757] Output: Parsed message data

[0758] Specific operation: The server uses MeCab to split the message "Today I went to the movies with a friend" into words.

[0759] 2. The server extracts and models the user's writing style and values ​​from the analysis results.

[0760] Input: Parsed message data

[0761] Output: Modeled writing style and values

[0762] Specific operation: Based on the analysis results, the server models that the user writes politely and has values ​​that value family.

[0763] Step 3: Recognize emotions

[0764] 1. The server uses a natural language processing library (NLTK) to analyze the sentiment from the message.

[0765] Input: Parsed message data

[0766] Output: Recognized emotion data

[0767] Specific operation: The server uses NLTK to recognize the emotion "joy" from the message "Today I went to the movies with a friend."

[0768] 2. The server stores the analyzed emotional information in a database.

[0769] Input: Recognized emotion data

[0770] Output: Emotion data stored in a database

[0771] Specific operation: The server stores the recognized emotion data of "joy" in the database.

[0772] Step 4: Build your chatbot

[0773] 1. The server feeds back the analyzed data (writing style, values, emotional information) to the generative AI model (GPT-4).

[0774] Input: Analyzed data (style, values, sentiment)

[0775] Output: Data fed back to the AI ​​model

[0776] Specific operation: The server inputs the following data into GPT-4: "User loves movies and is sociable. Emotion: joy."

[0777] 2. The server uses the generative AI model to generate a chatbot that reflects the user's characteristics and emotions.

[0778] Input: Feedback data

[0779] Output: The generated chatbot

[0780] How it works: The server uses GPT-4 to generate a chatbot that replicates the user's writing style and emotions.

[0781] Step 5: Serve the chatbot

[0782] 1. Once the server confirms the user's death, it will provide the chatbot to the surviving family at a pre-specified time.

[0783] Input: User death confirmation and specified timing

[0784] Output: Chatbot provided to the family

[0785] Specific operation: When the server confirms the user's death, it sends a chatbot access link to the surviving family member's LINE account at the specified time.

[0786] 2. Set up the chatbot so that bereaved family members can smoothly interact with it through the LINE app.

[0787] Input: LINE account of the bereaved family

[0788] Output: A chatbot that can interact with bereaved families

[0789] Specific operation: The bereaved family member uses the LINE app and clicks on the sent link to start a conversation with the chatbot.

[0790] (Application example 2)

[0791] 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."

[0792] The problem that this invention aims to solve is not only to enable continued dialogue with surviving family members after a user's death, but also to provide a means to ensure the user's safety while they are still alive. Current chatbot systems have difficulty automatically providing responses and safety measures tailored to the user in specific situations. Therefore, there is a need for a system that can generate a chatbot that responds appropriately to the user's writing style and values, and further analyzes information related to emotions and safety to provide appropriate responses.

[0793] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages and modeling the user's writing style and values, means for constructing a chatbot using a generation AI that generates user responses based on the analyzed data, means for providing the constructed chatbot to a party specified by the user, means for analyzing the user's emotions and storing the emotion information, and means for notifying a security service when the generated chatbot detects an abnormality or danger. This enables appropriate responses and measures that take into consideration the user's emotions and safety not only after the user's death but also while the user is still alive.

[0794] A "user's text message" is text or character string data sent by a user via a communication terminal.

[0795] A "collection method" is a system or program designed to store a user's text messages on a server or database.

[0796] "Means for analysis" refers to a system or program that uses natural language processing technology to analyze collected text messages and extract the user's writing style and values.

[0797] A "style and value modeling method" is a method or algorithm that identifies a user's specific style and values ​​from the analyzed text data.

[0798] "Generative AI" is a system that uses artificial intelligence technology to generate responses based on the user's writing style and values.

[0799] A "chatbot" is a program or application designed to interact with users.

[0800] "Means for providing to designated parties" refers to the method or process by which the generated chatbot is made available to parties designated in advance by the user.

[0801] "Means for analyzing emotions" refers to algorithms or systems that read and analyze emotions from users' text messages.

[0802] The "means for storing emotional information" refers to a method or device for storing analyzed emotional data of a user in a database or the like.

[0803] "Means for notifying when an abnormality or danger is detected" refers to a system or process for alerting or notifying security services when the chatbot detects suspicious signs in a user's message.

[0804] This system collects and analyzes users' text messages, builds a chatbot that models their writing style and values, and provides the built chatbot to a person specified by the user. It also has the ability to analyze the user's emotions and notify a security service if it detects an abnormality or danger.

[0805] The system includes the following major components:

[0806] 1. How to collect user text messages

[0807] Users use messaging applications on their smartphones, PCs, or other devices to send text messages to a server, which then collects the messages and stores them in a database.

[0808] 2. A means of analyzing collected text messages and modeling users' writing style and values

[0809] The server analyzes the collected text messages using natural language processing techniques, such as using Hugging Face's Transformers library to identify and model the user's writing style (e.g., polite or casual) and values.

[0810] 3. A means of analyzing user emotions and storing emotional information

[0811] The server uses an emotion engine to analyze the emotions (e.g., joy, sadness, anger, etc.) from the message sent by the user. This emotion information is stored in a database and used in the next step.

[0812] 4. A means to build chatbots using generative AI that generates user responses based on analyzed data

[0813] The server uses the analyzed data and emotional information to feed back into an AI model that generates a chatbot that reflects the user's characteristics and emotions. The generated chatbot then generates natural responses based on the user's writing style, values, and emotions.

[0814] 5. A means to provide the created chatbot to the person specified by the user

[0815] The server provides the chatbot at the time specified by the user. For example, if the user requests that the chatbot be provided to their family after their death, the server will provide the chatbot according to that request.

[0816] 6. A method for notifying security services when the generated chatbot detects an abnormality or danger.

[0817] If the chatbot detects any suspicious signs in the user's message, it will alert or notify the security service, for example, by sending a warning email using an SMTP server.

[0818] Examples:

[0819] When a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database. An analysis program then analyzes the message and models that the user is sociable and a movie lover. An emotion engine also detects "joy" and stores this information. A generative AI model uses this data to generate a chatbot that reflects the user's unique writing style and emotions. After the user's death, this chatbot is provided to the specified recipient. If a user sends a message such as "I feel like there's a suspicious person in my house," the chatbot will detect this as a danger signal and automatically notify security services.

[0820] Example prompt sentence:

[0821] Perform sentiment analysis on the following user messages and generate appropriate responses and security warnings where necessary:

[0822] Message: "I'm feeling a bit down today."

[0823] Message: "I feel like there's someone suspicious in my house."

[0824] This allows communication to continue while taking into consideration the user's feelings and safety, both during and after the user's death.

[0825] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0826] Step 1:

[0827] A user sends a text message using a messaging application on their smartphone or computer. The server receives this message and stores it in a database. The input is the user's text message, and the output is the message data stored in the database. Specifically, the server receives the message content and stores it as a record in the database along with a timestamp.

[0828] Step 2:

[0829] The server analyzes the collected text messages and uses natural language processing techniques to identify the user's writing style and values. The input is the text message stored in the database, and the output is the analysis results that indicate the user's writing style and values. Specifically, the server analyzes the messages using Hugging Face's Transformers library and stores the resulting writing style and values ​​data in a separate database.

[0830] Step 3:

[0831] The server uses an emotion engine to detect user emotions from collected text messages. The input is the text message to be analyzed, and the output is data indicating the user's emotions. Specifically, the server runs the emotion detection algorithm and stores the emotion information in a database.

[0832] Step 4:

[0833] Based on the analyzed data and emotional information, the server uses generative AI to generate a chatbot that reproduces the user's writing style and emotions. The input is the analyzed data and emotional information, and the output is a chatbot dedicated to the user. Specifically, the server feeds the data back into the AI ​​model, training and generating a chatbot that reflects the user's writing style and emotions.

[0834] Step 5:

[0835] The server provides the generated chatbot to the person specified by the user. The input is the generated chatbot and the user's specified information, and the output is the chatbot provided to the specified person. In concrete terms, the server provides the chatbot to the specified person in the form of a link, making it accessible.

[0836] Step 6:

[0837] If the server detects an abnormality or danger through the generated chatbot, it notifies the security service. The input is a text message indicating the abnormality or danger, and the output is warning information sent to the security service. Specifically, as soon as the server detects a suspicious message, it sends a warning email to the security service via the SMTP server.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] [Third embodiment]

[0842] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0843] 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.

[0844] 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).

[0845] 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.

[0846] 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.

[0847] 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).

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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."

[0854] The system of the present invention supports users' end-of-life planning and serves as a means to maintain communication with their surviving family members after the user's death. The system collects and analyzes the user's text messages to model the user's writing style and values. It then uses generative AI to build a chatbot with the user's characteristics and provides the chatbot to a contact specified by the user.

[0855] To implement the present invention, several major components are required, including:

[0856] 1. Programs that collect users' text messages

[0857] A user uses a messaging application such as LINE from their device (e.g., a smartphone or PC) to send a text message to the server.

[0858] The server collects messages sent by users and stores them in a database.

[0859] To give a concrete example, when a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database.

[0860] 2. A program that analyzes collected text messages and models the user's writing style and values.

[0861] The text messages collected by the server are analyzed using natural language processing technology.

[0862] Based on the analysis results, the server identifies the user's writing style (e.g., polite language or casual expressions) and values ​​(e.g., valuing family, liking adventure, etc.) and models them.

[0863] As a concrete example, a server analyzes the message "I went to the movies with a friend today" and models the user as being sociable and a movie lover.

[0864] 3. Program to build chatbots using generative AI

[0865] The server uses the analyzed data to feed it back into an AI model that generates a chatbot with the user's characteristics.

[0866] The generated chatbot will generate natural responses based on the user's writing style and values.

[0867] For example, if the chatbot is asked, "How was your day today?", it will respond by mimicking the user's writing style, saying, "Today I went to the movies with a friend."

[0868] 4. A program that provides the generated chatbot to the person specified by the user

[0869] After the user's death, the server provides the chatbot to the surviving family at a pre-specified time.

[0870] The chatbot will be set up so that bereaved family members can interact with it through the LINE app.

[0871] For example, if a user had previously requested, "When I die, please provide this chatbot to my daughter," the server would provide the chatbot to the daughter at the specified time as soon as it confirmed the user's death.

[0872] With these components, the system of the present invention allows the user to reflect their own values ​​and writing style in the chatbot during their end-of-life planning, and maintain an emotional connection with their surviving family members even after their death. This provides a valuable means for both the user and their surviving family members to process their emotions and remember the deceased.

[0873] The processing flow will be explained below.

[0874] Step 1:

[0875] The user sends a message saying "Start creating a chatbot" using the LINE application installed on their device (e.g., a smartphone).

[0876] Step 2:

[0877] The server receives the message sent by the user, records the user's LINE ID in the database, and replies to the user, asking "Please tell us your name and date of birth."

[0878] Step 3:

[0879] The user uses a terminal to enter "Yamada Taro, January 1, 1980" and sends it back to the server.

[0880] Step 4:

[0881] The server receives the user's name and date of birth and stores them in a database. The server then notifies the user that the settings are complete and that regular messages will be sent.

[0882] Step 5:

[0883] Every day at 7pm, the server sends a message to the user's device via LINE asking, "How was your day?"

[0884] Step 6:

[0885] The user uses the device to reply with a message saying, "I went to the movies with a friend today."

[0886] Step 7:

[0887] The server receives the user's replies and stores them in a database. The server then analyzes the collected messages using natural language processing technology to determine the user's writing style and values.

[0888] Step 8:

[0889] Based on the analysis results, the server models the user's writing style and values ​​and stores this information in a database.

[0890] Step 9:

[0891] The user uses the device to set up the device to send specific LINE chat conversation history to the server as "conversations with family" or similar.

[0892] Step 10:

[0893] The server receives the conversation history with a specific partner, stores it in a database, and analyzes the relationship and unique response patterns of each partner.

[0894] Step 11:

[0895] Based on the analyzed data, the server uses a generative AI model to generate a chatbot that reflects the user's characteristics. The generated chatbot then generates natural responses based on the user's writing style and values.

[0896] Step 12:

[0897] The user or agent notifies the server of the user's death.

[0898] Step 13:

[0899] The server receives the user's death notification and notifies pre-designated family members via LINE that "you have a message from the deceased."

[0900] Step 14:

[0901] The bereaved family member uses the device to send a message to the chatbot asking, "Dad, what do you think?"

[0902] Step 15:

[0903] The server inputs the message from the bereaved family into the chatbot and sends the generated response (e.g., "As a father, the most important thing is that you are happy") to the bereaved family.

[0904] Example 1

[0905] 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."

[0906] In modern society, people frequently communicate digitally, but there is a problem that there is a lack of ways to convey precious memories and values ​​to bereaved families after a user passes away.In addition, there is a problem that the emotional connection with bereaved families is severed because there is no communication method that reflects the user's writing style and values ​​when they were alive.

[0907] 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.

[0908] In this invention, the server includes a means for collecting the user's text messages, a means for analyzing the collected text messages and modeling the user's writing style and values, and a means for constructing a chatbot using a generative AI that generates responses to the user based on the analyzed data. This makes it possible to continue to emotionally connect with the bereaved family even after the user has passed away through a chatbot that inherits the user's writing style and values.

[0909] "User" means an individual or legal entity that uses the System.

[0910] "Text message" refers to textual information sent by a user using a device.

[0911] "Collection" refers to the process by which the server obtains and stores text messages sent from the device.

[0912] "Analysis" is the process by which the server uses natural language processing technology to analyze the content of collected text messages and extract important information.

[0913] "Writing style" refers to the language and style of expression used by a User in their text messages.

[0914] "Values" refer to the beliefs and principles of behavior held by users.

[0915] "Modeling" is the process of abstracting the user's writing style and values ​​from the analyzed data and expressing them as a data model.

[0916] "Generative AI" refers to artificial intelligence that generates new text based on the user's characteristics.

[0917] A "chatbot" is a program that converses with a user by recreating their writing style and values.

[0918] "Terminal" refers to an electronic device used by a user (e.g., a smartphone or PC).

[0919] A "server" is a computer system that collects, analyzes, and stores text messages and generates chatbots.

[0920] A "prompt sentence" is an input sentence given to a generation AI, and is the text that the AI ​​uses to generate a response based on those instructions.

[0921] "LINE API" refers to an interface that allows external use of the functions of the messaging application LINE.

[0922] "Natural language processing technology" refers to technology that automatically analyzes text data and understands and processes human language.

[0923] The system of the present invention supports users in their end-of-life planning and serves as a means for maintaining communication with their surviving family members after their death. The system collects and analyzes the user's text messages to model the user's writing style and values. It then uses generative AI to build a chatbot with the user's characteristics and provides the chatbot to a contact specified by the user.

[0924] Hardware and software used

[0925] Server: A computer system for collecting, analyzing, and storing messages, and generating chatbots.

[0926] Device: The electronic device used by the user (e.g. smartphone, computer).

[0927] Messaging applications: LINE, etc.

[0928] Natural language processing technologies: Google Cloud NLP API, IBM Watson, etc.

[0929] Generative AI model: OpenAI GPT-4.

[0930] LINE API: An interface for using LINE functions externally.

[0931] Program processing

[0932] 1. Text message collection

[0933] A user sends a text message using a messaging application such as LINE on their device. The server receives this message via the LINE API and stores it in a database.

[0934] Example: When a user sends a message saying "I went to the movies with a friend today," the server receives this message and automatically stores it in a database.

[0935] 2. Text Message Analysis and Modeling

[0936] The server analyzes the collected text messages using natural language processing technology, and based on the analysis results, identifies the user's writing style and values ​​and creates a model of them.

[0937] Example: A server parses the message "I went to the movies with a friend today" and models the user as sociable and movie-loving.

[0938] 3. Chatbot Creation

[0939] Based on the analyzed data, the server provides feedback on the user's characteristics to the generation AI (GPT-4), which then generates natural responses based on the user's writing style and values.

[0940] Example: When asked, "How was your day?", a chatbot responds in the user's style, "Today I went to the movies with a friend."

[0941] Example prompt: "If a user sends, 'I went to the movies with a friend today,' model their writing style and values ​​around this."

[0942] "Generate a chatbot with your writing style and have it respond to the question, 'How was your day?'"

[0943] 4. Providing chatbots

[0944] Before dying, the user specifies to the server who the chatbot should be provided to.

[0945] After a user's death, the server provides the chatbot to the family at a specified time. The family can then use the LINE API to interact with the chatbot through the LINE app.

[0946] Example: A user requests, "Please provide this chatbot to my daughter after I die." After confirming the user's death, the server provides the chatbot to the daughter's LINE account, and the chatbot speaks to the daughter, saying, "Let's talk about memories of Dad."

[0947] This system allows users to maintain an emotional connection with their families even after their death through a chatbot that inherits the user's values ​​and writing style, providing a valuable tool for both the user and their family to process their emotions and remember the deceased.

[0948] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0949] Step 1:

[0950] User-submitted messages

[0951] A user uses a device (smartphone or computer), launches a messaging application such as LINE, and types and sends a text message.

[0952] Input: A text message typed by the user (e.g., "I went to the movies with a friend today").

[0953] Output: The text message sent.

[0954] Specific operation: The user types a message in the LINE app such as "Today I went to the movies with a friend" and presses the send button.

[0955] Step 2:

[0956] Server receives and stores messages

[0957] The server uses the LINE API to receive messages sent from the user's device and save them in a database.

[0958] Input: A text message sent from the user's device.

[0959] Output: The text message stored in the database.

[0960] Specific operation: The server receives a message through the LINE API and automatically saves the message "I went to the movies with a friend today" in a database.

[0961] Step 3:

[0962] Message parsing by the server

[0963] The server analyzes the stored text messages using natural language processing technology (e.g., Google Cloud NLP API or IBM Watson).

[0964] Input: A text message stored in the database.

[0965] Output: Parsed message style and value data.

[0966] Specific operation: The server analyzes the message "I went to the movies with a friend today" using natural language processing technology and identifies that the user is sociable and loves movies.

[0967] Step 4:

[0968] Analysis data modeling

[0969] The server models the user's writing style and values ​​based on the data analyzed.

[0970] Input: Parsed message stylistic and value data.

[0971] Output: A model that reflects the user's writing style and values.

[0972] Specific operation: The server generates a user model with the characteristics of being "sociable" and "movie lover."

[0973] Step 5:

[0974] Chatbot generation by the server

[0975] The server feeds user characteristics into a generative AI model (e.g., OpenAI GPT-4) to build a chatbot that generates natural responses based on the user's writing style and values.

[0976] Input: Model data that reflects the user's writing style and values.

[0977] Output: A chatbot with the user's writing style and values.

[0978] Specific operation: The server inputs the feature data "sociable" and "movie lover" into GPT-4, and generates a chatbot that reproduces the user's writing style. When the chatbot is asked "How was your day?", it responds "Today, I went to the movies with a friend."

[0979] Step 6:

[0980] Chatbot provision settings

[0981] Before dying, the user specifies to the server who the chatbot should be provided to.

[0982] Input: User-specified information (e.g., contact information for recipient, timing of provision).

[0983] Output: Provided configuration data.

[0984] Specific operation: The user enters information into the server specifying, "Please provide this chatbot to my daughter after I die."

[0985] Step 7:

[0986] Providing chatbots

[0987] After a user's death, the server provides the chatbot to the surviving family at a specified time. Using the LINE API, the server sends permission to use the chatbot to the surviving family's LINE account.

[0988] Input: Provided configuration data, user death confirmation information.

[0989] Output: A chatbot provided to the family's LINE account.

[0990] Specific operation: After the server confirms the user's death, it provides a chatbot to the daughter's LINE account and asks her to "tell us about memories of your father."

[0991] (Application example 1)

[0992] 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."

[0993] It is difficult to provide a realistic interaction experience in a specific virtual environment or with customers, while inheriting the various values ​​and interaction styles that users developed during their lifetime.In addition, there is a lack of systems that can maintain emotional connections with users after their death and reproduce interactions in specific environments such as virtual stores.

[0994] 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.

[0995] In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages and modeling the user's writing style and values, means for constructing a chatbot using a generation AI that generates responses for the user based on the analyzed data, means for providing the constructed chatbot to a party designated by the user, means for the user to reproduce a specific response style, and means for the generated chatbot to respond in a virtual environment. This makes it possible to provide a realistic conversation experience in a designated virtual environment or in conversations with customers even after the user's death.

[0996] "User's text message" means a text message sent by a User using an information and communication terminal, including personal conversations, business correspondence, social interactions, etc.

[0997] The "means for collecting text messages" refers to a program or device for storing text messages sent from an information communication terminal in a database such as a server.

[0998] "Means for analyzing text messages and modeling a user's writing style and values" refers to an algorithm that uses natural language processing technology to extract, understand, and model a user's writing style, speech patterns, and the values ​​they value based on collected text messages.

[0999] A "means for building a chatbot using generative AI" is a program or system that feeds analyzed user data back into generative AI to create a chatbot that reproduces the user's writing style and values.

[1000] "Means for providing chatbots to people designated by users" refers to a system or service that makes the constructed chatbot available at the appropriate time to people designated by the user during their lifetime.

[1001] "Means for users to recreate a specific response style" refers to an algorithm that recreates the way a user would respond to a specific situation or person based on collected text messages.

[1002] "Means for responding in a virtual environment" refers to a program that enables the generated chatbot to respond in an online environment such as virtual reality or a virtual store while reproducing the user's writing style and response style.

[1003] To implement this invention, the following main components are required: a system that collects users' text messages, analyzes them to model their writing style and values, uses generative AI to build a chatbot, and responds in a virtual environment.

[1004] How to collect user text messages

[1005] The server collects text messages sent from the user's device (e.g., smartphone or PC). During this collection process, the server receives text messages using messaging applications (e.g., LINE or WhatsApp) and stores them in a database. For example, if a user sends a message saying, "Today, I went to the movies with a friend," the server receives this message and stores it in a database.

[1006] Text message analysis and modeling

[1007] The server analyzes the text messages stored in the database using natural language processing technology. Specifically, it uses NLP (natural language processing) libraries (such as spaCy and BERT) to extract and model the user's writing style and values. For example, the server analyzes the message "Today I went to the movies with a friend" and models the user as sociable and a movie lover.

[1008] Building a chatbot using generative AI

[1009] The server feeds user data obtained through natural language processing back to the generation AI, building a chatbot that reflects the user's unique writing style and values. The generation AI uses OpenAI's GPT-3 and other technologies. This allows the chatbot to respond in the user's writing style when asked, for example, "How was your day?", with, for example, "I went to the movies with a friend today."

[1010] Providing chatbots and responses in a virtual environment

[1011] The constructed chatbot is provided to a person specified by the user. The server can also provide the chatbot at a specified time after the user's death. Furthermore, the constructed chatbot can interact with the user in a virtual environment (for example, a VR shopping mall or online customer support), recreating the user's response style.

[1012] Specific examples

[1013] For example, text messages from store staff members' previous customer service interactions can be collected and analyzed to model their writing style and response style. The generated chatbot can respond to customers based on the prompt, "You are Staff A. We strive to provide our customers with the highest quality products. Customer: Hello, is the item in stock?"

[1014] Through the above components and processes, the system of this invention can inherit the user's values ​​and writing style from their previous life and provide a realistic conversational experience even in a specific virtual environment.

[1015] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1016] Step 1:

[1017] The server collects text messages sent from the user's device (e.g., smartphone or PC).

[1018] Input: A text message sent by a user through a messaging app.

[1019] Output: The collected text messages are stored in the server's database. The specific operation is to receive the text messages and store them in the database along with the user ID.

[1020] Step 2:

[1021] The server analyzes the text messages stored in the database using natural language processing techniques.

[1022] Input: The user's text message stored in the database.

[1023] Output: Data that models the user's writing style and values ​​as a result of the analysis. Specifically, it uses NLP libraries (e.g., spaCy, BERT) to extract writing style, keywords, sentiment, etc., and statistically models them.

[1024] Step 3:

[1025] Based on the analysis results, the server uses generative AI to build a chatbot that reflects the user's writing style and values.

[1026] Input: Data that models the user's writing style and values.

[1027] Output: A chatbot that reflects the user's writing style and values. The specific operation is to feed the analysis results into a generative AI model (e.g., OpenAI GPT-3) to train and adjust the chatbot so that it can have natural conversations.

[1028] Step 4:

[1029] The server configures the constructed chatbot so that it can respond in a virtual environment.

[1030] Input: The chatbot you built.

[1031] Output: Configuration data that enables the chatbot to operate in a virtual environment. The specific operation is to connect and place the chatbot in a virtual environment (e.g., VR shopping mall, online customer support) and enable it to respond through an appropriate interface.

[1032] Step 5:

[1033] The server provides the chatbot to the user after the user's death or to a specified person.

[1034] Input: Information about the recipient and timing of donation designated by the user during their lifetime.

[1035] Output: A chatbot provided to the specified person. The specific operation is to notify the specified person of the chatbot and make it available when the user's death is confirmed.

[1036] Step 6:

[1037] The server ensures that the chatbot responds in a virtual environment while replicating a particular interaction style.

[1038] Input: Model data for reproducing the user's response style.

[1039] Output: Natural and consistent responses in the virtual environment. The specific behavior is that the generated chatbot will conduct a dialogue based on a pre-modeled response style.

[1040] 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.

[1041] The system of the present invention supports users in planning for their end of life and provides a means for maintaining communication with their surviving family members after their death. The system collects and analyzes the user's text messages to model the user's writing style, values, and even emotions. Furthermore, it uses generative AI to build a chatbot that reflects the user's characteristics and emotions, and provides the chatbot to a contact specified by the user.

[1042] To implement the present invention, the following major components are required, including:

[1043] 1. Programs that collect users' text messages

[1044] A user uses a messaging application such as LINE from their device (e.g., a smartphone or PC) to send a text message to the server.

[1045] The server collects messages sent by users and stores them in a database.

[1046] To give a concrete example, when a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database.

[1047] 2. A program that analyzes collected text messages and models the user's writing style and values.

[1048] The text messages collected by the server are analyzed using natural language processing technology.

[1049] Based on the analysis results, the server identifies the user's writing style (e.g., polite language or casual expressions) and values ​​(e.g., valuing family, liking adventure, etc.) and models them.

[1050] As a concrete example, a server analyzes the message "I went to the movies with a friend today" and models the user as being sociable and a movie lover.

[1051] 3. Emotion engine that recognizes emotions from collected text messages

[1052] The server uses an emotion engine to analyze emotions (e.g., joy, sadness, anger, etc.) from messages sent by users.

[1053] The server stores the analyzed emotional information in a database and uses it in the next step.

[1054] For example, the server may recognize from a message such as "I went to the movies with a friend today" that the user is feeling "joy."

[1055] 4. Program for building chatbots using generative AI

[1056] The server uses the analyzed data and emotional information to feed back into an AI model to generate a chatbot that reflects the user's characteristics and emotions.

[1057] The resulting chatbot will generate natural responses based on the user's writing style, values, and even emotions.

[1058] For example, if a chatbot is asked, "How was your day today?", it will respond by mimicking the user's writing style and emotions, saying, "Today I'm going to the movies with a friend. I'm looking forward to it!"

[1059] 5. A program that provides the generated chatbot to the person specified by the user

[1060] After the user's death, the server provides the chatbot to the surviving family at a pre-specified time.

[1061] The chatbot will be set up so that bereaved family members can interact with it through the LINE app.

[1062] For example, if a user had previously requested, "When I die, please provide this chatbot to my daughter," the server would provide the chatbot to the daughter at the specified time as soon as it confirmed the user's death.

[1063] The above components allow the system of the present invention to reflect the user's own values, writing style, and even emotions in the chatbot throughout the end-of-life planning process. This allows the user to maintain an emotional connection and dialogue with their family members even after their death. This provides a valuable means for both the user and the family members to process their emotions and remember the deceased.

[1064] The processing flow will be explained below.

[1065] Step 1:

[1066] The user sends a message saying "Start creating a chatbot" using the LINE application installed on their device (e.g., a smartphone).

[1067] Step 2:

[1068] The server receives this message, records the user's LINE ID in the database, and replies to the user, "Please tell us your name and date of birth."

[1069] Step 3:

[1070] The user uses a terminal to enter "Yamada Taro, January 1, 1980" and sends it back to the server.

[1071] Step 4:

[1072] The server receives the user's name and date of birth and stores them in a database. The server then notifies the user that the settings are complete and that regular messages will be sent.

[1073] Step 5:

[1074] Every day at 7pm, the server sends a message to the user's device via LINE asking, "How was your day?"

[1075] Step 6:

[1076] The user uses the device to reply with a message saying, "I went to the movies with a friend today."

[1077] Step 7:

[1078] The server receives the user's replies and stores them in a database. The server then analyzes the collected messages using natural language processing technology to determine the user's writing style and values.

[1079] Step 8:

[1080] Based on the analysis results, the server models the user's writing style and values ​​and stores this information in a database.

[1081] Step 9:

[1082] The server analyzes the text message sent by the user using an emotion engine to recognize the user's emotions (e.g., joy, sadness, anger, etc.).

[1083] Step 10:

[1084] The server stores the emotional information obtained from the emotion engine in a database. The analyzed emotional information is also reflected in the modeling.

[1085] Step 11:

[1086] The user uses the device to set up the device to send specific LINE chat conversation history to the server as "conversations with family" or similar.

[1087] Step 12:

[1088] The server receives the conversation history with a specific partner, stores it in a database, and analyzes the relationship and unique response patterns of each partner.

[1089] Step 13:

[1090] The server uses a generative AI model to generate a chatbot that reflects the user's characteristics and emotions based on the analyzed data and emotional information. The generated chatbot then generates natural responses based on the user's writing style, values, and emotions.

[1091] Step 14:

[1092] The user or agent notifies the server of the user's death.

[1093] Step 15:

[1094] The server receives the user's death notification and notifies pre-designated family members via LINE that "you have a message from the deceased."

[1095] Step 16:

[1096] Family members can use the device to send messages to the chatbot, such as "Dad, how are you feeling today?"

[1097] Step 17:

[1098] The server inputs the message from the bereaved family into the chatbot and sends the generated response (e.g., "I feel a little lonely today, but as long as you're happy, that's all that matters") to the bereaved family.

[1099] Example 2

[1100] 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."

[1101] This invention relates to a system that generates a chatbot that reflects a user's writing style, values, and emotions based on text messages sent by the user while the user was alive, allowing the user to maintain an emotional connection with their surviving family members even after the user's death. Such a system allows the surviving family members to easily process their emotions by continuing to communicate with the user, providing a valuable means of remembering the deceased. However, existing systems face the challenge of effectively generating a chatbot that accurately reflects the user's writing style, values, and emotions.

[1102] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1103] In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages using natural language processing technology and modeling the user's writing style and values, means for recognizing emotions from the analyzed data, means for training a generative AI model based on the recognized emotions and the modeled writing style and values, means for constructing a chatbot that generates user responses using the generative AI model, and means for providing the constructed chatbot to a party designated by the user. This makes it possible to generate a chatbot that reproduces the user's characteristics and emotions and to maintain an emotional connection with the user's surviving family members after the user's death.

[1104] "User" refers to the person who uses the System and sends text messages.

[1105] "Text Message" refers to any written communication sent by a User through a Device.

[1106] "Means for collection" refers to the function or device for capturing text messages sent by users onto a server.

[1107] "Natural language processing technology" refers to computational methods and programming techniques for analyzing, understanding, and generating human language.

[1108] "Writing style" refers to the linguistic expression style used when a user sends a text message, including, for example, polite language or casual expressions.

[1109] "Values" refer to the beliefs and ways of thinking that users consider important, such as a love of family or a love of adventure.

[1110] "Means for analyzing" refers to a function or device for analyzing text messages and modeling writing styles and values.

[1111] "Emotion" refers to the state of mind expressed by a user in a text message, including, for example, joy, sadness, anger, etc.

[1112] "Means for recognizing emotions" refers to a function or device for analyzing and identifying a user's emotions from collected text messages.

[1113] A "generative AI model" refers to a machine learning model that automatically generates responses that reflect the user's writing style, values, and emotions.

[1114] "Training means" refers to the functionality or equipment that allows a generative AI model to learn based on the characteristics and emotions of the user.

[1115] A "chatbot" is a conversational agent that uses generative AI models to replicate a user's writing style, values, and emotions.

[1116] "Means for providing" refers to a function or device for delivering the constructed chatbot to a specified party in a usable state.

[1117] "Means for managing timing" refers to a function or device for managing the appropriate timing for providing the chatbot to a designated person after a user's death.

[1118] The system of the present invention supports users in planning their end of life and provides a means for maintaining communication with their surviving family members even after the user's death. The system collects and analyzes the user's text messages to model the user's writing style, values, and even emotions. Furthermore, it uses a generative AI model to build a chatbot that reflects the user's characteristics and emotions, and provides the chatbot to a contact specified by the user.

[1119] Hardware and Software Configuration

[1120] Server: The central processing unit of the system, responsible for collecting, analyzing, and modeling user text messages, training generative AI models, and building and serving chatbots.

[1121] Device: This refers to the smartphone or computer used by the user to send text messages.

[1122] Database: A data storage for saving collected text messages, analyzed data, and emotional information, using, for example, MySQL.

[1123] Natural language processing tools: Software used to analyze text messages, such as MeCab and NLTK.

[1124] Generative AI models: Machine learning models used to generate responses that reflect a user's writing style, values, and emotions, such as GPT-4.

[1125] Specific examples of program processing

[1126] 1. Text message collection

[1127] The user sends a message from their device through the LINE app saying, "Today I went to the movies with a friend."

[1128] The server receives this message using the LINE app API and stores it in a database (MySQL) in text format.

[1129] 2. Text message analysis

[1130] The message data collected by the server is analyzed using a morphological analysis tool (MeCab).

[1131] The server extracts the user's writing style (polite language, casual expressions) and values ​​(family-oriented, adventurous) from the analysis results and models them.

[1132] 3. Emotional Recognition

[1133] The server uses a natural language processing library (NLTK) to analyze the sentiment from the message.

[1134] For example, the server recognizes the emotion "joy" from the message "I went to the movies with a friend today" and stores the emotion information in a database.

[1135] 4. Building a Chatbot

[1136] The server feeds back the analyzed data (writing style, values, emotional information) to the generative AI model (GPT-4).

[1137] The server uses a generative AI model to generate a chatbot that reflects the user's writing style and emotions.

[1138] 5. Providing chatbots

[1139] Once the server confirms the user's death, it will provide the chatbot to the bereaved family at a pre-specified time.

[1140] The chatbot will be set up so that bereaved family members can smoothly communicate with it through the LINE app.

[1141] Examples of prompt statements

[1142] Prompts: Analyzing the user's writing style and values

[1143] "Parse the following message and model the user's writing style and values: 'Today I went to the movies with a friend. It was a great time.'"

[1144] Prompts: Chatbot response generation

[1145] "If a user is asked, 'How was your day?' generate a response based on the following text: 'User is a movie lover and sociable. Emotion: Joy'"

[1146] This system makes it possible to generate chatbots that replicate the characteristics and emotions of users, and to maintain an emotional connection with their surviving family members after the user's death.

[1147] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1148] Step 1: Collect text messages

[1149] 1. A user sends a text message using a messaging application such as LINE from their device (smartphone or PC).

[1150] Input: User's text message

[1151] Output: Text message sent to the server

[1152] Specific operation: The user sends a message using the LINE app saying, "Today I went to the movies with a friend."

[1153] 2. The server receives this message using the LINE app API.

[1154] Input: Text message from the LINE app

[1155] Output: Messages stored on the server

[1156] Specific operation: The server receives the message through the LINE API and saves it as text.

[1157] 3. The server stores the received message in a database.

[1158] Input: Received message

[1159] Output: Messages stored in the database

[1160] Specific operation: The server stores the received message in, for example, a MySQL database.

[1161] Step 2: Parsing the text messages

[1162] 1. The message data collected by the server is analyzed using a morphological analysis tool (MeCab).

[1163] Input: Message stored in the database

[1164] Output: Parsed message data

[1165] Specific operation: The server uses MeCab to split the message "Today I went to the movies with a friend" into words.

[1166] 2. The server extracts and models the user's writing style and values ​​from the analysis results.

[1167] Input: Parsed message data

[1168] Output: Modeled writing style and values

[1169] Specific operation: Based on the analysis results, the server models that the user writes politely and has values ​​that value family.

[1170] Step 3: Recognize emotions

[1171] 1. The server uses a natural language processing library (NLTK) to analyze the sentiment from the message.

[1172] Input: Parsed message data

[1173] Output: Recognized emotion data

[1174] Specific operation: The server uses NLTK to recognize the emotion "joy" from the message "Today I went to the movies with a friend."

[1175] 2. The server stores the analyzed emotional information in a database.

[1176] Input: Recognized emotion data

[1177] Output: Emotion data stored in a database

[1178] Specific operation: The server stores the recognized emotion data of "joy" in the database.

[1179] Step 4: Build your chatbot

[1180] 1. The server feeds back the analyzed data (writing style, values, emotional information) to the generative AI model (GPT-4).

[1181] Input: Analyzed data (style, values, sentiment)

[1182] Output: Data fed back to the AI ​​model

[1183] Specific operation: The server inputs the following data into GPT-4: "User loves movies and is sociable. Emotion: joy."

[1184] 2. The server uses the generative AI model to generate a chatbot that reflects the user's characteristics and emotions.

[1185] Input: Feedback data

[1186] Output: The generated chatbot

[1187] How it works: The server uses GPT-4 to generate a chatbot that replicates the user's writing style and emotions.

[1188] Step 5: Serve the chatbot

[1189] 1. Once the server confirms the user's death, it will provide the chatbot to the surviving family at a pre-specified time.

[1190] Input: User death confirmation and specified timing

[1191] Output: Chatbot provided to the family

[1192] Specific operation: When the server confirms the user's death, it sends a chatbot access link to the surviving family member's LINE account at the specified time.

[1193] 2. Set up the chatbot so that bereaved family members can smoothly interact with it through the LINE app.

[1194] Input: LINE account of the bereaved family

[1195] Output: A chatbot that can interact with bereaved families

[1196] Specific operation: The bereaved family member uses the LINE app and clicks on the sent link to start a conversation with the chatbot.

[1197] (Application example 2)

[1198] 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."

[1199] The problem that this invention aims to solve is not only to enable continued dialogue with surviving family members after a user's death, but also to provide a means to ensure the user's safety while they are still alive. Current chatbot systems have difficulty automatically providing responses and safety measures tailored to the user in specific situations. Therefore, there is a need for a system that can generate a chatbot that responds appropriately to the user's writing style and values, and further analyzes information related to emotions and safety to provide appropriate responses.

[1200] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages and modeling the user's writing style and values, means for constructing a chatbot using a generation AI that generates user responses based on the analyzed data, means for providing the constructed chatbot to a party specified by the user, means for analyzing the user's emotions and storing the emotion information, and means for notifying a security service when the generated chatbot detects an abnormality or danger. This enables appropriate responses and measures that take into consideration the user's emotions and safety not only after the user's death but also while the user is still alive.

[1201] A "user's text message" is text or character string data sent by a user via a communication terminal.

[1202] A "collection method" is a system or program designed to store a user's text messages on a server or database.

[1203] "Means for analysis" refers to a system or program that uses natural language processing technology to analyze collected text messages and extract the user's writing style and values.

[1204] A "style and value modeling method" is a method or algorithm that identifies a user's specific style and values ​​from the analyzed text data.

[1205] "Generative AI" is a system that uses artificial intelligence technology to generate responses based on the user's writing style and values.

[1206] A "chatbot" is a program or application designed to interact with users.

[1207] "Means for providing to designated parties" refers to the method or process by which the generated chatbot is made available to parties designated in advance by the user.

[1208] "Means for analyzing emotions" refers to algorithms or systems that read and analyze emotions from users' text messages.

[1209] The "means for storing emotional information" refers to a method or device for storing analyzed emotional data of a user in a database or the like.

[1210] "Means for notifying when an abnormality or danger is detected" refers to a system or process for alerting or notifying security services when the chatbot detects suspicious signs in a user's message.

[1211] This system collects and analyzes users' text messages, builds a chatbot that models their writing style and values, and provides the built chatbot to a person specified by the user. It also has the ability to analyze the user's emotions and notify a security service if it detects an abnormality or danger.

[1212] The system includes the following major components:

[1213] 1. How to collect user text messages

[1214] Users use messaging applications on their smartphones, PCs, or other devices to send text messages to a server, which then collects the messages and stores them in a database.

[1215] 2. A means of analyzing collected text messages and modeling users' writing style and values

[1216] The server analyzes the collected text messages using natural language processing techniques, such as using Hugging Face's Transformers library to identify and model the user's writing style (e.g., polite or casual) and values.

[1217] 3. A means of analyzing user emotions and storing emotional information

[1218] The server uses an emotion engine to analyze the emotions (e.g., joy, sadness, anger, etc.) from the message sent by the user. This emotion information is stored in a database and used in the next step.

[1219] 4. A means to build chatbots using generative AI that generates user responses based on analyzed data

[1220] The server uses the analyzed data and emotional information to feed back into an AI model that generates a chatbot that reflects the user's characteristics and emotions. The generated chatbot then generates natural responses based on the user's writing style, values, and emotions.

[1221] 5. A means to provide the created chatbot to the person specified by the user

[1222] The server provides the chatbot at the time specified by the user. For example, if the user requests that the chatbot be provided to their family after their death, the server will provide the chatbot according to that request.

[1223] 6. A method for notifying security services when the generated chatbot detects an abnormality or danger.

[1224] If the chatbot detects any suspicious signs in the user's message, it will alert or notify the security service, for example, by sending a warning email using an SMTP server.

[1225] Examples:

[1226] When a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database. An analysis program then analyzes the message and models that the user is sociable and a movie lover. An emotion engine also detects "joy" and stores this information. A generative AI model uses this data to generate a chatbot that reflects the user's unique writing style and emotions. After the user's death, this chatbot is provided to the specified recipient. If a user sends a message such as "I feel like there's a suspicious person in my house," the chatbot will detect this as a danger signal and automatically notify security services.

[1227] Example prompt sentence:

[1228] Perform sentiment analysis on the following user messages and generate appropriate responses and security warnings where necessary:

[1229] Message: "I'm feeling a bit down today."

[1230] Message: "I feel like there's someone suspicious in my house."

[1231] This allows communication to continue while taking into consideration the user's feelings and safety, both during and after the user's death.

[1232] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1233] Step 1:

[1234] A user sends a text message using a messaging application on their smartphone or computer. The server receives this message and stores it in a database. The input is the user's text message, and the output is the message data stored in the database. Specifically, the server receives the message content and stores it as a record in the database along with a timestamp.

[1235] Step 2:

[1236] The server analyzes the collected text messages and uses natural language processing techniques to identify the user's writing style and values. The input is the text message stored in the database, and the output is the analysis results that indicate the user's writing style and values. Specifically, the server analyzes the messages using Hugging Face's Transformers library and stores the resulting writing style and values ​​data in a separate database.

[1237] Step 3:

[1238] The server uses an emotion engine to detect user emotions from collected text messages. The input is the text message to be analyzed, and the output is data indicating the user's emotions. Specifically, the server runs the emotion detection algorithm and stores the emotion information in a database.

[1239] Step 4:

[1240] Based on the analyzed data and emotional information, the server uses generative AI to generate a chatbot that reproduces the user's writing style and emotions. The input is the analyzed data and emotional information, and the output is a chatbot dedicated to the user. Specifically, the server feeds the data back into the AI ​​model, training and generating a chatbot that reflects the user's writing style and emotions.

[1241] Step 5:

[1242] The server provides the generated chatbot to the person specified by the user. The input is the generated chatbot and the user's specified information, and the output is the chatbot provided to the specified person. In concrete terms, the server provides the chatbot to the specified person in the form of a link, making it accessible.

[1243] Step 6:

[1244] If the server detects an abnormality or danger through the generated chatbot, it notifies the security service. The input is a text message indicating the abnormality or danger, and the output is warning information sent to the security service. Specifically, as soon as the server detects a suspicious message, it sends a warning email to the security service via the SMTP server.

[1245] 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.

[1246] 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.

[1247] 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.

[1248] [Fourth embodiment]

[1249] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1250] 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.

[1251] 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).

[1252] 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.

[1253] 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.

[1254] 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).

[1255] 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.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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."

[1262] The system of the present invention supports users' end-of-life planning and serves as a means to maintain communication with their surviving family members after the user's death. The system collects and analyzes the user's text messages to model the user's writing style and values. It then uses generative AI to build a chatbot with the user's characteristics and provides the chatbot to a contact specified by the user.

[1263] To implement the present invention, several major components are required, including:

[1264] 1. Programs that collect users' text messages

[1265] A user uses a messaging application such as LINE from their device (e.g., a smartphone or PC) to send a text message to the server.

[1266] The server collects messages sent by users and stores them in a database.

[1267] To give a concrete example, when a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database.

[1268] 2. A program that analyzes collected text messages and models the user's writing style and values.

[1269] The text messages collected by the server are analyzed using natural language processing technology.

[1270] Based on the analysis results, the server identifies the user's writing style (e.g., polite language or casual expressions) and values ​​(e.g., valuing family, liking adventure, etc.) and models them.

[1271] As a concrete example, a server analyzes the message "I went to the movies with a friend today" and models the user as being sociable and a movie lover.

[1272] 3. Program to build chatbots using generative AI

[1273] The server uses the analyzed data to feed it back into an AI model that generates a chatbot with the user's characteristics.

[1274] The generated chatbot will generate natural responses based on the user's writing style and values.

[1275] For example, if the chatbot is asked, "How was your day today?", it will respond by mimicking the user's writing style, saying, "Today I went to the movies with a friend."

[1276] 4. A program that provides the generated chatbot to the person specified by the user

[1277] After the user's death, the server provides the chatbot to the surviving family at a pre-specified time.

[1278] The chatbot will be set up so that bereaved family members can interact with it through the LINE app.

[1279] For example, if a user had previously requested, "When I die, please provide this chatbot to my daughter," the server would provide the chatbot to the daughter at the specified time as soon as it confirmed the user's death.

[1280] With these components, the system of the present invention allows the user to reflect their own values ​​and writing style in the chatbot during their end-of-life planning, and maintain an emotional connection with their surviving family members even after their death. This provides a valuable means for both the user and their surviving family members to process their emotions and remember the deceased.

[1281] The processing flow will be explained below.

[1282] Step 1:

[1283] The user sends a message saying "Start creating a chatbot" using the LINE application installed on their device (e.g., a smartphone).

[1284] Step 2:

[1285] The server receives the message sent by the user, records the user's LINE ID in the database, and replies to the user, asking "Please tell us your name and date of birth."

[1286] Step 3:

[1287] The user uses a terminal to enter "Yamada Taro, January 1, 1980" and sends it back to the server.

[1288] Step 4:

[1289] The server receives the user's name and date of birth and stores them in a database. The server then notifies the user that the settings are complete and that regular messages will be sent.

[1290] Step 5:

[1291] Every day at 7pm, the server sends a message to the user's device via LINE asking, "How was your day?"

[1292] Step 6:

[1293] The user uses the device to reply with a message saying, "I went to the movies with a friend today."

[1294] Step 7:

[1295] The server receives the user's replies and stores them in a database. The server then analyzes the collected messages using natural language processing technology to determine the user's writing style and values.

[1296] Step 8:

[1297] Based on the analysis results, the server models the user's writing style and values ​​and stores this information in a database.

[1298] Step 9:

[1299] The user uses the device to set up the device to send specific LINE chat conversation history to the server as "conversations with family" or similar.

[1300] Step 10:

[1301] The server receives the conversation history with a specific partner, stores it in a database, and analyzes the relationship and unique response patterns of each partner.

[1302] Step 11:

[1303] Based on the analyzed data, the server uses a generative AI model to generate a chatbot that reflects the user's characteristics. The generated chatbot then generates natural responses based on the user's writing style and values.

[1304] Step 12:

[1305] The user or agent notifies the server of the user's death.

[1306] Step 13:

[1307] The server receives the user's death notification and notifies pre-designated family members via LINE that "you have a message from the deceased."

[1308] Step 14:

[1309] The bereaved family member uses the device to send a message to the chatbot asking, "Dad, what do you think?"

[1310] Step 15:

[1311] The server inputs the message from the bereaved family into the chatbot and sends the generated response (e.g., "As a father, the most important thing is that you are happy") to the bereaved family.

[1312] Example 1

[1313] 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."

[1314] In modern society, people frequently communicate digitally, but there is a problem that there is a lack of ways to convey precious memories and values ​​to bereaved families after a user passes away.In addition, there is a problem that the emotional connection with bereaved families is severed because there is no communication method that reflects the user's writing style and values ​​when they were alive.

[1315] 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.

[1316] In this invention, the server includes a means for collecting the user's text messages, a means for analyzing the collected text messages and modeling the user's writing style and values, and a means for constructing a chatbot using a generative AI that generates responses to the user based on the analyzed data. This makes it possible to continue to emotionally connect with the bereaved family even after the user has passed away through a chatbot that inherits the user's writing style and values.

[1317] "User" means an individual or legal entity that uses the System.

[1318] "Text message" refers to textual information sent by a user using a device.

[1319] "Collection" refers to the process by which the server obtains and stores text messages sent from the device.

[1320] "Analysis" is the process by which the server uses natural language processing technology to analyze the content of collected text messages and extract important information.

[1321] "Writing style" refers to the language and style of expression used by a User in their text messages.

[1322] "Values" refer to the beliefs and principles of behavior held by users.

[1323] "Modeling" is the process of abstracting the user's writing style and values ​​from the analyzed data and expressing them as a data model.

[1324] "Generative AI" refers to artificial intelligence that generates new text based on the user's characteristics.

[1325] A "chatbot" is a program that converses with a user by recreating their writing style and values.

[1326] "Terminal" refers to an electronic device used by a user (e.g., a smartphone or PC).

[1327] A "server" is a computer system that collects, analyzes, and stores text messages and generates chatbots.

[1328] A "prompt sentence" is an input sentence given to a generation AI, and is the text that the AI ​​uses to generate a response based on those instructions.

[1329] "LINE API" refers to an interface that allows external use of the functions of the messaging application LINE.

[1330] "Natural language processing technology" refers to technology that automatically analyzes text data and understands and processes human language.

[1331] The system of the present invention supports users in their end-of-life planning and serves as a means for maintaining communication with their surviving family members after their death. The system collects and analyzes the user's text messages to model the user's writing style and values. It then uses generative AI to build a chatbot with the user's characteristics and provides the chatbot to a contact specified by the user.

[1332] Hardware and software used

[1333] Server: A computer system for collecting, analyzing, and storing messages, and generating chatbots.

[1334] Device: The electronic device used by the user (e.g. smartphone, computer).

[1335] Messaging applications: LINE, etc.

[1336] Natural language processing technologies: Google Cloud NLP API, IBM Watson, etc.

[1337] Generative AI model: OpenAI GPT-4.

[1338] LINE API: An interface for using LINE functions externally.

[1339] Program processing

[1340] 1. Text message collection

[1341] A user sends a text message using a messaging application such as LINE on their device. The server receives this message via the LINE API and stores it in a database.

[1342] Example: When a user sends a message saying "I went to the movies with a friend today," the server receives this message and automatically stores it in a database.

[1343] 2. Text Message Analysis and Modeling

[1344] The server analyzes the collected text messages using natural language processing technology, and based on the analysis results, identifies the user's writing style and values ​​and creates a model of them.

[1345] Example: A server parses the message "I went to the movies with a friend today" and models the user as sociable and movie-loving.

[1346] 3. Chatbot Creation

[1347] Based on the analyzed data, the server provides feedback on the user's characteristics to the generation AI (GPT-4), which then generates natural responses based on the user's writing style and values.

[1348] Example: When asked, "How was your day?", a chatbot responds in the user's style, "Today I went to the movies with a friend."

[1349] Example prompt: "If a user sends, 'I went to the movies with a friend today,' model their writing style and values ​​around this."

[1350] "Generate a chatbot with your writing style and have it respond to the question, 'How was your day?'"

[1351] 4. Providing chatbots

[1352] Before dying, the user specifies to the server who the chatbot should be provided to.

[1353] After a user's death, the server provides the chatbot to the family at a specified time. The family can then use the LINE API to interact with the chatbot through the LINE app.

[1354] Example: A user requests, "Please provide this chatbot to my daughter after I die." After confirming the user's death, the server provides the chatbot to the daughter's LINE account, and the chatbot speaks to the daughter, saying, "Let's talk about memories of Dad."

[1355] This system allows users to maintain an emotional connection with their families even after their death through a chatbot that inherits the user's values ​​and writing style, providing a valuable tool for both the user and their family to process their emotions and remember the deceased.

[1356] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1357] Step 1:

[1358] User-submitted messages

[1359] A user uses a device (smartphone or computer), launches a messaging application such as LINE, and types and sends a text message.

[1360] Input: A text message typed by the user (e.g., "I went to the movies with a friend today").

[1361] Output: The text message sent.

[1362] Specific operation: The user types a message in the LINE app such as "Today I went to the movies with a friend" and presses the send button.

[1363] Step 2:

[1364] Server receives and stores messages

[1365] The server uses the LINE API to receive messages sent from the user's device and save them in a database.

[1366] Input: A text message sent from the user's device.

[1367] Output: The text message stored in the database.

[1368] Specific operation: The server receives a message through the LINE API and automatically saves the message "I went to the movies with a friend today" in a database.

[1369] Step 3:

[1370] Message parsing by the server

[1371] The server analyzes the stored text messages using natural language processing technology (e.g., Google Cloud NLP API or IBM Watson).

[1372] Input: A text message stored in the database.

[1373] Output: Parsed message style and value data.

[1374] Specific operation: The server analyzes the message "I went to the movies with a friend today" using natural language processing technology and identifies that the user is sociable and loves movies.

[1375] Step 4:

[1376] Analysis data modeling

[1377] The server models the user's writing style and values ​​based on the data analyzed.

[1378] Input: Parsed message stylistic and value data.

[1379] Output: A model that reflects the user's writing style and values.

[1380] Specific operation: The server generates a user model with the characteristics of being "sociable" and "movie lover."

[1381] Step 5:

[1382] Chatbot generation by the server

[1383] The server feeds user characteristics into a generative AI model (e.g., OpenAI GPT-4) to build a chatbot that generates natural responses based on the user's writing style and values.

[1384] Input: Model data that reflects the user's writing style and values.

[1385] Output: A chatbot with the user's writing style and values.

[1386] Specific operation: The server inputs the feature data "sociable" and "movie lover" into GPT-4, and generates a chatbot that reproduces the user's writing style. When the chatbot is asked "How was your day?", it responds "Today, I went to the movies with a friend."

[1387] Step 6:

[1388] Chatbot provision settings

[1389] Before dying, the user specifies to the server who the chatbot should be provided to.

[1390] Input: User-specified information (e.g., contact information for recipient, timing of provision).

[1391] Output: Provided configuration data.

[1392] Specific operation: The user enters information into the server specifying, "Please provide this chatbot to my daughter after I die."

[1393] Step 7:

[1394] Providing chatbots

[1395] After a user's death, the server provides the chatbot to the surviving family at a specified time. Using the LINE API, the server sends permission to use the chatbot to the surviving family's LINE account.

[1396] Input: Provided configuration data, user death confirmation information.

[1397] Output: A chatbot provided to the family's LINE account.

[1398] Specific operation: After the server confirms the user's death, it provides a chatbot to the daughter's LINE account and asks her to "tell us about memories of your father."

[1399] (Application example 1)

[1400] 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."

[1401] It is difficult to provide a realistic interaction experience in a specific virtual environment or with customers, while inheriting the various values ​​and interaction styles that users developed during their lifetime.In addition, there is a lack of systems that can maintain emotional connections with users after their death and reproduce interactions in specific environments such as virtual stores.

[1402] 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.

[1403] In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages and modeling the user's writing style and values, means for constructing a chatbot using a generation AI that generates responses for the user based on the analyzed data, means for providing the constructed chatbot to a party designated by the user, means for the user to reproduce a specific response style, and means for the generated chatbot to respond in a virtual environment. This makes it possible to provide a realistic conversation experience in a designated virtual environment or in conversations with customers even after the user's death.

[1404] "User's text message" means a text message sent by a User using an information and communication terminal, including personal conversations, business correspondence, social interactions, etc.

[1405] The "means for collecting text messages" refers to a program or device for storing text messages sent from an information communication terminal in a database such as a server.

[1406] "Means for analyzing text messages and modeling a user's writing style and values" refers to an algorithm that uses natural language processing technology to extract, understand, and model a user's writing style, speech patterns, and the values ​​they value based on collected text messages.

[1407] A "means for building a chatbot using generative AI" is a program or system that feeds analyzed user data back into generative AI to create a chatbot that reproduces the user's writing style and values.

[1408] "Means for providing chatbots to people designated by users" refers to a system or service that makes the constructed chatbot available at the appropriate time to people designated by the user during their lifetime.

[1409] "Means for users to recreate a specific response style" refers to an algorithm that recreates the way a user would respond to a specific situation or person based on collected text messages.

[1410] "Means for responding in a virtual environment" refers to a program that enables the generated chatbot to respond in an online environment such as virtual reality or a virtual store while reproducing the user's writing style and response style.

[1411] To implement this invention, the following main components are required: a system that collects users' text messages, analyzes them to model their writing style and values, uses generative AI to build a chatbot, and responds in a virtual environment.

[1412] How to collect user text messages

[1413] The server collects text messages sent from the user's device (e.g., smartphone or PC). During this collection process, the server receives text messages using messaging applications (e.g., LINE or WhatsApp) and stores them in a database. For example, if a user sends a message saying, "Today, I went to the movies with a friend," the server receives this message and stores it in a database.

[1414] Text message analysis and modeling

[1415] The server analyzes the text messages stored in the database using natural language processing technology. Specifically, it uses NLP (natural language processing) libraries (such as spaCy and BERT) to extract and model the user's writing style and values. For example, the server analyzes the message "Today I went to the movies with a friend" and models the user as sociable and a movie lover.

[1416] Building a chatbot using generative AI

[1417] The server feeds user data obtained through natural language processing back to the generation AI, building a chatbot that reflects the user's unique writing style and values. The generation AI uses OpenAI's GPT-3 and other technologies. This allows the chatbot to respond in the user's writing style when asked, for example, "How was your day?", with, for example, "I went to the movies with a friend today."

[1418] Providing chatbots and responses in a virtual environment

[1419] The constructed chatbot is provided to a person specified by the user. The server can also provide the chatbot at a specified time after the user's death. Furthermore, the constructed chatbot can interact with the user in a virtual environment (for example, a VR shopping mall or online customer support), recreating the user's response style.

[1420] Specific examples

[1421] For example, text messages from store staff members' previous customer service interactions can be collected and analyzed to model their writing style and response style. The generated chatbot can respond to customers based on the prompt, "You are Staff A. We strive to provide our customers with the highest quality products. Customer: Hello, is the item in stock?"

[1422] Through the above components and processes, the system of this invention can inherit the user's values ​​and writing style from their previous life and provide a realistic conversational experience even in a specific virtual environment.

[1423] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1424] Step 1:

[1425] The server collects text messages sent from the user's device (e.g., smartphone or PC).

[1426] Input: A text message sent by a user through a messaging app.

[1427] Output: The collected text messages are stored in the server's database. The specific operation is to receive the text messages and store them in the database along with the user ID.

[1428] Step 2:

[1429] The server analyzes the text messages stored in the database using natural language processing techniques.

[1430] Input: The user's text message stored in the database.

[1431] Output: Data that models the user's writing style and values ​​as a result of the analysis. Specifically, it uses NLP libraries (e.g., spaCy, BERT) to extract writing style, keywords, sentiment, etc., and statistically models them.

[1432] Step 3:

[1433] Based on the analysis results, the server uses generative AI to build a chatbot that reflects the user's writing style and values.

[1434] Input: Data that models the user's writing style and values.

[1435] Output: A chatbot that reflects the user's writing style and values. The specific operation is to feed the analysis results into a generative AI model (e.g., OpenAI GPT-3) to train and adjust the chatbot so that it can have natural conversations.

[1436] Step 4:

[1437] The server configures the constructed chatbot so that it can respond in a virtual environment.

[1438] Input: The chatbot you built.

[1439] Output: Configuration data that enables the chatbot to operate in a virtual environment. The specific operation is to connect and place the chatbot in a virtual environment (e.g., VR shopping mall, online customer support) and enable it to respond through an appropriate interface.

[1440] Step 5:

[1441] The server provides the chatbot to the user after the user's death or to a specified person.

[1442] Input: Information about the recipient and timing of donation designated by the user during their lifetime.

[1443] Output: A chatbot provided to the specified person. The specific operation is to notify the specified person of the chatbot and make it available when the user's death is confirmed.

[1444] Step 6:

[1445] The server ensures that the chatbot responds in a virtual environment while replicating a particular interaction style.

[1446] Input: Model data for reproducing the user's response style.

[1447] Output: Natural and consistent responses in the virtual environment. The specific behavior is that the generated chatbot will conduct a dialogue based on a pre-modeled response style.

[1448] 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.

[1449] The system of the present invention supports users in planning for their end of life and provides a means for maintaining communication with their surviving family members after their death. The system collects and analyzes the user's text messages to model the user's writing style, values, and even emotions. Furthermore, it uses generative AI to build a chatbot that reflects the user's characteristics and emotions, and provides the chatbot to a contact specified by the user.

[1450] To implement the present invention, the following major components are required, including:

[1451] 1. Programs that collect users' text messages

[1452] A user uses a messaging application such as LINE from their device (e.g., a smartphone or PC) to send a text message to the server.

[1453] The server collects messages sent by users and stores them in a database.

[1454] To give a concrete example, when a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database.

[1455] 2. A program that analyzes collected text messages and models the user's writing style and values.

[1456] The text messages collected by the server are analyzed using natural language processing technology.

[1457] Based on the analysis results, the server identifies the user's writing style (e.g., polite language or casual expressions) and values ​​(e.g., valuing family, liking adventure, etc.) and models them.

[1458] As a concrete example, a server analyzes the message "I went to the movies with a friend today" and models the user as being sociable and a movie lover.

[1459] 3. Emotion engine that recognizes emotions from collected text messages

[1460] The server uses an emotion engine to analyze emotions (e.g., joy, sadness, anger, etc.) from messages sent by users.

[1461] The server stores the analyzed emotional information in a database and uses it in the next step.

[1462] For example, the server may recognize from a message such as "I went to the movies with a friend today" that the user is feeling "joy."

[1463] 4. Program for building chatbots using generative AI

[1464] The server uses the analyzed data and emotional information to feed back into an AI model to generate a chatbot that reflects the user's characteristics and emotions.

[1465] The resulting chatbot will generate natural responses based on the user's writing style, values, and even emotions.

[1466] For example, if a chatbot is asked, "How was your day today?", it will respond by mimicking the user's writing style and emotions, saying, "Today I'm going to the movies with a friend. I'm looking forward to it!"

[1467] 5. A program that provides the generated chatbot to the person specified by the user

[1468] After the user's death, the server provides the chatbot to the surviving family at a pre-specified time.

[1469] The chatbot will be set up so that bereaved family members can interact with it through the LINE app.

[1470] For example, if a user had previously requested, "When I die, please provide this chatbot to my daughter," the server would provide the chatbot to the daughter at the specified time as soon as it confirmed the user's death.

[1471] The above components allow the system of the present invention to reflect the user's own values, writing style, and even emotions in the chatbot throughout the end-of-life planning process. This allows the user to maintain an emotional connection and dialogue with their family members even after their death. This provides a valuable means for both the user and the family members to process their emotions and remember the deceased.

[1472] The processing flow will be explained below.

[1473] Step 1:

[1474] The user sends a message saying "Start creating a chatbot" using the LINE application installed on their device (e.g., a smartphone).

[1475] Step 2:

[1476] The server receives this message, records the user's LINE ID in the database, and replies to the user, "Please tell us your name and date of birth."

[1477] Step 3:

[1478] The user uses a terminal to enter "Yamada Taro, January 1, 1980" and sends it back to the server.

[1479] Step 4:

[1480] The server receives the user's name and date of birth and stores them in a database. The server then notifies the user that the settings are complete and that regular messages will be sent.

[1481] Step 5:

[1482] Every day at 7pm, the server sends a message to the user's device via LINE asking, "How was your day?"

[1483] Step 6:

[1484] The user uses the device to reply with a message saying, "I went to the movies with a friend today."

[1485] Step 7:

[1486] The server receives the user's replies and stores them in a database. The server then analyzes the collected messages using natural language processing technology to determine the user's writing style and values.

[1487] Step 8:

[1488] Based on the analysis results, the server models the user's writing style and values ​​and stores this information in a database.

[1489] Step 9:

[1490] The server analyzes the text message sent by the user using an emotion engine to recognize the user's emotions (e.g., joy, sadness, anger, etc.).

[1491] Step 10:

[1492] The server stores the emotional information obtained from the emotion engine in a database. The analyzed emotional information is also reflected in the modeling.

[1493] Step 11:

[1494] The user uses the device to set up the device to send specific LINE chat conversation history to the server as "conversations with family" or similar.

[1495] Step 12:

[1496] The server receives the conversation history with a specific partner, stores it in a database, and analyzes the relationship and unique response patterns of each partner.

[1497] Step 13:

[1498] The server uses a generative AI model to generate a chatbot that reflects the user's characteristics and emotions based on the analyzed data and emotional information. The generated chatbot then generates natural responses based on the user's writing style, values, and emotions.

[1499] Step 14:

[1500] The user or agent notifies the server of the user's death.

[1501] Step 15:

[1502] The server receives the user's death notification and notifies pre-designated family members via LINE that "you have a message from the deceased."

[1503] Step 16:

[1504] Family members can use the device to send messages to the chatbot, such as "Dad, how are you feeling today?"

[1505] Step 17:

[1506] The server inputs the message from the bereaved family into the chatbot and sends the generated response (e.g., "I feel a little lonely today, but as long as you're happy, that's all that matters") to the bereaved family.

[1507] Example 2

[1508] 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."

[1509] This invention relates to a system that generates a chatbot that reflects a user's writing style, values, and emotions based on text messages sent by the user while the user was alive, allowing the user to maintain an emotional connection with their surviving family members even after the user's death. Such a system allows the surviving family members to easily process their emotions by continuing to communicate with the user, providing a valuable means of remembering the deceased. However, existing systems face the challenge of effectively generating a chatbot that accurately reflects the user's writing style, values, and emotions.

[1510] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1511] In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages using natural language processing technology and modeling the user's writing style and values, means for recognizing emotions from the analyzed data, means for training a generative AI model based on the recognized emotions and the modeled writing style and values, means for constructing a chatbot that generates user responses using the generative AI model, and means for providing the constructed chatbot to a party designated by the user. This makes it possible to generate a chatbot that reproduces the user's characteristics and emotions and to maintain an emotional connection with the user's surviving family members after the user's death.

[1512] "User" refers to the person who uses the System and sends text messages.

[1513] "Text Message" refers to any written communication sent by a User through a Device.

[1514] "Means for collection" refers to the function or device for capturing text messages sent by users onto a server.

[1515] "Natural language processing technology" refers to computational methods and programming techniques for analyzing, understanding, and generating human language.

[1516] "Writing style" refers to the linguistic expression style used when a user sends a text message, including, for example, polite language or casual expressions.

[1517] "Values" refer to the beliefs and ways of thinking that users consider important, such as a love of family or a love of adventure.

[1518] "Means for analyzing" refers to a function or device for analyzing text messages and modeling writing styles and values.

[1519] "Emotion" refers to the state of mind expressed by a user in a text message, including, for example, joy, sadness, anger, etc.

[1520] "Means for recognizing emotions" refers to a function or device for analyzing and identifying a user's emotions from collected text messages.

[1521] A "generative AI model" refers to a machine learning model that automatically generates responses that reflect the user's writing style, values, and emotions.

[1522] "Training means" refers to the functionality or equipment that allows a generative AI model to learn based on the characteristics and emotions of the user.

[1523] A "chatbot" is a conversational agent that uses generative AI models to replicate a user's writing style, values, and emotions.

[1524] "Means for providing" refers to a function or device for delivering the constructed chatbot to a specified party in a usable state.

[1525] "Means for managing timing" refers to a function or device for managing the appropriate timing for providing the chatbot to a designated person after a user's death.

[1526] The system of the present invention supports users in planning their end of life and provides a means for maintaining communication with their surviving family members even after the user's death. The system collects and analyzes the user's text messages to model the user's writing style, values, and even emotions. Furthermore, it uses a generative AI model to build a chatbot that reflects the user's characteristics and emotions, and provides the chatbot to a contact specified by the user.

[1527] Hardware and Software Configuration

[1528] Server: The central processing unit of the system, responsible for collecting, analyzing, and modeling user text messages, training generative AI models, and building and serving chatbots.

[1529] Device: This refers to the smartphone or computer used by the user to send text messages.

[1530] Database: A data storage for saving collected text messages, analyzed data, and emotional information, using, for example, MySQL.

[1531] Natural language processing tools: Software used to analyze text messages, such as MeCab and NLTK.

[1532] Generative AI models: Machine learning models used to generate responses that reflect a user's writing style, values, and emotions, such as GPT-4.

[1533] Specific examples of program processing

[1534] 1. Text message collection

[1535] The user sends a message from their device through the LINE app saying, "Today I went to the movies with a friend."

[1536] The server receives this message using the LINE app API and stores it in a database (MySQL) in text format.

[1537] 2. Text message analysis

[1538] The message data collected by the server is analyzed using a morphological analysis tool (MeCab).

[1539] The server extracts the user's writing style (polite language, casual expressions) and values ​​(family-oriented, adventurous) from the analysis results and models them.

[1540] 3. Emotional Recognition

[1541] The server uses a natural language processing library (NLTK) to analyze the sentiment from the message.

[1542] For example, the server recognizes the emotion "joy" from the message "I went to the movies with a friend today" and stores the emotion information in a database.

[1543] 4. Building a Chatbot

[1544] The server feeds back the analyzed data (writing style, values, emotional information) to the generative AI model (GPT-4).

[1545] The server uses a generative AI model to generate a chatbot that reflects the user's writing style and emotions.

[1546] 5. Providing chatbots

[1547] Once the server confirms the user's death, it will provide the chatbot to the bereaved family at a pre-specified time.

[1548] The chatbot will be set up so that bereaved family members can smoothly communicate with it through the LINE app.

[1549] Examples of prompt statements

[1550] Prompts: Analyzing the user's writing style and values

[1551] "Parse the following message and model the user's writing style and values: 'Today I went to the movies with a friend. It was a great time.'"

[1552] Prompts: Chatbot response generation

[1553] "If a user is asked, 'How was your day?' generate a response based on the following text: 'User is a movie lover and sociable. Emotion: Joy'"

[1554] This system makes it possible to generate chatbots that replicate the characteristics and emotions of users, and to maintain an emotional connection with their surviving family members after the user's death.

[1555] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1556] Step 1: Collect text messages

[1557] 1. A user sends a text message using a messaging application such as LINE from their device (smartphone or PC).

[1558] Input: User's text message

[1559] Output: Text message sent to the server

[1560] Specific operation: The user sends a message using the LINE app saying, "Today I went to the movies with a friend."

[1561] 2. The server receives this message using the LINE app API.

[1562] Input: Text message from the LINE app

[1563] Output: Messages stored on the server

[1564] Specific operation: The server receives the message through the LINE API and saves it as text.

[1565] 3. The server stores the received message in a database.

[1566] Input: Received message

[1567] Output: Messages stored in the database

[1568] Specific operation: The server stores the received message in, for example, a MySQL database.

[1569] Step 2: Parsing the text messages

[1570] 1. The message data collected by the server is analyzed using a morphological analysis tool (MeCab).

[1571] Input: Message stored in the database

[1572] Output: Parsed message data

[1573] Specific operation: The server uses MeCab to split the message "Today I went to the movies with a friend" into words.

[1574] 2. The server extracts and models the user's writing style and values ​​from the analysis results.

[1575] Input: Parsed message data

[1576] Output: Modeled writing style and values

[1577] Specific operation: Based on the analysis results, the server models that the user writes politely and has values ​​that value family.

[1578] Step 3: Recognize emotions

[1579] 1. The server uses a natural language processing library (NLTK) to analyze the sentiment from the message.

[1580] Input: Parsed message data

[1581] Output: Recognized emotion data

[1582] Specific operation: The server uses NLTK to recognize the emotion "joy" from the message "Today I went to the movies with a friend."

[1583] 2. The server stores the analyzed emotional information in a database.

[1584] Input: Recognized emotion data

[1585] Output: Emotion data stored in a database

[1586] Specific operation: The server stores the recognized emotion data of "joy" in the database.

[1587] Step 4: Build your chatbot

[1588] 1. The server feeds back the analyzed data (writing style, values, emotional information) to the generative AI model (GPT-4).

[1589] Input: Analyzed data (style, values, sentiment)

[1590] Output: Data fed back to the AI ​​model

[1591] Specific operation: The server inputs the following data into GPT-4: "User loves movies and is sociable. Emotion: joy."

[1592] 2. The server uses the generative AI model to generate a chatbot that reflects the user's characteristics and emotions.

[1593] Input: Feedback data

[1594] Output: The generated chatbot

[1595] How it works: The server uses GPT-4 to generate a chatbot that replicates the user's writing style and emotions.

[1596] Step 5: Serve the chatbot

[1597] 1. Once the server confirms the user's death, it will provide the chatbot to the surviving family at a pre-specified time.

[1598] Input: User death confirmation and specified timing

[1599] Output: Chatbot provided to the family

[1600] Specific operation: When the server confirms the user's death, it sends a chatbot access link to the surviving family member's LINE account at the specified time.

[1601] 2. Set up the chatbot so that bereaved family members can smoothly interact with it through the LINE app.

[1602] Input: LINE account of the bereaved family

[1603] Output: A chatbot that can interact with bereaved families

[1604] Specific operation: The bereaved family member uses the LINE app and clicks on the sent link to start a conversation with the chatbot.

[1605] (Application example 2)

[1606] 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."

[1607] The problem that this invention aims to solve is not only to enable continued dialogue with surviving family members after a user's death, but also to provide a means to ensure the user's safety while they are still alive. Current chatbot systems have difficulty automatically providing responses and safety measures tailored to the user in specific situations. Therefore, there is a need for a system that can generate a chatbot that responds appropriately to the user's writing style and values, and further analyzes information related to emotions and safety to provide appropriate responses.

[1608] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user text messages, means for analyzing the collected text messages and modeling the user's writing style and values, means for constructing a chatbot using a generation AI that generates user responses based on the analyzed data, means for providing the constructed chatbot to a party specified by the user, means for analyzing the user's emotions and storing the emotion information, and means for notifying a security service when the generated chatbot detects an abnormality or danger. This enables appropriate responses and measures that take into consideration the user's emotions and safety not only after the user's death but also while the user is still alive.

[1609] A "user's text message" is text or character string data sent by a user via a communication terminal.

[1610] A "collection method" is a system or program designed to store a user's text messages on a server or database.

[1611] "Means for analysis" refers to a system or program that uses natural language processing technology to analyze collected text messages and extract the user's writing style and values.

[1612] A "style and value modeling method" is a method or algorithm that identifies a user's specific style and values ​​from the analyzed text data.

[1613] "Generative AI" is a system that uses artificial intelligence technology to generate responses based on the user's writing style and values.

[1614] A "chatbot" is a program or application designed to interact with users.

[1615] "Means for providing to designated parties" refers to the method or process by which the generated chatbot is made available to parties designated in advance by the user.

[1616] "Means for analyzing emotions" refers to algorithms or systems that read and analyze emotions from users' text messages.

[1617] The "means for storing emotional information" refers to a method or device for storing analyzed emotional data of a user in a database or the like.

[1618] "Means for notifying when an abnormality or danger is detected" refers to a system or process for alerting or notifying security services when the chatbot detects suspicious signs in a user's message.

[1619] This system collects and analyzes users' text messages, builds a chatbot that models their writing style and values, and provides the built chatbot to a person specified by the user. It also has the ability to analyze the user's emotions and notify a security service if it detects an abnormality or danger.

[1620] The system includes the following major components:

[1621] 1. How to collect user text messages

[1622] Users use messaging applications on their smartphones, PCs, or other devices to send text messages to a server, which then collects the messages and stores them in a database.

[1623] 2. A means of analyzing collected text messages and modeling users' writing style and values

[1624] The server analyzes the collected text messages using natural language processing techniques, such as using Hugging Face's Transformers library to identify and model the user's writing style (e.g., polite or casual) and values.

[1625] 3. A means of analyzing user emotions and storing emotional information

[1626] The server uses an emotion engine to analyze the emotions (e.g., joy, sadness, anger, etc.) from the message sent by the user. This emotion information is stored in a database and used in the next step.

[1627] 4. A means to build chatbots using generative AI that generates user responses based on analyzed data

[1628] The server uses the analyzed data and emotional information to feed back into an AI model that generates a chatbot that reflects the user's characteristics and emotions. The generated chatbot then generates natural responses based on the user's writing style, values, and emotions.

[1629] 5. A means to provide the created chatbot to the person specified by the user

[1630] The server provides the chatbot at the time specified by the user. For example, if the user requests that the chatbot be provided to their family after their death, the server will provide the chatbot according to that request.

[1631] 6. A method for notifying security services when the generated chatbot detects an abnormality or danger.

[1632] If the chatbot detects any suspicious signs in the user's message, it will alert or notify the security service, for example, by sending a warning email using an SMTP server.

[1633] Examples:

[1634] When a user sends a message such as "I went to the movies with a friend today," the server receives this message and stores it in a database. An analysis program then analyzes the message and models that the user is sociable and a movie lover. An emotion engine also detects "joy" and stores this information. A generative AI model uses this data to generate a chatbot that reflects the user's unique writing style and emotions. After the user's death, this chatbot is provided to the specified recipient. If a user sends a message such as "I feel like there's a suspicious person in my house," the chatbot will detect this as a danger signal and automatically notify security services.

[1635] Example prompt sentence:

[1636] Perform sentiment analysis on the following user messages and generate appropriate responses and security warnings where necessary:

[1637] Message: "I'm feeling a bit down today."

[1638] Message: "I feel like there's someone suspicious in my house."

[1639] This allows communication to continue while taking into consideration the user's feelings and safety, both during and after the user's death.

[1640] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1641] Step 1:

[1642] A user sends a text message using a messaging application on their smartphone or computer. The server receives this message and stores it in a database. The input is the user's text message, and the output is the message data stored in the database. Specifically, the server receives the message content and stores it as a record in the database along with a timestamp.

[1643] Step 2:

[1644] The server analyzes the collected text messages and uses natural language processing techniques to identify the user's writing style and values. The input is the text message stored in the database, and the output is the analysis results that indicate the user's writing style and values. Specifically, the server analyzes the messages using Hugging Face's Transformers library and stores the resulting writing style and values ​​data in a separate database.

[1645] Step 3:

[1646] The server uses an emotion engine to detect user emotions from collected text messages. The input is the text message to be analyzed, and the output is data indicating the user's emotions. Specifically, the server runs the emotion detection algorithm and stores the emotion information in a database.

[1647] Step 4:

[1648] Based on the analyzed data and emotional information, the server uses generative AI to generate a chatbot that reproduces the user's writing style and emotions. The input is the analyzed data and emotional information, and the output is a chatbot dedicated to the user. Specifically, the server feeds the data back into the AI ​​model, training and generating a chatbot that reflects the user's writing style and emotions.

[1649] Step 5:

[1650] The server provides the generated chatbot to the person specified by the user. The input is the generated chatbot and the user's specified information, and the output is the chatbot provided to the specified person. In concrete terms, the server provides the chatbot to the specified person in the form of a link, making it accessible.

[1651] Step 6:

[1652] If the server detects an abnormality or danger through the generated chatbot, it notifies the security service. The input is a text message indicating the abnormality or danger, and the output is warning information sent to the security service. Specifically, as soon as the server detects a suspicious message, it sends a warning email to the security service via the SMTP server.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1658] 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.

[1659] 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).

[1660] 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.

[1661] 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."

[1662] 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.

[1663] 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).

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] 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.

[1674] The following is further disclosed regarding the above embodiment.

[1675] (Claim 1)

[1676] a means for collecting users' text messages;

[1677] A means of analyzing collected text messages and modeling the user's writing style and values;

[1678] A means for building a chatbot using generative AI that generates user responses based on the analyzed data;

[1679] A means for providing the constructed chatbot to a person designated by the user;

[1680] A system including:

[1681] (Claim 2)

[1682] A means for a user to provide a conversation history with a particular interlocutor;

[1683] A means of analyzing the provided conversation history and learning the relationship and unique response patterns of each interlocutor.

[1684] The system of claim 1 further comprising:

[1685] (Claim 3)

[1686] A way to control when the chatbot will be made available to designated people after a user's death;

[1687] A means to provide chatbots at the right time, and

[1688] The system of claim 1 further comprising:

[1689] "Example 1"

[1690] (Claim 1)

[1691] a means for collecting users' text messages;

[1692] A means of analyzing collected text messages and modeling the user's writing style and values;

[1693] A means for building a chatbot using generative AI that generates user responses based on the analyzed data;

[1694] A means for providing the constructed chatbot to a person designated by the user;

[1695] A means for a user to send a message from a terminal, and a server to receive the message and store it in a database;

[1696] A means for analyzing text messages collected by a server using natural language processing technology to identify and model the user's writing style and values;

[1697] The server provides feedback on the user's characteristics to the AI ​​generator, allowing it to generate natural responses that match the user's writing style.

[1698] A method to provide a chatbot to a designated person after a user's death using the LINE API based on the user's instructions while they were alive, and

[1699] A system including:

[1700] (Claim 2)

[1701] A means for a user to provide a conversation history with a particular interlocutor;

[1702] A means of analyzing the provided conversation history and learning the relationship and unique response patterns of each interlocutor.

[1703] The system of claim 1 further comprising:

[1704] (Claim 3)

[1705] A way to control when the chatbot will be made available to designated people after a user's death;

[1706] A means to provide chatbots at the right time, and

[1707] The system of claim 1 further comprising:

[1708] "Application Example 1"

[1709] (Claim 1)

[1710] a means for collecting users' text messages;

[1711] A means of analyzing collected text messages and modeling the user's writing style and values;

[1712] A means for building a chatbot using generative AI that generates user responses based on the analyzed data;

[1713] A means for providing the constructed chatbot to a person designated by the user;

[1714] A way for users to replicate a particular style of interaction;

[1715] a means for the generated chatbot to respond in the virtual environment;

[1716] A system including:

[1717] (Claim 2)

[1718] A means for a user to provide a conversation history with a particular interlocutor;

[1719] A means of analyzing the provided conversation history and learning the relationship and unique response patterns of each interlocutor.

[1720] The system of claim 1 further comprising:

[1721] (Claim 3)

[1722] A way to control when the chatbot will be made available to designated people after a user's death;

[1723] A means to provide chatbots at the right time, and

[1724] The system of claim 1 further comprising:

[1725] "Example 2: Combining Emotion Engines"

[1726] (Claim 1)

[1727] a means for collecting users' text messages;

[1728] A method for analyzing collected text messages using natural language processing technology to model the user's writing style and values;

[1729] A means for recognizing emotions from the analyzed data;

[1730] A means for training a generative AI model based on the recognized emotions and modeled writing styles and values; and

[1731] A means for building a chatbot that uses a generative AI model to generate user responses;

[1732] A means for providing the constructed chatbot to a person designated by the user;

[1733] A system including:

[1734] (Claim 2)

[1735] A means for a user to provide a conversation history with a particular interlocutor;

[1736] A means of analyzing the provided conversation history and learning the relationship and unique response patterns of each interlocutor.

[1737] The system of claim 1 further comprising:

[1738] (Claim 3)

[1739] A way to control when the chatbot will be made available to designated people after a user's death;

[1740] A means to provide chatbots at the right time, and

[1741] The system of claim 1 further comprising:

[1742] "Application example 2 when combining emotion engines"

[1743] (Claim 1)

[1744] a means for collecting users' text messages;

[1745] A means of analyzing collected text messages and modeling the user's writing style and values;

[1746] A means for building a chatbot using generative AI that generates user responses based on the analyzed data;

[1747] A means for providing the constructed chatbot to a person designated by the user;

[1748] A means for analyzing user emotions and storing emotional information;

[1749] A means for the generated chatbot to notify the security service when it detects an abnormality or danger,

[1750] A system including:

[1751] (Claim 2)

[1752] A means for a user to provide a conversation history with a particular interlocutor;

[1753] A means of analyzing the provided conversation history and learning the relationship and unique response patterns of each interlocutor.

[1754] The system of claim 1 further comprising:

[1755] (Claim 3)

[1756] A way to control when the chatbot will be made available to designated people after a user's death;

[1757] A means to provide chatbots at the right time, and

[1758] The system of claim 1 further comprising: [Explanation of symbols]

[1759] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting users' text messages; A means of analyzing collected text messages and modeling the user's writing style and values; A means for building a chatbot using generative AI that generates user responses based on the analyzed data; A means for providing the constructed chatbot to a person designated by the user; A system including:

2. A means for a user to provide a conversation history with a particular interlocutor; A means of analyzing the provided conversation history and learning the relationship and unique response patterns of each interlocutor. The system of claim 1 further comprising:

3. A way to control when the chatbot will be made available to designated people after a user's death; A means to provide chatbots at the right time, and The system of claim 1 further comprising:

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