system

Abstract

Provide a system. [Solution] a means for collecting digital data relating to the deceased; means for training a generative AI model based on the digital data; A means of recreating interactions with the deceased using a trained generative AI model; and means for enabling a user to interact with the deceased person using the generative AI model; A system including a means for centrally managing the digital data and organizing digital remains.

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 intense stress and sense of loss caused by bereavement place a long-term emotional burden on many people. Managing digital data related to the deceased is also difficult, and many problems arise in organizing their digital belongings. These issues are expected to become even more serious as the digital generation reaches the end of its lifespan. Therefore, there is a need for an effective system that allows for efficient organization of digital belongings while enabling reunions with the deceased. [Means for solving the problem]

[0005] This invention provides a means for collecting digital data about the deceased and training a generative AI model based on that data. The generative AI model learns the characteristics of the deceased using natural language processing and image recognition technologies. A system is constructed that allows users to recreate conversations with the deceased using the trained model. It also provides a means for centrally managing digital data and enabling the organization of digital remains. Specifically, it proposes a system that uploads, stores, and manages digital data and uses it to generate realistic conversations.

[0006] "Digital data" refers to any form of electronically stored information, including text, images, audio, and video.

[0007] A "generative AI model" refers to an artificial intelligence algorithm that has the ability to learn from data and generate new data.

[0008] "Natural language processing technology" refers to technology that allows computers to process, understand, and generate human language.

[0009] "Image recognition technology" refers to technology for extracting and recognizing specific information and features from image data.

[0010] "Centralized management" refers to the centralized management of distributed information and resources in one system or location.

[0011] "Dialogue" refers to verbal exchange or communication between two parties.

[0012] "Personal effects" refers to items and data that the deceased owned during their lifetime.

[0013] "Terminal" refers to an electronic device that allows a user to access and operate the system.

[0014] A "server" refers to a computer system on a network that provides services and data to a large number of clients. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. The system provides a means for users to reunite with their loved ones and assists in the organization of their digital remains.

[0037] Data collection implementation

[0038] Data Collection Module

[0039] A user logs in to their device and uploads photos, videos, message history, etc. related to the deceased. The server receives this data and temporarily stores it. The server then checks the file format and stores it in the appropriate format in the database accordingly. For example, photo data is stored in the image database, and text data is classified as a text database.

[0040] Data Learning Embodiment

[0041] Data Learning Module

[0042] The server uses the digital data stored in the database to train the generative AI model. Specifically, it uses natural language processing and image recognition technologies to learn the deceased's speaking style and facial expressions. This learning process allows the generative AI model to realistically reproduce the characteristics of the deceased. Once trained, the generative AI model is stored in dedicated storage.

[0043] Communication reproduction embodiment

[0044] Communication Reproduction Module

[0045] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device.

[0046] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[0047] Digital legacy management implementation example

[0048] Digital Legacy Management Module

[0049] Users log in to the server via their device and access all digital data related to the deceased. This allows them to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. In addition, they can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[0050] As described above, this system collects digital data about the deceased, trains a generative AI model based on that data, and recreates conversations with the deceased. It also centralizes digital data management and supports the organization and management of digital belongings, allowing users to reunite with their loved ones and provide emotional care.

[0051] The processing flow will be explained below.

[0052] Data collection module processing flow

[0053] Step 1:

[0054] The user launches the application on the device and accesses the login screen. The user enters the username and password and presses the login button.

[0055] Step 2:

[0056] The server receives the user's authentication information, checks it against the database, and if authentication is successful, redirects the user to the dashboard.

[0057] Step 3:

[0058] The user selects the deceased's digital data (photos, videos, message history, etc.) through the device interface. The user selects the files using a file selection dialog.

[0059] Step 4:

[0060] To upload the selected digital data, the user clicks the upload button, and the terminal transmits the selected file to the server.

[0061] Step 5:

[0062] The server temporarily stores the received digital data, determines the file format, and stores it in the appropriate database (image database, text database, etc.) depending on the file format.

[0063] Data Learning Module Processing Flow

[0064] Step 1:

[0065] The server retrieves the digital data of the deceased person stored in the database. The retrieved data is categorized into image files, text files, etc.

[0066] Step 2:

[0067] The server pre-processes the acquired data and converts it into a format suitable for input to the generative AI model: for example, image files are standardized in resolution, and text files are tokenized.

[0068] Step 3:

[0069] The server then uses the pre-processed data to begin the process of training a generative AI model, using natural language processing and image recognition techniques.

[0070] Step 4:

[0071] The server stores the trained generative AI model in storage, which has the ability to reproduce the features and speaking style of the deceased.

[0072] Processing flow of the communication reproduction module

[0073] Step 1:

[0074] The user accesses an interface on the terminal to initiate a dialogue with the deceased person. The user clicks a button to begin the conversation.

[0075] Step 2:

[0076] The device sends a request to the server to initiate a dialogue with the deceased person, and the server accepts the request and starts the dialogue session.

[0077] Step 3:

[0078] The user enters a message in the text box on the terminal and presses the send button. The terminal then sends this message to the server.

[0079] Step 4:

[0080] The server inputs the received message into a generative AI model to generate a response in the deceased person's voice, which is then sent back to the server.

[0081] Step 5:

[0082] The server sends the generated response to the user's terminal, where the user checks the response message from the deceased.

[0083] Digital Legacy Management Module Processing Flow

[0084] Step 1:

[0085] The user accesses the digital estate management interface on their device and clicks a button to view all digital data related to the deceased.

[0086] Step 2:

[0087] The device sends a request to access the digital remains to the server, which accepts the request and retrieves the relevant data from the database.

[0088] Step 3:

[0089] The server processes the digital data retrieved as search results to display them to the user in a visually understandable format, for example, images are displayed as thumbnails and message histories are displayed in a list format.

[0090] Step 4:

[0091] Users can view and organize their digital belongings on their devices, searching for data, sorting it into folders, and deleting it as needed.

[0092] Step 5:

[0093] The server updates the database based on user actions, e.g., deleted data is removed from the database, data moved to a different category is saved in a new folder, etc.

[0094] Example 1

[0095] 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."

[0096] While users can reminisce about their memories with the deceased, it was difficult to recreate the experience of interacting with the deceased. Furthermore, there was a lack of efficient ways to manage and organize the vast amount of digital data of the deceased, which required users to manually organize it. Additionally, managing the authentication information for the deceased's online accounts was difficult.

[0097] 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.

[0098] In this invention, the server includes a means for users to upload digital data via their terminals and for the server to store the data in a database, a means for training a generative AI model based on the digital data, and a means for recreating a conversation with the deceased using the trained generative AI model. This allows users to enjoy a realistic conversation experience with the deceased while efficiently managing and organizing the deceased's digital data. Furthermore, authentication information for the deceased's online accounts can also be centrally managed, facilitating the preparations for the deceased's digital legacy.

[0099] A "server" is a central processing unit that stores, processes, and shares data over a network.

[0100] A "terminal" is a device operated by a user, and includes a personal computer, a smartphone, etc.

[0101] A "user" is an individual who uses this system and has a relationship with the deceased.

[0102] "Digital data" refers to information stored electronically, including files such as photos, videos, and message histories.

[0103] A "database" is a system for efficiently storing, managing, and searching digital data.

[0104] A "generative AI model" is an algorithm that is trained using machine learning techniques to perform a specific task.

[0105] "Natural language processing technology" is a technology that enables computers to understand, generate, and analyze human language.

[0106] "Image recognition technology" is a technology that allows computers to analyze images and identify specific features or objects.

[0107] The "interactive experience" is an experience in which users can communicate with the deceased through a generative AI model.

[0108] "Digital legacy" refers to digital data and online account information left behind by a deceased person.

[0109] "Centralized management" refers to the integrated management of multiple digital data using a single system.

[0110] "Authentication information" refers to information such as ID and password required by a user to access an online account.

[0111] "Digital end-of-life planning" is the process of organizing and managing the digital data and online accounts left behind by the deceased.

[0112] This invention is a system that collects digital data about the deceased and trains a generative AI model based on that data. Specific embodiments of the system are described below.

[0113] Data collection implementation

[0114] Data Collection Module

[0115] First, the user enters authentication information to log in to the device, which can be a PC or smartphone. The server receives this authentication information and checks it against a database. If authentication is successful, the user is able to upload digital data.

[0116] Users upload photos, videos, message history, etc. related to the deceased person from their devices. The server receives this data and temporarily stores it in cloud storage. The server checks the file format and stores image data in an image database and text data in a text database.

[0117] The hardware used includes general PCs and smartphones, and the servers are cloud servers (such as AWS (registered trademark) and Google (registered trademark) Cloud), while the database systems used are MySQL (registered trademark) and PostgreSQL.

[0118] Data Learning Embodiment

[0119] Data Learning Module

[0120] The server retrieves the digital data stored in the database, processes it, and stores it in a workspace. The collected data is used to train a generative AI model (for example, using TENSORFLOW (registered trademark) or PyTorch). This process uses natural language processing techniques (such as BERT) and image recognition techniques (such as CNN).

[0121] During the learning process, the system uses text data to learn the deceased's speaking style and image data to learn their facial expressions and gestures. Once trained, the generated AI model is saved in the "trained_models" folder.

[0122] Communication reproduction embodiment

[0123] Communication Reproduction Module

[0124] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response based on the user's input. The generated response is then sent to the user's device via the server. This allows the user to enjoy a real-time dialogue experience with the deceased on their device.

[0125] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[0126] Digital legacy management implementation example

[0127] Digital Legacy Management Module

[0128] Users can log in to the server via their devices and access the deceased's digital data. This allows them to search for specific message history and view photos in album format. Furthermore, they can also manage the deceased's online account authentication information, making it easier to organize their digital belongings.

[0129] The hardware used includes PCs and smartphones, and cloud servers (AWS, Google Cloud, etc.) are used. MySQL and PostgreSQL are used as database management systems.

[0130] (Example of a prompt)

[0131] "Recreate the conversational style of the deceased person based on their message history, and generate answers that sound like the deceased person to the user's questions."

[0132] The above is a specific embodiment for carrying out the present invention.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1: User authentication

[0135] The user enters authentication information to log in to the terminal.

[0136] The server receives the authentication information and checks it against a database.

[0137] (Input) The ID and password entered by the user from the terminal.

[0138] (Processing) The server references the database and searches for records that match the input information.

[0139] (Output) If authentication is successful, the user is redirected to the data upload screen. If not, an error message is displayed.

[0140] (Specific operation) The user enters their ID and password into the login form on the terminal and presses the "Login" button. The server verifies the information in the database and returns the authentication result.

[0141] Step 2: Upload data

[0142] Users upload digital data about the deceased, such as photos, videos, and message history, from their devices.

[0143] The server receives this data and temporarily stores it in storage.

[0144] (Input) Digital data (file) selected by the user from the terminal.

[0145] (Processing) The server checks the file format and saves it in temporary storage.

[0146] (Output) A message notifying you that the data was saved successfully.

[0147] (Specific operation) The user clicks the upload button and selects a specific file from their PC. The selected file is uploaded to the server and saved in temporary storage.

[0148] Step 3: Check the data format and save it to the database

[0149] The server checks the format of the uploaded file.

[0150] Image data is stored in an image database, and text data is stored in a text database.

[0151] (Input) Digital data stored in temporary storage.

[0152] (Processing) The server checks the file extension, etc., and classifies and stores it in the appropriate database.

[0153] (Output) Message indicating completion of saving to the database.

[0154] (Specific operation) The server stores .jpg files in the image database and .txt files in the text database.

[0155] Step 4: Collecting data for training

[0156] The server collects data from an image database and a text database.

[0157] The collected data is stored in a temporary workspace.

[0158] (Input) Data stored in the image database and text database.

[0159] (Processing) The server retrieves the data using the specified query and moves it to the workspace.

[0160] (Output) The training data in the workspace.

[0161] (Specific operation) The server retrieves a specific number of photos and message history from the database and moves them to the workspace.

[0162] Step 5: Training the generative AI model

[0163] The server uses the data in the workspace to train a generative AI model.

[0164] It uses natural language processing technology and image recognition technology.

[0165] (Input) Digital data in the workspace.

[0166] The (processing) server analyzes the data using natural language processing models (such as BERT) and image recognition models (such as CNN) and trains the models.

[0167] (Output) A trained generative AI model.

[0168] (Specific operation) The server uses the BERT model to train text data and the CNN model to train image data. It then performs calculations to improve the accuracy of the model.

[0169] Step 6: Save the trained model

[0170] Once trained, the generative AI model is stored in dedicated storage.

[0171] (Input) A fully trained generative AI model.

[0172] (Processing) The server saves the trained model in storage.

[0173] (Output) Completion notification for the saved model.

[0174] (Specific operation) The server saves the generated AI model that has completed learning in the "trained_models" folder.

[0175] Step 7: Sending a conversation-initiating request

[0176] The user sends a dialogue start request to the server via the terminal.

[0177] (Input) A request from the user to start a conversation.

[0178] (Processing) The server receives the request and transitions to interactive mode.

[0179] (Output) Notification of conversation start.

[0180] (Specific operation) The user clicks the "talk to the deceased" button in the chat application, and the server processes the request.

[0181] Step 8: Response Generation

[0182] The server uses a trained generative AI model to generate a response based on the user's input.

[0183] (Input) A message from the user.

[0184] (Processing) The server uses the generative AI model to generate a response appropriate to the user's message.

[0185] (Output) The generated response message.

[0186] (Specific operation) When a user sends a message saying, "What are you doing now?", the server uses the generative AI model to generate a response saying, "I'm just taking a walk."

[0187] Step 9: Sending a Response

[0188] The generated response is sent to the user's terminal via the server.

[0189] (Input) The response generated by the generative AI model.

[0190] (Processing) The server sends the response to the user's terminal.

[0191] (Output) The response message displayed on the user's terminal.

[0192] (Specific operation) The server generates a message saying "I'm just taking a walk" and sends it to the chat application, which displays it on the user's device.

[0193] Step 10: Organizing and managing your digital legacy

[0194] Users log into the server via a terminal and access all digital data relating to the deceased.

[0195] (Input) User authentication information.

[0196] (Processing) The server authenticates and allows access to the digital data.

[0197] (Output) Display of digital data about the deceased.

[0198] (Specific operation) A user logs in on a terminal and accesses a dashboard of digital data.

[0199] Step 11: Search and view specific digital data

[0200] Users can search for specific message history and view photos in album format.

[0201] (Input) The user's search query.

[0202] The (processing) server searches the database and retrieves the relevant data.

[0203] (Output) Search results and photos in album format.

[0204] (Specific operation) The user types "travel" in the search box to display a list of relevant message history. Also, the user clicks the album tab to view photos.

[0205] Step 12: Manage your online account credentials

[0206] The server manages the IDs and passwords of the deceased person's online accounts.

[0207] (Enter) The deceased person's online account information.

[0208] The (processing) server securely stores and manages information.

[0209] (Output) Secure storage and management status of credentials.

[0210] (Specific operation) The user registers the deceased person's online account information on the server, and the server encrypts and stores this information.

[0211] These are the specific processing steps of the program for this system. Each step shows the specific operations and inputs / outputs of the user, terminal, and server.

[0212] (Application example 1)

[0213] 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."

[0214] Systems already exist that collect and manage digital data about the deceased and use generative AI models to recreate conversations with the deceased. However, no system exists that applies this technology to virtual stores, allowing users to enjoy shopping in a virtual space with the deceased. Such a system would allow users to enjoy new experiences while reminiscing on memories spent with the deceased, and would also efficiently organize digital belongings. Therefore, there is a need for a system that allows users to reunite with the deceased while also providing new experiences in a virtual space.

[0215] 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.

[0216] In this invention, the server includes means for collecting digital data about the deceased, means for training a generative AI model based on the digital data, means for recreating a conversation with the deceased using the trained generative AI model, means for enabling a user to converse with the deceased using the generative AI model, means for enabling the user to have the deceased recommend products to the user in a virtual space, and means for centrally managing the digital data and organizing the digital remains. This allows users to not only relive conversations with the deceased but also enjoy a shopping experience in a virtual space with the deceased.

[0217] "Digital data" refers to information stored or collected electronically, including photographs, videos, text messages, audio data, and other forms of multimedia.

[0218] A "generative AI model" is an artificial intelligence model trained using machine learning technology that has the ability to learn the characteristics of the deceased based on the digital data provided and reproduce their dialogue.

[0219] "Dialogue reproduction" is a process in which a user simulates a conversation with a deceased person, and refers to the generative AI model mimicking the deceased's tone of voice and facial expressions to respond to the user.

[0220] A "virtual space" is a computer-simulated, imaginary, three-dimensional environment within which a user can visually and interactively interact.

[0221] "Product introduction" refers to the process by which a deceased person uses a generative AI model to provide a user with a description or opinion about a product or service.

[0222] "Digital legacy" refers to all data in digital format that the deceased created, stored, and shared during their lifetime, including message history, photos, videos, and online account information.

[0223] MODE FOR CARRYING OUT THE INVENTION

[0224] A specific system for implementing the invention includes the following elements:

[0225] 1. Data Collection Module

[0226] The device provides an interface that allows users to upload digital data related to the deceased, such as photos, videos, and message history. When a user sends the digital data from the device to the server, the server temporarily stores the data, checks the file format, and then stores it in the appropriate database. For example, photo data is stored in an image database, and text data is classified as a text database.

[0227] 2. Data Learning Module

[0228] The server uses the digital data stored in the database to train the generative AI model. Using natural language processing and image recognition technologies with the TensorFlow library, the model learns the deceased's speaking style and facial expressions. This training process allows the generative AI model to realistically reproduce the characteristics of the deceased.

[0229] 3. Communication Reproduction Module

[0230] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device. For example, if the user sends a message from their device saying, "What are you doing now?", the server uses the generative AI model to generate a response such as "I'm just taking a walk," which is then displayed on the user's device. An example prompt is "The user said: What are you doing now? Your response:."

[0231] 4. Virtual Space Module

[0232] Users can enjoy shopping while interacting with the deceased in a virtual space. Specifically, when a user asks a question about a specific product, a generative AI model that mimics the deceased's style will explain the product. For example, if a user asks, "What do you think of this dress?", the generative AI model will respond, "It's very nice. It looks great on you." An example prompt is, "The user said: What do you think of this dress? Your response:"

[0233] 5. Digital Legacy Management Module

[0234] Users can log in via their device and access all digital data related to the deceased. Users can centrally manage this data and easily organize their digital belongings. For example, they can search for specific message history or view photos in album format. They can also manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[0235] This allows users to not only realistically recreate conversations with their deceased loved ones, but also enjoy a virtual shopping experience with them while efficiently organizing and managing their digital belongings.

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] DETAILED DESCRIPTION OF THE INVENTION - PROCESS STEPS

[0238] Step 1: Upload your digital data

[0239] Users use their devices to upload digital data about the deceased, such as photos, videos, and message history. The devices then send this data to a server. The input is the digital data provided by the user, and the output is temporary storage of the data by the server.

[0240] Specific operation: The user clicks the "Upload" button on the terminal application screen, selects a file, and sends it to the server.

[0241] Step 2: Check the format of the data and save it

[0242] The server checks the format of the uploaded data and stores it in the database in the appropriate format. The input is the digital data sent from the terminal, and the output is the data stored in the database.

[0243] Specific operation: The server checks the file format of the received data, classifies and stores the photo data in the image database and the text data in the text database.

[0244] Step 3: Training the generative AI model

[0245] The server uses the digital data stored in the database to train the generative AI model, with the input being the digital data in the database and the output being the trained generative AI model.

[0246] How it works: The server uses the TensorFlow library to perform image recognition and natural language processing techniques, learning the speech style and facial expressions of the deceased.

[0247] Step 4: Receiving an interaction request

[0248] The user sends a request to the server via a terminal to start a dialogue with the deceased. The input is the dialogue request from the user, and the output is the request received by the server.

[0249] Specific operation: The user clicks the "Start conversation" button on the application screen to send a conversation request to the server.

[0250] Step 5: Creating and Executing Dialogues

[0251] The server uses the trained generative AI model to generate a response from the deceased in response to the user's input. The input is a message from the user, and the output is a response from the generative AI model.

[0252] Specific operation: When a user sends a message saying "What are you doing now?", the server uses the generative AI model to generate a response saying "Just taking a walk" and sends it to the user's device.

[0253] Step 6: Interact in the virtual space

[0254] The user interacts with the deceased through a generative AI model in a virtual space, introducing products to each other. The input is the user's question, and the output is a product introduction by the generative AI model.

[0255] Specific operation: When a user asks in the virtual space, "What do you think of this dress?", the generative AI model replies, "It's very nice. It looks good on you."

[0256] Step 7: Organizing your digital legacy

[0257] Users access and organize their digital remains through a terminal. Input is search keywords and operations for organization, and output is a display of the organized data.

[0258] Specific operation: When a user searches for "view photos from 2022," the server retrieves the relevant photo data from the database and displays it on the device.

[0259] This processing step enables the recreation of conversations with the deceased and the experience of shopping in a virtual space, enabling the efficient management of digital belongings.

[0260] 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.

[0261] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. It also includes a combined emotion engine that recognizes user emotions and optimizes dialogue and display content accordingly. This system provides a means for users to reunite with their deceased loved ones and assists in the organization of their digital legacy.

[0262] Data collection implementation

[0263] Data Collection Module

[0264] A user logs in to their device and uploads photos, videos, message history, etc. related to the deceased. The server receives this data and temporarily stores it. The server then checks the file format and stores it in the appropriate format in the database accordingly. For example, photo data is stored in the image database, and text data is classified as a text database.

[0265] Data Learning Embodiment

[0266] Data Learning Module

[0267] The server uses the digital data stored in the database to train the generative AI model. Specifically, it uses natural language processing and image recognition technologies to learn the deceased's speaking style and facial expressions. This learning process allows the generative AI model to realistically reproduce the characteristics of the deceased. Once trained, the generative AI model is stored in dedicated storage.

[0268] Communication reproduction embodiment

[0269] Communication Reproduction Module

[0270] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device.

[0271] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[0272] Embodiment of Emotion Engine

[0273] Emotion Recognition Module

[0274] The server receives the text and voice data entered by the user and analyzes the user's emotions using an emotion engine. The emotion engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state from their speech and facial expressions. For example, if a user enters "I'm not feeling very good today" in a sad voice, the emotion engine will recognize that the user is in a "sad" state.

[0275] Optimizing dialogue based on emotions

[0276] Emotion-based communication reproduction module

[0277] The server adjusts the response from the generative AI model based on the user's emotional state analyzed by the emotion engine. For example, if the user appears sad, the server can generate a particularly compassionate response and send a message of comfort and encouragement to the user. This provides the user with a more personalized interaction experience and enhanced psychological support.

[0278] Digital legacy management implementation example

[0279] Digital Legacy Management Module

[0280] Users log in to the server via their device and access all digital data related to the deceased. This allows them to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. In addition, they can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[0281] As described above, this system collects digital data about the deceased and uses it to train a generative AI model to recreate conversations with the deceased. Furthermore, by incorporating an emotion engine, the system optimizes the content of the conversations according to the user's emotional state, providing more effective mental care. Furthermore, by centrally managing digital data and supporting the organization and management of digital belongings, the system allows users to reunite with their loved ones while also providing emotional care.

[0282] The processing flow will be explained below.

[0283] Data collection module processing flow

[0284] Step 1:

[0285] The user launches the application on the device and accesses the login screen. The user enters the username and password and presses the login button.

[0286] Step 2:

[0287] The server receives the user's authentication information, checks it against the database, and if authentication is successful, redirects the user to the dashboard.

[0288] Step 3:

[0289] The user selects the deceased's digital data (photos, videos, message history, etc.) through the device interface. The user selects the files using a file selection dialog.

[0290] Step 4:

[0291] To upload the selected digital data, the user clicks the upload button, and the terminal transmits the selected file to the server.

[0292] Step 5:

[0293] The server temporarily stores the received digital data, determines the file format, and stores it in the appropriate database (image database, text database, etc.) depending on the file format.

[0294] Data Learning Module Processing Flow

[0295] Step 1:

[0296] The server retrieves the digital data of the deceased person stored in the database. The retrieved data is categorized into image files, text files, etc.

[0297] Step 2:

[0298] The server pre-processes the acquired data and converts it into a format suitable for input to the generative AI model: for example, image files are standardized in resolution, and text files are tokenized.

[0299] Step 3:

[0300] The server then uses the pre-processed data to begin the process of training a generative AI model, using natural language processing and image recognition techniques.

[0301] Step 4:

[0302] The server stores the trained generative AI model in storage, which has the ability to reproduce the features and speaking style of the deceased.

[0303] Processing flow of the communication reproduction module

[0304] Step 1:

[0305] The user accesses an interface on the terminal to initiate a dialogue with the deceased person. The user clicks a button to begin the conversation.

[0306] Step 2:

[0307] The device sends a request to the server to initiate a dialogue with the deceased person, and the server accepts the request and starts the dialogue session.

[0308] Step 3:

[0309] The user enters a message in the text box on the terminal and presses the send button. The terminal then sends this message to the server.

[0310] Step 4:

[0311] The server inputs the received message into a generative AI model to generate a response in the deceased person's voice, which is then sent back to the server.

[0312] Step 5:

[0313] The server sends the generated response to the user's terminal, where the user checks the response message from the deceased.

[0314] Emotion Recognition Module Processing Flow

[0315] Step 1:

[0316] The user inputs a message on the terminal and sends it to the server, which then sends a request to the emotion engine to analyze the received message.

[0317] Step 2:

[0318] The emotion engine analyzes the received message using natural language processing technology and emotion analysis algorithms, and classifies the user's emotional state into categories such as "sad," "happy," or "angry."

[0319] Step 3:

[0320] The emotion engine sends the analysis results back to the server, which receives the emotion analysis results and adjusts the generated response content based on the user's emotional state.

[0321] Step 4:

[0322] The server inputs the response message corresponding to the generated emotion into the generative AI model, which generates a response tailored to the tone of the deceased person. The generated response is returned to the server.

[0323] Step 5:

[0324] The server sends the tailored response message to the user's terminal, and the user checks the tailored response message on the terminal.

[0325] Digital Legacy Management Module Processing Flow

[0326] Step 1:

[0327] The user accesses the digital estate management interface on their device and clicks a button to view all digital data related to the deceased.

[0328] Step 2:

[0329] The device sends a request to access the digital remains to the server, which accepts the request and retrieves the relevant data from the database.

[0330] Step 3:

[0331] The server processes the digital data retrieved as search results to display them to the user in a visually understandable format, for example, images are displayed as thumbnails and message histories are displayed in a list format.

[0332] Step 4:

[0333] Users can view and organize their digital belongings on their devices, searching for data, sorting it into folders, and deleting it as needed.

[0334] Step 5:

[0335] The server updates the database based on user actions, e.g., deleted data is removed from the database, data moved to a different category is saved in a new folder, etc.

[0336] Example 2

[0337] 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."

[0338] In the digital age, there is a huge amount of digital data about the deceased, but it is difficult to effectively utilize this data and provide a dialogue experience with the deceased. It is also a challenge to optimize dialogue based on the user's emotions and strengthen psychological support. Furthermore, it is also important to centrally manage digital data in various formats and organize digital remains.

[0339] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting digital data related to the deceased; means for training a generative AI model based on the digital data; means for recreating a dialogue with the deceased using the trained generative AI model; means for enabling a user to engage in a dialogue with the deceased using the generative AI model; means for centrally managing the digital data and organizing the digital remains; means for the user to send a request to the server to initiate a dialogue with the deceased; means for the server to confirm the data format and store the data in an appropriate database; means for analyzing the user's emotions using an emotion recognition engine; means for optimizing the dialogue content based on the analyzed emotions; and means for generating a response to the user's input in real time using the generative AI model. This enables the user to have a dialogue experience with the deceased and optimize the dialogue according to the user's emotions. Furthermore, centralized management of digital data facilitates the organization of the digital remains.

[0340] "Digital data relating to the deceased" refers to digital information such as photographs, videos, and message history that the deceased created or saved during their lifetime.

[0341] A "generative AI model" is a machine learning model that has been trained using artificial intelligence techniques to perform a specific task, in this case, recreating the speaking style and facial expressions of a deceased person.

[0342] A "user" is an individual or group who logs in and operates this system to interact with the deceased.

[0343] "Logging in" is the process by which a user enters the authentication information (such as a user ID and password) required to access a system.

[0344] A "database" is a system for efficiently storing, managing, and searching large amounts of digital data.

[0345] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language.

[0346] "Image recognition technology" is a technology that allows computers to detect and identify objects and patterns in images.

[0347] An "emotion recognition engine" is an algorithm or software that analyzes and identifies emotions from text or voice input by a user.

[0348] "Centralized management" refers to the centralized management of data of different formats and types in one place.

[0349] "Digital legacy" refers to digital assets left behind by a deceased person, such as photos, messages, and online account information.

[0350] A "request" refers to an instruction sent by a user to the system to request a particular operation.

[0351] "Real-time" refers to immediate response to user operations and inputs without delay.

[0352] "Dialogue optimization" refers to adjusting the dialogue content and generating the optimal response according to the user's emotions and situation.

[0353] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. It also includes a combined emotion engine that recognizes user emotions and optimizes dialogue and display content accordingly. This system provides a means for users to reunite with their deceased loved ones and assists in the organization of their digital legacy.

[0354] Data collection

[0355] Users log in to the system using a terminal. When logging in, they are required to enter their user ID and password for authentication. Once authentication is complete, the user uploads digital data related to the deceased, such as photos, videos, and message history. The terminal sends this data to the server, which then stores the received data in a temporary file system. The server checks the data format (e.g., image file, video file, text file), and stores it in the appropriate database for each format.

[0356] Data Learning

[0357] The server uses the digital data of the deceased stored in the database to train the generative AI model. This training uses natural language processing (NLP) and image recognition technologies. Specifically, the server extracts from the data the features necessary to reproduce the deceased's speaking style and facial expressions, and trains the AI ​​model. Once training is complete, the trained generative AI model is stored in dedicated storage.

[0358] Communication Reproduction

[0359] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a generative AI model to generate a response based on the user's input, in the tone and style of the deceased. The generated response is then sent from the server to the user's device and displayed in real time.

[0360] As a concrete example, if a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a reply saying, "I'm just taking a walk," and displays this on the user's device.

[0361] Example prompt sentence:

[0362] "What are you doing now?"

[0363] "I'm just taking a walk."

[0364] Combining Emotion Engines

[0365] The server analyzes the text and voice data entered by the user using an emotion engine to confirm the user's emotions. The emotion engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state. For example, if a user enters "I'm not feeling very good today" in a sad voice, the emotion engine will recognize that the user is in a "sad" state.

[0366] Optimizing dialogue based on emotions

[0367] The server adjusts the response of the generative AI model based on the analysis results of the emotion engine. If the user appears sad, the server will generate a more compassionate response and send a comforting or encouraging message to the user. This allows the user to have a personalized interaction experience based on their emotions and provides enhanced psychological support.

[0368] Digital legacy management

[0369] Users can log in to the server via their device and access all digital data related to the deceased. This allows users to organize and manage the deceased's digital belongings. Specifically, users can search for specific message history and view photos in album format. In addition, users can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[0370] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0371] Step 1:

[0372] A user logs in to the system using a terminal. They enter their user ID and password and go through the authentication procedure. The server receives the entered authentication information and authenticates the user. If authentication is successful, the user is allowed to access the system. The input here is the user ID and password, and the output is a message indicating whether authentication was successful or failed.

[0373] Step 2:

[0374] Users use their devices to upload digital data about the deceased, such as photos, videos, and message history, to the system. The devices then send the uploaded data to the server, which then receives the data and stores it in a temporary file system. The input is digital data such as photos, videos, and message history, and the output is the data stored in the temporary file system.

[0375] Step 3:

[0376] The server checks the format of the data stored in the temporary file system. For example, it identifies the format as an image file, video file, text file, etc., and classifies and stores it in a database. The server analyzes the data format and stores each piece of data appropriately in the image database, video database, or text database. The input is data stored in the temporary file system, and the output is data classified and stored in the database.

[0377] Step 4:

[0378] The server retrieves the data stored in the database and trains the generative AI model. This training process uses natural language processing and image recognition technologies. Specifically, data is fed into the generative AI model to learn the deceased's speech style and facial expressions. Once trained, the generative AI model is stored in dedicated storage. The input is the data stored in the database, and the output is the trained generative AI model.

[0379] Step 5:

[0380] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server receives this request and uses a generative AI model to generate a response based on the user's input. The generated response is sent from the server to the user's device and displayed in real time. The input is the user's dialogue request and its content, and the output is the response generated using the generative AI model.

[0381] Step 6:

[0382] The server receives text or voice data entered by the user and analyzes it with an emotion recognition engine. The emotion recognition engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state. For example, if a user enters "I'm not feeling very good today," the emotion recognition engine will determine that this is "sad." The input is text or voice data, and the output is the analyzed emotional state.

[0383] Step 7:

[0384] The server optimizes the response of the generative AI model based on the analysis results of the emotion recognition engine. If the user looks sad, the server generates a more compassionate response and sends a message of comfort or encouragement to the user. The input is the emotion analysis result, and the output is the optimized response.

[0385] Step 8:

[0386] Users can log in to the server via their devices and access all digital data related to the deceased. This allows users to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. The input is the user's login information and request, and the output is the display and management function for the corresponding digital data.

[0387] (Application example 2)

[0388] 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."

[0389] Previously, there were systems that collected digital data of the deceased and used it to recreate conversations with the deceased, but the content of the conversations was not optimized to reflect the user's emotions, and psychological support for the user was insufficient.In addition, there was a lack of easy ways to converse with the deceased using smart devices, which limited the user experience.

[0390] 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 digital data related to the deceased, means for training a generative AI model based on the digital data, means for recreating a dialogue with the deceased using the trained generative AI model, means for enabling a user to interact with the deceased using the generative AI model, means for centrally managing the digital data and organizing the digital remains, means for analyzing the user's emotions and optimizing the dialogue content based on the analysis, and means for enabling the user to engage in dialogue using a smart device. This enables a personalized dialogue experience based on the user's emotions, enhancing psychological support. Furthermore, by utilizing a smart device, users can easily enjoy dialogue with the deceased anytime, anywhere.

[0391] "Digital data" refers to electronic files such as photos, videos, and message history related to the deceased.

[0392] A "generative AI model" refers to an artificial intelligence model that learns the characteristics of the deceased based on collected digital data and recreates conversations with the deceased.

[0393] "Sentiment analysis" refers to the process of analyzing text or voice data entered by a user to identify their emotional state.

[0394] "Optimizing dialogue content" refers to adjusting the responses of the generative AI model based on the user's emotional state to provide a more personalized dialogue experience.

[0395] "Digital legacy" refers to all digital data and online account information related to the deceased.

[0396] "Centralized management" refers to managing multiple digital data in a unified system or database.

[0397] "Smart devices" refers to devices that have internet connectivity and advanced computing capabilities, such as smartphones, head-mounted displays, and smart glasses.

[0398] This invention provides a system that collects digital data about the deceased, trains a generative AI model based on that data, and recreates the conversation between the user and the deceased. Furthermore, it performs emotion analysis and optimizes the conversation content according to the user's emotional state, providing psychological support to the user. The following describes specific embodiments of this invention.

[0399] Hardware and software used

[0400] Hardware:

[0401] Server: Stores data and trains generative AI models.

[0402] User device: Any device with internet connectivity, such as a smartphone, head-mounted display, or smart glasses.

[0403] software:

[0404] Data collection module: Python

[0405] Data learning module: TensorFlow or PyTorch

[0406] Emotion Recognition Module: NLP, emotion analysis library (e.g. Hugging Face Transformers)

[0407] Communication Reproduction Module: Python

[0408] Digital legacy management module: Django

[0409] Data collection

[0410] The server collects data by allowing users to upload digital data about the deceased via their devices. The collected data is temporarily stored on the server and then categorized and stored in the appropriate database depending on its format. For example, photo data is stored in an image database, and text data is categorized in a text database.

[0411] Data Learning

[0412] The server uses the collected digital data to train a generative AI model. Using deep learning frameworks such as TensorFlow and PyTorch, it uses natural language processing and image recognition techniques to learn the deceased's speaking style and facial expressions. This training process allows the generative AI model to realistically reproduce the deceased's characteristics.

[0413] Communication Reproduction

[0414] When a user initiates a conversation via their device, the server uses the trained generative AI model to generate the deceased's tone of voice and responses based on the user's input. The generated responses are sent to the user's device in real time, allowing the user to enjoy the experience of interacting with the deceased.

[0415] Emotion recognition and dialogue content optimization

[0416] The server receives text and voice data entered by the user and analyzes the user's emotional state using emotion analysis libraries such as Hugging Face Transformers. Based on the analyzed emotional state, the generative AI model adjusts its response. For example, if the user is sad, it generates an encouraging message.

[0417] Digital legacy management

[0418] Users can log in to the server via their devices and access all digital data related to the deceased, allowing them to organize and manage their digital belongings, search for specific message history, and view photos in album format.

[0419] Specific examples

[0420] Example 1: A user reminisces about a deceased loved one

[0421] User: "Check this out" and upload a photo.

[0422] Deceased AI: "Oh, this brings back memories. Those trips were fun."

[0423] Example prompt: "Tell me about your memories of our trip."

[0424] Example 2: Dialogue when the user is feeling down

[0425] User: "I'm not feeling well today."

[0426] Deceased AI: "Are you OK? Tell me what happened."

[0427] Example prompt: "Tell me how you would comfort a user when they're feeling down."

[0428] With the above configuration, the present invention can provide a personalized conversation experience according to the user's emotional state and enhance psychological support. Furthermore, by utilizing smart devices, users can enjoy conversation with the deceased anytime, anywhere.

[0429] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0430] Step 1: Collect digital data

[0431] Input: The user uploads photos, videos, and message history about the deceased via the device.

[0432] Processing: The server temporarily stores the data uploaded by the user and checks its format (image, text, video, etc.). For example, photos are stored in an image database, and text data is classified in a text database.

[0433] Output: Each data is stored in a corresponding database and made available for further processing.

[0434] Step 2: Train the data

[0435] Input: Digital data about the deceased, including photos, videos, and message history, stored in a database.

[0436] Processing: The server uses the stored digital data to train a generative AI model using deep learning frameworks such as TensorFlow and PyTorch, using natural language processing and image recognition techniques to learn the speech style and facial expressions of the deceased.

[0437] Output: The trained generative AI model is saved in dedicated storage and used to recreate user interactions.

[0438] Step 3: Sentiment Analysis

[0439] Input: Text and voice data entered by the user through the device.

[0440] Processing: The server performs sentiment analysis on the user's input data using emotion analysis libraries such as Hugging Face Transformers, thereby identifying the user's emotional state (e.g., sad, happy, angry, etc.).

[0441] Output: The user's emotional state is analyzed and used in the next dialogue generation step.

[0442] Step 4: Communication reproduction and dialogue content optimization

[0443] Input: User sentiment analysis results and user-entered text or voice data.

[0444] Processing: The server uses a trained generative AI model to generate the optimal response for the user based on the analyzed emotional state. For example, if the user seems sad, it generates a message of comfort or encouragement.

[0445] Output: The optimized response is sent to the user's device in real time, allowing the user to experience a natural conversation with the deceased.

[0446] Step 5: Digital Legacy Management

[0447] Input: The digital data that a user logs into a server via a terminal and requests access to.

[0448] Processing: The server authenticates the user and provides the authenticated user with the deceased's digital data (photos, message history, account information, etc.), allowing the user to view and organize their digital belongings.

[0449] Output: Users can access, organize and manage all digital data related to the deceased.

[0450] The above are the specific processing steps for implementing this invention, which allow users to receive psychological support through dialogue with the deceased and to properly manage the deceased's digital remains.

[0451] 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.

[0452] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0453] 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.

[0454] [Second embodiment]

[0455] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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."

[0467] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. The system provides a means for users to reunite with their loved ones and assists in the organization of their digital remains.

[0468] Data collection implementation

[0469] Data Collection Module

[0470] A user logs in to their device and uploads photos, videos, message history, etc. related to the deceased. The server receives this data and temporarily stores it. The server then checks the file format and stores it in the appropriate format in the database accordingly. For example, photo data is stored in the image database, and text data is classified as a text database.

[0471] Data Learning Embodiment

[0472] Data Learning Module

[0473] The server uses the digital data stored in the database to train the generative AI model. Specifically, it uses natural language processing and image recognition technologies to learn the deceased's speaking style and facial expressions. This learning process allows the generative AI model to realistically reproduce the characteristics of the deceased. Once trained, the generative AI model is stored in dedicated storage.

[0474] Communication reproduction embodiment

[0475] Communication Reproduction Module

[0476] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device.

[0477] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[0478] Digital legacy management implementation example

[0479] Digital Legacy Management Module

[0480] Users log in to the server via their device and access all digital data related to the deceased. This allows them to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. In addition, they can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[0481] As described above, this system collects digital data about the deceased, trains a generative AI model based on that data, and recreates conversations with the deceased. It also centralizes digital data management and supports the organization and management of digital belongings, allowing users to reunite with their loved ones and provide emotional care.

[0482] The processing flow will be explained below.

[0483] Data collection module processing flow

[0484] Step 1:

[0485] The user launches the application on the device and accesses the login screen. The user enters the username and password and presses the login button.

[0486] Step 2:

[0487] The server receives the user's authentication information, checks it against the database, and if authentication is successful, redirects the user to the dashboard.

[0488] Step 3:

[0489] The user selects the deceased's digital data (photos, videos, message history, etc.) through the device interface. The user selects the files using a file selection dialog.

[0490] Step 4:

[0491] To upload the selected digital data, the user clicks the upload button, and the terminal transmits the selected file to the server.

[0492] Step 5:

[0493] The server temporarily stores the received digital data, determines the file format, and stores it in the appropriate database (image database, text database, etc.) depending on the file format.

[0494] Data Learning Module Processing Flow

[0495] Step 1:

[0496] The server retrieves the digital data of the deceased person stored in the database. The retrieved data is categorized into image files, text files, etc.

[0497] Step 2:

[0498] The server pre-processes the acquired data and converts it into a format suitable for input to the generative AI model: for example, image files are standardized in resolution, and text files are tokenized.

[0499] Step 3:

[0500] The server then uses the pre-processed data to begin the process of training a generative AI model, using natural language processing and image recognition techniques.

[0501] Step 4:

[0502] The server stores the trained generative AI model in storage, which has the ability to reproduce the features and speaking style of the deceased.

[0503] Processing flow of the communication reproduction module

[0504] Step 1:

[0505] The user accesses an interface on the terminal to initiate a dialogue with the deceased person. The user clicks a button to begin the conversation.

[0506] Step 2:

[0507] The device sends a request to the server to initiate a dialogue with the deceased person, and the server accepts the request and starts the dialogue session.

[0508] Step 3:

[0509] The user enters a message in the text box on the terminal and presses the send button. The terminal then sends this message to the server.

[0510] Step 4:

[0511] The server inputs the received message into a generative AI model to generate a response in the deceased person's voice, which is then sent back to the server.

[0512] Step 5:

[0513] The server sends the generated response to the user's terminal, where the user checks the response message from the deceased.

[0514] Digital Legacy Management Module Processing Flow

[0515] Step 1:

[0516] The user accesses the digital estate management interface on their device and clicks a button to view all digital data related to the deceased.

[0517] Step 2:

[0518] The device sends a request to access the digital remains to the server, which accepts the request and retrieves the relevant data from the database.

[0519] Step 3:

[0520] The server processes the digital data retrieved as search results to display them to the user in a visually understandable format, for example, images are displayed as thumbnails and message histories are displayed in a list format.

[0521] Step 4:

[0522] Users can view and organize their digital belongings on their devices, searching for data, sorting it into folders, and deleting it as needed.

[0523] Step 5:

[0524] The server updates the database based on user actions, e.g., deleted data is removed from the database, data moved to a different category is saved in a new folder, etc.

[0525] Example 1

[0526] 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."

[0527] While users can reminisce about their memories with the deceased, it was difficult to recreate the experience of interacting with the deceased. Furthermore, there was a lack of efficient ways to manage and organize the vast amount of digital data of the deceased, which required users to manually organize it. Additionally, managing the authentication information for the deceased's online accounts was difficult.

[0528] 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.

[0529] In this invention, the server includes a means for users to upload digital data via their terminals and for the server to store the data in a database, a means for training a generative AI model based on the digital data, and a means for recreating a conversation with the deceased using the trained generative AI model. This allows users to enjoy a realistic conversation experience with the deceased while efficiently managing and organizing the deceased's digital data. Furthermore, authentication information for the deceased's online accounts can also be centrally managed, facilitating the preparations for the deceased's digital legacy.

[0530] A "server" is a central processing unit that stores, processes, and shares data over a network.

[0531] A "terminal" is a device operated by a user, and includes a personal computer, a smartphone, etc.

[0532] A "user" is an individual who uses this system and has a relationship with the deceased.

[0533] "Digital data" refers to information stored electronically, including files such as photos, videos, and message histories.

[0534] A "database" is a system for efficiently storing, managing, and searching digital data.

[0535] A "generative AI model" is an algorithm that is trained using machine learning techniques to perform a specific task.

[0536] "Natural language processing technology" is a technology that enables computers to understand, generate, and analyze human language.

[0537] "Image recognition technology" is a technology that allows computers to analyze images and identify specific features or objects.

[0538] The "interactive experience" is an experience in which users can communicate with the deceased through a generative AI model.

[0539] "Digital legacy" refers to digital data and online account information left behind by a deceased person.

[0540] "Centralized management" refers to the integrated management of multiple digital data using a single system.

[0541] "Authentication information" refers to information such as ID and password required by a user to access an online account.

[0542] "Digital end-of-life planning" is the process of organizing and managing the digital data and online accounts left behind by the deceased.

[0543] This invention is a system that collects digital data about the deceased and trains a generative AI model based on that data. Specific embodiments of the system are described below.

[0544] Data collection implementation

[0545] Data Collection Module

[0546] First, the user enters authentication information to log in to the device, which can be a PC or smartphone. The server receives this authentication information and checks it against a database. If authentication is successful, the user is able to upload digital data.

[0547] Users upload photos, videos, message history, etc. related to the deceased person from their devices. The server receives this data and temporarily stores it in cloud storage. The server checks the file format and stores image data in an image database and text data in a text database.

[0548] The hardware used includes general PCs and smartphones, and the servers are cloud servers (e.g., AWS, Google Cloud, etc.), and the database systems used are MySQL and PostgreSQL.

[0549] Data Learning Embodiment

[0550] Data Learning Module

[0551] The server retrieves the digital data stored in the database, processes it, and stores it in a workspace. The collected data is used to train a generative AI model (using, for example, TensorFlow or PyTorch). This process uses natural language processing techniques (such as BERT) and image recognition techniques (such as CNN).

[0552] During the learning process, the system uses text data to learn the deceased's speaking style and image data to learn their facial expressions and gestures. Once trained, the generated AI model is saved in the "trained_models" folder.

[0553] Communication reproduction embodiment

[0554] Communication Reproduction Module

[0555] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response based on the user's input. The generated response is then sent to the user's device via the server. This allows the user to enjoy a real-time dialogue experience with the deceased on their device.

[0556] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[0557] Digital legacy management implementation example

[0558] Digital Legacy Management Module

[0559] Users can log in to the server via their devices and access the deceased's digital data. This allows them to search for specific message history and view photos in album format. Furthermore, they can also manage the deceased's online account authentication information, making it easier to organize their digital belongings.

[0560] The hardware used includes PCs and smartphones, and cloud servers (AWS, Google Cloud, etc.) are used. MySQL and PostgreSQL are used as database management systems.

[0561] (Example of a prompt)

[0562] "Recreate the conversational style of the deceased person based on their message history, and generate answers that sound like the deceased person to the user's questions."

[0563] The above is a specific embodiment for carrying out the present invention.

[0564] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0565] Step 1: User authentication

[0566] The user enters authentication information to log in to the terminal.

[0567] The server receives the authentication information and checks it against a database.

[0568] (Input) The ID and password entered by the user from the terminal.

[0569] (Processing) The server references the database and searches for records that match the input information.

[0570] (Output) If authentication is successful, the user is redirected to the data upload screen. If not, an error message is displayed.

[0571] (Specific operation) The user enters their ID and password into the login form on the terminal and presses the "Login" button. The server verifies the information in the database and returns the authentication result.

[0572] Step 2: Upload data

[0573] Users upload digital data about the deceased, such as photos, videos, and message history, from their devices.

[0574] The server receives this data and temporarily stores it in storage.

[0575] (Input) Digital data (file) selected by the user from the terminal.

[0576] (Processing) The server checks the file format and saves it in temporary storage.

[0577] (Output) A message notifying you that the data was saved successfully.

[0578] (Specific operation) The user clicks the upload button and selects a specific file from their PC. The selected file is uploaded to the server and saved in temporary storage.

[0579] Step 3: Check the data format and save it to the database

[0580] The server checks the format of the uploaded file.

[0581] Image data is stored in an image database, and text data is stored in a text database.

[0582] (Input) Digital data stored in temporary storage.

[0583] (Processing) The server checks the file extension, etc., and classifies and stores it in the appropriate database.

[0584] (Output) Message indicating completion of saving to the database.

[0585] (Specific operation) The server stores .jpg files in the image database and .txt files in the text database.

[0586] Step 4: Collecting data for training

[0587] The server collects data from an image database and a text database.

[0588] The collected data is stored in a temporary workspace.

[0589] (Input) Data stored in the image database and text database.

[0590] (Processing) The server retrieves the data using the specified query and moves it to the workspace.

[0591] (Output) The training data in the workspace.

[0592] (Specific operation) The server retrieves a specific number of photos and message history from the database and moves them to the workspace.

[0593] Step 5: Training the generative AI model

[0594] The server uses the data in the workspace to train a generative AI model.

[0595] It uses natural language processing technology and image recognition technology.

[0596] (Input) Digital data in the workspace.

[0597] The (processing) server analyzes the data using natural language processing models (such as BERT) and image recognition models (such as CNN) and trains the models.

[0598] (Output) A trained generative AI model.

[0599] (Specific operation) The server uses the BERT model to train text data and the CNN model to train image data. It then performs calculations to improve the accuracy of the model.

[0600] Step 6: Save the trained model

[0601] Once trained, the generative AI model is stored in dedicated storage.

[0602] (Input) A fully trained generative AI model.

[0603] (Processing) The server saves the trained model in storage.

[0604] (Output) Completion notification for the saved model.

[0605] (Specific operation) The server saves the generated AI model that has completed learning in the "trained_models" folder.

[0606] Step 7: Sending a conversation-initiating request

[0607] The user sends a dialogue start request to the server via the terminal.

[0608] (Input) A request from the user to start a conversation.

[0609] (Processing) The server receives the request and transitions to interactive mode.

[0610] (Output) Notification of conversation start.

[0611] (Specific operation) The user clicks the "talk to the deceased" button in the chat application, and the server processes the request.

[0612] Step 8: Response Generation

[0613] The server uses a trained generative AI model to generate a response based on the user's input.

[0614] (Input) A message from the user.

[0615] (Processing) The server uses the generative AI model to generate a response appropriate to the user's message.

[0616] (Output) The generated response message.

[0617] (Specific operation) When a user sends a message saying, "What are you doing now?", the server uses the generative AI model to generate a response saying, "I'm just taking a walk."

[0618] Step 9: Sending a Response

[0619] The generated response is sent to the user's terminal via the server.

[0620] (Input) The response generated by the generative AI model.

[0621] (Processing) The server sends the response to the user's terminal.

[0622] (Output) The response message displayed on the user's terminal.

[0623] (Specific operation) The server generates a message saying "I'm just taking a walk" and sends it to the chat application, which displays it on the user's device.

[0624] Step 10: Organizing and managing your digital legacy

[0625] Users log into the server via a terminal and access all digital data relating to the deceased.

[0626] (Input) User authentication information.

[0627] (Processing) The server authenticates and allows access to the digital data.

[0628] (Output) Display of digital data about the deceased.

[0629] (Specific operation) A user logs in on a terminal and accesses a dashboard of digital data.

[0630] Step 11: Search and view specific digital data

[0631] Users can search for specific message history and view photos in album format.

[0632] (Input) The user's search query.

[0633] The (processing) server searches the database and retrieves the relevant data.

[0634] (Output) Search results and photos in album format.

[0635] (Specific operation) The user types "travel" in the search box to display a list of relevant message history. Also, the user clicks the album tab to view photos.

[0636] Step 12: Manage your online account credentials

[0637] The server manages the IDs and passwords of the deceased person's online accounts.

[0638] (Enter) The deceased person's online account information.

[0639] The (processing) server securely stores and manages information.

[0640] (Output) Secure storage and management status of credentials.

[0641] (Specific operation) The user registers the deceased person's online account information on the server, and the server encrypts and stores this information.

[0642] These are the specific processing steps of the program for this system. Each step shows the specific operations and inputs / outputs of the user, terminal, and server.

[0643] (Application example 1)

[0644] 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."

[0645] Systems already exist that collect and manage digital data about the deceased and use generative AI models to recreate conversations with the deceased. However, no system exists that applies this technology to virtual stores, allowing users to enjoy shopping in a virtual space with the deceased. Such a system would allow users to enjoy new experiences while reminiscing on memories spent with the deceased, and would also efficiently organize digital belongings. Therefore, there is a need for a system that allows users to reunite with the deceased while also providing new experiences in a virtual space.

[0646] 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.

[0647] In this invention, the server includes means for collecting digital data about the deceased, means for training a generative AI model based on the digital data, means for recreating a conversation with the deceased using the trained generative AI model, means for enabling a user to converse with the deceased using the generative AI model, means for enabling the user to have the deceased recommend products to the user in a virtual space, and means for centrally managing the digital data and organizing the digital remains. This allows users to not only relive conversations with the deceased but also enjoy a shopping experience in a virtual space with the deceased.

[0648] "Digital data" refers to information stored or collected electronically, including photographs, videos, text messages, audio data, and other forms of multimedia.

[0649] A "generative AI model" is an artificial intelligence model trained using machine learning technology that has the ability to learn the characteristics of the deceased based on the digital data provided and reproduce their dialogue.

[0650] "Dialogue reproduction" is a process in which a user simulates a conversation with a deceased person, and refers to the generative AI model mimicking the deceased's tone of voice and facial expressions to respond to the user.

[0651] A "virtual space" is a computer-simulated, imaginary, three-dimensional environment within which a user can visually and interactively interact.

[0652] "Product introduction" refers to the process by which a deceased person uses a generative AI model to provide a user with a description or opinion about a product or service.

[0653] "Digital legacy" refers to all data in digital format that the deceased created, stored, and shared during their lifetime, including message history, photos, videos, and online account information.

[0654] MODE FOR CARRYING OUT THE INVENTION

[0655] A specific system for implementing the invention includes the following elements:

[0656] 1. Data Collection Module

[0657] The device provides an interface that allows users to upload digital data related to the deceased, such as photos, videos, and message history. When a user sends the digital data from the device to the server, the server temporarily stores the data, checks the file format, and then stores it in the appropriate database. For example, photo data is stored in an image database, and text data is classified as a text database.

[0658] 2. Data Learning Module

[0659] The server uses the digital data stored in the database to train the generative AI model. Using natural language processing and image recognition technologies with the TensorFlow library, the model learns the deceased's speaking style and facial expressions. This training process allows the generative AI model to realistically reproduce the characteristics of the deceased.

[0660] 3. Communication Reproduction Module

[0661] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device. For example, if the user sends a message from their device saying, "What are you doing now?", the server uses the generative AI model to generate a response such as "I'm just taking a walk," which is then displayed on the user's device. An example prompt is "The user said: What are you doing now? Your response:."

[0662] 4. Virtual Space Module

[0663] Users can enjoy shopping while interacting with the deceased in a virtual space. Specifically, when a user asks a question about a specific product, a generative AI model that mimics the deceased's style will explain the product. For example, if a user asks, "What do you think of this dress?", the generative AI model will respond, "It's very nice. It looks great on you." An example prompt is, "The user said: What do you think of this dress? Your response:"

[0664] 5. Digital Legacy Management Module

[0665] Users can log in via their device and access all digital data related to the deceased. Users can centrally manage this data and easily organize their digital belongings. For example, they can search for specific message history or view photos in album format. They can also manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[0666] This allows users to not only realistically recreate conversations with their deceased loved ones, but also enjoy a virtual shopping experience with them while efficiently organizing and managing their digital belongings.

[0667] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0668] DETAILED DESCRIPTION OF THE INVENTION - PROCESS STEPS

[0669] Step 1: Upload your digital data

[0670] Users use their devices to upload digital data about the deceased, such as photos, videos, and message history. The devices then send this data to a server. The input is the digital data provided by the user, and the output is temporary storage of the data by the server.

[0671] Specific operation: The user clicks the "Upload" button on the terminal application screen, selects a file, and sends it to the server.

[0672] Step 2: Check the format of the data and save it

[0673] The server checks the format of the uploaded data and stores it in the database in the appropriate format. The input is the digital data sent from the terminal, and the output is the data stored in the database.

[0674] Specific operation: The server checks the file format of the received data, classifies and stores the photo data in the image database and the text data in the text database.

[0675] Step 3: Training the generative AI model

[0676] The server uses the digital data stored in the database to train the generative AI model, with the input being the digital data in the database and the output being the trained generative AI model.

[0677] How it works: The server uses the TensorFlow library to perform image recognition and natural language processing techniques, learning the speech style and facial expressions of the deceased.

[0678] Step 4: Receiving an interaction request

[0679] The user sends a request to the server via a terminal to start a dialogue with the deceased. The input is the dialogue request from the user, and the output is the request received by the server.

[0680] Specific operation: The user clicks the "Start conversation" button on the application screen to send a conversation request to the server.

[0681] Step 5: Creating and Executing Dialogues

[0682] The server uses the trained generative AI model to generate a response from the deceased in response to the user's input. The input is a message from the user, and the output is a response from the generative AI model.

[0683] Specific operation: When a user sends a message saying "What are you doing now?", the server uses the generative AI model to generate a response saying "Just taking a walk" and sends it to the user's device.

[0684] Step 6: Interact in the virtual space

[0685] The user interacts with the deceased through a generative AI model in a virtual space, introducing products to each other. The input is the user's question, and the output is a product introduction by the generative AI model.

[0686] Specific operation: When a user asks in the virtual space, "What do you think of this dress?", the generative AI model replies, "It's very nice. It looks good on you."

[0687] Step 7: Organizing your digital legacy

[0688] Users access and organize their digital remains through a terminal. Input is search keywords and operations for organization, and output is a display of the organized data.

[0689] Specific operation: When a user searches for "view photos from 2022," the server retrieves the relevant photo data from the database and displays it on the device.

[0690] This processing step enables the recreation of conversations with the deceased and the experience of shopping in a virtual space, enabling the efficient management of digital belongings.

[0691] 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.

[0692] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. It also includes a combined emotion engine that recognizes user emotions and optimizes dialogue and display content accordingly. This system provides a means for users to reunite with their deceased loved ones and assists in the organization of their digital legacy.

[0693] Data collection implementation

[0694] Data Collection Module

[0695] A user logs in to their device and uploads photos, videos, message history, etc. related to the deceased. The server receives this data and temporarily stores it. The server then checks the file format and stores it in the appropriate format in the database accordingly. For example, photo data is stored in the image database, and text data is classified as a text database.

[0696] Data Learning Embodiment

[0697] Data Learning Module

[0698] The server uses the digital data stored in the database to train the generative AI model. Specifically, it uses natural language processing and image recognition technologies to learn the deceased's speaking style and facial expressions. This learning process allows the generative AI model to realistically reproduce the characteristics of the deceased. Once trained, the generative AI model is stored in dedicated storage.

[0699] Communication reproduction embodiment

[0700] Communication Reproduction Module

[0701] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device.

[0702] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[0703] Embodiment of Emotion Engine

[0704] Emotion Recognition Module

[0705] The server receives the text and voice data entered by the user and analyzes the user's emotions using an emotion engine. The emotion engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state from their speech and facial expressions. For example, if a user enters "I'm not feeling very good today" in a sad voice, the emotion engine will recognize that the user is in a "sad" state.

[0706] Optimizing dialogue based on emotions

[0707] Emotion-based communication reproduction module

[0708] The server adjusts the response from the generative AI model based on the user's emotional state analyzed by the emotion engine. For example, if the user appears sad, the server can generate a particularly compassionate response and send a message of comfort and encouragement to the user. This provides the user with a more personalized interaction experience and enhanced psychological support.

[0709] Digital legacy management implementation example

[0710] Digital Legacy Management Module

[0711] Users log in to the server via their device and access all digital data related to the deceased. This allows them to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. In addition, they can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[0712] As described above, this system collects digital data about the deceased and uses it to train a generative AI model to recreate conversations with the deceased. Furthermore, by incorporating an emotion engine, the system optimizes the content of the conversations according to the user's emotional state, providing more effective mental care. Furthermore, by centrally managing digital data and supporting the organization and management of digital belongings, the system allows users to reunite with their loved ones while also providing emotional care.

[0713] The processing flow will be explained below.

[0714] Data collection module processing flow

[0715] Step 1:

[0716] The user launches the application on the device and accesses the login screen. The user enters the username and password and presses the login button.

[0717] Step 2:

[0718] The server receives the user's authentication information, checks it against the database, and if authentication is successful, redirects the user to the dashboard.

[0719] Step 3:

[0720] The user selects the deceased's digital data (photos, videos, message history, etc.) through the device interface. The user selects the files using a file selection dialog.

[0721] Step 4:

[0722] To upload the selected digital data, the user clicks the upload button, and the terminal transmits the selected file to the server.

[0723] Step 5:

[0724] The server temporarily stores the received digital data, determines the file format, and stores it in the appropriate database (image database, text database, etc.) depending on the file format.

[0725] Data Learning Module Processing Flow

[0726] Step 1:

[0727] The server retrieves the digital data of the deceased person stored in the database. The retrieved data is categorized into image files, text files, etc.

[0728] Step 2:

[0729] The server pre-processes the acquired data and converts it into a format suitable for input to the generative AI model: for example, image files are standardized in resolution, and text files are tokenized.

[0730] Step 3:

[0731] The server then uses the pre-processed data to begin the process of training a generative AI model, using natural language processing and image recognition techniques.

[0732] Step 4:

[0733] The server stores the trained generative AI model in storage, which has the ability to reproduce the features and speaking style of the deceased.

[0734] Processing flow of the communication reproduction module

[0735] Step 1:

[0736] The user accesses an interface on the terminal to initiate a dialogue with the deceased person. The user clicks a button to begin the conversation.

[0737] Step 2:

[0738] The device sends a request to the server to initiate a dialogue with the deceased person, and the server accepts the request and starts the dialogue session.

[0739] Step 3:

[0740] The user enters a message in the text box on the terminal and presses the send button. The terminal then sends this message to the server.

[0741] Step 4:

[0742] The server inputs the received message into a generative AI model to generate a response in the deceased person's voice, which is then sent back to the server.

[0743] Step 5:

[0744] The server sends the generated response to the user's terminal, where the user checks the response message from the deceased.

[0745] Emotion Recognition Module Processing Flow

[0746] Step 1:

[0747] The user inputs a message on the terminal and sends it to the server, which then sends a request to the emotion engine to analyze the received message.

[0748] Step 2:

[0749] The emotion engine analyzes the received message using natural language processing technology and emotion analysis algorithms, and classifies the user's emotional state into categories such as "sad," "happy," or "angry."

[0750] Step 3:

[0751] The emotion engine sends the analysis results back to the server, which receives the emotion analysis results and adjusts the generated response content based on the user's emotional state.

[0752] Step 4:

[0753] The server inputs the response message corresponding to the generated emotion into the generative AI model, which generates a response tailored to the tone of the deceased person. The generated response is returned to the server.

[0754] Step 5:

[0755] The server sends the tailored response message to the user's terminal, and the user checks the tailored response message on the terminal.

[0756] Digital Legacy Management Module Processing Flow

[0757] Step 1:

[0758] The user accesses the digital estate management interface on their device and clicks a button to view all digital data related to the deceased.

[0759] Step 2:

[0760] The device sends a request to access the digital remains to the server, which accepts the request and retrieves the relevant data from the database.

[0761] Step 3:

[0762] The server processes the digital data retrieved as search results to display them to the user in a visually understandable format, for example, images are displayed as thumbnails and message histories are displayed in a list format.

[0763] Step 4:

[0764] Users can view and organize their digital belongings on their devices, searching for data, sorting it into folders, and deleting it as needed.

[0765] Step 5:

[0766] The server updates the database based on user actions, e.g., deleted data is removed from the database, data moved to a different category is saved in a new folder, etc.

[0767] Example 2

[0768] 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."

[0769] In the digital age, there is a huge amount of digital data about the deceased, but it is difficult to effectively utilize this data and provide a dialogue experience with the deceased. It is also a challenge to optimize dialogue based on the user's emotions and strengthen psychological support. Furthermore, it is also important to centrally manage digital data in various formats and organize digital remains.

[0770] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting digital data related to the deceased; means for training a generative AI model based on the digital data; means for recreating a dialogue with the deceased using the trained generative AI model; means for enabling a user to engage in a dialogue with the deceased using the generative AI model; means for centrally managing the digital data and organizing the digital remains; means for the user to send a request to the server to initiate a dialogue with the deceased; means for the server to confirm the data format and store the data in an appropriate database; means for analyzing the user's emotions using an emotion recognition engine; means for optimizing the dialogue content based on the analyzed emotions; and means for generating a response to the user's input in real time using the generative AI model. This enables the user to have a dialogue experience with the deceased and optimize the dialogue according to the user's emotions. Furthermore, centralized management of digital data facilitates the organization of the digital remains.

[0771] "Digital data relating to the deceased" refers to digital information such as photographs, videos, and message history that the deceased created or saved during their lifetime.

[0772] A "generative AI model" is a machine learning model that has been trained using artificial intelligence techniques to perform a specific task, in this case, recreating the speaking style and facial expressions of a deceased person.

[0773] A "user" is an individual or group who logs in and operates this system to interact with the deceased.

[0774] "Logging in" is the process by which a user enters the authentication information (such as a user ID and password) required to access a system.

[0775] A "database" is a system for efficiently storing, managing, and searching large amounts of digital data.

[0776] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language.

[0777] "Image recognition technology" is a technology that allows computers to detect and identify objects and patterns in images.

[0778] An "emotion recognition engine" is an algorithm or software that analyzes and identifies emotions from text or voice input by a user.

[0779] "Centralized management" refers to the centralized management of data of different formats and types in one place.

[0780] "Digital legacy" refers to digital assets left behind by a deceased person, such as photos, messages, and online account information.

[0781] A "request" refers to an instruction sent by a user to the system to request a particular operation.

[0782] "Real-time" refers to immediate response to user operations and inputs without delay.

[0783] "Dialogue optimization" refers to adjusting the dialogue content and generating the optimal response according to the user's emotions and situation.

[0784] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. It also includes a combined emotion engine that recognizes user emotions and optimizes dialogue and display content accordingly. This system provides a means for users to reunite with their deceased loved ones and assists in the organization of their digital legacy.

[0785] Data collection

[0786] Users log in to the system using a terminal. When logging in, they are required to enter their user ID and password for authentication. Once authentication is complete, the user uploads digital data related to the deceased, such as photos, videos, and message history. The terminal sends this data to the server, which then stores the received data in a temporary file system. The server checks the data format (e.g., image file, video file, text file), and stores it in the appropriate database for each format.

[0787] Data Learning

[0788] The server uses the digital data of the deceased stored in the database to train the generative AI model. This training uses natural language processing (NLP) and image recognition technologies. Specifically, the server extracts from the data the features necessary to reproduce the deceased's speaking style and facial expressions, and trains the AI ​​model. Once training is complete, the trained generative AI model is stored in dedicated storage.

[0789] Communication Reproduction

[0790] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a generative AI model to generate a response based on the user's input, in the tone and style of the deceased. The generated response is then sent from the server to the user's device and displayed in real time.

[0791] As a concrete example, if a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a reply saying, "I'm just taking a walk," and displays this on the user's device.

[0792] Example prompt sentence:

[0793] "What are you doing now?"

[0794] "I'm just taking a walk."

[0795] Combining Emotion Engines

[0796] The server analyzes the text and voice data entered by the user using an emotion engine to confirm the user's emotions. The emotion engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state. For example, if a user enters "I'm not feeling very good today" in a sad voice, the emotion engine will recognize that the user is in a "sad" state.

[0797] Optimizing dialogue based on emotions

[0798] The server adjusts the response of the generative AI model based on the analysis results of the emotion engine. If the user appears sad, the server will generate a more compassionate response and send a comforting or encouraging message to the user. This allows the user to have a personalized interaction experience based on their emotions and provides enhanced psychological support.

[0799] Digital legacy management

[0800] Users can log in to the server via their device and access all digital data related to the deceased. This allows users to organize and manage the deceased's digital belongings. Specifically, users can search for specific message history and view photos in album format. In addition, users can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[0801] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0802] Step 1:

[0803] A user logs in to the system using a terminal. They enter their user ID and password and go through the authentication procedure. The server receives the entered authentication information and authenticates the user. If authentication is successful, the user is allowed to access the system. The input here is the user ID and password, and the output is a message indicating whether authentication was successful or failed.

[0804] Step 2:

[0805] Users use their devices to upload digital data about the deceased, such as photos, videos, and message history, to the system. The devices then send the uploaded data to the server, which then receives the data and stores it in a temporary file system. The input is digital data such as photos, videos, and message history, and the output is the data stored in the temporary file system.

[0806] Step 3:

[0807] The server checks the format of the data stored in the temporary file system. For example, it identifies the format as an image file, video file, text file, etc., and classifies and stores it in a database. The server analyzes the data format and stores each piece of data appropriately in the image database, video database, or text database. The input is data stored in the temporary file system, and the output is data classified and stored in the database.

[0808] Step 4:

[0809] The server retrieves the data stored in the database and trains the generative AI model. This training process uses natural language processing and image recognition technologies. Specifically, data is fed into the generative AI model to learn the deceased's speech style and facial expressions. Once trained, the generative AI model is stored in dedicated storage. The input is the data stored in the database, and the output is the trained generative AI model.

[0810] Step 5:

[0811] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server receives this request and uses a generative AI model to generate a response based on the user's input. The generated response is sent from the server to the user's device and displayed in real time. The input is the user's dialogue request and its content, and the output is the response generated using the generative AI model.

[0812] Step 6:

[0813] The server receives text or voice data entered by the user and analyzes it with an emotion recognition engine. The emotion recognition engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state. For example, if a user enters "I'm not feeling very good today," the emotion recognition engine will determine that this is "sad." The input is text or voice data, and the output is the analyzed emotional state.

[0814] Step 7:

[0815] The server optimizes the response of the generative AI model based on the analysis results of the emotion recognition engine. If the user looks sad, the server generates a more compassionate response and sends a message of comfort or encouragement to the user. The input is the emotion analysis result, and the output is the optimized response.

[0816] Step 8:

[0817] Users can log in to the server via their devices and access all digital data related to the deceased. This allows users to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. The input is the user's login information and request, and the output is the display and management function for the corresponding digital data.

[0818] (Application example 2)

[0819] 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."

[0820] Previously, there were systems that collected digital data of the deceased and used it to recreate conversations with the deceased, but the content of the conversations was not optimized to reflect the user's emotions, and psychological support for the user was insufficient.In addition, there was a lack of easy ways to converse with the deceased using smart devices, which limited the user experience.

[0821] 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 digital data related to the deceased, means for training a generative AI model based on the digital data, means for recreating a dialogue with the deceased using the trained generative AI model, means for enabling a user to interact with the deceased using the generative AI model, means for centrally managing the digital data and organizing the digital remains, means for analyzing the user's emotions and optimizing the dialogue content based on the analysis, and means for enabling the user to engage in dialogue using a smart device. This enables a personalized dialogue experience based on the user's emotions, enhancing psychological support. Furthermore, by utilizing a smart device, users can easily enjoy dialogue with the deceased anytime, anywhere.

[0822] "Digital data" refers to electronic files such as photos, videos, and message history related to the deceased.

[0823] A "generative AI model" refers to an artificial intelligence model that learns the characteristics of the deceased based on collected digital data and recreates conversations with the deceased.

[0824] "Sentiment analysis" refers to the process of analyzing text or voice data entered by a user to identify their emotional state.

[0825] "Optimizing dialogue content" refers to adjusting the responses of the generative AI model based on the user's emotional state to provide a more personalized dialogue experience.

[0826] "Digital legacy" refers to all digital data and online account information related to the deceased.

[0827] "Centralized management" refers to managing multiple digital data in a unified system or database.

[0828] "Smart devices" refers to devices that have internet connectivity and advanced computing capabilities, such as smartphones, head-mounted displays, and smart glasses.

[0829] This invention provides a system that collects digital data about the deceased, trains a generative AI model based on that data, and recreates the conversation between the user and the deceased. Furthermore, it performs emotion analysis and optimizes the conversation content according to the user's emotional state, providing psychological support to the user. The following describes specific embodiments of this invention.

[0830] Hardware and software used

[0831] Hardware:

[0832] Server: Stores data and trains generative AI models.

[0833] User device: Any device with internet connectivity, such as a smartphone, head-mounted display, or smart glasses.

[0834] software:

[0835] Data collection module: Python

[0836] Data learning module: TensorFlow or PyTorch

[0837] Emotion Recognition Module: NLP, emotion analysis library (e.g. Hugging Face Transformers)

[0838] Communication Reproduction Module: Python

[0839] Digital legacy management module: Django

[0840] Data collection

[0841] The server collects data by allowing users to upload digital data about the deceased via their devices. The collected data is temporarily stored on the server and then categorized and stored in the appropriate database depending on its format. For example, photo data is stored in an image database, and text data is categorized in a text database.

[0842] Data Learning

[0843] The server uses the collected digital data to train a generative AI model. Using deep learning frameworks such as TensorFlow and PyTorch, it uses natural language processing and image recognition techniques to learn the deceased's speaking style and facial expressions. This training process allows the generative AI model to realistically reproduce the deceased's characteristics.

[0844] Communication Reproduction

[0845] When a user initiates a conversation via their device, the server uses the trained generative AI model to generate the deceased's tone of voice and responses based on the user's input. The generated responses are sent to the user's device in real time, allowing the user to enjoy the experience of interacting with the deceased.

[0846] Emotion recognition and dialogue content optimization

[0847] The server receives text and voice data entered by the user and analyzes the user's emotional state using emotion analysis libraries such as Hugging Face Transformers. Based on the analyzed emotional state, the generative AI model adjusts its response. For example, if the user is sad, it generates an encouraging message.

[0848] Digital legacy management

[0849] Users can log in to the server via their devices and access all digital data related to the deceased, allowing them to organize and manage their digital belongings, search for specific message history, and view photos in album format.

[0850] Specific examples

[0851] Example 1: A user reminisces about a deceased loved one

[0852] User: "Check this out" and upload a photo.

[0853] Deceased AI: "Oh, this brings back memories. Those trips were fun."

[0854] Example prompt: "Tell me about your memories of our trip."

[0855] Example 2: Dialogue when the user is feeling down

[0856] User: "I'm not feeling well today."

[0857] Deceased AI: "Are you OK? Tell me what happened."

[0858] Example prompt: "Tell me how you would comfort a user when they're feeling down."

[0859] With the above configuration, the present invention can provide a personalized conversation experience according to the user's emotional state and enhance psychological support. Furthermore, by utilizing smart devices, users can enjoy conversation with the deceased anytime, anywhere.

[0860] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0861] Step 1: Collect digital data

[0862] Input: The user uploads photos, videos, and message history about the deceased via the device.

[0863] Processing: The server temporarily stores the data uploaded by the user and checks its format (image, text, video, etc.). For example, photos are stored in an image database, and text data is classified in a text database.

[0864] Output: Each data is stored in a corresponding database and made available for further processing.

[0865] Step 2: Train the data

[0866] Input: Digital data about the deceased, including photos, videos, and message history, stored in a database.

[0867] Processing: The server uses the stored digital data to train a generative AI model using deep learning frameworks such as TensorFlow and PyTorch, using natural language processing and image recognition techniques to learn the speech style and facial expressions of the deceased.

[0868] Output: The trained generative AI model is saved in dedicated storage and used to recreate user interactions.

[0869] Step 3: Sentiment Analysis

[0870] Input: Text and voice data entered by the user through the device.

[0871] Processing: The server performs sentiment analysis on the user's input data using emotion analysis libraries such as Hugging Face Transformers, thereby identifying the user's emotional state (e.g., sad, happy, angry, etc.).

[0872] Output: The user's emotional state is analyzed and used in the next dialogue generation step.

[0873] Step 4: Communication reproduction and dialogue content optimization

[0874] Input: User sentiment analysis results and user-entered text or voice data.

[0875] Processing: The server uses a trained generative AI model to generate the optimal response for the user based on the analyzed emotional state. For example, if the user seems sad, it generates a message of comfort or encouragement.

[0876] Output: The optimized response is sent to the user's device in real time, allowing the user to experience a natural conversation with the deceased.

[0877] Step 5: Digital Legacy Management

[0878] Input: The digital data that a user logs into a server via a terminal and requests access to.

[0879] Processing: The server authenticates the user and provides the authenticated user with the deceased's digital data (photos, message history, account information, etc.), allowing the user to view and organize their digital belongings.

[0880] Output: Users can access, organize and manage all digital data related to the deceased.

[0881] The above are the specific processing steps for implementing this invention, which allow users to receive psychological support through dialogue with the deceased and to properly manage the deceased's digital remains.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] [Third embodiment]

[0886] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0887] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0888] 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).

[0889] 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.

[0890] 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.

[0891] 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).

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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."

[0898] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. The system provides a means for users to reunite with their loved ones and assists in the organization of their digital remains.

[0899] Data collection implementation

[0900] Data Collection Module

[0901] A user logs in to their device and uploads photos, videos, message history, etc. related to the deceased. The server receives this data and temporarily stores it. The server then checks the file format and stores it in the appropriate format in the database accordingly. For example, photo data is stored in the image database, and text data is classified as a text database.

[0902] Data Learning Embodiment

[0903] Data Learning Module

[0904] The server uses the digital data stored in the database to train the generative AI model. Specifically, it uses natural language processing and image recognition technologies to learn the deceased's speaking style and facial expressions. This learning process allows the generative AI model to realistically reproduce the characteristics of the deceased. Once trained, the generative AI model is stored in dedicated storage.

[0905] Communication reproduction embodiment

[0906] Communication Reproduction Module

[0907] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device.

[0908] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[0909] Digital legacy management implementation example

[0910] Digital Legacy Management Module

[0911] Users log in to the server via their device and access all digital data related to the deceased. This allows them to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. In addition, they can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[0912] As described above, this system collects digital data about the deceased, trains a generative AI model based on that data, and recreates conversations with the deceased. It also centralizes digital data management and supports the organization and management of digital belongings, allowing users to reunite with their loved ones and provide emotional care.

[0913] The processing flow will be explained below.

[0914] Data collection module processing flow

[0915] Step 1:

[0916] The user launches the application on the device and accesses the login screen. The user enters the username and password and presses the login button.

[0917] Step 2:

[0918] The server receives the user's authentication information, checks it against the database, and if authentication is successful, redirects the user to the dashboard.

[0919] Step 3:

[0920] The user selects the deceased's digital data (photos, videos, message history, etc.) through the device interface. The user selects the files using a file selection dialog.

[0921] Step 4:

[0922] To upload the selected digital data, the user clicks the upload button, and the terminal transmits the selected file to the server.

[0923] Step 5:

[0924] The server temporarily stores the received digital data, determines the file format, and stores it in the appropriate database (image database, text database, etc.) depending on the file format.

[0925] Data Learning Module Processing Flow

[0926] Step 1:

[0927] The server retrieves the digital data of the deceased person stored in the database. The retrieved data is categorized into image files, text files, etc.

[0928] Step 2:

[0929] The server pre-processes the acquired data and converts it into a format suitable for input to the generative AI model: for example, image files are standardized in resolution, and text files are tokenized.

[0930] Step 3:

[0931] The server then uses the pre-processed data to begin the process of training a generative AI model, using natural language processing and image recognition techniques.

[0932] Step 4:

[0933] The server stores the trained generative AI model in storage, which has the ability to reproduce the features and speaking style of the deceased.

[0934] Processing flow of the communication reproduction module

[0935] Step 1:

[0936] The user accesses an interface on the terminal to initiate a dialogue with the deceased person. The user clicks a button to begin the conversation.

[0937] Step 2:

[0938] The device sends a request to the server to initiate a dialogue with the deceased person, and the server accepts the request and starts the dialogue session.

[0939] Step 3:

[0940] The user enters a message in the text box on the terminal and presses the send button. The terminal then sends this message to the server.

[0941] Step 4:

[0942] The server inputs the received message into a generative AI model to generate a response in the deceased person's voice, which is then sent back to the server.

[0943] Step 5:

[0944] The server sends the generated response to the user's terminal, where the user checks the response message from the deceased.

[0945] Digital Legacy Management Module Processing Flow

[0946] Step 1:

[0947] The user accesses the digital estate management interface on their device and clicks a button to view all digital data related to the deceased.

[0948] Step 2:

[0949] The device sends a request to access the digital remains to the server, which accepts the request and retrieves the relevant data from the database.

[0950] Step 3:

[0951] The server processes the digital data retrieved as search results to display them to the user in a visually understandable format, for example, images are displayed as thumbnails and message histories are displayed in a list format.

[0952] Step 4:

[0953] Users can view and organize their digital belongings on their devices, searching for data, sorting it into folders, and deleting it as needed.

[0954] Step 5:

[0955] The server updates the database based on user actions, e.g., deleted data is removed from the database, data moved to a different category is saved in a new folder, etc.

[0956] Example 1

[0957] 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."

[0958] While users can reminisce about their memories with the deceased, it was difficult to recreate the experience of interacting with the deceased. Furthermore, there was a lack of efficient ways to manage and organize the vast amount of digital data of the deceased, which required users to manually organize it. Additionally, managing the authentication information for the deceased's online accounts was difficult.

[0959] 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.

[0960] In this invention, the server includes a means for users to upload digital data via their terminals and for the server to store the data in a database, a means for training a generative AI model based on the digital data, and a means for recreating a conversation with the deceased using the trained generative AI model. This allows users to enjoy a realistic conversation experience with the deceased while efficiently managing and organizing the deceased's digital data. Furthermore, authentication information for the deceased's online accounts can also be centrally managed, facilitating the preparations for the deceased's digital legacy.

[0961] A "server" is a central processing unit that stores, processes, and shares data over a network.

[0962] A "terminal" is a device operated by a user, and includes a personal computer, a smartphone, etc.

[0963] A "user" is an individual who uses this system and has a relationship with the deceased.

[0964] "Digital data" refers to information stored electronically, including files such as photos, videos, and message histories.

[0965] A "database" is a system for efficiently storing, managing, and searching digital data.

[0966] A "generative AI model" is an algorithm that is trained using machine learning techniques to perform a specific task.

[0967] "Natural language processing technology" is a technology that enables computers to understand, generate, and analyze human language.

[0968] "Image recognition technology" is a technology that allows computers to analyze images and identify specific features or objects.

[0969] The "interactive experience" is an experience in which users can communicate with the deceased through a generative AI model.

[0970] "Digital legacy" refers to digital data and online account information left behind by a deceased person.

[0971] "Centralized management" refers to the integrated management of multiple digital data using a single system.

[0972] "Authentication information" refers to information such as ID and password required by a user to access an online account.

[0973] "Digital end-of-life planning" is the process of organizing and managing the digital data and online accounts left behind by the deceased.

[0974] This invention is a system that collects digital data about the deceased and trains a generative AI model based on that data. Specific embodiments of the system are described below.

[0975] Data collection implementation

[0976] Data Collection Module

[0977] First, the user enters authentication information to log in to the device, which can be a PC or smartphone. The server receives this authentication information and checks it against a database. If authentication is successful, the user is able to upload digital data.

[0978] Users upload photos, videos, message history, etc. related to the deceased person from their devices. The server receives this data and temporarily stores it in cloud storage. The server checks the file format and stores image data in an image database and text data in a text database.

[0979] The hardware used includes general PCs and smartphones, and the servers are cloud servers (e.g., AWS, Google Cloud, etc.), and the database systems used are MySQL and PostgreSQL.

[0980] Data Learning Embodiment

[0981] Data Learning Module

[0982] The server retrieves the digital data stored in the database, processes it, and stores it in a workspace. The collected data is used to train a generative AI model (using, for example, TensorFlow or PyTorch). This process uses natural language processing techniques (such as BERT) and image recognition techniques (such as CNN).

[0983] During the learning process, the system uses text data to learn the deceased's speaking style and image data to learn their facial expressions and gestures. Once trained, the generated AI model is saved in the "trained_models" folder.

[0984] Communication reproduction embodiment

[0985] Communication Reproduction Module

[0986] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response based on the user's input. The generated response is then sent to the user's device via the server. This allows the user to enjoy a real-time dialogue experience with the deceased on their device.

[0987] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[0988] Digital legacy management implementation example

[0989] Digital Legacy Management Module

[0990] Users can log in to the server via their devices and access the deceased's digital data. This allows them to search for specific message history and view photos in album format. Furthermore, they can also manage the deceased's online account authentication information, making it easier to organize their digital belongings.

[0991] The hardware used includes PCs and smartphones, and cloud servers (AWS, Google Cloud, etc.) are used. MySQL and PostgreSQL are used as database management systems.

[0992] (Example of a prompt)

[0993] "Recreate the conversational style of the deceased person based on their message history, and generate answers that sound like the deceased person to the user's questions."

[0994] The above is a specific embodiment for carrying out the present invention.

[0995] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0996] Step 1: User authentication

[0997] The user enters authentication information to log in to the terminal.

[0998] The server receives the authentication information and checks it against a database.

[0999] (Input) The ID and password entered by the user from the terminal.

[1000] (Processing) The server references the database and searches for records that match the input information.

[1001] (Output) If authentication is successful, the user is redirected to the data upload screen. If not, an error message is displayed.

[1002] (Specific operation) The user enters their ID and password into the login form on the terminal and presses the "Login" button. The server verifies the information in the database and returns the authentication result.

[1003] Step 2: Upload data

[1004] Users upload digital data about the deceased, such as photos, videos, and message history, from their devices.

[1005] The server receives this data and temporarily stores it in storage.

[1006] (Input) Digital data (file) selected by the user from the terminal.

[1007] (Processing) The server checks the file format and saves it in temporary storage.

[1008] (Output) A message notifying you that the data was saved successfully.

[1009] (Specific operation) The user clicks the upload button and selects a specific file from their PC. The selected file is uploaded to the server and saved in temporary storage.

[1010] Step 3: Check the data format and save it to the database

[1011] The server checks the format of the uploaded file.

[1012] Image data is stored in an image database, and text data is stored in a text database.

[1013] (Input) Digital data stored in temporary storage.

[1014] (Processing) The server checks the file extension, etc., and classifies and stores it in the appropriate database.

[1015] (Output) Message indicating completion of saving to the database.

[1016] (Specific operation) The server stores .jpg files in the image database and .txt files in the text database.

[1017] Step 4: Collecting data for training

[1018] The server collects data from an image database and a text database.

[1019] The collected data is stored in a temporary workspace.

[1020] (Input) Data stored in the image database and text database.

[1021] (Processing) The server retrieves the data using the specified query and moves it to the workspace.

[1022] (Output) The training data in the workspace.

[1023] (Specific operation) The server retrieves a specific number of photos and message history from the database and moves them to the workspace.

[1024] Step 5: Training the generative AI model

[1025] The server uses the data in the workspace to train a generative AI model.

[1026] It uses natural language processing technology and image recognition technology.

[1027] (Input) Digital data in the workspace.

[1028] The (processing) server analyzes the data using natural language processing models (such as BERT) and image recognition models (such as CNN) and trains the models.

[1029] (Output) A trained generative AI model.

[1030] (Specific operation) The server uses the BERT model to train text data and the CNN model to train image data. It then performs calculations to improve the accuracy of the model.

[1031] Step 6: Save the trained model

[1032] Once trained, the generative AI model is stored in dedicated storage.

[1033] (Input) A fully trained generative AI model.

[1034] (Processing) The server saves the trained model in storage.

[1035] (Output) Completion notification for the saved model.

[1036] (Specific operation) The server saves the generated AI model that has completed learning in the "trained_models" folder.

[1037] Step 7: Sending a conversation-initiating request

[1038] The user sends a dialogue start request to the server via the terminal.

[1039] (Input) A request from the user to start a conversation.

[1040] (Processing) The server receives the request and transitions to interactive mode.

[1041] (Output) Notification of conversation start.

[1042] (Specific operation) The user clicks the "talk to the deceased" button in the chat application, and the server processes the request.

[1043] Step 8: Response Generation

[1044] The server uses a trained generative AI model to generate a response based on the user's input.

[1045] (Input) A message from the user.

[1046] (Processing) The server uses the generative AI model to generate a response appropriate to the user's message.

[1047] (Output) The generated response message.

[1048] (Specific operation) When a user sends a message saying, "What are you doing now?", the server uses the generative AI model to generate a response saying, "I'm just taking a walk."

[1049] Step 9: Sending a Response

[1050] The generated response is sent to the user's terminal via the server.

[1051] (Input) The response generated by the generative AI model.

[1052] (Processing) The server sends the response to the user's terminal.

[1053] (Output) The response message displayed on the user's terminal.

[1054] (Specific operation) The server generates a message saying "I'm just taking a walk" and sends it to the chat application, which displays it on the user's device.

[1055] Step 10: Organizing and managing your digital legacy

[1056] Users log into the server via a terminal and access all digital data relating to the deceased.

[1057] (Input) User authentication information.

[1058] (Processing) The server authenticates and allows access to the digital data.

[1059] (Output) Display of digital data about the deceased.

[1060] (Specific operation) A user logs in on a terminal and accesses a dashboard of digital data.

[1061] Step 11: Search and view specific digital data

[1062] Users can search for specific message history and view photos in album format.

[1063] (Input) The user's search query.

[1064] The (processing) server searches the database and retrieves the relevant data.

[1065] (Output) Search results and photos in album format.

[1066] (Specific operation) The user types "travel" in the search box to display a list of relevant message history. Also, the user clicks the album tab to view photos.

[1067] Step 12: Manage your online account credentials

[1068] The server manages the IDs and passwords of the deceased person's online accounts.

[1069] (Enter) The deceased person's online account information.

[1070] The (processing) server securely stores and manages information.

[1071] (Output) Secure storage and management status of credentials.

[1072] (Specific operation) The user registers the deceased person's online account information on the server, and the server encrypts and stores this information.

[1073] These are the specific processing steps of the program for this system. Each step shows the specific operations and inputs / outputs of the user, terminal, and server.

[1074] (Application example 1)

[1075] 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."

[1076] Systems already exist that collect and manage digital data about the deceased and use generative AI models to recreate conversations with the deceased. However, no system exists that applies this technology to virtual stores, allowing users to enjoy shopping in a virtual space with the deceased. Such a system would allow users to enjoy new experiences while reminiscing on memories spent with the deceased, and would also efficiently organize digital belongings. Therefore, there is a need for a system that allows users to reunite with the deceased while also providing new experiences in a virtual space.

[1077] 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.

[1078] In this invention, the server includes means for collecting digital data about the deceased, means for training a generative AI model based on the digital data, means for recreating a conversation with the deceased using the trained generative AI model, means for enabling a user to converse with the deceased using the generative AI model, means for enabling the user to have the deceased recommend products to the user in a virtual space, and means for centrally managing the digital data and organizing the digital remains. This allows users to not only relive conversations with the deceased but also enjoy a shopping experience in a virtual space with the deceased.

[1079] "Digital data" refers to information stored or collected electronically, including photographs, videos, text messages, audio data, and other forms of multimedia.

[1080] A "generative AI model" is an artificial intelligence model trained using machine learning technology that has the ability to learn the characteristics of the deceased based on the digital data provided and reproduce their dialogue.

[1081] "Dialogue reproduction" is a process in which a user simulates a conversation with a deceased person, and refers to the generative AI model mimicking the deceased's tone of voice and facial expressions to respond to the user.

[1082] A "virtual space" is a computer-simulated, imaginary, three-dimensional environment within which a user can visually and interactively interact.

[1083] "Product introduction" refers to the process by which a deceased person uses a generative AI model to provide a user with a description or opinion about a product or service.

[1084] "Digital legacy" refers to all data in digital format that the deceased created, stored, and shared during their lifetime, including message history, photos, videos, and online account information.

[1085] MODE FOR CARRYING OUT THE INVENTION

[1086] A specific system for implementing the invention includes the following elements:

[1087] 1. Data Collection Module

[1088] The device provides an interface that allows users to upload digital data related to the deceased, such as photos, videos, and message history. When a user sends the digital data from the device to the server, the server temporarily stores the data, checks the file format, and then stores it in the appropriate database. For example, photo data is stored in an image database, and text data is classified as a text database.

[1089] 2. Data Learning Module

[1090] The server uses the digital data stored in the database to train the generative AI model. Using natural language processing and image recognition technologies with the TensorFlow library, the model learns the deceased's speaking style and facial expressions. This training process allows the generative AI model to realistically reproduce the characteristics of the deceased.

[1091] 3. Communication Reproduction Module

[1092] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device. For example, if the user sends a message from their device saying, "What are you doing now?", the server uses the generative AI model to generate a response such as "I'm just taking a walk," which is then displayed on the user's device. An example prompt is "The user said: What are you doing now? Your response:."

[1093] 4. Virtual Space Module

[1094] Users can enjoy shopping while interacting with the deceased in a virtual space. Specifically, when a user asks a question about a specific product, a generative AI model that mimics the deceased's style will explain the product. For example, if a user asks, "What do you think of this dress?", the generative AI model will respond, "It's very nice. It looks great on you." An example prompt is, "The user said: What do you think of this dress? Your response:"

[1095] 5. Digital Legacy Management Module

[1096] Users can log in via their device and access all digital data related to the deceased. Users can centrally manage this data and easily organize their digital belongings. For example, they can search for specific message history or view photos in album format. They can also manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[1097] This allows users to not only realistically recreate conversations with their deceased loved ones, but also enjoy a virtual shopping experience with them while efficiently organizing and managing their digital belongings.

[1098] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1099] DETAILED DESCRIPTION OF THE INVENTION - PROCESS STEPS

[1100] Step 1: Upload your digital data

[1101] Users use their devices to upload digital data about the deceased, such as photos, videos, and message history. The devices then send this data to a server. The input is the digital data provided by the user, and the output is temporary storage of the data by the server.

[1102] Specific operation: The user clicks the "Upload" button on the terminal application screen, selects a file, and sends it to the server.

[1103] Step 2: Check the format of the data and save it

[1104] The server checks the format of the uploaded data and stores it in the database in the appropriate format. The input is the digital data sent from the terminal, and the output is the data stored in the database.

[1105] Specific operation: The server checks the file format of the received data, classifies and stores the photo data in the image database and the text data in the text database.

[1106] Step 3: Training the generative AI model

[1107] The server uses the digital data stored in the database to train the generative AI model, with the input being the digital data in the database and the output being the trained generative AI model.

[1108] How it works: The server uses the TensorFlow library to perform image recognition and natural language processing techniques, learning the speech style and facial expressions of the deceased.

[1109] Step 4: Receiving an interaction request

[1110] The user sends a request to the server via a terminal to start a dialogue with the deceased. The input is the dialogue request from the user, and the output is the request received by the server.

[1111] Specific operation: The user clicks the "Start conversation" button on the application screen to send a conversation request to the server.

[1112] Step 5: Creating and Executing Dialogues

[1113] The server uses the trained generative AI model to generate a response from the deceased in response to the user's input. The input is a message from the user, and the output is a response from the generative AI model.

[1114] Specific operation: When a user sends a message saying "What are you doing now?", the server uses the generative AI model to generate a response saying "Just taking a walk" and sends it to the user's device.

[1115] Step 6: Interact in the virtual space

[1116] The user interacts with the deceased through a generative AI model in a virtual space, introducing products to each other. The input is the user's question, and the output is a product introduction by the generative AI model.

[1117] Specific operation: When a user asks in the virtual space, "What do you think of this dress?", the generative AI model replies, "It's very nice. It looks good on you."

[1118] Step 7: Organizing your digital legacy

[1119] Users access and organize their digital remains through a terminal. Input is search keywords and operations for organization, and output is a display of the organized data.

[1120] Specific operation: When a user searches for "view photos from 2022," the server retrieves the relevant photo data from the database and displays it on the device.

[1121] This processing step enables the recreation of conversations with the deceased and the experience of shopping in a virtual space, enabling the efficient management of digital belongings.

[1122] 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.

[1123] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. It also includes a combined emotion engine that recognizes user emotions and optimizes dialogue and display content accordingly. This system provides a means for users to reunite with their deceased loved ones and assists in the organization of their digital legacy.

[1124] Data collection implementation

[1125] Data Collection Module

[1126] A user logs in to their device and uploads photos, videos, message history, etc. related to the deceased. The server receives this data and temporarily stores it. The server then checks the file format and stores it in the appropriate format in the database accordingly. For example, photo data is stored in the image database, and text data is classified as a text database.

[1127] Data Learning Embodiment

[1128] Data Learning Module

[1129] The server uses the digital data stored in the database to train the generative AI model. Specifically, it uses natural language processing and image recognition technologies to learn the deceased's speaking style and facial expressions. This learning process allows the generative AI model to realistically reproduce the characteristics of the deceased. Once trained, the generative AI model is stored in dedicated storage.

[1130] Communication reproduction embodiment

[1131] Communication Reproduction Module

[1132] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device.

[1133] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[1134] Embodiment of Emotion Engine

[1135] Emotion Recognition Module

[1136] The server receives the text and voice data entered by the user and analyzes the user's emotions using an emotion engine. The emotion engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state from their speech and facial expressions. For example, if a user enters "I'm not feeling very good today" in a sad voice, the emotion engine will recognize that the user is in a "sad" state.

[1137] Optimizing dialogue based on emotions

[1138] Emotion-based communication reproduction module

[1139] The server adjusts the response from the generative AI model based on the user's emotional state analyzed by the emotion engine. For example, if the user appears sad, the server can generate a particularly compassionate response and send a message of comfort and encouragement to the user. This provides the user with a more personalized interaction experience and enhanced psychological support.

[1140] Digital legacy management implementation example

[1141] Digital Legacy Management Module

[1142] Users log in to the server via their device and access all digital data related to the deceased. This allows them to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. In addition, they can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[1143] As described above, this system collects digital data about the deceased and uses it to train a generative AI model to recreate conversations with the deceased. Furthermore, by incorporating an emotion engine, the system optimizes the content of the conversations according to the user's emotional state, providing more effective mental care. Furthermore, by centrally managing digital data and supporting the organization and management of digital belongings, the system allows users to reunite with their loved ones while also providing emotional care.

[1144] The processing flow will be explained below.

[1145] Data collection module processing flow

[1146] Step 1:

[1147] The user launches the application on the device and accesses the login screen. The user enters the username and password and presses the login button.

[1148] Step 2:

[1149] The server receives the user's authentication information, checks it against the database, and if authentication is successful, redirects the user to the dashboard.

[1150] Step 3:

[1151] The user selects the deceased's digital data (photos, videos, message history, etc.) through the device interface. The user selects the files using a file selection dialog.

[1152] Step 4:

[1153] To upload the selected digital data, the user clicks the upload button, and the terminal transmits the selected file to the server.

[1154] Step 5:

[1155] The server temporarily stores the received digital data, determines the file format, and stores it in the appropriate database (image database, text database, etc.) depending on the file format.

[1156] Data Learning Module Processing Flow

[1157] Step 1:

[1158] The server retrieves the digital data of the deceased person stored in the database. The retrieved data is categorized into image files, text files, etc.

[1159] Step 2:

[1160] The server pre-processes the acquired data and converts it into a format suitable for input to the generative AI model: for example, image files are standardized in resolution, and text files are tokenized.

[1161] Step 3:

[1162] The server then uses the pre-processed data to begin the process of training a generative AI model, using natural language processing and image recognition techniques.

[1163] Step 4:

[1164] The server stores the trained generative AI model in storage, which has the ability to reproduce the features and speaking style of the deceased.

[1165] Processing flow of the communication reproduction module

[1166] Step 1:

[1167] The user accesses an interface on the terminal to initiate a dialogue with the deceased person. The user clicks a button to begin the conversation.

[1168] Step 2:

[1169] The device sends a request to the server to initiate a dialogue with the deceased person, and the server accepts the request and starts the dialogue session.

[1170] Step 3:

[1171] The user enters a message in the text box on the terminal and presses the send button. The terminal then sends this message to the server.

[1172] Step 4:

[1173] The server inputs the received message into a generative AI model to generate a response in the deceased person's voice, which is then sent back to the server.

[1174] Step 5:

[1175] The server sends the generated response to the user's terminal, where the user checks the response message from the deceased.

[1176] Emotion Recognition Module Processing Flow

[1177] Step 1:

[1178] The user inputs a message on the terminal and sends it to the server, which then sends a request to the emotion engine to analyze the received message.

[1179] Step 2:

[1180] The emotion engine analyzes the received message using natural language processing technology and emotion analysis algorithms, and classifies the user's emotional state into categories such as "sad," "happy," or "angry."

[1181] Step 3:

[1182] The emotion engine sends the analysis results back to the server, which receives the emotion analysis results and adjusts the generated response content based on the user's emotional state.

[1183] Step 4:

[1184] The server inputs the response message corresponding to the generated emotion into the generative AI model, which generates a response tailored to the tone of the deceased person. The generated response is returned to the server.

[1185] Step 5:

[1186] The server sends the tailored response message to the user's terminal, and the user checks the tailored response message on the terminal.

[1187] Digital Legacy Management Module Processing Flow

[1188] Step 1:

[1189] The user accesses the digital estate management interface on their device and clicks a button to view all digital data related to the deceased.

[1190] Step 2:

[1191] The device sends a request to access the digital remains to the server, which accepts the request and retrieves the relevant data from the database.

[1192] Step 3:

[1193] The server processes the digital data retrieved as search results to display them to the user in a visually understandable format, for example, images are displayed as thumbnails and message histories are displayed in a list format.

[1194] Step 4:

[1195] Users can view and organize their digital belongings on their devices, searching for data, sorting it into folders, and deleting it as needed.

[1196] Step 5:

[1197] The server updates the database based on user actions, e.g., deleted data is removed from the database, data moved to a different category is saved in a new folder, etc.

[1198] Example 2

[1199] 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."

[1200] In the digital age, there is a huge amount of digital data about the deceased, but it is difficult to effectively utilize this data and provide a dialogue experience with the deceased. It is also a challenge to optimize dialogue based on the user's emotions and strengthen psychological support. Furthermore, it is also important to centrally manage digital data in various formats and organize digital remains.

[1201] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting digital data related to the deceased; means for training a generative AI model based on the digital data; means for recreating a dialogue with the deceased using the trained generative AI model; means for enabling a user to engage in a dialogue with the deceased using the generative AI model; means for centrally managing the digital data and organizing the digital remains; means for the user to send a request to the server to initiate a dialogue with the deceased; means for the server to confirm the data format and store the data in an appropriate database; means for analyzing the user's emotions using an emotion recognition engine; means for optimizing the dialogue content based on the analyzed emotions; and means for generating a response to the user's input in real time using the generative AI model. This enables the user to have a dialogue experience with the deceased and optimize the dialogue according to the user's emotions. Furthermore, centralized management of digital data facilitates the organization of the digital remains.

[1202] "Digital data relating to the deceased" refers to digital information such as photographs, videos, and message history that the deceased created or saved during their lifetime.

[1203] A "generative AI model" is a machine learning model that has been trained using artificial intelligence techniques to perform a specific task, in this case, recreating the speaking style and facial expressions of a deceased person.

[1204] A "user" is an individual or group who logs in and operates this system to interact with the deceased.

[1205] "Logging in" is the process by which a user enters the authentication information (such as a user ID and password) required to access a system.

[1206] A "database" is a system for efficiently storing, managing, and searching large amounts of digital data.

[1207] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language.

[1208] "Image recognition technology" is a technology that allows computers to detect and identify objects and patterns in images.

[1209] An "emotion recognition engine" is an algorithm or software that analyzes and identifies emotions from text or voice input by a user.

[1210] "Centralized management" refers to the centralized management of data of different formats and types in one place.

[1211] "Digital legacy" refers to digital assets left behind by a deceased person, such as photos, messages, and online account information.

[1212] A "request" refers to an instruction sent by a user to the system to request a particular operation.

[1213] "Real-time" refers to immediate response to user operations and inputs without delay.

[1214] "Dialogue optimization" refers to adjusting the dialogue content and generating the optimal response according to the user's emotions and situation.

[1215] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. It also includes a combined emotion engine that recognizes user emotions and optimizes dialogue and display content accordingly. This system provides a means for users to reunite with their deceased loved ones and assists in the organization of their digital legacy.

[1216] Data collection

[1217] Users log in to the system using a terminal. When logging in, they are required to enter their user ID and password for authentication. Once authentication is complete, the user uploads digital data related to the deceased, such as photos, videos, and message history. The terminal sends this data to the server, which then stores the received data in a temporary file system. The server checks the data format (e.g., image file, video file, text file), and stores it in the appropriate database for each format.

[1218] Data Learning

[1219] The server uses the digital data of the deceased stored in the database to train the generative AI model. This training uses natural language processing (NLP) and image recognition technologies. Specifically, the server extracts from the data the features necessary to reproduce the deceased's speaking style and facial expressions, and trains the AI ​​model. Once training is complete, the trained generative AI model is stored in dedicated storage.

[1220] Communication Reproduction

[1221] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a generative AI model to generate a response based on the user's input, in the tone and style of the deceased. The generated response is then sent from the server to the user's device and displayed in real time.

[1222] As a concrete example, if a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a reply saying, "I'm just taking a walk," and displays this on the user's device.

[1223] Example prompt sentence:

[1224] "What are you doing now?"

[1225] "I'm just taking a walk."

[1226] Combining Emotion Engines

[1227] The server analyzes the text and voice data entered by the user using an emotion engine to confirm the user's emotions. The emotion engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state. For example, if a user enters "I'm not feeling very good today" in a sad voice, the emotion engine will recognize that the user is in a "sad" state.

[1228] Optimizing dialogue based on emotions

[1229] The server adjusts the response of the generative AI model based on the analysis results of the emotion engine. If the user appears sad, the server will generate a more compassionate response and send a comforting or encouraging message to the user. This allows the user to have a personalized interaction experience based on their emotions and provides enhanced psychological support.

[1230] Digital legacy management

[1231] Users can log in to the server via their device and access all digital data related to the deceased. This allows users to organize and manage the deceased's digital belongings. Specifically, users can search for specific message history and view photos in album format. In addition, users can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[1232] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1233] Step 1:

[1234] A user logs in to the system using a terminal. They enter their user ID and password and go through the authentication procedure. The server receives the entered authentication information and authenticates the user. If authentication is successful, the user is allowed to access the system. The input here is the user ID and password, and the output is a message indicating whether authentication was successful or failed.

[1235] Step 2:

[1236] Users use their devices to upload digital data about the deceased, such as photos, videos, and message history, to the system. The devices then send the uploaded data to the server, which then receives the data and stores it in a temporary file system. The input is digital data such as photos, videos, and message history, and the output is the data stored in the temporary file system.

[1237] Step 3:

[1238] The server checks the format of the data stored in the temporary file system. For example, it identifies the format as an image file, video file, text file, etc., and classifies and stores it in a database. The server analyzes the data format and stores each piece of data appropriately in the image database, video database, or text database. The input is data stored in the temporary file system, and the output is data classified and stored in the database.

[1239] Step 4:

[1240] The server retrieves the data stored in the database and trains the generative AI model. This training process uses natural language processing and image recognition technologies. Specifically, data is fed into the generative AI model to learn the deceased's speech style and facial expressions. Once trained, the generative AI model is stored in dedicated storage. The input is the data stored in the database, and the output is the trained generative AI model.

[1241] Step 5:

[1242] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server receives this request and uses a generative AI model to generate a response based on the user's input. The generated response is sent from the server to the user's device and displayed in real time. The input is the user's dialogue request and its content, and the output is the response generated using the generative AI model.

[1243] Step 6:

[1244] The server receives text or voice data entered by the user and analyzes it with an emotion recognition engine. The emotion recognition engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state. For example, if a user enters "I'm not feeling very good today," the emotion recognition engine will determine that this is "sad." The input is text or voice data, and the output is the analyzed emotional state.

[1245] Step 7:

[1246] The server optimizes the response of the generative AI model based on the analysis results of the emotion recognition engine. If the user looks sad, the server generates a more compassionate response and sends a message of comfort or encouragement to the user. The input is the emotion analysis result, and the output is the optimized response.

[1247] Step 8:

[1248] Users can log in to the server via their devices and access all digital data related to the deceased. This allows users to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. The input is the user's login information and request, and the output is the display and management function for the corresponding digital data.

[1249] (Application example 2)

[1250] 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."

[1251] Previously, there were systems that collected digital data of the deceased and used it to recreate conversations with the deceased, but the content of the conversations was not optimized to reflect the user's emotions, and psychological support for the user was insufficient.In addition, there was a lack of easy ways to converse with the deceased using smart devices, which limited the user experience.

[1252] 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 digital data related to the deceased, means for training a generative AI model based on the digital data, means for recreating a dialogue with the deceased using the trained generative AI model, means for enabling a user to interact with the deceased using the generative AI model, means for centrally managing the digital data and organizing the digital remains, means for analyzing the user's emotions and optimizing the dialogue content based on the analysis, and means for enabling the user to engage in dialogue using a smart device. This enables a personalized dialogue experience based on the user's emotions, enhancing psychological support. Furthermore, by utilizing a smart device, users can easily enjoy dialogue with the deceased anytime, anywhere.

[1253] "Digital data" refers to electronic files such as photos, videos, and message history related to the deceased.

[1254] A "generative AI model" refers to an artificial intelligence model that learns the characteristics of the deceased based on collected digital data and recreates conversations with the deceased.

[1255] "Sentiment analysis" refers to the process of analyzing text or voice data entered by a user to identify their emotional state.

[1256] "Optimizing dialogue content" refers to adjusting the responses of the generative AI model based on the user's emotional state to provide a more personalized dialogue experience.

[1257] "Digital legacy" refers to all digital data and online account information related to the deceased.

[1258] "Centralized management" refers to managing multiple digital data in a unified system or database.

[1259] "Smart devices" refers to devices that have internet connectivity and advanced computing capabilities, such as smartphones, head-mounted displays, and smart glasses.

[1260] This invention provides a system that collects digital data about the deceased, trains a generative AI model based on that data, and recreates the conversation between the user and the deceased. Furthermore, it performs emotion analysis and optimizes the conversation content according to the user's emotional state, providing psychological support to the user. The following describes specific embodiments of this invention.

[1261] Hardware and software used

[1262] Hardware:

[1263] Server: Stores data and trains generative AI models.

[1264] User device: Any device with internet connectivity, such as a smartphone, head-mounted display, or smart glasses.

[1265] software:

[1266] Data collection module: Python

[1267] Data learning module: TensorFlow or PyTorch

[1268] Emotion Recognition Module: NLP, emotion analysis library (e.g. Hugging Face Transformers)

[1269] Communication Reproduction Module: Python

[1270] Digital legacy management module: Django

[1271] Data collection

[1272] The server collects data by allowing users to upload digital data about the deceased via their devices. The collected data is temporarily stored on the server and then categorized and stored in the appropriate database depending on its format. For example, photo data is stored in an image database, and text data is categorized in a text database.

[1273] Data Learning

[1274] The server uses the collected digital data to train a generative AI model. Using deep learning frameworks such as TensorFlow and PyTorch, it uses natural language processing and image recognition techniques to learn the deceased's speaking style and facial expressions. This training process allows the generative AI model to realistically reproduce the deceased's characteristics.

[1275] Communication Reproduction

[1276] When a user initiates a conversation via their device, the server uses the trained generative AI model to generate the deceased's tone of voice and responses based on the user's input. The generated responses are sent to the user's device in real time, allowing the user to enjoy the experience of interacting with the deceased.

[1277] Emotion recognition and dialogue content optimization

[1278] The server receives text and voice data entered by the user and analyzes the user's emotional state using emotion analysis libraries such as Hugging Face Transformers. Based on the analyzed emotional state, the generative AI model adjusts its response. For example, if the user is sad, it generates an encouraging message.

[1279] Digital legacy management

[1280] Users can log in to the server via their devices and access all digital data related to the deceased, allowing them to organize and manage their digital belongings, search for specific message history, and view photos in album format.

[1281] Specific examples

[1282] Example 1: A user reminisces about a deceased loved one

[1283] User: "Check this out" and upload a photo.

[1284] Deceased AI: "Oh, this brings back memories. Those trips were fun."

[1285] Example prompt: "Tell me about your memories of our trip."

[1286] Example 2: Dialogue when the user is feeling down

[1287] User: "I'm not feeling well today."

[1288] Deceased AI: "Are you OK? Tell me what happened."

[1289] Example prompt: "Tell me how you would comfort a user when they're feeling down."

[1290] With the above configuration, the present invention can provide a personalized conversation experience according to the user's emotional state and enhance psychological support. Furthermore, by utilizing smart devices, users can enjoy conversation with the deceased anytime, anywhere.

[1291] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1292] Step 1: Collect digital data

[1293] Input: The user uploads photos, videos, and message history about the deceased via the device.

[1294] Processing: The server temporarily stores the data uploaded by the user and checks its format (image, text, video, etc.). For example, photos are stored in an image database, and text data is classified in a text database.

[1295] Output: Each data is stored in a corresponding database and made available for further processing.

[1296] Step 2: Train the data

[1297] Input: Digital data about the deceased, including photos, videos, and message history, stored in a database.

[1298] Processing: The server uses the stored digital data to train a generative AI model using deep learning frameworks such as TensorFlow and PyTorch, using natural language processing and image recognition techniques to learn the speech style and facial expressions of the deceased.

[1299] Output: The trained generative AI model is saved in dedicated storage and used to recreate user interactions.

[1300] Step 3: Sentiment Analysis

[1301] Input: Text and voice data entered by the user through the device.

[1302] Processing: The server performs sentiment analysis on the user's input data using emotion analysis libraries such as Hugging Face Transformers, thereby identifying the user's emotional state (e.g., sad, happy, angry, etc.).

[1303] Output: The user's emotional state is analyzed and used in the next dialogue generation step.

[1304] Step 4: Communication reproduction and dialogue content optimization

[1305] Input: User sentiment analysis results and user-entered text or voice data.

[1306] Processing: The server uses a trained generative AI model to generate the optimal response for the user based on the analyzed emotional state. For example, if the user seems sad, it generates a message of comfort or encouragement.

[1307] Output: The optimized response is sent to the user's device in real time, allowing the user to experience a natural conversation with the deceased.

[1308] Step 5: Digital Legacy Management

[1309] Input: The digital data that a user logs into a server via a terminal and requests access to.

[1310] Processing: The server authenticates the user and provides the authenticated user with the deceased's digital data (photos, message history, account information, etc.), allowing the user to view and organize their digital belongings.

[1311] Output: Users can access, organize and manage all digital data related to the deceased.

[1312] The above are the specific processing steps for implementing this invention, which allow users to receive psychological support through dialogue with the deceased and to properly manage the deceased's digital remains.

[1313] 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.

[1314] 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.

[1315] 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.

[1316] [Fourth embodiment]

[1317] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1318] 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.

[1319] 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).

[1320] 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.

[1321] 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.

[1322] 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).

[1323] 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.

[1324] 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.

[1325] 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.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] 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."

[1330] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. The system provides a means for users to reunite with their loved ones and assists in the organization of their digital remains.

[1331] Data collection implementation

[1332] Data Collection Module

[1333] A user logs in to their device and uploads photos, videos, message history, etc. related to the deceased. The server receives this data and temporarily stores it. The server then checks the file format and stores it in the appropriate format in the database accordingly. For example, photo data is stored in the image database, and text data is classified as a text database.

[1334] Data Learning Embodiment

[1335] Data Learning Module

[1336] The server uses the digital data stored in the database to train the generative AI model. Specifically, it uses natural language processing and image recognition technologies to learn the deceased's speaking style and facial expressions. This learning process allows the generative AI model to realistically reproduce the characteristics of the deceased. Once trained, the generative AI model is stored in dedicated storage.

[1337] Communication reproduction embodiment

[1338] Communication Reproduction Module

[1339] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device.

[1340] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[1341] Digital legacy management implementation example

[1342] Digital Legacy Management Module

[1343] Users log in to the server via their device and access all digital data related to the deceased. This allows them to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. In addition, they can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[1344] As described above, this system collects digital data about the deceased, trains a generative AI model based on that data, and recreates conversations with the deceased. It also centralizes digital data management and supports the organization and management of digital belongings, allowing users to reunite with their loved ones and provide emotional care.

[1345] The processing flow will be explained below.

[1346] Data collection module processing flow

[1347] Step 1:

[1348] The user launches the application on the device and accesses the login screen. The user enters the username and password and presses the login button.

[1349] Step 2:

[1350] The server receives the user's authentication information, checks it against the database, and if authentication is successful, redirects the user to the dashboard.

[1351] Step 3:

[1352] The user selects the deceased's digital data (photos, videos, message history, etc.) through the device interface. The user selects the files using a file selection dialog.

[1353] Step 4:

[1354] To upload the selected digital data, the user clicks the upload button, and the terminal transmits the selected file to the server.

[1355] Step 5:

[1356] The server temporarily stores the received digital data, determines the file format, and stores it in the appropriate database (image database, text database, etc.) depending on the file format.

[1357] Data Learning Module Processing Flow

[1358] Step 1:

[1359] The server retrieves the digital data of the deceased person stored in the database. The retrieved data is categorized into image files, text files, etc.

[1360] Step 2:

[1361] The server pre-processes the acquired data and converts it into a format suitable for input to the generative AI model: for example, image files are standardized in resolution, and text files are tokenized.

[1362] Step 3:

[1363] The server then uses the pre-processed data to begin the process of training a generative AI model, using natural language processing and image recognition techniques.

[1364] Step 4:

[1365] The server stores the trained generative AI model in storage, which has the ability to reproduce the features and speaking style of the deceased.

[1366] Processing flow of the communication reproduction module

[1367] Step 1:

[1368] The user accesses an interface on the terminal to initiate a dialogue with the deceased person. The user clicks a button to begin the conversation.

[1369] Step 2:

[1370] The device sends a request to the server to initiate a dialogue with the deceased person, and the server accepts the request and starts the dialogue session.

[1371] Step 3:

[1372] The user enters a message in the text box on the terminal and presses the send button. The terminal then sends this message to the server.

[1373] Step 4:

[1374] The server inputs the received message into a generative AI model to generate a response in the deceased person's voice, which is then sent back to the server.

[1375] Step 5:

[1376] The server sends the generated response to the user's terminal, where the user checks the response message from the deceased.

[1377] Digital Legacy Management Module Processing Flow

[1378] Step 1:

[1379] The user accesses the digital estate management interface on their device and clicks a button to view all digital data related to the deceased.

[1380] Step 2:

[1381] The device sends a request to access the digital remains to the server, which accepts the request and retrieves the relevant data from the database.

[1382] Step 3:

[1383] The server processes the digital data retrieved as search results to display them to the user in a visually understandable format, for example, images are displayed as thumbnails and message histories are displayed in a list format.

[1384] Step 4:

[1385] Users can view and organize their digital belongings on their devices, searching for data, sorting it into folders, and deleting it as needed.

[1386] Step 5:

[1387] The server updates the database based on user actions, e.g., deleted data is removed from the database, data moved to a different category is saved in a new folder, etc.

[1388] Example 1

[1389] 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."

[1390] While users can reminisce about their memories with the deceased, it was difficult to recreate the experience of interacting with the deceased. Furthermore, there was a lack of efficient ways to manage and organize the vast amount of digital data of the deceased, which required users to manually organize it. Additionally, managing the authentication information for the deceased's online accounts was difficult.

[1391] 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.

[1392] In this invention, the server includes a means for users to upload digital data via their terminals and for the server to store the data in a database, a means for training a generative AI model based on the digital data, and a means for recreating a conversation with the deceased using the trained generative AI model. This allows users to enjoy a realistic conversation experience with the deceased while efficiently managing and organizing the deceased's digital data. Furthermore, authentication information for the deceased's online accounts can also be centrally managed, facilitating the preparations for the deceased's digital legacy.

[1393] A "server" is a central processing unit that stores, processes, and shares data over a network.

[1394] A "terminal" is a device operated by a user, and includes a personal computer, a smartphone, etc.

[1395] A "user" is an individual who uses this system and has a relationship with the deceased.

[1396] "Digital data" refers to information stored electronically, including files such as photos, videos, and message histories.

[1397] A "database" is a system for efficiently storing, managing, and searching digital data.

[1398] A "generative AI model" is an algorithm that is trained using machine learning techniques to perform a specific task.

[1399] "Natural language processing technology" is a technology that enables computers to understand, generate, and analyze human language.

[1400] "Image recognition technology" is a technology that allows computers to analyze images and identify specific features or objects.

[1401] The "interactive experience" is an experience in which users can communicate with the deceased through a generative AI model.

[1402] "Digital legacy" refers to digital data and online account information left behind by a deceased person.

[1403] "Centralized management" refers to the integrated management of multiple digital data using a single system.

[1404] "Authentication information" refers to information such as ID and password required by a user to access an online account.

[1405] "Digital end-of-life planning" is the process of organizing and managing the digital data and online accounts left behind by the deceased.

[1406] This invention is a system that collects digital data about the deceased and trains a generative AI model based on that data. Specific embodiments of the system are described below.

[1407] Data collection implementation

[1408] Data Collection Module

[1409] First, the user enters authentication information to log in to the device, which can be a PC or smartphone. The server receives this authentication information and checks it against a database. If authentication is successful, the user is able to upload digital data.

[1410] Users upload photos, videos, message history, etc. related to the deceased person from their devices. The server receives this data and temporarily stores it in cloud storage. The server checks the file format and stores image data in an image database and text data in a text database.

[1411] The hardware used includes general PCs and smartphones, and the servers are cloud servers (e.g., AWS, Google Cloud, etc.), and the database systems used are MySQL and PostgreSQL.

[1412] Data Learning Embodiment

[1413] Data Learning Module

[1414] The server retrieves the digital data stored in the database, processes it, and stores it in a workspace. The collected data is used to train a generative AI model (using, for example, TensorFlow or PyTorch). This process uses natural language processing techniques (such as BERT) and image recognition techniques (such as CNN).

[1415] During the learning process, the system uses text data to learn the deceased's speaking style and image data to learn their facial expressions and gestures. Once trained, the generated AI model is saved in the "trained_models" folder.

[1416] Communication reproduction embodiment

[1417] Communication Reproduction Module

[1418] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response based on the user's input. The generated response is then sent to the user's device via the server. This allows the user to enjoy a real-time dialogue experience with the deceased on their device.

[1419] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[1420] Digital legacy management implementation example

[1421] Digital Legacy Management Module

[1422] Users can log in to the server via their devices and access the deceased's digital data. This allows them to search for specific message history and view photos in album format. Furthermore, they can also manage the deceased's online account authentication information, making it easier to organize their digital belongings.

[1423] The hardware used includes PCs and smartphones, and cloud servers (AWS, Google Cloud, etc.) are used. MySQL and PostgreSQL are used as database management systems.

[1424] (Example of a prompt)

[1425] "Recreate the conversational style of the deceased person based on their message history, and generate answers that sound like the deceased person to the user's questions."

[1426] The above is a specific embodiment for carrying out the present invention.

[1427] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1428] Step 1: User authentication

[1429] The user enters authentication information to log in to the terminal.

[1430] The server receives the authentication information and checks it against a database.

[1431] (Input) The ID and password entered by the user from the terminal.

[1432] (Processing) The server references the database and searches for records that match the input information.

[1433] (Output) If authentication is successful, the user is redirected to the data upload screen. If not, an error message is displayed.

[1434] (Specific operation) The user enters their ID and password into the login form on the terminal and presses the "Login" button. The server verifies the information in the database and returns the authentication result.

[1435] Step 2: Upload data

[1436] Users upload digital data about the deceased, such as photos, videos, and message history, from their devices.

[1437] The server receives this data and temporarily stores it in storage.

[1438] (Input) Digital data (file) selected by the user from the terminal.

[1439] (Processing) The server checks the file format and saves it in temporary storage.

[1440] (Output) A message notifying you that the data was saved successfully.

[1441] (Specific operation) The user clicks the upload button and selects a specific file from their PC. The selected file is uploaded to the server and saved in temporary storage.

[1442] Step 3: Check the data format and save it to the database

[1443] The server checks the format of the uploaded file.

[1444] Image data is stored in an image database, and text data is stored in a text database.

[1445] (Input) Digital data stored in temporary storage.

[1446] (Processing) The server checks the file extension, etc., and classifies and stores it in the appropriate database.

[1447] (Output) Message indicating completion of saving to the database.

[1448] (Specific operation) The server stores .jpg files in the image database and .txt files in the text database.

[1449] Step 4: Collecting data for training

[1450] The server collects data from an image database and a text database.

[1451] The collected data is stored in a temporary workspace.

[1452] (Input) Data stored in the image database and text database.

[1453] (Processing) The server retrieves the data using the specified query and moves it to the workspace.

[1454] (Output) The training data in the workspace.

[1455] (Specific operation) The server retrieves a specific number of photos and message history from the database and moves them to the workspace.

[1456] Step 5: Training the generative AI model

[1457] The server uses the data in the workspace to train a generative AI model.

[1458] It uses natural language processing technology and image recognition technology.

[1459] (Input) Digital data in the workspace.

[1460] The (processing) server analyzes the data using natural language processing models (such as BERT) and image recognition models (such as CNN) and trains the models.

[1461] (Output) A trained generative AI model.

[1462] (Specific operation) The server uses the BERT model to train text data and the CNN model to train image data. It then performs calculations to improve the accuracy of the model.

[1463] Step 6: Save the trained model

[1464] Once trained, the generative AI model is stored in dedicated storage.

[1465] (Input) A fully trained generative AI model.

[1466] (Processing) The server saves the trained model in storage.

[1467] (Output) Completion notification for the saved model.

[1468] (Specific operation) The server saves the generated AI model that has completed learning in the "trained_models" folder.

[1469] Step 7: Sending a conversation-initiating request

[1470] The user sends a dialogue start request to the server via the terminal.

[1471] (Input) A request from the user to start a conversation.

[1472] (Processing) The server receives the request and transitions to interactive mode.

[1473] (Output) Notification of conversation start.

[1474] (Specific operation) The user clicks the "talk to the deceased" button in the chat application, and the server processes the request.

[1475] Step 8: Response Generation

[1476] The server uses a trained generative AI model to generate a response based on the user's input.

[1477] (Input) A message from the user.

[1478] (Processing) The server uses the generative AI model to generate a response appropriate to the user's message.

[1479] (Output) The generated response message.

[1480] (Specific operation) When a user sends a message saying, "What are you doing now?", the server uses the generative AI model to generate a response saying, "I'm just taking a walk."

[1481] Step 9: Sending a Response

[1482] The generated response is sent to the user's terminal via the server.

[1483] (Input) The response generated by the generative AI model.

[1484] (Processing) The server sends the response to the user's terminal.

[1485] (Output) The response message displayed on the user's terminal.

[1486] (Specific operation) The server generates a message saying "I'm just taking a walk" and sends it to the chat application, which displays it on the user's device.

[1487] Step 10: Organizing and managing your digital legacy

[1488] Users log into the server via a terminal and access all digital data relating to the deceased.

[1489] (Input) User authentication information.

[1490] (Processing) The server authenticates and allows access to the digital data.

[1491] (Output) Display of digital data about the deceased.

[1492] (Specific operation) A user logs in on a terminal and accesses a dashboard of digital data.

[1493] Step 11: Search and view specific digital data

[1494] Users can search for specific message history and view photos in album format.

[1495] (Input) The user's search query.

[1496] The (processing) server searches the database and retrieves the relevant data.

[1497] (Output) Search results and photos in album format.

[1498] (Specific operation) The user types "travel" in the search box to display a list of relevant message history. Also, the user clicks the album tab to view photos.

[1499] Step 12: Manage your online account credentials

[1500] The server manages the IDs and passwords of the deceased person's online accounts.

[1501] (Enter) The deceased person's online account information.

[1502] The (processing) server securely stores and manages information.

[1503] (Output) Secure storage and management status of credentials.

[1504] (Specific operation) The user registers the deceased person's online account information on the server, and the server encrypts and stores this information.

[1505] These are the specific processing steps of the program for this system. Each step shows the specific operations and inputs / outputs of the user, terminal, and server.

[1506] (Application example 1)

[1507] 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."

[1508] Systems already exist that collect and manage digital data about the deceased and use generative AI models to recreate conversations with the deceased. However, no system exists that applies this technology to virtual stores, allowing users to enjoy shopping in a virtual space with the deceased. Such a system would allow users to enjoy new experiences while reminiscing on memories spent with the deceased, and would also efficiently organize digital belongings. Therefore, there is a need for a system that allows users to reunite with the deceased while also providing new experiences in a virtual space.

[1509] 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.

[1510] In this invention, the server includes means for collecting digital data about the deceased, means for training a generative AI model based on the digital data, means for recreating a conversation with the deceased using the trained generative AI model, means for enabling a user to converse with the deceased using the generative AI model, means for enabling the user to have the deceased recommend products to the user in a virtual space, and means for centrally managing the digital data and organizing the digital remains. This allows users to not only relive conversations with the deceased but also enjoy a shopping experience in a virtual space with the deceased.

[1511] "Digital data" refers to information stored or collected electronically, including photographs, videos, text messages, audio data, and other forms of multimedia.

[1512] A "generative AI model" is an artificial intelligence model trained using machine learning technology that has the ability to learn the characteristics of the deceased based on the digital data provided and reproduce their dialogue.

[1513] "Dialogue reproduction" is a process in which a user simulates a conversation with a deceased person, and refers to the generative AI model mimicking the deceased's tone of voice and facial expressions to respond to the user.

[1514] A "virtual space" is a computer-simulated, imaginary, three-dimensional environment within which a user can visually and interactively interact.

[1515] "Product introduction" refers to the process by which a deceased person uses a generative AI model to provide a user with a description or opinion about a product or service.

[1516] "Digital legacy" refers to all data in digital format that the deceased created, stored, and shared during their lifetime, including message history, photos, videos, and online account information.

[1517] MODE FOR CARRYING OUT THE INVENTION

[1518] A specific system for implementing the invention includes the following elements:

[1519] 1. Data Collection Module

[1520] The device provides an interface that allows users to upload digital data related to the deceased, such as photos, videos, and message history. When a user sends the digital data from the device to the server, the server temporarily stores the data, checks the file format, and then stores it in the appropriate database. For example, photo data is stored in an image database, and text data is classified as a text database.

[1521] 2. Data Learning Module

[1522] The server uses the digital data stored in the database to train the generative AI model. Using natural language processing and image recognition technologies with the TensorFlow library, the model learns the deceased's speaking style and facial expressions. This training process allows the generative AI model to realistically reproduce the characteristics of the deceased.

[1523] 3. Communication Reproduction Module

[1524] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device. For example, if the user sends a message from their device saying, "What are you doing now?", the server uses the generative AI model to generate a response such as "I'm just taking a walk," which is then displayed on the user's device. An example prompt is "The user said: What are you doing now? Your response:."

[1525] 4. Virtual Space Module

[1526] Users can enjoy shopping while interacting with the deceased in a virtual space. Specifically, when a user asks a question about a specific product, a generative AI model that mimics the deceased's style will explain the product. For example, if a user asks, "What do you think of this dress?", the generative AI model will respond, "It's very nice. It looks great on you." An example prompt is, "The user said: What do you think of this dress? Your response:"

[1527] 5. Digital Legacy Management Module

[1528] Users can log in via their device and access all digital data related to the deceased. Users can centrally manage this data and easily organize their digital belongings. For example, they can search for specific message history or view photos in album format. They can also manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[1529] This allows users to not only realistically recreate conversations with their deceased loved ones, but also enjoy a virtual shopping experience with them while efficiently organizing and managing their digital belongings.

[1530] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1531] DETAILED DESCRIPTION OF THE INVENTION - PROCESS STEPS

[1532] Step 1: Upload your digital data

[1533] Users use their devices to upload digital data about the deceased, such as photos, videos, and message history. The devices then send this data to a server. The input is the digital data provided by the user, and the output is temporary storage of the data by the server.

[1534] Specific operation: The user clicks the "Upload" button on the terminal application screen, selects a file, and sends it to the server.

[1535] Step 2: Check the format of the data and save it

[1536] The server checks the format of the uploaded data and stores it in the database in the appropriate format. The input is the digital data sent from the terminal, and the output is the data stored in the database.

[1537] Specific operation: The server checks the file format of the received data, classifies and stores the photo data in the image database and the text data in the text database.

[1538] Step 3: Training the generative AI model

[1539] The server uses the digital data stored in the database to train the generative AI model, with the input being the digital data in the database and the output being the trained generative AI model.

[1540] How it works: The server uses the TensorFlow library to perform image recognition and natural language processing techniques, learning the speech style and facial expressions of the deceased.

[1541] Step 4: Receiving an interaction request

[1542] The user sends a request to the server via a terminal to start a dialogue with the deceased. The input is the dialogue request from the user, and the output is the request received by the server.

[1543] Specific operation: The user clicks the "Start conversation" button on the application screen to send a conversation request to the server.

[1544] Step 5: Creating and Executing Dialogues

[1545] The server uses the trained generative AI model to generate a response from the deceased in response to the user's input. The input is a message from the user, and the output is a response from the generative AI model.

[1546] Specific operation: When a user sends a message saying "What are you doing now?", the server uses the generative AI model to generate a response saying "Just taking a walk" and sends it to the user's device.

[1547] Step 6: Interact in the virtual space

[1548] The user interacts with the deceased through a generative AI model in a virtual space, introducing products to each other. The input is the user's question, and the output is a product introduction by the generative AI model.

[1549] Specific operation: When a user asks in the virtual space, "What do you think of this dress?", the generative AI model replies, "It's very nice. It looks good on you."

[1550] Step 7: Organizing your digital legacy

[1551] Users access and organize their digital remains through a terminal. Input is search keywords and operations for organization, and output is a display of the organized data.

[1552] Specific operation: When a user searches for "view photos from 2022," the server retrieves the relevant photo data from the database and displays it on the device.

[1553] This processing step enables the recreation of conversations with the deceased and the experience of shopping in a virtual space, enabling the efficient management of digital belongings.

[1554] 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.

[1555] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. It also includes a combined emotion engine that recognizes user emotions and optimizes dialogue and display content accordingly. This system provides a means for users to reunite with their deceased loved ones and assists in the organization of their digital legacy.

[1556] Data collection implementation

[1557] Data Collection Module

[1558] A user logs in to their device and uploads photos, videos, message history, etc. related to the deceased. The server receives this data and temporarily stores it. The server then checks the file format and stores it in the appropriate format in the database accordingly. For example, photo data is stored in the image database, and text data is classified as a text database.

[1559] Data Learning Embodiment

[1560] Data Learning Module

[1561] The server uses the digital data stored in the database to train the generative AI model. Specifically, it uses natural language processing and image recognition technologies to learn the deceased's speaking style and facial expressions. This learning process allows the generative AI model to realistically reproduce the characteristics of the deceased. Once trained, the generative AI model is stored in dedicated storage.

[1562] Communication reproduction embodiment

[1563] Communication Reproduction Module

[1564] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a trained generative AI model to generate a response in the deceased's voice based on the user's input. The generated response is sent to the user's device via the server, allowing the user to enjoy a real-time dialogue experience with the deceased on their device.

[1565] As a concrete example, when a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a response saying, "Just taking a walk," and displays this on the user's device.

[1566] Embodiment of Emotion Engine

[1567] Emotion Recognition Module

[1568] The server receives the text and voice data entered by the user and analyzes the user's emotions using an emotion engine. The emotion engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state from their speech and facial expressions. For example, if a user enters "I'm not feeling very good today" in a sad voice, the emotion engine will recognize that the user is in a "sad" state.

[1569] Optimizing dialogue based on emotions

[1570] Emotion-based communication reproduction module

[1571] The server adjusts the response from the generative AI model based on the user's emotional state analyzed by the emotion engine. For example, if the user appears sad, the server can generate a particularly compassionate response and send a message of comfort and encouragement to the user. This provides the user with a more personalized interaction experience and enhanced psychological support.

[1572] Digital legacy management implementation example

[1573] Digital Legacy Management Module

[1574] Users log in to the server via their device and access all digital data related to the deceased. This allows them to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. In addition, they can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[1575] As described above, this system collects digital data about the deceased and uses it to train a generative AI model to recreate conversations with the deceased. Furthermore, by incorporating an emotion engine, the system optimizes the content of the conversations according to the user's emotional state, providing more effective mental care. Furthermore, by centrally managing digital data and supporting the organization and management of digital belongings, the system allows users to reunite with their loved ones while also providing emotional care.

[1576] The processing flow will be explained below.

[1577] Data collection module processing flow

[1578] Step 1:

[1579] The user launches the application on the device and accesses the login screen. The user enters the username and password and presses the login button.

[1580] Step 2:

[1581] The server receives the user's authentication information, checks it against the database, and if authentication is successful, redirects the user to the dashboard.

[1582] Step 3:

[1583] The user selects the deceased's digital data (photos, videos, message history, etc.) through the device interface. The user selects the files using a file selection dialog.

[1584] Step 4:

[1585] To upload the selected digital data, the user clicks the upload button, and the terminal transmits the selected file to the server.

[1586] Step 5:

[1587] The server temporarily stores the received digital data, determines the file format, and stores it in the appropriate database (image database, text database, etc.) depending on the file format.

[1588] Data Learning Module Processing Flow

[1589] Step 1:

[1590] The server retrieves the digital data of the deceased person stored in the database. The retrieved data is categorized into image files, text files, etc.

[1591] Step 2:

[1592] The server pre-processes the acquired data and converts it into a format suitable for input to the generative AI model: for example, image files are standardized in resolution, and text files are tokenized.

[1593] Step 3:

[1594] The server then uses the pre-processed data to begin the process of training a generative AI model, using natural language processing and image recognition techniques.

[1595] Step 4:

[1596] The server stores the trained generative AI model in storage, which has the ability to reproduce the features and speaking style of the deceased.

[1597] Processing flow of the communication reproduction module

[1598] Step 1:

[1599] The user accesses an interface on the terminal to initiate a dialogue with the deceased person. The user clicks a button to begin the conversation.

[1600] Step 2:

[1601] The device sends a request to the server to initiate a dialogue with the deceased person, and the server accepts the request and starts the dialogue session.

[1602] Step 3:

[1603] The user enters a message in the text box on the terminal and presses the send button. The terminal then sends this message to the server.

[1604] Step 4:

[1605] The server inputs the received message into a generative AI model to generate a response in the deceased person's voice, which is then sent back to the server.

[1606] Step 5:

[1607] The server sends the generated response to the user's terminal, where the user checks the response message from the deceased.

[1608] Emotion Recognition Module Processing Flow

[1609] Step 1:

[1610] The user inputs a message on the terminal and sends it to the server, which then sends a request to the emotion engine to analyze the received message.

[1611] Step 2:

[1612] The emotion engine analyzes the received message using natural language processing technology and emotion analysis algorithms, and classifies the user's emotional state into categories such as "sad," "happy," or "angry."

[1613] Step 3:

[1614] The emotion engine sends the analysis results back to the server, which receives the emotion analysis results and adjusts the generated response content based on the user's emotional state.

[1615] Step 4:

[1616] The server inputs the response message corresponding to the generated emotion into the generative AI model, which generates a response tailored to the tone of the deceased person. The generated response is returned to the server.

[1617] Step 5:

[1618] The server sends the tailored response message to the user's terminal, and the user checks the tailored response message on the terminal.

[1619] Digital Legacy Management Module Processing Flow

[1620] Step 1:

[1621] The user accesses the digital estate management interface on their device and clicks a button to view all digital data related to the deceased.

[1622] Step 2:

[1623] The device sends a request to access the digital remains to the server, which accepts the request and retrieves the relevant data from the database.

[1624] Step 3:

[1625] The server processes the digital data retrieved as search results to display them to the user in a visually understandable format, for example, images are displayed as thumbnails and message histories are displayed in a list format.

[1626] Step 4:

[1627] Users can view and organize their digital belongings on their devices, searching for data, sorting it into folders, and deleting it as needed.

[1628] Step 5:

[1629] The server updates the database based on user actions, e.g., deleted data is removed from the database, data moved to a different category is saved in a new folder, etc.

[1630] Example 2

[1631] 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."

[1632] In the digital age, there is a huge amount of digital data about the deceased, but it is difficult to effectively utilize this data and provide a dialogue experience with the deceased. It is also a challenge to optimize dialogue based on the user's emotions and strengthen psychological support. Furthermore, it is also important to centrally manage digital data in various formats and organize digital remains.

[1633] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting digital data related to the deceased; means for training a generative AI model based on the digital data; means for recreating a dialogue with the deceased using the trained generative AI model; means for enabling a user to engage in a dialogue with the deceased using the generative AI model; means for centrally managing the digital data and organizing the digital remains; means for the user to send a request to the server to initiate a dialogue with the deceased; means for the server to confirm the data format and store the data in an appropriate database; means for analyzing the user's emotions using an emotion recognition engine; means for optimizing the dialogue content based on the analyzed emotions; and means for generating a response to the user's input in real time using the generative AI model. This enables the user to have a dialogue experience with the deceased and optimize the dialogue according to the user's emotions. Furthermore, centralized management of digital data facilitates the organization of the digital remains.

[1634] "Digital data relating to the deceased" refers to digital information such as photographs, videos, and message history that the deceased created or saved during their lifetime.

[1635] A "generative AI model" is a machine learning model that has been trained using artificial intelligence techniques to perform a specific task, in this case, recreating the speaking style and facial expressions of a deceased person.

[1636] A "user" is an individual or group who logs in and operates this system to interact with the deceased.

[1637] "Logging in" is the process by which a user enters the authentication information (such as a user ID and password) required to access a system.

[1638] A "database" is a system for efficiently storing, managing, and searching large amounts of digital data.

[1639] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language.

[1640] "Image recognition technology" is a technology that allows computers to detect and identify objects and patterns in images.

[1641] An "emotion recognition engine" is an algorithm or software that analyzes and identifies emotions from text or voice input by a user.

[1642] "Centralized management" refers to the centralized management of data of different formats and types in one place.

[1643] "Digital legacy" refers to digital assets left behind by a deceased person, such as photos, messages, and online account information.

[1644] A "request" refers to an instruction sent by a user to the system to request a particular operation.

[1645] "Real-time" refers to immediate response to user operations and inputs without delay.

[1646] "Dialogue optimization" refers to adjusting the dialogue content and generating the optimal response according to the user's emotions and situation.

[1647] This invention relates to a system that collects digital data about the deceased and trains a generative AI model based on that data. It also includes a combined emotion engine that recognizes user emotions and optimizes dialogue and display content accordingly. This system provides a means for users to reunite with their deceased loved ones and assists in the organization of their digital legacy.

[1648] Data collection

[1649] Users log in to the system using a terminal. When logging in, they are required to enter their user ID and password for authentication. Once authentication is complete, the user uploads digital data related to the deceased, such as photos, videos, and message history. The terminal sends this data to the server, which then stores the received data in a temporary file system. The server checks the data format (e.g., image file, video file, text file), and stores it in the appropriate database for each format.

[1650] Data Learning

[1651] The server uses the digital data of the deceased stored in the database to train the generative AI model. This training uses natural language processing (NLP) and image recognition technologies. Specifically, the server extracts from the data the features necessary to reproduce the deceased's speaking style and facial expressions, and trains the AI ​​model. Once training is complete, the trained generative AI model is stored in dedicated storage.

[1652] Communication Reproduction

[1653] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server uses a generative AI model to generate a response based on the user's input, in the tone and style of the deceased. The generated response is then sent from the server to the user's device and displayed in real time.

[1654] As a concrete example, if a user sends a message from their device asking, "What are you doing now?", the server uses a generative AI model to generate a reply saying, "I'm just taking a walk," and displays this on the user's device.

[1655] Example prompt sentence:

[1656] "What are you doing now?"

[1657] "I'm just taking a walk."

[1658] Combining Emotion Engines

[1659] The server analyzes the text and voice data entered by the user using an emotion engine to confirm the user's emotions. The emotion engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state. For example, if a user enters "I'm not feeling very good today" in a sad voice, the emotion engine will recognize that the user is in a "sad" state.

[1660] Optimizing dialogue based on emotions

[1661] The server adjusts the response of the generative AI model based on the analysis results of the emotion engine. If the user appears sad, the server will generate a more compassionate response and send a comforting or encouraging message to the user. This allows the user to have a personalized interaction experience based on their emotions and provides enhanced psychological support.

[1662] Digital legacy management

[1663] Users can log in to the server via their device and access all digital data related to the deceased. This allows users to organize and manage the deceased's digital belongings. Specifically, users can search for specific message history and view photos in album format. In addition, users can manage the IDs and passwords of the deceased's online accounts, making it suitable for digital end-of-life planning.

[1664] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1665] Step 1:

[1666] A user logs in to the system using a terminal. They enter their user ID and password and go through the authentication procedure. The server receives the entered authentication information and authenticates the user. If authentication is successful, the user is allowed to access the system. The input here is the user ID and password, and the output is a message indicating whether authentication was successful or failed.

[1667] Step 2:

[1668] Users use their devices to upload digital data about the deceased, such as photos, videos, and message history, to the system. The devices then send the uploaded data to the server, which then receives the data and stores it in a temporary file system. The input is digital data such as photos, videos, and message history, and the output is the data stored in the temporary file system.

[1669] Step 3:

[1670] The server checks the format of the data stored in the temporary file system. For example, it identifies the format as an image file, video file, text file, etc., and classifies and stores it in a database. The server analyzes the data format and stores each piece of data appropriately in the image database, video database, or text database. The input is data stored in the temporary file system, and the output is data classified and stored in the database.

[1671] Step 4:

[1672] The server retrieves the data stored in the database and trains the generative AI model. This training process uses natural language processing and image recognition technologies. Specifically, data is fed into the generative AI model to learn the deceased's speech style and facial expressions. Once trained, the generative AI model is stored in dedicated storage. The input is the data stored in the database, and the output is the trained generative AI model.

[1673] Step 5:

[1674] The user sends a request to the server via their device to initiate a dialogue with the deceased. The server receives this request and uses a generative AI model to generate a response based on the user's input. The generated response is sent from the server to the user's device and displayed in real time. The input is the user's dialogue request and its content, and the output is the response generated using the generative AI model.

[1675] Step 6:

[1676] The server receives text or voice data entered by the user and analyzes it with an emotion recognition engine. The emotion recognition engine uses natural language processing technology and emotion analysis algorithms to identify the user's emotional state. For example, if a user enters "I'm not feeling very good today," the emotion recognition engine will determine that this is "sad." The input is text or voice data, and the output is the analyzed emotional state.

[1677] Step 7:

[1678] The server optimizes the response of the generative AI model based on the analysis results of the emotion recognition engine. If the user looks sad, the server generates a more compassionate response and sends a message of comfort or encouragement to the user. The input is the emotion analysis result, and the output is the optimized response.

[1679] Step 8:

[1680] Users can log in to the server via their devices and access all digital data related to the deceased. This allows users to organize and manage the deceased's digital belongings. For example, they can search for specific message history or view photos in album format. The input is the user's login information and request, and the output is the display and management function for the corresponding digital data.

[1681] (Application example 2)

[1682] 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."

[1683] Previously, there were systems that collected digital data of the deceased and used it to recreate conversations with the deceased, but the content of the conversations was not optimized to reflect the user's emotions, and psychological support for the user was insufficient.In addition, there was a lack of easy ways to converse with the deceased using smart devices, which limited the user experience.

[1684] 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 digital data related to the deceased, means for training a generative AI model based on the digital data, means for recreating a dialogue with the deceased using the trained generative AI model, means for enabling a user to interact with the deceased using the generative AI model, means for centrally managing the digital data and organizing the digital remains, means for analyzing the user's emotions and optimizing the dialogue content based on the analysis, and means for enabling the user to engage in dialogue using a smart device. This enables a personalized dialogue experience based on the user's emotions, enhancing psychological support. Furthermore, by utilizing a smart device, users can easily enjoy dialogue with the deceased anytime, anywhere.

[1685] "Digital data" refers to electronic files such as photos, videos, and message history related to the deceased.

[1686] A "generative AI model" refers to an artificial intelligence model that learns the characteristics of the deceased based on collected digital data and recreates conversations with the deceased.

[1687] "Sentiment analysis" refers to the process of analyzing text or voice data entered by a user to identify their emotional state.

[1688] "Optimizing dialogue content" refers to adjusting the responses of the generative AI model based on the user's emotional state to provide a more personalized dialogue experience.

[1689] "Digital legacy" refers to all digital data and online account information related to the deceased.

[1690] "Centralized management" refers to managing multiple digital data in a unified system or database.

[1691] "Smart devices" refers to devices that have internet connectivity and advanced computing capabilities, such as smartphones, head-mounted displays, and smart glasses.

[1692] This invention provides a system that collects digital data about the deceased, trains a generative AI model based on that data, and recreates the conversation between the user and the deceased. Furthermore, it performs emotion analysis and optimizes the conversation content according to the user's emotional state, providing psychological support to the user. The following describes specific embodiments of this invention.

[1693] Hardware and software used

[1694] Hardware:

[1695] Server: Stores data and trains generative AI models.

[1696] User device: Any device with internet connectivity, such as a smartphone, head-mounted display, or smart glasses.

[1697] software:

[1698] Data collection module: Python

[1699] Data learning module: TensorFlow or PyTorch

[1700] Emotion Recognition Module: NLP, emotion analysis library (e.g. Hugging Face Transformers)

[1701] Communication Reproduction Module: Python

[1702] Digital legacy management module: Django

[1703] Data collection

[1704] The server collects data by allowing users to upload digital data about the deceased via their devices. The collected data is temporarily stored on the server and then categorized and stored in the appropriate database depending on its format. For example, photo data is stored in an image database, and text data is categorized in a text database.

[1705] Data Learning

[1706] The server uses the collected digital data to train a generative AI model. Using deep learning frameworks such as TensorFlow and PyTorch, it uses natural language processing and image recognition techniques to learn the deceased's speaking style and facial expressions. This training process allows the generative AI model to realistically reproduce the deceased's characteristics.

[1707] Communication Reproduction

[1708] When a user initiates a conversation via their device, the server uses the trained generative AI model to generate the deceased's tone of voice and responses based on the user's input. The generated responses are sent to the user's device in real time, allowing the user to enjoy the experience of interacting with the deceased.

[1709] Emotion recognition and dialogue content optimization

[1710] The server receives text and voice data entered by the user and analyzes the user's emotional state using emotion analysis libraries such as Hugging Face Transformers. Based on the analyzed emotional state, the generative AI model adjusts its response. For example, if the user is sad, it generates an encouraging message.

[1711] Digital legacy management

[1712] Users can log in to the server via their devices and access all digital data related to the deceased, allowing them to organize and manage their digital belongings, search for specific message history, and view photos in album format.

[1713] Specific examples

[1714] Example 1: A user reminisces about a deceased loved one

[1715] User: "Check this out" and upload a photo.

[1716] Deceased AI: "Oh, this brings back memories. Those trips were fun."

[1717] Example prompt: "Tell me about your memories of our trip."

[1718] Example 2: Dialogue when the user is feeling down

[1719] User: "I'm not feeling well today."

[1720] Deceased AI: "Are you OK? Tell me what happened."

[1721] Example prompt: "Tell me how you would comfort a user when they're feeling down."

[1722] With the above configuration, the present invention can provide a personalized conversation experience according to the user's emotional state and enhance psychological support. Furthermore, by utilizing smart devices, users can enjoy conversation with the deceased anytime, anywhere.

[1723] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1724] Step 1: Collect digital data

[1725] Input: The user uploads photos, videos, and message history about the deceased via the device.

[1726] Processing: The server temporarily stores the data uploaded by the user and checks its format (image, text, video, etc.). For example, photos are stored in an image database, and text data is classified in a text database.

[1727] Output: Each data is stored in a corresponding database and made available for further processing.

[1728] Step 2: Train the data

[1729] Input: Digital data about the deceased, including photos, videos, and message history, stored in a database.

[1730] Processing: The server uses the stored digital data to train a generative AI model using deep learning frameworks such as TensorFlow and PyTorch, using natural language processing and image recognition techniques to learn the speech style and facial expressions of the deceased.

[1731] Output: The trained generative AI model is saved in dedicated storage and used to recreate user interactions.

[1732] Step 3: Sentiment Analysis

[1733] Input: Text and voice data entered by the user through the device.

[1734] Processing: The server performs sentiment analysis on the user's input data using emotion analysis libraries such as Hugging Face Transformers, thereby identifying the user's emotional state (e.g., sad, happy, angry, etc.).

[1735] Output: The user's emotional state is analyzed and used in the next dialogue generation step.

[1736] Step 4: Communication reproduction and dialogue content optimization

[1737] Input: User sentiment analysis results and user-entered text or voice data.

[1738] Processing: The server uses a trained generative AI model to generate the optimal response for the user based on the analyzed emotional state. For example, if the user seems sad, it generates a message of comfort or encouragement.

[1739] Output: The optimized response is sent to the user's device in real time, allowing the user to experience a natural conversation with the deceased.

[1740] Step 5: Digital Legacy Management

[1741] Input: The digital data that a user logs into a server via a terminal and requests access to.

[1742] Processing: The server authenticates the user and provides the authenticated user with the deceased's digital data (photos, message history, account information, etc.), allowing the user to view and organize their digital belongings.

[1743] Output: Users can access, organize and manage all digital data related to the deceased.

[1744] The above are the specific processing steps for implementing this invention, which allow users to receive psychological support through dialogue with the deceased and to properly manage the deceased's digital remains.

[1745] 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.

[1746] 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.

[1747] 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.

[1748] 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.

[1749] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1750] 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.

[1751] 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).

[1752] 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.

[1753] 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."

[1754] 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.

[1755] 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).

[1756] 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.

[1757] 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.

[1758] 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.

[1759] 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.

[1760] 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.

[1761] 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.

[1762] 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.

[1763] 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.

[1764] 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.

[1765] 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.

[1766] The following is further disclosed regarding the above embodiment.

[1767] (Claim 1)

[1768] a means for collecting digital data relating to the deceased;

[1769] means for training a generative AI model based on the digital data;

[1770] A means of recreating interactions with the deceased using a trained generative AI model; and

[1771] means for enabling a user to interact with the deceased person using the generative AI model;

[1772] A system including a means for centrally managing the digital data and organizing digital remains.

[1773] (Claim 2)

[1774] The generative AI model learns the characteristics of the deceased using natural language processing technology and image recognition technology.

[1775] 10. The system of claim 1.

[1776] (Claim 3)

[1777] The system includes a means for a user to upload digital data via a terminal and for a server to store the data in a database.

[1778] 10. The system of claim 1.

[1779] "Example 1"

[1780] (Claim 1)

[1781] a means for users to upload digital data via a terminal and for a server to store the data in a database;

[1782] means for training a generative AI model based on the digital data;

[1783] A means of recreating interactions with the deceased using a trained generative AI model; and

[1784] means for enabling a user to interact with the deceased person using the generative AI model;

[1785] a means for unifying the management of the digital data and organizing the digital remains;

[1786] A means to access the deceased person's digital data and search for specific message histories;

[1787] A means to view the digital data of the deceased in album format,

[1788] A system including a means for managing the credentials of a deceased person's online accounts.

[1789] (Claim 2)

[1790] The generative AI model learns the characteristics of the deceased using natural language processing technology and image recognition technology.

[1791] 10. The system of claim 1.

[1792] (Claim 3)

[1793] The system is characterized in that it includes a means for a user to send a request to a server via a terminal to start a dialogue with the deceased, and the server generates a response according to the user's input using a trained generative AI model, and transmits the response to the user's terminal.

[1794] 10. The system of claim 1.

[1795] "Application Example 1"

[1796] (Claim 1)

[1797] a means for collecting digital data relating to the deceased;

[1798] means for training a generative AI model based on the digital data;

[1799] A means of recreating interactions with the deceased using a trained generative AI model; and

[1800] means for enabling a user to interact with the deceased person using the generative AI model;

[1801] A means for enabling a user to have a deceased person recommend products to the user in a virtual space;

[1802] A system including a means for centrally managing the digital data and organizing digital remains.

[1803] (Claim 2)

[1804] The generative AI model learns the characteristics of the deceased using natural language processing technology and image recognition technology.

[1805] 10. The system of claim 1.

[1806] (Claim 3)

[1807] The system includes a means for a user to upload digital data via a terminal and for a server to store the data in a database.

[1808] 10. The system of claim 1.

[1809] "Example 2: Combining Emotion Engines"

[1810] (Claim 1)

[1811] a means for collecting digital data relating to the deceased;

[1812] means for training a generative AI model based on the digital data;

[1813] A means of recreating interactions with the deceased using a trained generative AI model; and

[1814] means for enabling a user to interact with the deceased person using the generative AI model;

[1815] a means for unifying the management of the digital data and organizing the digital remains;

[1816] means for transmitting a request to a server for a user to initiate a dialogue with the deceased person;

[1817] A means for the server to check the data format and store it in the appropriate database;

[1818] means for analyzing a user's emotions using an emotion recognition engine;

[1819] A means for optimizing dialogue content based on the analyzed emotions;

[1820] A system including means for generating responses in real time to user input using a generative AI model.

[1821] (Claim 2)

[1822] The system of claim 1, wherein the generative AI model learns the characteristics of the deceased using natural language processing and image recognition techniques.

[1823] (Claim 3)

[1824] 10. The system of claim 1, further comprising means for users to upload digital data via terminals and for the server to store the data in a database.

[1825] "Application example 2 when combining emotion engines"

[1826] (Claim 1)

[1827] a means for collecting digital data relating to the deceased;

[1828] means for training a generative AI model based on the digital data;

[1829] A means of recreating interactions with the deceased using a trained generative AI model; and

[1830] means for enabling a user to interact with the deceased person using the generative AI model;

[1831] a means for unifying the management of the digital data and organizing the digital remains;

[1832] A means for analyzing user emotions and optimizing dialogue content based on the analysis;

[1833] A system including means for enabling a user to interact with a smart device.

[1834] (Claim 2)

[1835] The generative AI model learns the characteristics of the deceased using natural language processing technology and image recognition technology.

[1836] 10. The system of claim 1.

[1837] (Claim 3)

[1838] The system includes a means for a user to upload digital data via a terminal and for a server to store the data in a database.

[1839] 10. The system of claim 1. [Explanation of symbols]

[1840] 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 digital data relating to the deceased; means for training a generative AI model based on the digital data; A means of recreating interactions with the deceased using a trained generative AI model; and means for enabling a user to interact with the deceased person using the generative AI model; A system including a means for centrally managing the digital data and organizing digital remains.

2. The generative AI model learns the characteristics of the deceased using natural language processing technology and image recognition technology. The system of claim 1 .

3. The system includes a means for a user to upload digital data via a terminal and for a server to store the data in a database. The system of claim 1 .

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