System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting a moving image, a photo, and a chat of a deceased person; analyzing means for extracting a facial feature, a vocal feature, and a language pattern of the deceased person; generation and AI means for generating a digital clone of the deceased person based on the extracted features; means for providing an interface for a user to interact with the digital clone; and means for displaying or outputting by voice a reply generated by the generation and AI means to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Approximately 63 million people die every year around the world, and many feel lonely after their loved ones pass away, with no one to rely on or talk to. Currently, it is possible to review videos and photos of the deceased, but it is difficult to communicate directly, making it difficult to feel a sense of connection. There is a need to solve this problem and provide a way to communicate with the deceased at any time. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including means for collecting videos, photos, and chat data of the deceased; analysis means for extracting the facial features, voice features, and language patterns of the deceased from the collected data; generation AI means for generating a digital clone of the deceased based on the extracted features; means for providing an interface for the user to interact with the digital clone; and means for displaying or outputting the responses generated by the generation AI means to the user as audio. Furthermore, by providing generation means for recreating the face and voice of the deceased using DeepFake technology and analysis means for analyzing the deceased's language usage and unique expressions using natural language processing technology, a more realistic and reliable digital clone is realized.

[0006] A "deceased person" refers to a person who was alive but is now deceased.

[0007] "Video" refers to video data that records the deceased person moving.

[0008] "Photograph" refers to data that records the appearance of the deceased as a still image.

[0009] "Chat data" refers to text-based records of communications made by a deceased person during their lifetime.

[0010] "Collection means" refers to methods and devices for collecting and storing information such as videos, photos, and chat data of the deceased.

[0011] "Analysis means" refers to a method or device for extracting specific features from collected data and processing the data.

[0012] "Generative AI means" refers to artificial intelligence technology for generating a digital clone of a deceased person based on data extracted by analytical means.

[0013] "Interface" refers to the operation screen and input device that allow the user to interact with the digital clone.

[0014] "DeepFake technology" refers to deep learning technology that realistically recreates the face and voice of a target person (deceased person) based on existing video and audio data.

[0015] "Natural language processing technology" refers to technology for analyzing text data and understanding and generating word usage and unique expressions.

[0016] "Generation means" refers to a method or device for recreating the face and voice of a deceased person using DeepFake technology. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention provides a system for generating a digital clone of a deceased person and allowing a user to interact with the deceased person in a digital space. Specific embodiments of the system are described below.

[0039] System configuration

[0040] The system includes the following main components:

[0041] 1. Data collection method: Users can use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The server receives this data and stores it in storage.

[0042] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased. Facial features and voice features are analyzed using computer vision and speech recognition technologies, and language patterns are extracted using natural language processing technology.

[0043] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[0044] 4. User interface provision means: Users can access and start interacting with the digital clone through a dedicated application or web portal. Users can ask questions to the digital clone using text input or voice input.

[0045] 5. Response generation means: The server receives the user's question and generates an appropriate response using the generative AI model. The generated response is displayed or played aloud to the user through the user interface.

[0046] Program processing flow (overview)

[0047] When a user uploads the deceased's data to the system, the server begins analyzing the data. Once the analysis is complete, a generative AI means generates a digital clone of the deceased. When a user accesses the digital clone and asks a question, the server analyzes the question and generates a response using the generative AI model. The generated response is displayed on the user's device or played aloud.

[0048] Specific examples

[0049] For example, if a user wanted to create a digital clone of their deceased mother, they would use the system as follows: The user would upload videos, photos, and chat logs of the mother from when she was alive to the system. The server would analyze this data and extract the mother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to create a digital clone that reflects the mother's speaking style and personality.

[0050] The user opens a dedicated application and asks their mother's digital clone, "How was the weather today?" The device sends this question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. It was a perfect day for a walk." This response is then displayed on the user's device, allowing the user to enjoy a conversation with their mother.

[0051] The above is a specific embodiment of the present invention. This system allows you to continue to connect with the deceased and receive comfort and support through conversation.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] Users: Upload videos, photos, chat data, etc. of the deceased using a dedicated web portal or application. Users can complete the upload by dragging and dropping files or using a file selection dialog.

[0055] Step 2:

[0056] Server: Stores the uploaded data in storage. Records file metadata (file name, size, upload date and time, etc.) in a database.

[0057] Step 3:

[0058] Server: Analyzes the stored data, extracting facial features of the deceased from videos and photos, and voice features from audio files. This involves computer vision and voice recognition techniques. For example, facial recognition algorithms are used to identify facial landmarks, and voice analysis algorithms are used to extract voice tone and pitch.

[0059] Step 4:

[0060] Server: Analyzes chat data and text data using natural language processing (NLP) techniques to extract the deceased's language patterns and unique expressions. For example, it uses keyword frequency analysis and language models to identify commonly used phrases and vocabulary.

[0061] Step 5:

[0062] Server: Using DeepFake technology, a digital clone of the deceased is created based on the extracted facial and vocal features. The clone is then optimized to reproduce the deceased's face and voice in real time. For example, a video generative model is used to realistically recreate the deceased's facial expressions.

[0063] Step 6:

[0064] Server: A generative AI model is used to learn the behavioral patterns and personality of the deceased. This model is trained to mimic the deceased's response patterns and thought process based on the analyzed chat data. For example, a deep learning model is used to understand the context and generate appropriate responses.

[0065] Step 7:

[0066] User: Accesses the digital clone through a dedicated application or web portal. The user can talk to the digital clone using text or voice chat. For example, they can type "Mom, how was the weather today?" into the chat box within the app.

[0067] Step 8:

[0068] Terminal: Sends user input to the server. This includes text questions and voice data. The terminal captures user input in real time and forwards it to the server.

[0069] Step 9:

[0070] Server: Analyzes the user's question and generates an appropriate response using a generative AI model. For example, in response to a question about the weather, the server generates a response such as "Today was a beautiful sunny day, perfect for a walk."

[0071] Step 10:

[0072] Server: Generates and sends the response to the terminal. The response is sent in text or audio format.

[0073] Step 11:

[0074] Terminal: Displays the received response to the user or plays it aloud. For example, it may show the text on a display and use a voice playback function to play the response in the deceased's voice.

[0075] Step 12:

[0076] User: Enjoys interacting with the digital clone. The user can re-enter questions or continue the conversation. This process repeats, continuing the interaction with the user.

[0077] The above are the specific processing steps of the digital clone generation system according to the present invention, which allows users to feel a connection with the deceased through conversation.

[0078] Example 1

[0079] 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."

[0080] In modern society, there is a growing emotional need to reminisce and converse with deceased loved ones. However, the technology to effectively utilize the digital data left behind by the deceased is underdeveloped. Therefore, there is a need for a system that enables communication with the deceased through a computer. Furthermore, advanced data analysis and generation technologies are required to accurately reproduce the facial features, vocal characteristics, and language patterns of the deceased, allowing bereaved families to have a natural and emotionally rich experience.

[0081] 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.

[0082] In this invention, the server includes means for collecting image data, voice data, and text data of the deceased, analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, artificial intelligence (AI) means for generating a digital clone of the deceased based on the extracted features, means for providing a user interface for the user to interact with the digital clone, and means for displaying or outputting to the user the responses generated by the AI ​​generating means by voice, thereby enabling a highly simulated interaction with the deceased and enabling the bereaved family to maintain an emotional connection.

[0083] "Deceased" refers to a person who has passed away.

[0084] "Image data" refers to digital image information stored in the form of photographs or videos.

[0085] "Audio Data" means digital acoustic information stored in voice or audio recording form.

[0086] "Text data" refers to digital information in text format stored as chat logs or documents.

[0087] "Facial features" refers to feature information about the shape and structure of a person's face extracted from image data.

[0088] "Voice features" refer to the pitch, timbre, and pronunciation characteristics of a person's voice extracted from audio data.

[0089] "Language patterns" refer to a person's language and unique ways of expression extracted from text data.

[0090] "Analysis means" refers to a processing mechanism for extracting necessary feature information from collected data.

[0091] "Generative artificial intelligence means" refers to artificial intelligence technology for generating a digital clone based on extracted feature information.

[0092] "User interface" refers to the means that provides input and output mechanisms for a user to interact with a system.

[0093] A "digital clone" refers to a digital model that recreates the face, voice, and language patterns of a deceased person.

[0094] "Deepfake technology" refers to the technology of synthesizing images and audio data using deep learning technology.

[0095] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0096] System Overview

[0097] This invention is a system that creates a digital clone of a deceased person and allows users to interact with the deceased in a digital space. The system is mainly composed of three components: a server, a terminal, and a user, with each component playing a specific role. The entire system functions through the following stages: data collection, data analysis, digital clone creation, user interaction, and response generation.

[0098] Data collection methods

[0099] Users use a dedicated web portal or application to collect image, audio, and text data of the deceased, which is then securely uploaded to a server via HTTPS, where the server stores the data.

[0100] Data Analysis Methods

[0101] The server analyzes the received data and extracts facial features, voice features, and language patterns using the following software and techniques:

[0102] Facial features: Extract facial features from image data using OpenCV.

[0103] Speech features: Extract speech features from the audio data using the Google Speech-to-Text API.

[0104] Language Patterns: Extract language patterns from text data using SpaCy or NLTK.

[0105] Generation AI means

[0106] The server then uses DeepFake technology and generative AI technology (such as GPT-3) to create a digital clone of the deceased person based on the analyzed characteristics. The digital clone faithfully reproduces the appearance, voice, and manner of speech of the deceased.

[0107] User interface providing means

[0108] Users can access the digital clone through a dedicated application or a web portal. The user interface is built on React and Vue.js and is designed to allow users to ask questions and make comments to the digital clone via text or voice input.

[0109] Response Generation Method

[0110] The server analyzes questions received through the user interface and generates appropriate responses using a generative AI model (e.g., GPT-3). The responses are then sent to the device and displayed as text or played aloud.

[0111] Specific examples

[0112] For example, if a user wants to create a digital clone of their deceased mother, they can upload her image, voice, and text data from before she died to the system. The server analyzes this data and extracts her facial features, voice characteristics, and speech patterns. It then uses DeepFake technology and generative AI technology to create a digital clone of the mother.

[0113] A user opens the application and asks their mother's digital clone, "How was the weather today?" The device sends the question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. A perfect day for a walk." This response is then displayed on the user's device, allowing them to enjoy a conversation with their deceased loved one.

[0114] Prompt Sentence Examples

[0115] "To a digital clone of your deceased mother, please type: 'What was the weather like today?'"

[0116] The above is a specific embodiment of the present invention. This system allows you to continue to connect with the deceased and receive comfort and support through conversation.

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1: Data collection

[0119] Users use a dedicated web portal or application to upload image, audio, and text data of the deceased to the system, which then transfers the data to the server.

[0120] Input: Image data, audio data, and text data of the deceased.

[0121] Data processing: splitting, compression, secure data transfer using HTTP protocol.

[0122] Output: Data saved to storage on the server.

[0123] Specific behavior:

[0124] A user logs into a web portal and clicks the file selection button.

[0125] The device displays a file selection dialog, and the user selects the required file.

[0126] The device uploads the selected file to the server.

[0127] The server stores the received data in storage.

[0128] Step 2: Data analysis

[0129] The server analyzes the uploaded data, extracting facial features, voice features, and language patterns.

[0130] Input: Image data, audio data, and text data saved in step 1.

[0131] Data computing: image recognition, speech analysis, natural language processing.

[0132] Output: Extraction results of facial features, audio features, and language patterns.

[0133] Specific behavior:

[0134] The server uses the OpenCV library to extract facial features from the image data.

[0135] The server uses the Google Speech-to-Text API to extract speech features from the audio data.

[0136] The server uses SpaCy to extract language patterns from the text data.

[0137] Step 3: Digital cloning

[0138] Based on the analyzed characteristic information, the server uses DeepFake technology and generative AI technology to generate a digital clone.

[0139] Input: Facial features, audio features, and language pattern extraction results.

[0140] Data computation: face and voice synthesis, generation of conversation models.

[0141] Output: A digital clone of the deceased person.

[0142] Specific behavior:

[0143] The server uses the facial features in DeepFaceLab to generate a digital clone's face.

[0144] The voice of the digital clone is reproduced based on the voice model generated by the server.

[0145] The server uses GPT-3 to generate a conversation model that reflects the speaking style and personality of the deceased.

[0146] Step 4: Configuring User Interaction

[0147] Users access their digital clone by opening a dedicated application or web portal, where they can ask questions or make comments using text or voice input.

[0148] Input: User text or voice input.

[0149] Data processing: Accepting input and forwarding it to the server.

[0150] Output: User input transmitted to the server.

[0151] Specific behavior:

[0152] The user launches the application and logs in.

[0153] It opens an interface that allows the user to begin interacting with the digital clone.

[0154] The device accepts the user's text input or voice input and sends it to the server.

[0155] Step 5: Response Generation

[0156] The server analyzes the input received from the user and uses a generative AI model to generate an appropriate response, which is then sent to the device for display or audio playback.

[0157] Input: User text or voice input.

[0158] Data Computing: Input analysis and response generation using natural language processing.

[0159] Output: The generated text or audio response.

[0160] Specific behavior:

[0161] The server uses GPT-3 to analyze the user's input and generate an appropriate response.

[0162] The server generates a response that is then encoded and sent to the terminal.

[0163] The terminal displays or plays the response to the user in text or audio.

[0164] The above are the specific processing steps of the program of this system.

[0165] (Application example 1)

[0166] 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."

[0167] In modern times, methods of remembering the deceased rely on static records such as photographs and videos, making it difficult to recreate conversations and memories with the deceased. Furthermore, there are limited opportunities to share memories with the deceased. This creates a need for a way to vividly recreate conversations and memories with the deceased, allowing family and friends to deepen their connection with them.

[0168] 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.

[0169] In this invention, the server includes means for collecting videos, photos, and chat data of the deceased, analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, generation AI means for generating a digital clone of the deceased based on the extracted features, means for providing an interface through which the user can interact with the digital clone, means for displaying or outputting to the user by voice the responses generated by the generation AI means, and means for providing a virtual environment through which the user can enjoy interacting with the deceased in a virtual space. This allows the user to relive vivid memories and remember the deceased in a virtual space by interacting with the digital clone of the deceased.

[0170] "Video" is digital data that contains moving images.

[0171] A "photograph" is digital data of a captured still image.

[0172] "Chat data" is digital data of communication records including text message history.

[0173] "Means for collecting data" refers to the means by which users upload video, image, and text data of the deceased to the system.

[0174] "Analysis means" refers to means for extracting facial features, voice features, and language patterns of the deceased from the collected data.

[0175] "Generative AI means" refers to means that use artificial intelligence technology to generate a digital clone of a deceased person based on extracted characteristics.

[0176] The "means for providing an interface" is a means for providing an operation screen for a user to interact with a digital clone.

[0177] "Means for displaying or audibly outputting the generated response to the user" refers to means for displaying or audibly playing the response generated by the generation AI means on the user's terminal.

[0178] The "virtual environment providing means" is a means for providing a virtual space or environment in which a user can enjoy a conversation with a deceased person in a virtual space.

[0179] This invention provides a system for generating a digital clone of a deceased person and allowing interaction with the deceased person in a virtual space. The system includes the following main components:

[0180] 1. Data Collection Methods

[0181] Users use a dedicated web portal or application to upload videos, photos, and chat data of the deceased, which is then sent to a server and stored.

[0182] 2. Data analysis methods

[0183] The server analyzes the collected data, specifically by using computer vision technology to extract facial features, speech recognition technology to extract voice features, and natural language processing technology to analyze language patterns.

[0184] 3. Generation AI means

[0185] Based on the analyzed features, the server uses a generative AI model to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and manner of speech of the deceased.

[0186] 4. User Interface Provisioning Method

[0187] Users can access and interact with their digital clone through a dedicated application or web portal, using text and voice input.

[0188] 5. A means to display and speak the generated response

[0189] The server receives the user's question and uses a generative AI model to generate an appropriate response, which is then displayed or played aloud on the user's device.

[0190] 6. Means of providing virtual environments

[0191] Users can enjoy interacting with the deceased in a virtual space, which is provided using a dedicated application and a head-mounted display.

[0192] Hardware and software used

[0193] Hardware: General web servers, user devices (PCs, smartphones, tablets)

[0194] Software: Flask (web application framework), DeepFake technology (deepfake_gen), generative AI technology (ai_response_gen)

[0195] Specific examples

[0196] For example, if a user wanted to interact with a digital clone of their deceased grandmother, they would use the system as follows: The user would upload videos, photos, and chat data from when the grandmother was alive to the system. The server would analyze this data and extract the grandmother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to generate a digital clone that reflected her speaking style and personality.

[0197] The user accesses the grandmother's digital clone through a dedicated application and asks, "How are you doing these days?" The server analyzes the question and generates an appropriate response using a generative AI model. As a result, the user receives the response, "I'm fine. I water the flowers every day," allowing them to enjoy a conversation with the deceased.

[0198] Prompt Sentence Examples

[0199] "How are you doing these days?"

[0200] "Tell me your memories."

[0201] "Tell me your favorite dish."

[0202] In this way, the user can relive memories of the deceased while remembering them in a virtual space through dialogue with the deceased.

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] Users upload videos, photos, and chat data of the deceased person using a dedicated web portal or application. Specifically, users click the upload button to send video files, photo files, and text chat history to the system. This data is sent to the server and stored in storage.

[0206] Input: Video, photos, chat data

[0207] Output: Data stored on the server

[0208] Step 2:

[0209] The server then begins the process of analyzing the collected data, first using computer vision technology to extract the deceased's facial features from the videos and photos, then using speech recognition technology to extract the deceased's voice features from the videos, and finally using natural language processing technology to analyze the deceased's language patterns from the chat data.

[0210] Input: Videos, photos, and chat data stored on the server

[0211] Output: facial features, voice features, and language patterns of the deceased

[0212] Step 3:

[0213] The server then uses augmented reality technology (such as DeepFake technology) to create a digital clone of the deceased based on the extracted features. This process combines facial and vocal features to realistically recreate the appearance and voice of the deceased, and uses a generative AI model to create a digital clone with a speaking style based on the deceased's language patterns.

[0214] Input: facial features, voice features, and language patterns of the deceased

[0215] Output: Digital clone of the deceased

[0216] Step 4:

[0217] The user accesses the generated digital clone through a dedicated application or web portal and begins interacting with it. Specifically, the user opens the application and asks the digital clone questions by text input or voice input. The user's input is sent from the device to the server.

[0218] Input: User question (text or voice)

[0219] Output: Query data sent to the server

[0220] Step 5:

[0221] The server receives the user's question and generates an appropriate response using a pre-trained generative AI model, which generates a specific answer to the user's question while recreating the deceased's language patterns.

[0222] Input: User question data

[0223] Output: Digital clone response data

[0224] Step 6:

[0225] Once the response data is generated, the server sends it back to the user's device, where it is displayed or played audibly, allowing the user to receive responses from the deceased's digital clone and enjoy a conversation.

[0226] Input: Digital clone response data

[0227] Output: The response that is displayed or played aloud on the user's device

[0228] Step 7:

[0229] To further deepen the interaction in the virtual space, users can continue to ask questions using specific prompts. Examples include "How are you doing these days?", "Tell me a memory of your life," and "Tell me your favorite dish." These questions can encourage a more emotional interaction with the deceased and deepen the experience in the virtual space.

[0230] Input: New prompt question

[0231] Output: Continuing dialogue and responses

[0232] In this way, users can relive memories through dialogue with the deceased and deepen their emotional connection while remembering them.

[0233] 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.

[0234] The present invention combines a system that generates a digital clone of a deceased person, allows a user to interact with the deceased in a digital space, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[0235] System configuration

[0236] The system includes the following main components:

[0237] 1. Data collection method: Users use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The server receives this data and stores it in storage.

[0238] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased. Facial features and voice features are analyzed using computer vision and speech recognition technologies, and language patterns are extracted using natural language processing technology.

[0239] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[0240] 4. Emotion Engine: The server analyzes user input and speech and recognizes the user's emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning techniques to determine the user's emotional state.

[0241] 5. User interface provision means: Users can access and start interacting with the digital clone through a dedicated application or web portal. Users can ask questions to the digital clone using text input or voice input.

[0242] 6. Response generation means: The server analyzes the user's question and generates an appropriate response using the generative AI model. The generated response is displayed to the user through the user interface or played aloud. The response content can be adjusted according to the user's emotions recognized by the emotion engine.

[0243] 7. Database Construction Method: The user's emotion recognition results are stored in a database to create a personalized user profile. This profile is used for future interactions and to improve the user experience.

[0244] Program processing flow (overview)

[0245] When a user uploads the deceased's data to the system, the server begins analyzing the data. Once the analysis is complete, a generative AI means generates a digital clone of the deceased. When the user accesses the digital clone and asks a question, the server analyzes the question and generates a response using the generative AI model. In parallel, an emotion engine recognizes emotions from the user's input and speech and adjusts the response. The generated response is displayed to the user through the user interface or played aloud. The user's emotion recognition results are stored in a database, and a personalized user profile is constructed.

[0246] Specific examples

[0247] For example, if a user wanted to create a digital clone of their deceased mother, they would use the system as follows: The user would upload videos, photos, and chat logs of the mother from when she was alive to the system. The server would analyze this data and extract the mother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to create a digital clone that reflects the mother's speaking style and personality.

[0248] The user opens a dedicated application and asks their mother's digital clone, "Mom, how was the weather today?" The device sends this question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. It was a perfect day for a walk." At the same time, the emotion engine analyzes the user's input and voice and recognizes that the user is expressing a happy emotion. Based on this information, the server adjusts the response content and generates a warmer one. This response is displayed on the user's device, allowing the user to enjoy a conversation with their mother.

[0249] The above is a specific embodiment of the digital clone generation system according to the present invention, which allows users to continue to connect with their deceased loved ones and receive emotional support through conversation.

[0250] The processing flow will be explained below.

[0251] Step 1:

[0252] Users: Upload videos, photos, chat data, etc. of the deceased using a dedicated web portal or application. Users can complete the upload by dragging and dropping files or using a file selection dialog.

[0253] Step 2:

[0254] Server: Stores the uploaded data in storage. Records file metadata (file name, size, upload date and time, etc.) in a database.

[0255] Step 3:

[0256] Server: Analyzes the stored data. Extracts facial features of the deceased from videos and photos, and voice features from audio files. Using computer vision and voice recognition technologies, facial recognition algorithms identify facial landmarks, and voice analysis algorithms extract voice tone and pitch.

[0257] Step 4:

[0258] Server: Analyzes chat data and text data using natural language processing (NLP) techniques to extract the deceased's language patterns and unique expressions. Keyword frequency analysis and language models are used to identify commonly used phrases and vocabulary.

[0259] Step 5:

[0260] Server: Using DeepFake technology, a digital clone of the deceased is created based on the extracted facial and vocal features. A video generation model is used to realistically recreate the facial expressions of the deceased, and an audio reproduction model is used to faithfully recreate the voice.

[0261] Step 6:

[0262] Server: A generative AI model is used to learn the behavioral patterns and personality of the deceased. This model is trained to mimic the deceased's response patterns and thought process based on the analyzed chat data.

[0263] Step 7:

[0264] User: Accesses the digital clone through a dedicated application or web portal. The user can talk to the digital clone using text or voice chat. For example, they can type "Mom, how was the weather today?" into the chat box within the app.

[0265] Step 8:

[0266] Terminal: Sends user input to the server. Captures text questions and voice data in real time and transfers them to the server.

[0267] Step 9:

[0268] Server: Analyzes the user's question and generates an appropriate response using a generative AI model, such as "Today was a beautiful sunny day, perfect for a walk."

[0269] Step 10:

[0270] Server: In parallel, the emotion engine analyzes the user's input and speech, recognizing emotions from voice tone, facial expressions, and text input. The recognized emotions are used as information to tailor the response. For example, if the user has a happy expression, a warmer response will be generated accordingly.

[0271] Step 11:

[0272] Server: Sends the responses generated by the generative AI model and emotion engine to the device in text or voice format.

[0273] Step 12:

[0274] Terminal: Displays the received response to the user or plays it aloud. Shows the text on the display and uses the audio playback function to play the response in the voice of the deceased.

[0275] Step 13:

[0276] Server: Stores the user's emotion recognition results in a database and creates a personalized user profile that is used for future interactions and to improve the user experience.

[0277] Step 14:

[0278] User: Enjoys interacting with the digital clone. The user can re-enter questions or continue the conversation. This process repeats, continuing the interaction with the user.

[0279] The above are the specific processing steps of the digital clone generation system according to the present invention, which allows users to obtain emotional support through conversations with the deceased.

[0280] Example 2

[0281] 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."

[0282] In recent years, digital approaches to remembering the deceased have been gaining attention, but existing systems cannot faithfully reproduce the facial expressions, voice, and language of the deceased, and they cannot adjust the dialogue to match the user's emotions, making it difficult to realize effective dialogue to provide emotional support.In addition, systems that can personalize and optimize the user experience are not well developed.

[0283] 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 videos, photos, and chat data of the deceased; analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data; generation AI means for generating a digital clone of the deceased based on the extracted features; means for providing an interface through which the user interacts with the digital clone; emotion recognition means for recognizing emotions from the user's input and speech; means for adjusting responses generated by the generation AI means according to the user's emotional state; means for displaying or outputting the responses generated by the generation AI means to the user as audio; and means for storing the user's emotion recognition results in a database and creating a personalized user profile. This allows the appearance, voice, and speaking style of the deceased to be faithfully reproduced and dialogue tailored to the user's emotions can be provided, thereby personalizing and optimizing the user experience.

[0284] "Means of collection" refers to a function that allows users to upload videos, photos, and chat data of the deceased and send that data to a server.

[0285] "Analysis means" refers to a function that extracts the facial features, voice features, and language patterns of the deceased from the collected data, and includes computer vision technology, voice recognition technology, and natural language processing technology.

[0286] "Generative AI means" is a function that generates a digital clone based on extracted characteristics of the deceased, and utilizes DeepFake technology and generative AI technology to faithfully reproduce the appearance, voice, and speaking style of the deceased.

[0287] "Means for providing an interface" refers to the ability to provide an input and output interface for a user to interact with the digital clone, such as a dedicated application or a web portal.

[0288] The "emotion recognition means" is a function that uses deep learning technology to recognize emotions by analyzing voice tone, facial expressions, and text input from user input and speech.

[0289] The "means for adjusting a response" is a function for appropriately adjusting the response generated by the generation AI means in accordance with the emotional state of the user recognized by the emotion recognition means.

[0290] The "means for displaying or outputting by voice" is a function for visually displaying the generated response to the user or playing it back as voice.

[0291] The "means for saving in a database and creating a personalized user profile" is a function for saving the user's emotion recognition results in a database and building a personalized user profile based on the results, which will be used in future interactions.

[0292] As an example of a mode for implementing the invention, a system will be described that combines a system that generates a digital clone of a deceased person and allows a user to interact with the deceased in a digital space with an emotion engine that recognizes the user's emotions.

[0293] The system includes the following main components:

[0294] 1. Data Collection Methods

[0295] Users use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The device sends this data to a server, which then stores it in storage. The hardware used in this process is the user's device (e.g., a PC or smartphone), and the software includes a web portal or mobile app.

[0296] 2. Data analysis methods

[0297] The server analyzes the collected data and extracts the facial features, voice characteristics, and language patterns of the deceased using computer vision, speech recognition, and natural language processing technologies, such as OpenCV (computer vision), Google Cloud Speech-to-Text API (speech recognition), and SpaCy (natural language processing).

[0298] 3. Generation AI means

[0299] Based on the analyzed features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased person. This digital clone faithfully reproduces the appearance, voice, and speaking style of the deceased, and generative AI models such as DeepFaceLab and GPT-3 are used.

[0300] 4. Emotion recognition means

[0301] The server analyzes user input and speech and recognizes the user's emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning technology, including TensorFlow and PyTorch.

[0302] 5. User Interface Provisioning Method

[0303] Users can access the digital clone through a dedicated application or web portal and begin interacting with it by asking questions using text or voice input.

[0304] 6. Response Coordination

[0305] The server analyzes the questions and utterances entered by the user and generates appropriate responses using a generative AI model. It also adjusts the response content according to the user's emotional state, as recognized by emotion recognition means.

[0306] 7. Display or audio output means

[0307] The generated response is displayed to the user through a user interface or played aloud.

[0308] 8. Means of storing in a database and creating individualized user profiles

[0309] The server stores the user's emotion recognition results in a database and builds an individualized user profile, which enables personalized interactions to enhance the user experience.

[0310] Specific examples

[0311] For example, if a user wants to create a digital clone of their deceased mother, they would use the system as follows: The program processing of this system is as follows.

[0312] 1. The user uploads videos, photos, and chat logs of their mother before she died to the system. The device sends this data to the server, which then stores it.

[0313] 2. The server analyzes the stored data and extracts the mother's facial features, voice characteristics, and phrasing using OpenCV, Google Cloud Speech-to-Text API, and SpaCy.

[0314] 3. Based on the extraction results, the server uses DeepFake technology to recreate the mother's face and voice, and uses a generative AI model to create a digital clone that reflects the mother's speaking style and personality.

[0315] 4. The user opens a dedicated application and asks the digital clone of their mother, "Mom, how was the weather today?" The prompt can be entered via text or voice.

[0316] 5. The device sends the question to the server, which analyzes it and uses a generative AI model to generate a response: "It was a beautiful sunny day today. A perfect day for a walk."

[0317] 6. The emotion recognition means analyzes the user's input and voice and recognizes that the user is feeling happy. As a result, the server adjusts the response content to be warmer.

[0318] 7. The adjusted response will be displayed on the device and played back in the mother's voice.

[0319] This system allows users to continue connecting with the deceased and receive emotional support through conversations. Furthermore, the system stores the user's emotion recognition results in a database, allowing for more personalized conversations and a better user experience.

[0320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0321] Step 1:

[0322] Users use a dedicated web portal or application to upload videos, photos and chat data of the deceased.

[0323] Input: Videos, photos, and chat data of the deceased

[0324] Output: Data sent to the server

[0325] Specific operation: A user accesses the portal, clicks the upload button, selects a file from a local folder, and uploads it. The device sends the uploaded data to the server and stores it.

[0326] Step 2:

[0327] The server analyzes the collected data and extracts the facial features, voice characteristics, and language patterns of the deceased.

[0328] Input: Videos, photos, and chat data stored on the server

[0329] Output: Extracted facial features, voice features, and language patterns

[0330] How it works: The server uses OpenCV to extract facial features from video, Google Cloud Speech-to-Text API to convert audio data to text, and SpaCy to analyze language patterns from chat data.

[0331] Step 3:

[0332] Based on the analyzed characteristics, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased person.

[0333] Input: extracted facial features, voice features, language patterns

[0334] Output: Generated digital clone

[0335] How it works: The server uses DeepFaceLab to recreate the face and voice of the deceased, and then uses GPT-3 to generate a text-based dialogue model that reflects the deceased's speaking style and personality.

[0336] Step 4:

[0337] Users access and interact with their digital clone through a dedicated application or web portal.

[0338] Input: User text or voice input

[0339] Output: The user's question sent to the server

[0340] How it works: The user opens the application, clicks on the digital clone icon, and enters a question via text or voice. The device then sends the question to the server.

[0341] Step 5:

[0342] The server analyzes the user's question and uses a generative AI model to generate an appropriate response.

[0343] Input: User question

[0344] Output: The generated response

[0345] What it does: The server uses GPT-3 to analyze the user's input and generates a response like, "It was a beautiful sunny day today. A perfect day for a walk."

[0346] Step 6:

[0347] The server recognizes emotions from the user's input and speech and performs adaptive processing.

[0348] Input: User input (text or voice)

[0349] Output: User's emotional state

[0350] Specific operation: The server uses TensorFlow to analyze the user's voice tone and text, and recognizes emotions such as whether the user is happy.

[0351] Step 7:

[0352] The server adjusts the response content according to the emotion recognition results.

[0353] Input: Initial generated response, user's emotional state

[0354] Output: Adjusted response

[0355] What it does: The server adjusts the initial response, "It was a beautiful sunny day today," to something like "It was a beautiful sunny day today. A perfect day for a walk, did you enjoy it?" depending on the emotion.

[0356] Step 8:

[0357] The generated response is displayed to the user through a user interface or played aloud.

[0358] Input: Adjusted response

[0359] Output: Display to user or play audio

[0360] Specific behavior: The device receives the response from the server and displays it on the screen as text or plays it back in the mother's voice.

[0361] Step 9:

[0362] The server stores the user's emotion recognition results in a database and builds a personalized user profile.

[0363] Input: User emotion recognition results

[0364] Output: Updated user profile

[0365] Specific operation: The server stores the emotion recognition results in a database and updates the personalized profile for the next interaction.

[0366] (Application example 2)

[0367] 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."

[0368] In factories and production lines, there is a challenge in efficiently transferring the knowledge and skills of experts to new employees and automated machines. Furthermore, when experts retire or pass away, there is a risk that their valuable skills and know-how will be lost. Furthermore, when new employees receive training from experts, it is difficult to provide appropriate guidance that takes into account their emotions and level of understanding.

[0369] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0370] In this invention, the server includes a means for collecting videos, photos, and chat data of the deceased, an analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, and a generation AI means for generating a digital clone of the deceased based on the extracted features, which makes it possible to educate and train new employees and automated machines in the knowledge and skills of an expert through the digital clone.

[0371] A "deceased person" is someone who was active in a certain workplace or society during their lifetime, but who is now deceased.

[0372] A "video" is a video file that records the actions and statements of the deceased.

[0373] A "photograph" is still image data that captures the face or figure of the deceased.

[0374] "Chat data" is a record of text-based conversations that the deceased had while alive.

[0375] "Analysis means" refers to technology or equipment for extracting and analyzing specific information from collected data.

[0376] "Facial features" are the external facial characteristics and identifying information of the deceased.

[0377] "Voice features" are characteristics or patterns related to the deceased person's voice.

[0378] A "language pattern" is the particular language or way of expressing oneself that the deceased used.

[0379] "Generative AI means" refers to a means for generating a digital clone based on information about a deceased person using artificial intelligence technology.

[0380] An "interface providing means" is a technique or device that provides a way for a user to interact with a digital clone.

[0381] "Means for displaying or audibly outputting the generated response to the user" refers to technology or devices for visually or audibly conveying the response generated by the digital clone to the user.

[0382] A "digital clone" is a digital representation that recreates the appearance, voice, and manner of speaking of a deceased person.

[0383] An "expert" is someone who has advanced knowledge and skills in a particular workplace or field.

[0384] A "new employee" is someone who has recently joined a workplace or organization and needs to learn the knowledge and skills of an expert.

[0385] An "automatic machine" is a mechanical device that automatically performs a specific task or process.

[0386] This invention relates to a digital coaching system that aims to transfer the knowledge and skills of experts to new employees and automated machines.

[0387] System configuration

[0388] The system includes the following main components:

[0389] 1. Data collection method: The server collects videos, photos, and chat data of the deceased and stores them in storage. Users upload these data using a dedicated web portal or application.

[0390] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased person. Facial features and voice features are extracted using computer vision and voice recognition technologies, and language patterns are extracted using natural language processing technology.

[0391] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[0392] 4. Emotion Engine: The server analyzes user and automated machine input and speech and recognizes emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning techniques to determine emotional states.

[0393] 5. User interface provision means: Users or automated machines can access and initiate a dialogue with the digital clone through a dedicated application or head-mounted display (HMD). Users can ask questions or give instructions to the digital clone using text input or voice input.

[0394] 6. Response generation means: The server analyzes the questions posed by the user or automated machine and generates an appropriate response using the generative AI model. The generated response is displayed or output as voice through the user interface. The response content can be adjusted according to the emotions recognized by the emotion engine.

[0395] 7. Database construction method: Emotion recognition results and dialogue data from users and automated machines are stored in a database to create personalized user profiles and work logs. These profiles are used for future dialogues and work assistance, improving the user experience.

[0396] Usage example

[0397] For example, if a new employee wants to learn how to maintain a new machine in a factory, they can use the system as follows: The user uploads a video of the expert working, along with audio commentary and instructions. The server analyzes this data and extracts the expert's facial features, vocal characteristics, and language patterns. DeepFake technology is then used to faithfully reproduce the expert's appearance and voice, and a generative AI model is used to create a digital clone that reflects the expert's speaking style and teaching style.

[0398] A user or automated machine wears an HMD and asks a digital clone of an expert, "How do I maintain this machine?" The server analyzes the question and uses a generative AI model to generate a response such as, "First, loosen this nut, then remove the filter." At the same time, an emotion engine analyzes the user's voice and movements to recognize confusion. Based on this information, the server adjusts the response, adding more detailed instructions and diagrams. The response is displayed on the HMD display, allowing the user to proceed with the task with confidence while receiving visual guidance.

[0399] The above is a specific embodiment of the digital coaching system based on the present invention. This system enables the knowledge and skills of experts to be efficiently passed on to the next generation, and improves the productivity of factories and production lines through education and guidance.

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Step 1:

[0402] The server collects videos, photos, and chat data of the deceased uploaded by users using a dedicated web portal or application. The input data are the deceased's video files, photo files, and chat text data. The server stores these files in storage and prepares them for analysis. The output is that this data is centrally managed on the server.

[0403] Step 2:

[0404] The server analyzes the collected video, photo, and chat data. It uses computer vision technology to extract facial features and speech recognition technology to identify voice features. It also uses natural language processing technology to extract language patterns from the chat data. The input data is the video, photo, and chat data collected in Step 1. Data processing involves facial feature mapping, speech spectrum analysis, text tokenization, and pattern recognition. The output is facial, voice, and language feature data of the deceased.

[0405] Step 3:

[0406] The server uses deepfake technology and generative AI technology to generate a digital clone based on the facial features, voice features, and language patterns of the deceased. The input data is the feature data extracted in step 2. Data calculations involve generating a face and voice based on an AI model and integrating language patterns. The output is a digital clone that faithfully reproduces the deceased.

[0407] Step 4:

[0408] The server accepts input from users or automated machines. In particular, users or robots access it through dedicated applications or head-mounted displays (HMDs) and begin interacting with their digital clones. The input in this step is a question or instruction from the user or robot. The server recognizes this and prepares to proceed to the next step. The output is a confirmation that the question or instruction was received.

[0409] Step 5:

[0410] The server analyzes questions and instructions from users or automated machines and generates appropriate responses using a generative AI model. In parallel, an emotion engine recognizes emotions from the user's voice tone, facial expressions, and text input. The input is the question or instruction and emotion data received in step 4. Data calculations involve question analysis using natural language processing, emotion analysis using the emotion engine, and response generation using an AI model. The output is an appropriate response text or voice data according to the emotion.

[0411] Step 6:

[0412] The server displays or outputs the generated response to the user or automated machine. The input is the output of step 5. The specific operation is to display text or video on the HMD display and play audio using the audio output device. The output is the response received by the user or automated machine.

[0413] Step 7:

[0414] The server stores the emotion recognition results and dialogue data of the user and the automated machine in a database. The input data is the output of steps 5 and 6. As data storage, personalized user profiles and work logs are recorded in the database. The output is an updated database, which can be used for future dialogue and guidance.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Second embodiment]

[0419] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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. 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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."

[0431] The present invention provides a system for generating a digital clone of a deceased person and allowing a user to interact with the deceased person in a digital space. Specific embodiments of the system are described below.

[0432] System configuration

[0433] The system includes the following main components:

[0434] 1. Data collection method: Users can use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The server receives this data and stores it in storage.

[0435] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased. Facial features and voice features are analyzed using computer vision and speech recognition technologies, and language patterns are extracted using natural language processing technology.

[0436] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[0437] 4. User interface provision means: Users can access and start interacting with the digital clone through a dedicated application or web portal. Users can ask questions to the digital clone using text input or voice input.

[0438] 5. Response generation means: The server receives the user's question and generates an appropriate response using the generative AI model. The generated response is displayed or played aloud to the user through the user interface.

[0439] Program processing flow (overview)

[0440] When a user uploads the deceased's data to the system, the server begins analyzing the data. Once the analysis is complete, a generative AI means generates a digital clone of the deceased. When a user accesses the digital clone and asks a question, the server analyzes the question and generates a response using the generative AI model. The generated response is displayed on the user's device or played aloud.

[0441] Specific examples

[0442] For example, if a user wanted to create a digital clone of their deceased mother, they would use the system as follows: The user would upload videos, photos, and chat logs of the mother from when she was alive to the system. The server would analyze this data and extract the mother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to create a digital clone that reflects the mother's speaking style and personality.

[0443] The user opens a dedicated application and asks their mother's digital clone, "How was the weather today?" The device sends this question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. It was a perfect day for a walk." This response is then displayed on the user's device, allowing the user to enjoy a conversation with their mother.

[0444] The above is a specific embodiment of the present invention. This system allows you to continue to connect with the deceased and receive comfort and support through conversation.

[0445] The processing flow will be explained below.

[0446] Step 1:

[0447] Users: Upload videos, photos, chat data, etc. of the deceased using a dedicated web portal or application. Users can complete the upload by dragging and dropping files or using a file selection dialog.

[0448] Step 2:

[0449] Server: Stores the uploaded data in storage. Records file metadata (file name, size, upload date and time, etc.) in a database.

[0450] Step 3:

[0451] Server: Analyzes the stored data, extracting facial features of the deceased from videos and photos, and voice features from audio files. This involves computer vision and voice recognition techniques. For example, facial recognition algorithms are used to identify facial landmarks, and voice analysis algorithms are used to extract voice tone and pitch.

[0452] Step 4:

[0453] Server: Analyzes chat data and text data using natural language processing (NLP) techniques to extract the deceased's language patterns and unique expressions. For example, it uses keyword frequency analysis and language models to identify commonly used phrases and vocabulary.

[0454] Step 5:

[0455] Server: Using DeepFake technology, a digital clone of the deceased is created based on the extracted facial and vocal features. The clone is then optimized to reproduce the deceased's face and voice in real time. For example, a video generative model is used to realistically recreate the deceased's facial expressions.

[0456] Step 6:

[0457] Server: A generative AI model is used to learn the behavioral patterns and personality of the deceased. This model is trained to mimic the deceased's response patterns and thought process based on the analyzed chat data. For example, a deep learning model is used to understand the context and generate appropriate responses.

[0458] Step 7:

[0459] User: Accesses the digital clone through a dedicated application or web portal. The user can talk to the digital clone using text or voice chat. For example, they can type "Mom, how was the weather today?" into the chat box within the app.

[0460] Step 8:

[0461] Terminal: Sends user input to the server. This includes text questions and voice data. The terminal captures user input in real time and forwards it to the server.

[0462] Step 9:

[0463] Server: Analyzes the user's question and generates an appropriate response using a generative AI model. For example, in response to a question about the weather, the server generates a response such as "Today was a beautiful sunny day, perfect for a walk."

[0464] Step 10:

[0465] Server: Generates and sends the response to the terminal. The response is sent in text or audio format.

[0466] Step 11:

[0467] Terminal: Displays the received response to the user or plays it aloud. For example, it may show the text on a display and use a voice playback function to play the response in the deceased's voice.

[0468] Step 12:

[0469] User: Enjoys interacting with the digital clone. The user can re-enter questions or continue the conversation. This process repeats, continuing the interaction with the user.

[0470] The above are the specific processing steps of the digital clone generation system according to the present invention, which allows users to feel a connection with the deceased through conversation.

[0471] Example 1

[0472] 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."

[0473] In modern society, there is a growing emotional need to reminisce and converse with deceased loved ones. However, the technology to effectively utilize the digital data left behind by the deceased is underdeveloped. Therefore, there is a need for a system that enables communication with the deceased through a computer. Furthermore, advanced data analysis and generation technologies are required to accurately reproduce the facial features, vocal characteristics, and language patterns of the deceased, allowing bereaved families to have a natural and emotionally rich experience.

[0474] 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.

[0475] In this invention, the server includes means for collecting image data, voice data, and text data of the deceased, analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, artificial intelligence (AI) means for generating a digital clone of the deceased based on the extracted features, means for providing a user interface for the user to interact with the digital clone, and means for displaying or outputting to the user the responses generated by the AI ​​generating means by voice, thereby enabling a highly simulated interaction with the deceased and enabling the bereaved family to maintain an emotional connection.

[0476] "Deceased" refers to a person who has passed away.

[0477] "Image data" refers to digital image information stored in the form of photographs or videos.

[0478] "Audio Data" means digital acoustic information stored in voice or audio recording form.

[0479] "Text data" refers to digital information in text format stored as chat logs or documents.

[0480] "Facial features" refers to feature information about the shape and structure of a person's face extracted from image data.

[0481] "Voice features" refer to the pitch, timbre, and pronunciation characteristics of a person's voice extracted from audio data.

[0482] "Language patterns" refer to a person's language and unique ways of expression extracted from text data.

[0483] "Analysis means" refers to a processing mechanism for extracting necessary feature information from collected data.

[0484] "Generative artificial intelligence means" refers to artificial intelligence technology for generating a digital clone based on extracted feature information.

[0485] "User interface" refers to the means that provides input and output mechanisms for a user to interact with a system.

[0486] A "digital clone" refers to a digital model that recreates the face, voice, and language patterns of a deceased person.

[0487] "Deepfake technology" refers to the technology of synthesizing images and audio data using deep learning technology.

[0488] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0489] System Overview

[0490] This invention is a system that creates a digital clone of a deceased person and allows users to interact with the deceased in a digital space. The system is mainly composed of three components: a server, a terminal, and a user, with each component playing a specific role. The entire system functions through the following stages: data collection, data analysis, digital clone creation, user interaction, and response generation.

[0491] Data collection methods

[0492] Users use a dedicated web portal or application to collect image, audio, and text data of the deceased, which is then securely uploaded to a server via HTTPS, where the server stores the data.

[0493] Data Analysis Methods

[0494] The server analyzes the received data and extracts facial features, voice features, and language patterns using the following software and techniques:

[0495] Facial features: Extract facial features from image data using OpenCV.

[0496] Speech features: Extract speech features from the audio data using the Google Speech-to-Text API.

[0497] Language Patterns: Extract language patterns from text data using SpaCy or NLTK.

[0498] Generation AI means

[0499] The server then uses DeepFake technology and generative AI technology (such as GPT-3) to create a digital clone of the deceased person based on the analyzed characteristics. The digital clone faithfully reproduces the appearance, voice, and manner of speech of the deceased.

[0500] User interface providing means

[0501] Users can access the digital clone through a dedicated application or a web portal. The user interface is built on React and Vue.js and is designed to allow users to ask questions and make comments to the digital clone via text or voice input.

[0502] Response Generation Method

[0503] The server analyzes questions received through the user interface and generates appropriate responses using a generative AI model (e.g., GPT-3). The responses are then sent to the device and displayed as text or played aloud.

[0504] Specific examples

[0505] For example, if a user wants to create a digital clone of their deceased mother, they can upload her image, voice, and text data from before she died to the system. The server analyzes this data and extracts her facial features, voice characteristics, and speech patterns. It then uses DeepFake technology and generative AI technology to create a digital clone of the mother.

[0506] A user opens the application and asks their mother's digital clone, "How was the weather today?" The device sends the question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. A perfect day for a walk." This response is then displayed on the user's device, allowing them to enjoy a conversation with their deceased loved one.

[0507] Prompt Sentence Examples

[0508] "To a digital clone of your deceased mother, please type: 'What was the weather like today?'"

[0509] The above is a specific embodiment of the present invention. This system allows you to continue to connect with the deceased and receive comfort and support through conversation.

[0510] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0511] Step 1: Data collection

[0512] Users use a dedicated web portal or application to upload image, audio, and text data of the deceased to the system, which then transfers the data to the server.

[0513] Input: Image data, audio data, and text data of the deceased.

[0514] Data processing: splitting, compression, secure data transfer using HTTP protocol.

[0515] Output: Data saved to storage on the server.

[0516] Specific behavior:

[0517] A user logs into a web portal and clicks the file selection button.

[0518] The device displays a file selection dialog, and the user selects the required file.

[0519] The device uploads the selected file to the server.

[0520] The server stores the received data in storage.

[0521] Step 2: Data analysis

[0522] The server analyzes the uploaded data, extracting facial features, voice features, and language patterns.

[0523] Input: Image data, audio data, and text data saved in step 1.

[0524] Data computing: image recognition, speech analysis, natural language processing.

[0525] Output: Extraction results of facial features, audio features, and language patterns.

[0526] Specific behavior:

[0527] The server uses the OpenCV library to extract facial features from the image data.

[0528] The server uses the Google Speech-to-Text API to extract speech features from the audio data.

[0529] The server uses SpaCy to extract language patterns from the text data.

[0530] Step 3: Digital cloning

[0531] Based on the analyzed characteristic information, the server uses DeepFake technology and generative AI technology to generate a digital clone.

[0532] Input: Facial features, audio features, and language pattern extraction results.

[0533] Data computation: face and voice synthesis, generation of conversation models.

[0534] Output: A digital clone of the deceased person.

[0535] Specific behavior:

[0536] The server uses the facial features in DeepFaceLab to generate a digital clone's face.

[0537] The voice of the digital clone is reproduced based on the voice model generated by the server.

[0538] The server uses GPT-3 to generate a conversation model that reflects the speaking style and personality of the deceased.

[0539] Step 4: Configuring User Interaction

[0540] Users access their digital clone by opening a dedicated application or web portal, where they can ask questions or make comments using text or voice input.

[0541] Input: User text or voice input.

[0542] Data processing: Accepting input and forwarding it to the server.

[0543] Output: User input transmitted to the server.

[0544] Specific behavior:

[0545] The user launches the application and logs in.

[0546] It opens an interface that allows the user to begin interacting with the digital clone.

[0547] The device accepts the user's text input or voice input and sends it to the server.

[0548] Step 5: Response Generation

[0549] The server analyzes the input received from the user and uses a generative AI model to generate an appropriate response, which is then sent to the device for display or audio playback.

[0550] Input: User text or voice input.

[0551] Data Computing: Input analysis and response generation using natural language processing.

[0552] Output: The generated text or audio response.

[0553] Specific behavior:

[0554] The server uses GPT-3 to analyze the user's input and generate an appropriate response.

[0555] The server generates a response that is then encoded and sent to the terminal.

[0556] The terminal displays or plays the response to the user in text or audio.

[0557] The above are the specific processing steps of the program of this system.

[0558] (Application example 1)

[0559] 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."

[0560] In modern times, methods of remembering the deceased rely on static records such as photographs and videos, making it difficult to recreate conversations and memories with the deceased. Furthermore, there are limited opportunities to share memories with the deceased. This creates a need for a way to vividly recreate conversations and memories with the deceased, allowing family and friends to deepen their connection with them.

[0561] 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.

[0562] In this invention, the server includes means for collecting videos, photos, and chat data of the deceased, analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, generation AI means for generating a digital clone of the deceased based on the extracted features, means for providing an interface through which the user can interact with the digital clone, means for displaying or outputting to the user by voice the responses generated by the generation AI means, and means for providing a virtual environment through which the user can enjoy interacting with the deceased in a virtual space. This allows the user to relive vivid memories and remember the deceased in a virtual space by interacting with the digital clone of the deceased.

[0563] "Video" is digital data that contains moving images.

[0564] A "photograph" is digital data of a captured still image.

[0565] "Chat data" is digital data of communication records including text message history.

[0566] "Means for collecting data" refers to the means by which users upload video, image, and text data of the deceased to the system.

[0567] "Analysis means" refers to means for extracting facial features, voice features, and language patterns of the deceased from the collected data.

[0568] "Generative AI means" refers to means that use artificial intelligence technology to generate a digital clone of a deceased person based on extracted characteristics.

[0569] The "means for providing an interface" is a means for providing an operation screen for a user to interact with a digital clone.

[0570] "Means for displaying or audibly outputting the generated response to the user" refers to means for displaying or audibly playing the response generated by the generation AI means on the user's terminal.

[0571] The "virtual environment providing means" is a means for providing a virtual space or environment in which a user can enjoy a conversation with a deceased person in a virtual space.

[0572] This invention provides a system for generating a digital clone of a deceased person and allowing interaction with the deceased person in a virtual space. The system includes the following main components:

[0573] 1. Data Collection Methods

[0574] Users use a dedicated web portal or application to upload videos, photos, and chat data of the deceased, which is then sent to a server and stored.

[0575] 2. Data analysis methods

[0576] The server analyzes the collected data, specifically by using computer vision technology to extract facial features, speech recognition technology to extract voice features, and natural language processing technology to analyze language patterns.

[0577] 3. Generation AI means

[0578] Based on the analyzed features, the server uses a generative AI model to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and manner of speech of the deceased.

[0579] 4. User Interface Provisioning Method

[0580] Users can access and interact with their digital clone through a dedicated application or web portal, using text and voice input.

[0581] 5. A means to display and speak the generated response

[0582] The server receives the user's question and uses a generative AI model to generate an appropriate response, which is then displayed or played aloud on the user's device.

[0583] 6. Means of providing virtual environments

[0584] Users can enjoy interacting with the deceased in a virtual space, which is provided using a dedicated application and a head-mounted display.

[0585] Hardware and software used

[0586] Hardware: General web servers, user devices (PCs, smartphones, tablets)

[0587] Software: Flask (web application framework), DeepFake technology (deepfake_gen), generative AI technology (ai_response_gen)

[0588] Specific examples

[0589] For example, if a user wanted to interact with a digital clone of their deceased grandmother, they would use the system as follows: The user would upload videos, photos, and chat data from when the grandmother was alive to the system. The server would analyze this data and extract the grandmother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to generate a digital clone that reflected her speaking style and personality.

[0590] The user accesses the grandmother's digital clone through a dedicated application and asks, "How are you doing these days?" The server analyzes the question and generates an appropriate response using a generative AI model. As a result, the user receives the response, "I'm fine. I water the flowers every day," allowing them to enjoy a conversation with the deceased.

[0591] Prompt Sentence Examples

[0592] "How are you doing these days?"

[0593] "Tell me your memories."

[0594] "Tell me your favorite dish."

[0595] In this way, the user can relive memories of the deceased while remembering them in a virtual space through dialogue with the deceased.

[0596] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0597] Step 1:

[0598] Users upload videos, photos, and chat data of the deceased person using a dedicated web portal or application. Specifically, users click the upload button to send video files, photo files, and text chat history to the system. This data is sent to the server and stored in storage.

[0599] Input: Video, photos, chat data

[0600] Output: Data stored on the server

[0601] Step 2:

[0602] The server then begins the process of analyzing the collected data, first using computer vision technology to extract the deceased's facial features from the videos and photos, then using speech recognition technology to extract the deceased's voice features from the videos, and finally using natural language processing technology to analyze the deceased's language patterns from the chat data.

[0603] Input: Videos, photos, and chat data stored on the server

[0604] Output: facial features, voice features, and language patterns of the deceased

[0605] Step 3:

[0606] The server then uses augmented reality technology (such as DeepFake technology) to create a digital clone of the deceased based on the extracted features. This process combines facial and vocal features to realistically recreate the appearance and voice of the deceased, and uses a generative AI model to create a digital clone with a speaking style based on the deceased's language patterns.

[0607] Input: facial features, voice features, and language patterns of the deceased

[0608] Output: Digital clone of the deceased

[0609] Step 4:

[0610] The user accesses the generated digital clone through a dedicated application or web portal and begins interacting with it. Specifically, the user opens the application and asks the digital clone questions by text input or voice input. The user's input is sent from the device to the server.

[0611] Input: User question (text or voice)

[0612] Output: Query data sent to the server

[0613] Step 5:

[0614] The server receives the user's question and generates an appropriate response using a pre-trained generative AI model, which generates a specific answer to the user's question while recreating the deceased's language patterns.

[0615] Input: User question data

[0616] Output: Digital clone response data

[0617] Step 6:

[0618] Once the response data is generated, the server sends it back to the user's device, where it is displayed or played audibly, allowing the user to receive responses from the deceased's digital clone and enjoy a conversation.

[0619] Input: Digital clone response data

[0620] Output: The response that is displayed or played aloud on the user's device

[0621] Step 7:

[0622] To further deepen the interaction in the virtual space, users can continue to ask questions using specific prompts. Examples include "How are you doing these days?", "Tell me a memory of your life," and "Tell me your favorite dish." These questions can encourage a more emotional interaction with the deceased and deepen the experience in the virtual space.

[0623] Input: New prompt question

[0624] Output: Continuing dialogue and responses

[0625] In this way, users can relive memories through dialogue with the deceased and deepen their emotional connection while remembering them.

[0626] 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.

[0627] The present invention combines a system that generates a digital clone of a deceased person, allows a user to interact with the deceased in a digital space, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[0628] System configuration

[0629] The system includes the following main components:

[0630] 1. Data collection method: Users use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The server receives this data and stores it in storage.

[0631] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased. Facial features and voice features are analyzed using computer vision and speech recognition technologies, and language patterns are extracted using natural language processing technology.

[0632] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[0633] 4. Emotion Engine: The server analyzes user input and speech and recognizes the user's emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning techniques to determine the user's emotional state.

[0634] 5. User interface provision means: Users can access and start interacting with the digital clone through a dedicated application or web portal. Users can ask questions to the digital clone using text input or voice input.

[0635] 6. Response generation means: The server analyzes the user's question and generates an appropriate response using the generative AI model. The generated response is displayed to the user through the user interface or played aloud. The response content can be adjusted according to the user's emotions recognized by the emotion engine.

[0636] 7. Database Construction Method: The user's emotion recognition results are stored in a database to create a personalized user profile. This profile is used for future interactions and to improve the user experience.

[0637] Program processing flow (overview)

[0638] When a user uploads the deceased's data to the system, the server begins analyzing the data. Once the analysis is complete, a generative AI means generates a digital clone of the deceased. When the user accesses the digital clone and asks a question, the server analyzes the question and generates a response using the generative AI model. In parallel, an emotion engine recognizes emotions from the user's input and speech and adjusts the response. The generated response is displayed to the user through the user interface or played aloud. The user's emotion recognition results are stored in a database, and a personalized user profile is constructed.

[0639] Specific examples

[0640] For example, if a user wanted to create a digital clone of their deceased mother, they would use the system as follows: The user would upload videos, photos, and chat logs of the mother from when she was alive to the system. The server would analyze this data and extract the mother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to create a digital clone that reflects the mother's speaking style and personality.

[0641] The user opens a dedicated application and asks their mother's digital clone, "Mom, how was the weather today?" The device sends this question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. It was a perfect day for a walk." At the same time, the emotion engine analyzes the user's input and voice and recognizes that the user is expressing a happy emotion. Based on this information, the server adjusts the response content and generates a warmer one. This response is displayed on the user's device, allowing the user to enjoy a conversation with their mother.

[0642] The above is a specific embodiment of the digital clone generation system according to the present invention, which allows users to continue to connect with their deceased loved ones and receive emotional support through conversation.

[0643] The processing flow will be explained below.

[0644] Step 1:

[0645] Users: Upload videos, photos, chat data, etc. of the deceased using a dedicated web portal or application. Users can complete the upload by dragging and dropping files or using a file selection dialog.

[0646] Step 2:

[0647] Server: Stores the uploaded data in storage. Records file metadata (file name, size, upload date and time, etc.) in a database.

[0648] Step 3:

[0649] Server: Analyzes the stored data. Extracts facial features of the deceased from videos and photos, and voice features from audio files. Using computer vision and voice recognition technologies, facial recognition algorithms identify facial landmarks, and voice analysis algorithms extract voice tone and pitch.

[0650] Step 4:

[0651] Server: Analyzes chat data and text data using natural language processing (NLP) techniques to extract the deceased's language patterns and unique expressions. Keyword frequency analysis and language models are used to identify commonly used phrases and vocabulary.

[0652] Step 5:

[0653] Server: Using DeepFake technology, a digital clone of the deceased is created based on the extracted facial and vocal features. A video generation model is used to realistically recreate the facial expressions of the deceased, and an audio reproduction model is used to faithfully recreate the voice.

[0654] Step 6:

[0655] Server: A generative AI model is used to learn the behavioral patterns and personality of the deceased. This model is trained to mimic the deceased's response patterns and thought process based on the analyzed chat data.

[0656] Step 7:

[0657] User: Accesses the digital clone through a dedicated application or web portal. The user can talk to the digital clone using text or voice chat. For example, they can type "Mom, how was the weather today?" into the chat box within the app.

[0658] Step 8:

[0659] Terminal: Sends user input to the server. Captures text questions and voice data in real time and transfers them to the server.

[0660] Step 9:

[0661] Server: Analyzes the user's question and generates an appropriate response using a generative AI model, such as "Today was a beautiful sunny day, perfect for a walk."

[0662] Step 10:

[0663] Server: In parallel, the emotion engine analyzes the user's input and speech, recognizing emotions from voice tone, facial expressions, and text input. The recognized emotions are used as information to tailor the response. For example, if the user has a happy expression, a warmer response will be generated accordingly.

[0664] Step 11:

[0665] Server: Sends the responses generated by the generative AI model and emotion engine to the device in text or voice format.

[0666] Step 12:

[0667] Terminal: Displays the received response to the user or plays it aloud. Shows the text on the display and uses the audio playback function to play the response in the voice of the deceased.

[0668] Step 13:

[0669] Server: Stores the user's emotion recognition results in a database and creates a personalized user profile that is used for future interactions and to improve the user experience.

[0670] Step 14:

[0671] User: Enjoys interacting with the digital clone. The user can re-enter questions or continue the conversation. This process repeats, continuing the interaction with the user.

[0672] The above are the specific processing steps of the digital clone generation system according to the present invention, which allows users to obtain emotional support through conversations with the deceased.

[0673] Example 2

[0674] 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."

[0675] In recent years, digital approaches to remembering the deceased have been gaining attention, but existing systems cannot faithfully reproduce the facial expressions, voice, and language of the deceased, and they cannot adjust the dialogue to match the user's emotions, making it difficult to realize effective dialogue to provide emotional support.In addition, systems that can personalize and optimize the user experience are not well developed.

[0676] 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 videos, photos, and chat data of the deceased; analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data; generation AI means for generating a digital clone of the deceased based on the extracted features; means for providing an interface through which the user interacts with the digital clone; emotion recognition means for recognizing emotions from the user's input and speech; means for adjusting responses generated by the generation AI means according to the user's emotional state; means for displaying or outputting the responses generated by the generation AI means to the user as audio; and means for storing the user's emotion recognition results in a database and creating a personalized user profile. This allows the appearance, voice, and speaking style of the deceased to be faithfully reproduced and dialogue tailored to the user's emotions can be provided, thereby personalizing and optimizing the user experience.

[0677] "Means of collection" refers to a function that allows users to upload videos, photos, and chat data of the deceased and send that data to a server.

[0678] "Analysis means" refers to a function that extracts the facial features, voice features, and language patterns of the deceased from the collected data, and includes computer vision technology, voice recognition technology, and natural language processing technology.

[0679] "Generative AI means" is a function that generates a digital clone based on extracted characteristics of the deceased, and utilizes DeepFake technology and generative AI technology to faithfully reproduce the appearance, voice, and speaking style of the deceased.

[0680] "Means for providing an interface" refers to the ability to provide an input and output interface for a user to interact with the digital clone, such as a dedicated application or a web portal.

[0681] The "emotion recognition means" is a function that uses deep learning technology to recognize emotions by analyzing voice tone, facial expressions, and text input from user input and speech.

[0682] The "means for adjusting a response" is a function for appropriately adjusting the response generated by the generation AI means in accordance with the emotional state of the user recognized by the emotion recognition means.

[0683] The "means for displaying or outputting by voice" is a function for visually displaying the generated response to the user or playing it back as voice.

[0684] The "means for saving in a database and creating a personalized user profile" is a function for saving the user's emotion recognition results in a database and building a personalized user profile based on the results, which will be used in future interactions.

[0685] As an example of a mode for implementing the invention, a system will be described that combines a system that generates a digital clone of a deceased person and allows a user to interact with the deceased in a digital space with an emotion engine that recognizes the user's emotions.

[0686] The system includes the following main components:

[0687] 1. Data Collection Methods

[0688] Users use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The device sends this data to a server, which then stores it in storage. The hardware used in this process is the user's device (e.g., a PC or smartphone), and the software includes a web portal or mobile app.

[0689] 2. Data analysis methods

[0690] The server analyzes the collected data and extracts the facial features, voice characteristics, and language patterns of the deceased using computer vision, speech recognition, and natural language processing technologies, such as OpenCV (computer vision), Google Cloud Speech-to-Text API (speech recognition), and SpaCy (natural language processing).

[0691] 3. Generation AI means

[0692] Based on the analyzed features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased person. This digital clone faithfully reproduces the appearance, voice, and speaking style of the deceased, and generative AI models such as DeepFaceLab and GPT-3 are used.

[0693] 4. Emotion recognition means

[0694] The server analyzes user input and speech and recognizes the user's emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning technology, including TensorFlow and PyTorch.

[0695] 5. User Interface Provisioning Method

[0696] Users can access the digital clone through a dedicated application or web portal and begin interacting with it by asking questions using text or voice input.

[0697] 6. Response Coordination

[0698] The server analyzes the questions and utterances entered by the user and generates appropriate responses using a generative AI model. It also adjusts the response content according to the user's emotional state, as recognized by emotion recognition means.

[0699] 7. Display or audio output means

[0700] The generated response is displayed to the user through a user interface or played aloud.

[0701] 8. Means of storing in a database and creating individualized user profiles

[0702] The server stores the user's emotion recognition results in a database and builds an individualized user profile, which enables personalized interactions to enhance the user experience.

[0703] Specific examples

[0704] For example, if a user wants to create a digital clone of their deceased mother, they would use the system as follows: The program processing of this system is as follows.

[0705] 1. The user uploads videos, photos, and chat logs of their mother before she died to the system. The device sends this data to the server, which then stores it.

[0706] 2. The server analyzes the stored data and extracts the mother's facial features, voice characteristics, and phrasing using OpenCV, Google Cloud Speech-to-Text API, and SpaCy.

[0707] 3. Based on the extraction results, the server uses DeepFake technology to recreate the mother's face and voice, and uses a generative AI model to create a digital clone that reflects the mother's speaking style and personality.

[0708] 4. The user opens a dedicated application and asks the digital clone of their mother, "Mom, how was the weather today?" The prompt can be entered via text or voice.

[0709] 5. The device sends the question to the server, which analyzes it and uses a generative AI model to generate a response: "It was a beautiful sunny day today. A perfect day for a walk."

[0710] 6. The emotion recognition means analyzes the user's input and voice and recognizes that the user is feeling happy. As a result, the server adjusts the response content to be warmer.

[0711] 7. The adjusted response will be displayed on the device and played back in the mother's voice.

[0712] This system allows users to continue connecting with the deceased and receive emotional support through conversations. Furthermore, the system stores the user's emotion recognition results in a database, allowing for more personalized conversations and a better user experience.

[0713] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0714] Step 1:

[0715] Users use a dedicated web portal or application to upload videos, photos and chat data of the deceased.

[0716] Input: Videos, photos, and chat data of the deceased

[0717] Output: Data sent to the server

[0718] Specific operation: A user accesses the portal, clicks the upload button, selects a file from a local folder, and uploads it. The device sends the uploaded data to the server and stores it.

[0719] Step 2:

[0720] The server analyzes the collected data and extracts the facial features, voice characteristics, and language patterns of the deceased.

[0721] Input: Videos, photos, and chat data stored on the server

[0722] Output: Extracted facial features, voice features, and language patterns

[0723] How it works: The server uses OpenCV to extract facial features from video, Google Cloud Speech-to-Text API to convert audio data to text, and SpaCy to analyze language patterns from chat data.

[0724] Step 3:

[0725] Based on the analyzed characteristics, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased person.

[0726] Input: extracted facial features, voice features, language patterns

[0727] Output: Generated digital clone

[0728] How it works: The server uses DeepFaceLab to recreate the face and voice of the deceased, and then uses GPT-3 to generate a text-based dialogue model that reflects the deceased's speaking style and personality.

[0729] Step 4:

[0730] Users access and interact with their digital clone through a dedicated application or web portal.

[0731] Input: User text or voice input

[0732] Output: The user's question sent to the server

[0733] How it works: The user opens the application, clicks on the digital clone icon, and enters a question via text or voice. The device then sends the question to the server.

[0734] Step 5:

[0735] The server analyzes the user's question and uses a generative AI model to generate an appropriate response.

[0736] Input: User question

[0737] Output: The generated response

[0738] What it does: The server uses GPT-3 to analyze the user's input and generates a response like, "It was a beautiful sunny day today. A perfect day for a walk."

[0739] Step 6:

[0740] The server recognizes emotions from the user's input and speech and performs adaptive processing.

[0741] Input: User input (text or voice)

[0742] Output: User's emotional state

[0743] Specific operation: The server uses TensorFlow to analyze the user's voice tone and text, and recognizes emotions such as whether the user is happy.

[0744] Step 7:

[0745] The server adjusts the response content according to the emotion recognition results.

[0746] Input: Initial generated response, user's emotional state

[0747] Output: Adjusted response

[0748] What it does: The server adjusts the initial response, "It was a beautiful sunny day today," to something like "It was a beautiful sunny day today. A perfect day for a walk, did you enjoy it?" depending on the emotion.

[0749] Step 8:

[0750] The generated response is displayed to the user through a user interface or played aloud.

[0751] Input: Adjusted response

[0752] Output: Display to user or play audio

[0753] Specific behavior: The device receives the response from the server and displays it on the screen as text or plays it back in the mother's voice.

[0754] Step 9:

[0755] The server stores the user's emotion recognition results in a database and builds a personalized user profile.

[0756] Input: User emotion recognition results

[0757] Output: Updated user profile

[0758] Specific operation: The server stores the emotion recognition results in a database and updates the personalized profile for the next interaction.

[0759] (Application example 2)

[0760] 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."

[0761] In factories and production lines, there is a challenge in efficiently transferring the knowledge and skills of experts to new employees and automated machines. Furthermore, when experts retire or pass away, there is a risk that their valuable skills and know-how will be lost. Furthermore, when new employees receive training from experts, it is difficult to provide appropriate guidance that takes into account their emotions and level of understanding.

[0762] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0763] In this invention, the server includes a means for collecting videos, photos, and chat data of the deceased, an analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, and a generation AI means for generating a digital clone of the deceased based on the extracted features, which makes it possible to educate and train new employees and automated machines in the knowledge and skills of an expert through the digital clone.

[0764] A "deceased person" is someone who was active in a certain workplace or society during their lifetime, but who is now deceased.

[0765] A "video" is a video file that records the actions and statements of the deceased.

[0766] A "photograph" is still image data that captures the face or figure of the deceased.

[0767] "Chat data" is a record of text-based conversations that the deceased had while alive.

[0768] "Analysis means" refers to technology or equipment for extracting and analyzing specific information from collected data.

[0769] "Facial features" are the external facial characteristics and identifying information of the deceased.

[0770] "Voice features" are characteristics or patterns related to the deceased person's voice.

[0771] A "language pattern" is the particular language or way of expressing oneself that the deceased used.

[0772] "Generative AI means" refers to a means for generating a digital clone based on information about a deceased person using artificial intelligence technology.

[0773] An "interface providing means" is a technique or device that provides a way for a user to interact with a digital clone.

[0774] "Means for displaying or audibly outputting the generated response to the user" refers to technology or devices for visually or audibly conveying the response generated by the digital clone to the user.

[0775] A "digital clone" is a digital representation that recreates the appearance, voice, and manner of speaking of a deceased person.

[0776] An "expert" is someone who has advanced knowledge and skills in a particular workplace or field.

[0777] A "new employee" is someone who has recently joined a workplace or organization and needs to learn the knowledge and skills of an expert.

[0778] An "automatic machine" is a mechanical device that automatically performs a specific task or process.

[0779] This invention relates to a digital coaching system that aims to transfer the knowledge and skills of experts to new employees and automated machines.

[0780] System configuration

[0781] The system includes the following main components:

[0782] 1. Data collection method: The server collects videos, photos, and chat data of the deceased and stores them in storage. Users upload these data using a dedicated web portal or application.

[0783] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased person. Facial features and voice features are extracted using computer vision and voice recognition technologies, and language patterns are extracted using natural language processing technology.

[0784] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[0785] 4. Emotion Engine: The server analyzes user and automated machine input and speech and recognizes emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning techniques to determine emotional states.

[0786] 5. User interface provision means: Users or automated machines can access and initiate a dialogue with the digital clone through a dedicated application or head-mounted display (HMD). Users can ask questions or give instructions to the digital clone using text input or voice input.

[0787] 6. Response generation means: The server analyzes the questions posed by the user or automated machine and generates an appropriate response using the generative AI model. The generated response is displayed or output as voice through the user interface. The response content can be adjusted according to the emotions recognized by the emotion engine.

[0788] 7. Database construction method: Emotion recognition results and dialogue data from users and automated machines are stored in a database to create personalized user profiles and work logs. These profiles are used for future dialogues and work assistance, improving the user experience.

[0789] Usage example

[0790] For example, if a new employee wants to learn how to maintain a new machine in a factory, they can use the system as follows: The user uploads a video of the expert working, along with audio commentary and instructions. The server analyzes this data and extracts the expert's facial features, vocal characteristics, and language patterns. DeepFake technology is then used to faithfully reproduce the expert's appearance and voice, and a generative AI model is used to create a digital clone that reflects the expert's speaking style and teaching style.

[0791] A user or automated machine wears an HMD and asks a digital clone of an expert, "How do I maintain this machine?" The server analyzes the question and uses a generative AI model to generate a response such as, "First, loosen this nut, then remove the filter." At the same time, an emotion engine analyzes the user's voice and movements to recognize confusion. Based on this information, the server adjusts the response, adding more detailed instructions and diagrams. The response is displayed on the HMD display, allowing the user to proceed with the task with confidence while receiving visual guidance.

[0792] The above is a specific embodiment of the digital coaching system based on the present invention. This system enables the knowledge and skills of experts to be efficiently passed on to the next generation, and improves the productivity of factories and production lines through education and guidance.

[0793] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0794] Step 1:

[0795] The server collects videos, photos, and chat data of the deceased uploaded by users using a dedicated web portal or application. The input data are the deceased's video files, photo files, and chat text data. The server stores these files in storage and prepares them for analysis. The output is that this data is centrally managed on the server.

[0796] Step 2:

[0797] The server analyzes the collected video, photo, and chat data. It uses computer vision technology to extract facial features and speech recognition technology to identify voice features. It also uses natural language processing technology to extract language patterns from the chat data. The input data is the video, photo, and chat data collected in Step 1. Data processing involves facial feature mapping, speech spectrum analysis, text tokenization, and pattern recognition. The output is facial, voice, and language feature data of the deceased.

[0798] Step 3:

[0799] The server uses deepfake technology and generative AI technology to generate a digital clone based on the facial features, voice features, and language patterns of the deceased. The input data is the feature data extracted in step 2. Data calculations involve generating a face and voice based on an AI model and integrating language patterns. The output is a digital clone that faithfully reproduces the deceased.

[0800] Step 4:

[0801] The server accepts input from users or automated machines. In particular, users or robots access it through dedicated applications or head-mounted displays (HMDs) and begin interacting with their digital clones. The input in this step is a question or instruction from the user or robot. The server recognizes this and prepares to proceed to the next step. The output is a confirmation that the question or instruction was received.

[0802] Step 5:

[0803] The server analyzes questions and instructions from users or automated machines and generates appropriate responses using a generative AI model. In parallel, an emotion engine recognizes emotions from the user's voice tone, facial expressions, and text input. The input is the question or instruction and emotion data received in step 4. Data calculations involve question analysis using natural language processing, emotion analysis using the emotion engine, and response generation using an AI model. The output is an appropriate response text or voice data according to the emotion.

[0804] Step 6:

[0805] The server displays or outputs the generated response to the user or automated machine. The input is the output of step 5. The specific operation is to display text or video on the HMD display and play audio using the audio output device. The output is the response received by the user or automated machine.

[0806] Step 7:

[0807] The server stores the emotion recognition results and dialogue data of the user and the automated machine in a database. The input data is the output of steps 5 and 6. As data storage, personalized user profiles and work logs are recorded in the database. The output is an updated database, which can be used for future dialogue and guidance.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] [Third embodiment]

[0812] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0813] 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.

[0814] 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).

[0815] 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.

[0816] 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.

[0817] 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).

[0818] 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. 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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."

[0824] The present invention provides a system for generating a digital clone of a deceased person and allowing a user to interact with the deceased person in a digital space. Specific embodiments of the system are described below.

[0825] System configuration

[0826] The system includes the following main components:

[0827] 1. Data collection method: Users can use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The server receives this data and stores it in storage.

[0828] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased. Facial features and voice features are analyzed using computer vision and speech recognition technologies, and language patterns are extracted using natural language processing technology.

[0829] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[0830] 4. User interface provision means: Users can access and start interacting with the digital clone through a dedicated application or web portal. Users can ask questions to the digital clone using text input or voice input.

[0831] 5. Response generation means: The server receives the user's question and generates an appropriate response using the generative AI model. The generated response is displayed or played aloud to the user through the user interface.

[0832] Program processing flow (overview)

[0833] When a user uploads the deceased's data to the system, the server begins analyzing the data. Once the analysis is complete, a generative AI means generates a digital clone of the deceased. When a user accesses the digital clone and asks a question, the server analyzes the question and generates a response using the generative AI model. The generated response is displayed on the user's device or played aloud.

[0834] Specific examples

[0835] For example, if a user wanted to create a digital clone of their deceased mother, they would use the system as follows: The user would upload videos, photos, and chat logs of the mother from when she was alive to the system. The server would analyze this data and extract the mother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to create a digital clone that reflects the mother's speaking style and personality.

[0836] The user opens a dedicated application and asks their mother's digital clone, "How was the weather today?" The device sends this question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. It was a perfect day for a walk." This response is then displayed on the user's device, allowing the user to enjoy a conversation with their mother.

[0837] The above is a specific embodiment of the present invention. This system allows you to continue to connect with the deceased and receive comfort and support through conversation.

[0838] The processing flow will be explained below.

[0839] Step 1:

[0840] Users: Upload videos, photos, chat data, etc. of the deceased using a dedicated web portal or application. Users can complete the upload by dragging and dropping files or using a file selection dialog.

[0841] Step 2:

[0842] Server: Stores the uploaded data in storage. Records file metadata (file name, size, upload date and time, etc.) in a database.

[0843] Step 3:

[0844] Server: Analyzes the stored data, extracting facial features of the deceased from videos and photos, and voice features from audio files. This involves computer vision and voice recognition techniques. For example, facial recognition algorithms are used to identify facial landmarks, and voice analysis algorithms are used to extract voice tone and pitch.

[0845] Step 4:

[0846] Server: Analyzes chat data and text data using natural language processing (NLP) techniques to extract the deceased's language patterns and unique expressions. For example, it uses keyword frequency analysis and language models to identify commonly used phrases and vocabulary.

[0847] Step 5:

[0848] Server: Using DeepFake technology, a digital clone of the deceased is created based on the extracted facial and vocal features. The clone is then optimized to reproduce the deceased's face and voice in real time. For example, a video generative model is used to realistically recreate the deceased's facial expressions.

[0849] Step 6:

[0850] Server: A generative AI model is used to learn the behavioral patterns and personality of the deceased. This model is trained to mimic the deceased's response patterns and thought process based on the analyzed chat data. For example, a deep learning model is used to understand the context and generate appropriate responses.

[0851] Step 7:

[0852] User: Accesses the digital clone through a dedicated application or web portal. The user can talk to the digital clone using text or voice chat. For example, they can type "Mom, how was the weather today?" into the chat box within the app.

[0853] Step 8:

[0854] Terminal: Sends user input to the server. This includes text questions and voice data. The terminal captures user input in real time and forwards it to the server.

[0855] Step 9:

[0856] Server: Analyzes the user's question and generates an appropriate response using a generative AI model. For example, in response to a question about the weather, the server generates a response such as "Today was a beautiful sunny day, perfect for a walk."

[0857] Step 10:

[0858] Server: Generates and sends the response to the terminal. The response is sent in text or audio format.

[0859] Step 11:

[0860] Terminal: Displays the received response to the user or plays it aloud. For example, it may show the text on a display and use a voice playback function to play the response in the deceased's voice.

[0861] Step 12:

[0862] User: Enjoys interacting with the digital clone. The user can re-enter questions or continue the conversation. This process repeats, continuing the interaction with the user.

[0863] The above are the specific processing steps of the digital clone generation system according to the present invention, which allows users to feel a connection with the deceased through conversation.

[0864] Example 1

[0865] 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."

[0866] In modern society, there is a growing emotional need to reminisce and converse with deceased loved ones. However, the technology to effectively utilize the digital data left behind by the deceased is underdeveloped. Therefore, there is a need for a system that enables communication with the deceased through a computer. Furthermore, advanced data analysis and generation technologies are required to accurately reproduce the facial features, vocal characteristics, and language patterns of the deceased, allowing bereaved families to have a natural and emotionally rich experience.

[0867] 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.

[0868] In this invention, the server includes means for collecting image data, voice data, and text data of the deceased, analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, artificial intelligence (AI) means for generating a digital clone of the deceased based on the extracted features, means for providing a user interface for the user to interact with the digital clone, and means for displaying or outputting to the user the responses generated by the AI ​​generating means by voice, thereby enabling a highly simulated interaction with the deceased and enabling the bereaved family to maintain an emotional connection.

[0869] "Deceased" refers to a person who has passed away.

[0870] "Image data" refers to digital image information stored in the form of photographs or videos.

[0871] "Audio Data" means digital acoustic information stored in voice or audio recording form.

[0872] "Text data" refers to digital information in text format stored as chat logs or documents.

[0873] "Facial features" refers to feature information about the shape and structure of a person's face extracted from image data.

[0874] "Voice features" refer to the pitch, timbre, and pronunciation characteristics of a person's voice extracted from audio data.

[0875] "Language patterns" refer to a person's language and unique ways of expression extracted from text data.

[0876] "Analysis means" refers to a processing mechanism for extracting necessary feature information from collected data.

[0877] "Generative artificial intelligence means" refers to artificial intelligence technology for generating a digital clone based on extracted feature information.

[0878] "User interface" refers to the means that provides input and output mechanisms for a user to interact with a system.

[0879] A "digital clone" refers to a digital model that recreates the face, voice, and language patterns of a deceased person.

[0880] "Deepfake technology" refers to the technology of synthesizing images and audio data using deep learning technology.

[0881] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0882] System Overview

[0883] This invention is a system that creates a digital clone of a deceased person and allows users to interact with the deceased in a digital space. The system is mainly composed of three components: a server, a terminal, and a user, with each component playing a specific role. The entire system functions through the following stages: data collection, data analysis, digital clone creation, user interaction, and response generation.

[0884] Data collection methods

[0885] Users use a dedicated web portal or application to collect image, audio, and text data of the deceased, which is then securely uploaded to a server via HTTPS, where the server stores the data.

[0886] Data Analysis Methods

[0887] The server analyzes the received data and extracts facial features, voice features, and language patterns using the following software and techniques:

[0888] Facial features: Extract facial features from image data using OpenCV.

[0889] Speech features: Extract speech features from the audio data using the Google Speech-to-Text API.

[0890] Language Patterns: Extract language patterns from text data using SpaCy or NLTK.

[0891] Generation AI means

[0892] The server then uses DeepFake technology and generative AI technology (such as GPT-3) to create a digital clone of the deceased person based on the analyzed characteristics. The digital clone faithfully reproduces the appearance, voice, and manner of speech of the deceased.

[0893] User interface providing means

[0894] Users can access the digital clone through a dedicated application or a web portal. The user interface is built on React and Vue.js and is designed to allow users to ask questions and make comments to the digital clone via text or voice input.

[0895] Response Generation Method

[0896] The server analyzes questions received through the user interface and generates appropriate responses using a generative AI model (e.g., GPT-3). The responses are then sent to the device and displayed as text or played aloud.

[0897] Specific examples

[0898] For example, if a user wants to create a digital clone of their deceased mother, they can upload her image, voice, and text data from before she died to the system. The server analyzes this data and extracts her facial features, voice characteristics, and speech patterns. It then uses DeepFake technology and generative AI technology to create a digital clone of the mother.

[0899] A user opens the application and asks their mother's digital clone, "How was the weather today?" The device sends the question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. A perfect day for a walk." This response is then displayed on the user's device, allowing them to enjoy a conversation with their deceased loved one.

[0900] Prompt Sentence Examples

[0901] "To a digital clone of your deceased mother, please type: 'What was the weather like today?'"

[0902] The above is a specific embodiment of the present invention. This system allows you to continue to connect with the deceased and receive comfort and support through conversation.

[0903] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0904] Step 1: Data collection

[0905] Users use a dedicated web portal or application to upload image, audio, and text data of the deceased to the system, which then transfers the data to the server.

[0906] Input: Image data, audio data, and text data of the deceased.

[0907] Data processing: splitting, compression, secure data transfer using HTTP protocol.

[0908] Output: Data saved to storage on the server.

[0909] Specific behavior:

[0910] A user logs into a web portal and clicks the file selection button.

[0911] The device displays a file selection dialog, and the user selects the required file.

[0912] The device uploads the selected file to the server.

[0913] The server stores the received data in storage.

[0914] Step 2: Data analysis

[0915] The server analyzes the uploaded data, extracting facial features, voice features, and language patterns.

[0916] Input: Image data, audio data, and text data saved in step 1.

[0917] Data computing: image recognition, speech analysis, natural language processing.

[0918] Output: Extraction results of facial features, audio features, and language patterns.

[0919] Specific behavior:

[0920] The server uses the OpenCV library to extract facial features from the image data.

[0921] The server uses the Google Speech-to-Text API to extract speech features from the audio data.

[0922] The server uses SpaCy to extract language patterns from the text data.

[0923] Step 3: Digital cloning

[0924] Based on the analyzed characteristic information, the server uses DeepFake technology and generative AI technology to generate a digital clone.

[0925] Input: Facial features, audio features, and language pattern extraction results.

[0926] Data computation: face and voice synthesis, generation of conversation models.

[0927] Output: A digital clone of the deceased person.

[0928] Specific behavior:

[0929] The server uses the facial features in DeepFaceLab to generate a digital clone's face.

[0930] The voice of the digital clone is reproduced based on the voice model generated by the server.

[0931] The server uses GPT-3 to generate a conversation model that reflects the speaking style and personality of the deceased.

[0932] Step 4: Configuring User Interaction

[0933] Users access their digital clone by opening a dedicated application or web portal, where they can ask questions or make comments using text or voice input.

[0934] Input: User text or voice input.

[0935] Data processing: Accepting input and forwarding it to the server.

[0936] Output: User input transmitted to the server.

[0937] Specific behavior:

[0938] The user launches the application and logs in.

[0939] It opens an interface that allows the user to begin interacting with the digital clone.

[0940] The device accepts the user's text input or voice input and sends it to the server.

[0941] Step 5: Response Generation

[0942] The server analyzes the input received from the user and uses a generative AI model to generate an appropriate response, which is then sent to the device for display or audio playback.

[0943] Input: User text or voice input.

[0944] Data Computing: Input analysis and response generation using natural language processing.

[0945] Output: The generated text or audio response.

[0946] Specific behavior:

[0947] The server uses GPT-3 to analyze the user's input and generate an appropriate response.

[0948] The server generates a response that is then encoded and sent to the terminal.

[0949] The terminal displays or plays the response to the user in text or audio.

[0950] The above are the specific processing steps of the program of this system.

[0951] (Application example 1)

[0952] 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."

[0953] In modern times, methods of remembering the deceased rely on static records such as photographs and videos, making it difficult to recreate conversations and memories with the deceased. Furthermore, there are limited opportunities to share memories with the deceased. This creates a need for a way to vividly recreate conversations and memories with the deceased, allowing family and friends to deepen their connection with them.

[0954] 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.

[0955] In this invention, the server includes means for collecting videos, photos, and chat data of the deceased, analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, generation AI means for generating a digital clone of the deceased based on the extracted features, means for providing an interface through which the user can interact with the digital clone, means for displaying or outputting to the user by voice the responses generated by the generation AI means, and means for providing a virtual environment through which the user can enjoy interacting with the deceased in a virtual space. This allows the user to relive vivid memories and remember the deceased in a virtual space by interacting with the digital clone of the deceased.

[0956] "Video" is digital data that contains moving images.

[0957] A "photograph" is digital data of a captured still image.

[0958] "Chat data" is digital data of communication records including text message history.

[0959] "Means for collecting data" refers to the means by which users upload video, image, and text data of the deceased to the system.

[0960] "Analysis means" refers to means for extracting facial features, voice features, and language patterns of the deceased from the collected data.

[0961] "Generative AI means" refers to means that use artificial intelligence technology to generate a digital clone of a deceased person based on extracted characteristics.

[0962] The "means for providing an interface" is a means for providing an operation screen for a user to interact with a digital clone.

[0963] "Means for displaying or audibly outputting the generated response to the user" refers to means for displaying or audibly playing the response generated by the generation AI means on the user's terminal.

[0964] The "virtual environment providing means" is a means for providing a virtual space or environment in which a user can enjoy a conversation with a deceased person in a virtual space.

[0965] This invention provides a system for generating a digital clone of a deceased person and allowing interaction with the deceased person in a virtual space. The system includes the following main components:

[0966] 1. Data Collection Methods

[0967] Users use a dedicated web portal or application to upload videos, photos, and chat data of the deceased, which is then sent to a server and stored.

[0968] 2. Data analysis methods

[0969] The server analyzes the collected data, specifically by using computer vision technology to extract facial features, speech recognition technology to extract voice features, and natural language processing technology to analyze language patterns.

[0970] 3. Generation AI means

[0971] Based on the analyzed features, the server uses a generative AI model to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and manner of speech of the deceased.

[0972] 4. User Interface Provisioning Method

[0973] Users can access and interact with their digital clone through a dedicated application or web portal, using text and voice input.

[0974] 5. A means to display and speak the generated response

[0975] The server receives the user's question and uses a generative AI model to generate an appropriate response, which is then displayed or played aloud on the user's device.

[0976] 6. Means of providing virtual environments

[0977] Users can enjoy interacting with the deceased in a virtual space, which is provided using a dedicated application and a head-mounted display.

[0978] Hardware and software used

[0979] Hardware: General web servers, user devices (PCs, smartphones, tablets)

[0980] Software: Flask (web application framework), DeepFake technology (deepfake_gen), generative AI technology (ai_response_gen)

[0981] Specific examples

[0982] For example, if a user wanted to interact with a digital clone of their deceased grandmother, they would use the system as follows: The user would upload videos, photos, and chat data from when the grandmother was alive to the system. The server would analyze this data and extract the grandmother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to generate a digital clone that reflected her speaking style and personality.

[0983] The user accesses the grandmother's digital clone through a dedicated application and asks, "How are you doing these days?" The server analyzes the question and generates an appropriate response using a generative AI model. As a result, the user receives the response, "I'm fine. I water the flowers every day," allowing them to enjoy a conversation with the deceased.

[0984] Prompt Sentence Examples

[0985] "How are you doing these days?"

[0986] "Tell me your memories."

[0987] "Tell me your favorite dish."

[0988] In this way, the user can relive memories of the deceased while remembering them in a virtual space through dialogue with the deceased.

[0989] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0990] Step 1:

[0991] Users upload videos, photos, and chat data of the deceased person using a dedicated web portal or application. Specifically, users click the upload button to send video files, photo files, and text chat history to the system. This data is sent to the server and stored in storage.

[0992] Input: Video, photos, chat data

[0993] Output: Data stored on the server

[0994] Step 2:

[0995] The server then begins the process of analyzing the collected data, first using computer vision technology to extract the deceased's facial features from the videos and photos, then using speech recognition technology to extract the deceased's voice features from the videos, and finally using natural language processing technology to analyze the deceased's language patterns from the chat data.

[0996] Input: Videos, photos, and chat data stored on the server

[0997] Output: facial features, voice features, and language patterns of the deceased

[0998] Step 3:

[0999] The server then uses augmented reality technology (such as DeepFake technology) to create a digital clone of the deceased based on the extracted features. This process combines facial and vocal features to realistically recreate the appearance and voice of the deceased, and uses a generative AI model to create a digital clone with a speaking style based on the deceased's language patterns.

[1000] Input: facial features, voice features, and language patterns of the deceased

[1001] Output: Digital clone of the deceased

[1002] Step 4:

[1003] The user accesses the generated digital clone through a dedicated application or web portal and begins interacting with it. Specifically, the user opens the application and asks the digital clone questions by text input or voice input. The user's input is sent from the device to the server.

[1004] Input: User question (text or voice)

[1005] Output: Query data sent to the server

[1006] Step 5:

[1007] The server receives the user's question and generates an appropriate response using a pre-trained generative AI model, which generates a specific answer to the user's question while recreating the deceased's language patterns.

[1008] Input: User question data

[1009] Output: Digital clone response data

[1010] Step 6:

[1011] Once the response data is generated, the server sends it back to the user's device, where it is displayed or played audibly, allowing the user to receive responses from the deceased's digital clone and enjoy a conversation.

[1012] Input: Digital clone response data

[1013] Output: The response that is displayed or played aloud on the user's device

[1014] Step 7:

[1015] To further deepen the interaction in the virtual space, users can continue to ask questions using specific prompts. Examples include "How are you doing these days?", "Tell me a memory of your life," and "Tell me your favorite dish." These questions can encourage a more emotional interaction with the deceased and deepen the experience in the virtual space.

[1016] Input: New prompt question

[1017] Output: Continuing dialogue and responses

[1018] In this way, users can relive memories through dialogue with the deceased and deepen their emotional connection while remembering them.

[1019] 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.

[1020] The present invention combines a system that generates a digital clone of a deceased person, allows a user to interact with the deceased in a digital space, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[1021] System configuration

[1022] The system includes the following main components:

[1023] 1. Data collection method: Users use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The server receives this data and stores it in storage.

[1024] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased. Facial features and voice features are analyzed using computer vision and speech recognition technologies, and language patterns are extracted using natural language processing technology.

[1025] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[1026] 4. Emotion Engine: The server analyzes user input and speech and recognizes the user's emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning techniques to determine the user's emotional state.

[1027] 5. User interface provision means: Users can access and start interacting with the digital clone through a dedicated application or web portal. Users can ask questions to the digital clone using text input or voice input.

[1028] 6. Response generation means: The server analyzes the user's question and generates an appropriate response using the generative AI model. The generated response is displayed to the user through the user interface or played aloud. The response content can be adjusted according to the user's emotions recognized by the emotion engine.

[1029] 7. Database Construction Method: The user's emotion recognition results are stored in a database to create a personalized user profile. This profile is used for future interactions and to improve the user experience.

[1030] Program processing flow (overview)

[1031] When a user uploads the deceased's data to the system, the server begins analyzing the data. Once the analysis is complete, a generative AI means generates a digital clone of the deceased. When the user accesses the digital clone and asks a question, the server analyzes the question and generates a response using the generative AI model. In parallel, an emotion engine recognizes emotions from the user's input and speech and adjusts the response. The generated response is displayed to the user through the user interface or played aloud. The user's emotion recognition results are stored in a database, and a personalized user profile is constructed.

[1032] Specific examples

[1033] For example, if a user wanted to create a digital clone of their deceased mother, they would use the system as follows: The user would upload videos, photos, and chat logs of the mother from when she was alive to the system. The server would analyze this data and extract the mother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to create a digital clone that reflects the mother's speaking style and personality.

[1034] The user opens a dedicated application and asks their mother's digital clone, "Mom, how was the weather today?" The device sends this question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. It was a perfect day for a walk." At the same time, the emotion engine analyzes the user's input and voice and recognizes that the user is expressing a happy emotion. Based on this information, the server adjusts the response content and generates a warmer one. This response is displayed on the user's device, allowing the user to enjoy a conversation with their mother.

[1035] The above is a specific embodiment of the digital clone generation system according to the present invention, which allows users to continue to connect with their deceased loved ones and receive emotional support through conversation.

[1036] The processing flow will be explained below.

[1037] Step 1:

[1038] Users: Upload videos, photos, chat data, etc. of the deceased using a dedicated web portal or application. Users can complete the upload by dragging and dropping files or using a file selection dialog.

[1039] Step 2:

[1040] Server: Stores the uploaded data in storage. Records file metadata (file name, size, upload date and time, etc.) in a database.

[1041] Step 3:

[1042] Server: Analyzes the stored data. Extracts facial features of the deceased from videos and photos, and voice features from audio files. Using computer vision and voice recognition technologies, facial recognition algorithms identify facial landmarks, and voice analysis algorithms extract voice tone and pitch.

[1043] Step 4:

[1044] Server: Analyzes chat data and text data using natural language processing (NLP) techniques to extract the deceased's language patterns and unique expressions. Keyword frequency analysis and language models are used to identify commonly used phrases and vocabulary.

[1045] Step 5:

[1046] Server: Using DeepFake technology, a digital clone of the deceased is created based on the extracted facial and vocal features. A video generation model is used to realistically recreate the facial expressions of the deceased, and an audio reproduction model is used to faithfully recreate the voice.

[1047] Step 6:

[1048] Server: A generative AI model is used to learn the behavioral patterns and personality of the deceased. This model is trained to mimic the deceased's response patterns and thought process based on the analyzed chat data.

[1049] Step 7:

[1050] User: Accesses the digital clone through a dedicated application or web portal. The user can talk to the digital clone using text or voice chat. For example, they can type "Mom, how was the weather today?" into the chat box within the app.

[1051] Step 8:

[1052] Terminal: Sends user input to the server. Captures text questions and voice data in real time and transfers them to the server.

[1053] Step 9:

[1054] Server: Analyzes the user's question and generates an appropriate response using a generative AI model, such as "Today was a beautiful sunny day, perfect for a walk."

[1055] Step 10:

[1056] Server: In parallel, the emotion engine analyzes the user's input and speech, recognizing emotions from voice tone, facial expressions, and text input. The recognized emotions are used as information to tailor the response. For example, if the user has a happy expression, a warmer response will be generated accordingly.

[1057] Step 11:

[1058] Server: Sends the responses generated by the generative AI model and emotion engine to the device in text or voice format.

[1059] Step 12:

[1060] Terminal: Displays the received response to the user or plays it aloud. Shows the text on the display and uses the audio playback function to play the response in the voice of the deceased.

[1061] Step 13:

[1062] Server: Stores the user's emotion recognition results in a database and creates a personalized user profile that is used for future interactions and to improve the user experience.

[1063] Step 14:

[1064] User: Enjoys interacting with the digital clone. The user can re-enter questions or continue the conversation. This process repeats, continuing the interaction with the user.

[1065] The above are the specific processing steps of the digital clone generation system according to the present invention, which allows users to obtain emotional support through conversations with the deceased.

[1066] Example 2

[1067] 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."

[1068] In recent years, digital approaches to remembering the deceased have been gaining attention, but existing systems cannot faithfully reproduce the facial expressions, voice, and language of the deceased, and they cannot adjust the dialogue to match the user's emotions, making it difficult to realize effective dialogue to provide emotional support.In addition, systems that can personalize and optimize the user experience are not well developed.

[1069] 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 videos, photos, and chat data of the deceased; analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data; generation AI means for generating a digital clone of the deceased based on the extracted features; means for providing an interface through which the user interacts with the digital clone; emotion recognition means for recognizing emotions from the user's input and speech; means for adjusting responses generated by the generation AI means according to the user's emotional state; means for displaying or outputting the responses generated by the generation AI means to the user as audio; and means for storing the user's emotion recognition results in a database and creating a personalized user profile. This allows the appearance, voice, and speaking style of the deceased to be faithfully reproduced and dialogue tailored to the user's emotions can be provided, thereby personalizing and optimizing the user experience.

[1070] "Means of collection" refers to a function that allows users to upload videos, photos, and chat data of the deceased and send that data to a server.

[1071] "Analysis means" refers to a function that extracts the facial features, voice features, and language patterns of the deceased from the collected data, and includes computer vision technology, voice recognition technology, and natural language processing technology.

[1072] "Generative AI means" is a function that generates a digital clone based on extracted characteristics of the deceased, and utilizes DeepFake technology and generative AI technology to faithfully reproduce the appearance, voice, and speaking style of the deceased.

[1073] "Means for providing an interface" refers to the ability to provide an input and output interface for a user to interact with the digital clone, such as a dedicated application or a web portal.

[1074] The "emotion recognition means" is a function that uses deep learning technology to recognize emotions by analyzing voice tone, facial expressions, and text input from user input and speech.

[1075] The "means for adjusting a response" is a function for appropriately adjusting the response generated by the generation AI means in accordance with the emotional state of the user recognized by the emotion recognition means.

[1076] The "means for displaying or outputting by voice" is a function for visually displaying the generated response to the user or playing it back as voice.

[1077] The "means for saving in a database and creating a personalized user profile" is a function for saving the user's emotion recognition results in a database and building a personalized user profile based on the results, which will be used in future interactions.

[1078] As an example of a mode for implementing the invention, a system will be described that combines a system that generates a digital clone of a deceased person and allows a user to interact with the deceased in a digital space with an emotion engine that recognizes the user's emotions.

[1079] The system includes the following main components:

[1080] 1. Data Collection Methods

[1081] Users use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The device sends this data to a server, which then stores it in storage. The hardware used in this process is the user's device (e.g., a PC or smartphone), and the software includes a web portal or mobile app.

[1082] 2. Data analysis methods

[1083] The server analyzes the collected data and extracts the facial features, voice characteristics, and language patterns of the deceased using computer vision, speech recognition, and natural language processing technologies, such as OpenCV (computer vision), Google Cloud Speech-to-Text API (speech recognition), and SpaCy (natural language processing).

[1084] 3. Generation AI means

[1085] Based on the analyzed features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased person. This digital clone faithfully reproduces the appearance, voice, and speaking style of the deceased, and generative AI models such as DeepFaceLab and GPT-3 are used.

[1086] 4. Emotion recognition means

[1087] The server analyzes user input and speech and recognizes the user's emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning technology, including TensorFlow and PyTorch.

[1088] 5. User Interface Provisioning Method

[1089] Users can access the digital clone through a dedicated application or web portal and begin interacting with it by asking questions using text or voice input.

[1090] 6. Response Coordination

[1091] The server analyzes the questions and utterances entered by the user and generates appropriate responses using a generative AI model. It also adjusts the response content according to the user's emotional state, as recognized by emotion recognition means.

[1092] 7. Display or audio output means

[1093] The generated response is displayed to the user through a user interface or played aloud.

[1094] 8. Means of storing in a database and creating individualized user profiles

[1095] The server stores the user's emotion recognition results in a database and builds an individualized user profile, which enables personalized interactions to enhance the user experience.

[1096] Specific examples

[1097] For example, if a user wants to create a digital clone of their deceased mother, they would use the system as follows: The program processing of this system is as follows.

[1098] 1. The user uploads videos, photos, and chat logs of their mother before she died to the system. The device sends this data to the server, which then stores it.

[1099] 2. The server analyzes the stored data and extracts the mother's facial features, voice characteristics, and phrasing using OpenCV, Google Cloud Speech-to-Text API, and SpaCy.

[1100] 3. Based on the extraction results, the server uses DeepFake technology to recreate the mother's face and voice, and uses a generative AI model to create a digital clone that reflects the mother's speaking style and personality.

[1101] 4. The user opens a dedicated application and asks the digital clone of their mother, "Mom, how was the weather today?" The prompt can be entered via text or voice.

[1102] 5. The device sends the question to the server, which analyzes it and uses a generative AI model to generate a response: "It was a beautiful sunny day today. A perfect day for a walk."

[1103] 6. The emotion recognition means analyzes the user's input and voice and recognizes that the user is feeling happy. As a result, the server adjusts the response content to be warmer.

[1104] 7. The adjusted response will be displayed on the device and played back in the mother's voice.

[1105] This system allows users to continue connecting with the deceased and receive emotional support through conversations. Furthermore, the system stores the user's emotion recognition results in a database, allowing for more personalized conversations and a better user experience.

[1106] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1107] Step 1:

[1108] Users use a dedicated web portal or application to upload videos, photos and chat data of the deceased.

[1109] Input: Videos, photos, and chat data of the deceased

[1110] Output: Data sent to the server

[1111] Specific operation: A user accesses the portal, clicks the upload button, selects a file from a local folder, and uploads it. The device sends the uploaded data to the server and stores it.

[1112] Step 2:

[1113] The server analyzes the collected data and extracts the facial features, voice characteristics, and language patterns of the deceased.

[1114] Input: Videos, photos, and chat data stored on the server

[1115] Output: Extracted facial features, voice features, and language patterns

[1116] How it works: The server uses OpenCV to extract facial features from video, Google Cloud Speech-to-Text API to convert audio data to text, and SpaCy to analyze language patterns from chat data.

[1117] Step 3:

[1118] Based on the analyzed characteristics, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased person.

[1119] Input: extracted facial features, voice features, language patterns

[1120] Output: Generated digital clone

[1121] How it works: The server uses DeepFaceLab to recreate the face and voice of the deceased, and then uses GPT-3 to generate a text-based dialogue model that reflects the deceased's speaking style and personality.

[1122] Step 4:

[1123] Users access and interact with their digital clone through a dedicated application or web portal.

[1124] Input: User text or voice input

[1125] Output: The user's question sent to the server

[1126] How it works: The user opens the application, clicks on the digital clone icon, and enters a question via text or voice. The device then sends the question to the server.

[1127] Step 5:

[1128] The server analyzes the user's question and uses a generative AI model to generate an appropriate response.

[1129] Input: User question

[1130] Output: The generated response

[1131] What it does: The server uses GPT-3 to analyze the user's input and generates a response like, "It was a beautiful sunny day today. A perfect day for a walk."

[1132] Step 6:

[1133] The server recognizes emotions from the user's input and speech and performs adaptive processing.

[1134] Input: User input (text or voice)

[1135] Output: User's emotional state

[1136] Specific operation: The server uses TensorFlow to analyze the user's voice tone and text, and recognizes emotions such as whether the user is happy.

[1137] Step 7:

[1138] The server adjusts the response content according to the emotion recognition results.

[1139] Input: Initial generated response, user's emotional state

[1140] Output: Adjusted response

[1141] What it does: The server adjusts the initial response, "It was a beautiful sunny day today," to something like "It was a beautiful sunny day today. A perfect day for a walk, did you enjoy it?" depending on the emotion.

[1142] Step 8:

[1143] The generated response is displayed to the user through a user interface or played aloud.

[1144] Input: Adjusted response

[1145] Output: Display to user or play audio

[1146] Specific behavior: The device receives the response from the server and displays it on the screen as text or plays it back in the mother's voice.

[1147] Step 9:

[1148] The server stores the user's emotion recognition results in a database and builds a personalized user profile.

[1149] Input: User emotion recognition results

[1150] Output: Updated user profile

[1151] Specific operation: The server stores the emotion recognition results in a database and updates the personalized profile for the next interaction.

[1152] (Application example 2)

[1153] 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."

[1154] In factories and production lines, there is a challenge in efficiently transferring the knowledge and skills of experts to new employees and automated machines. Furthermore, when experts retire or pass away, there is a risk that their valuable skills and know-how will be lost. Furthermore, when new employees receive training from experts, it is difficult to provide appropriate guidance that takes into account their emotions and level of understanding.

[1155] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1156] In this invention, the server includes a means for collecting videos, photos, and chat data of the deceased, an analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, and a generation AI means for generating a digital clone of the deceased based on the extracted features, which makes it possible to educate and train new employees and automated machines in the knowledge and skills of an expert through the digital clone.

[1157] A "deceased person" is someone who was active in a certain workplace or society during their lifetime, but who is now deceased.

[1158] A "video" is a video file that records the actions and statements of the deceased.

[1159] A "photograph" is still image data that captures the face or figure of the deceased.

[1160] "Chat data" is a record of text-based conversations that the deceased had while alive.

[1161] "Analysis means" refers to technology or equipment for extracting and analyzing specific information from collected data.

[1162] "Facial features" are the external facial characteristics and identifying information of the deceased.

[1163] "Voice features" are characteristics or patterns related to the deceased person's voice.

[1164] A "language pattern" is the particular language or way of expressing oneself that the deceased used.

[1165] "Generative AI means" refers to a means for generating a digital clone based on information about a deceased person using artificial intelligence technology.

[1166] An "interface providing means" is a technique or device that provides a way for a user to interact with a digital clone.

[1167] "Means for displaying or audibly outputting the generated response to the user" refers to technology or devices for visually or audibly conveying the response generated by the digital clone to the user.

[1168] A "digital clone" is a digital representation that recreates the appearance, voice, and manner of speaking of a deceased person.

[1169] An "expert" is someone who has advanced knowledge and skills in a particular workplace or field.

[1170] A "new employee" is someone who has recently joined a workplace or organization and needs to learn the knowledge and skills of an expert.

[1171] An "automatic machine" is a mechanical device that automatically performs a specific task or process.

[1172] This invention relates to a digital coaching system that aims to transfer the knowledge and skills of experts to new employees and automated machines.

[1173] System configuration

[1174] The system includes the following main components:

[1175] 1. Data collection method: The server collects videos, photos, and chat data of the deceased and stores them in storage. Users upload these data using a dedicated web portal or application.

[1176] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased person. Facial features and voice features are extracted using computer vision and voice recognition technologies, and language patterns are extracted using natural language processing technology.

[1177] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[1178] 4. Emotion Engine: The server analyzes user and automated machine input and speech and recognizes emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning techniques to determine emotional states.

[1179] 5. User interface provision means: Users or automated machines can access and initiate a dialogue with the digital clone through a dedicated application or head-mounted display (HMD). Users can ask questions or give instructions to the digital clone using text input or voice input.

[1180] 6. Response generation means: The server analyzes the questions posed by the user or automated machine and generates an appropriate response using the generative AI model. The generated response is displayed or output as voice through the user interface. The response content can be adjusted according to the emotions recognized by the emotion engine.

[1181] 7. Database construction method: Emotion recognition results and dialogue data from users and automated machines are stored in a database to create personalized user profiles and work logs. These profiles are used for future dialogues and work assistance, improving the user experience.

[1182] Usage example

[1183] For example, if a new employee wants to learn how to maintain a new machine in a factory, they can use the system as follows: The user uploads a video of the expert working, along with audio commentary and instructions. The server analyzes this data and extracts the expert's facial features, vocal characteristics, and language patterns. DeepFake technology is then used to faithfully reproduce the expert's appearance and voice, and a generative AI model is used to create a digital clone that reflects the expert's speaking style and teaching style.

[1184] A user or automated machine wears an HMD and asks a digital clone of an expert, "How do I maintain this machine?" The server analyzes the question and uses a generative AI model to generate a response such as, "First, loosen this nut, then remove the filter." At the same time, an emotion engine analyzes the user's voice and movements to recognize confusion. Based on this information, the server adjusts the response, adding more detailed instructions and diagrams. The response is displayed on the HMD display, allowing the user to proceed with the task with confidence while receiving visual guidance.

[1185] The above is a specific embodiment of the digital coaching system based on the present invention. This system enables the knowledge and skills of experts to be efficiently passed on to the next generation, and improves the productivity of factories and production lines through education and guidance.

[1186] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1187] Step 1:

[1188] The server collects videos, photos, and chat data of the deceased uploaded by users using a dedicated web portal or application. The input data are the deceased's video files, photo files, and chat text data. The server stores these files in storage and prepares them for analysis. The output is that this data is centrally managed on the server.

[1189] Step 2:

[1190] The server analyzes the collected video, photo, and chat data. It uses computer vision technology to extract facial features and speech recognition technology to identify voice features. It also uses natural language processing technology to extract language patterns from the chat data. The input data is the video, photo, and chat data collected in Step 1. Data processing involves facial feature mapping, speech spectrum analysis, text tokenization, and pattern recognition. The output is facial, voice, and language feature data of the deceased.

[1191] Step 3:

[1192] The server uses deepfake technology and generative AI technology to generate a digital clone based on the facial features, voice features, and language patterns of the deceased. The input data is the feature data extracted in step 2. Data calculations involve generating a face and voice based on an AI model and integrating language patterns. The output is a digital clone that faithfully reproduces the deceased.

[1193] Step 4:

[1194] The server accepts input from users or automated machines. In particular, users or robots access it through dedicated applications or head-mounted displays (HMDs) and begin interacting with their digital clones. The input in this step is a question or instruction from the user or robot. The server recognizes this and prepares to proceed to the next step. The output is a confirmation that the question or instruction was received.

[1195] Step 5:

[1196] The server analyzes questions and instructions from users or automated machines and generates appropriate responses using a generative AI model. In parallel, an emotion engine recognizes emotions from the user's voice tone, facial expressions, and text input. The input is the question or instruction and emotion data received in step 4. Data calculations involve question analysis using natural language processing, emotion analysis using the emotion engine, and response generation using an AI model. The output is an appropriate response text or voice data according to the emotion.

[1197] Step 6:

[1198] The server displays or outputs the generated response to the user or automated machine. The input is the output of step 5. The specific operation is to display text or video on the HMD display and play audio using the audio output device. The output is the response received by the user or automated machine.

[1199] Step 7:

[1200] The server stores the emotion recognition results and dialogue data of the user and the automated machine in a database. The input data is the output of steps 5 and 6. As data storage, personalized user profiles and work logs are recorded in the database. The output is an updated database, which can be used for future dialogue and guidance.

[1201] 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.

[1202] 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.

[1203] 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.

[1204] [Fourth embodiment]

[1205] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1206] 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.

[1207] 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).

[1208] 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.

[1209] 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.

[1210] 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).

[1211] 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. 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.

[1212] 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.

[1213] 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.

[1214] 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.

[1215] 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.

[1216] 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.

[1217] 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."

[1218] The present invention provides a system for generating a digital clone of a deceased person and allowing a user to interact with the deceased person in a digital space. Specific embodiments of the system are described below.

[1219] System configuration

[1220] The system includes the following main components:

[1221] 1. Data collection method: Users can use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The server receives this data and stores it in storage.

[1222] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased. Facial features and voice features are analyzed using computer vision and speech recognition technologies, and language patterns are extracted using natural language processing technology.

[1223] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[1224] 4. User interface provision means: Users can access and start interacting with the digital clone through a dedicated application or web portal. Users can ask questions to the digital clone using text input or voice input.

[1225] 5. Response generation means: The server receives the user's question and generates an appropriate response using the generative AI model. The generated response is displayed or played aloud to the user through the user interface.

[1226] Program processing flow (overview)

[1227] When a user uploads the deceased's data to the system, the server begins analyzing the data. Once the analysis is complete, a generative AI means generates a digital clone of the deceased. When a user accesses the digital clone and asks a question, the server analyzes the question and generates a response using the generative AI model. The generated response is displayed on the user's device or played aloud.

[1228] Specific examples

[1229] For example, if a user wanted to create a digital clone of their deceased mother, they would use the system as follows: The user would upload videos, photos, and chat logs of the mother from when she was alive to the system. The server would analyze this data and extract the mother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to create a digital clone that reflects the mother's speaking style and personality.

[1230] The user opens a dedicated application and asks their mother's digital clone, "How was the weather today?" The device sends this question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. It was a perfect day for a walk." This response is then displayed on the user's device, allowing the user to enjoy a conversation with their mother.

[1231] The above is a specific embodiment of the present invention. This system allows you to continue to connect with the deceased and receive comfort and support through conversation.

[1232] The processing flow will be explained below.

[1233] Step 1:

[1234] Users: Upload videos, photos, chat data, etc. of the deceased using a dedicated web portal or application. Users can complete the upload by dragging and dropping files or using a file selection dialog.

[1235] Step 2:

[1236] Server: Stores the uploaded data in storage. Records file metadata (file name, size, upload date and time, etc.) in a database.

[1237] Step 3:

[1238] Server: Analyzes the stored data, extracting facial features of the deceased from videos and photos, and voice features from audio files. This involves computer vision and voice recognition techniques. For example, facial recognition algorithms are used to identify facial landmarks, and voice analysis algorithms are used to extract voice tone and pitch.

[1239] Step 4:

[1240] Server: Analyzes chat data and text data using natural language processing (NLP) techniques to extract the deceased's language patterns and unique expressions. For example, it uses keyword frequency analysis and language models to identify commonly used phrases and vocabulary.

[1241] Step 5:

[1242] Server: Using DeepFake technology, a digital clone of the deceased is created based on the extracted facial and vocal features. The clone is then optimized to reproduce the deceased's face and voice in real time. For example, a video generative model is used to realistically recreate the deceased's facial expressions.

[1243] Step 6:

[1244] Server: A generative AI model is used to learn the behavioral patterns and personality of the deceased. This model is trained to mimic the deceased's response patterns and thought process based on the analyzed chat data. For example, a deep learning model is used to understand the context and generate appropriate responses.

[1245] Step 7:

[1246] User: Accesses the digital clone through a dedicated application or web portal. The user can talk to the digital clone using text or voice chat. For example, they can type "Mom, how was the weather today?" into the chat box within the app.

[1247] Step 8:

[1248] Terminal: Sends user input to the server. This includes text questions and voice data. The terminal captures user input in real time and forwards it to the server.

[1249] Step 9:

[1250] Server: Analyzes the user's question and generates an appropriate response using a generative AI model. For example, in response to a question about the weather, the server generates a response such as "Today was a beautiful sunny day, perfect for a walk."

[1251] Step 10:

[1252] Server: Generates and sends the response to the terminal. The response is sent in text or audio format.

[1253] Step 11:

[1254] Terminal: Displays the received response to the user or plays it aloud. For example, it may show the text on a display and use a voice playback function to play the response in the deceased's voice.

[1255] Step 12:

[1256] User: Enjoys interacting with the digital clone. The user can re-enter questions or continue the conversation. This process repeats, continuing the interaction with the user.

[1257] The above are the specific processing steps of the digital clone generation system according to the present invention, which allows users to feel a connection with the deceased through conversation.

[1258] Example 1

[1259] 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."

[1260] In modern society, there is a growing emotional need to reminisce and converse with deceased loved ones. However, the technology to effectively utilize the digital data left behind by the deceased is underdeveloped. Therefore, there is a need for a system that enables communication with the deceased through a computer. Furthermore, advanced data analysis and generation technologies are required to accurately reproduce the facial features, vocal characteristics, and language patterns of the deceased, allowing bereaved families to have a natural and emotionally rich experience.

[1261] 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.

[1262] In this invention, the server includes means for collecting image data, voice data, and text data of the deceased, analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, artificial intelligence (AI) means for generating a digital clone of the deceased based on the extracted features, means for providing a user interface for the user to interact with the digital clone, and means for displaying or outputting to the user the responses generated by the AI ​​generating means by voice, thereby enabling a highly simulated interaction with the deceased and enabling the bereaved family to maintain an emotional connection.

[1263] "Deceased" refers to a person who has passed away.

[1264] "Image data" refers to digital image information stored in the form of photographs or videos.

[1265] "Audio Data" means digital acoustic information stored in voice or audio recording form.

[1266] "Text data" refers to digital information in text format stored as chat logs or documents.

[1267] "Facial features" refers to feature information about the shape and structure of a person's face extracted from image data.

[1268] "Voice features" refer to the pitch, timbre, and pronunciation characteristics of a person's voice extracted from audio data.

[1269] "Language patterns" refer to a person's language and unique ways of expression extracted from text data.

[1270] "Analysis means" refers to a processing mechanism for extracting necessary feature information from collected data.

[1271] "Generative artificial intelligence means" refers to artificial intelligence technology for generating a digital clone based on extracted feature information.

[1272] "User interface" refers to the means that provides input and output mechanisms for a user to interact with a system.

[1273] A "digital clone" refers to a digital model that recreates the face, voice, and language patterns of a deceased person.

[1274] "Deepfake technology" refers to the technology of synthesizing images and audio data using deep learning technology.

[1275] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[1276] System Overview

[1277] This invention is a system that creates a digital clone of a deceased person and allows users to interact with the deceased in a digital space. The system is mainly composed of three components: a server, a terminal, and a user, with each component playing a specific role. The entire system functions through the following stages: data collection, data analysis, digital clone creation, user interaction, and response generation.

[1278] Data collection methods

[1279] Users use a dedicated web portal or application to collect image, audio, and text data of the deceased, which is then securely uploaded to a server via HTTPS, where the server stores the data.

[1280] Data Analysis Methods

[1281] The server analyzes the received data and extracts facial features, voice features, and language patterns using the following software and techniques:

[1282] Facial features: Extract facial features from image data using OpenCV.

[1283] Speech features: Extract speech features from the audio data using the Google Speech-to-Text API.

[1284] Language Patterns: Extract language patterns from text data using SpaCy or NLTK.

[1285] Generation AI means

[1286] The server then uses DeepFake technology and generative AI technology (such as GPT-3) to create a digital clone of the deceased person based on the analyzed characteristics. The digital clone faithfully reproduces the appearance, voice, and manner of speech of the deceased.

[1287] User interface providing means

[1288] Users can access the digital clone through a dedicated application or a web portal. The user interface is built on React and Vue.js and is designed to allow users to ask questions and make comments to the digital clone via text or voice input.

[1289] Response Generation Method

[1290] The server analyzes questions received through the user interface and generates appropriate responses using a generative AI model (e.g., GPT-3). The responses are then sent to the device and displayed as text or played aloud.

[1291] Specific examples

[1292] For example, if a user wants to create a digital clone of their deceased mother, they can upload her image, voice, and text data from before she died to the system. The server analyzes this data and extracts her facial features, voice characteristics, and speech patterns. It then uses DeepFake technology and generative AI technology to create a digital clone of the mother.

[1293] A user opens the application and asks their mother's digital clone, "How was the weather today?" The device sends the question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. A perfect day for a walk." This response is then displayed on the user's device, allowing them to enjoy a conversation with their deceased loved one.

[1294] Prompt Sentence Examples

[1295] "To a digital clone of your deceased mother, please type: 'What was the weather like today?'"

[1296] The above is a specific embodiment of the present invention. This system allows you to continue to connect with the deceased and receive comfort and support through conversation.

[1297] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1298] Step 1: Data collection

[1299] Users use a dedicated web portal or application to upload image, audio, and text data of the deceased to the system, which then transfers the data to the server.

[1300] Input: Image data, audio data, and text data of the deceased.

[1301] Data processing: splitting, compression, secure data transfer using HTTP protocol.

[1302] Output: Data saved to storage on the server.

[1303] Specific behavior:

[1304] A user logs into a web portal and clicks the file selection button.

[1305] The device displays a file selection dialog, and the user selects the required file.

[1306] The device uploads the selected file to the server.

[1307] The server stores the received data in storage.

[1308] Step 2: Data analysis

[1309] The server analyzes the uploaded data, extracting facial features, voice features, and language patterns.

[1310] Input: Image data, audio data, and text data saved in step 1.

[1311] Data computing: image recognition, speech analysis, natural language processing.

[1312] Output: Extraction results of facial features, audio features, and language patterns.

[1313] Specific behavior:

[1314] The server uses the OpenCV library to extract facial features from the image data.

[1315] The server uses the Google Speech-to-Text API to extract speech features from the audio data.

[1316] The server uses SpaCy to extract language patterns from the text data.

[1317] Step 3: Digital cloning

[1318] Based on the analyzed characteristic information, the server uses DeepFake technology and generative AI technology to generate a digital clone.

[1319] Input: Facial features, audio features, and language pattern extraction results.

[1320] Data computation: face and voice synthesis, generation of conversation models.

[1321] Output: A digital clone of the deceased person.

[1322] Specific behavior:

[1323] The server uses the facial features in DeepFaceLab to generate a digital clone's face.

[1324] The voice of the digital clone is reproduced based on the voice model generated by the server.

[1325] The server uses GPT-3 to generate a conversation model that reflects the speaking style and personality of the deceased.

[1326] Step 4: Configuring User Interaction

[1327] Users access their digital clone by opening a dedicated application or web portal, where they can ask questions or make comments using text or voice input.

[1328] Input: User text or voice input.

[1329] Data processing: Accepting input and forwarding it to the server.

[1330] Output: User input transmitted to the server.

[1331] Specific behavior:

[1332] The user launches the application and logs in.

[1333] It opens an interface that allows the user to begin interacting with the digital clone.

[1334] The device accepts the user's text input or voice input and sends it to the server.

[1335] Step 5: Response Generation

[1336] The server analyzes the input received from the user and uses a generative AI model to generate an appropriate response, which is then sent to the device for display or audio playback.

[1337] Input: User text or voice input.

[1338] Data Computing: Input analysis and response generation using natural language processing.

[1339] Output: The generated text or audio response.

[1340] Specific behavior:

[1341] The server uses GPT-3 to analyze the user's input and generate an appropriate response.

[1342] The server generates a response that is then encoded and sent to the terminal.

[1343] The terminal displays or plays the response to the user in text or audio.

[1344] The above are the specific processing steps of the program of this system.

[1345] (Application example 1)

[1346] 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."

[1347] In modern times, methods of remembering the deceased rely on static records such as photographs and videos, making it difficult to recreate conversations and memories with the deceased. Furthermore, there are limited opportunities to share memories with the deceased. This creates a need for a way to vividly recreate conversations and memories with the deceased, allowing family and friends to deepen their connection with them.

[1348] 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.

[1349] In this invention, the server includes means for collecting videos, photos, and chat data of the deceased, analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, generation AI means for generating a digital clone of the deceased based on the extracted features, means for providing an interface through which the user can interact with the digital clone, means for displaying or outputting to the user by voice the responses generated by the generation AI means, and means for providing a virtual environment through which the user can enjoy interacting with the deceased in a virtual space. This allows the user to relive vivid memories and remember the deceased in a virtual space by interacting with the digital clone of the deceased.

[1350] "Video" is digital data that contains moving images.

[1351] A "photograph" is digital data of a captured still image.

[1352] "Chat data" is digital data of communication records including text message history.

[1353] "Means for collecting data" refers to the means by which users upload video, image, and text data of the deceased to the system.

[1354] "Analysis means" refers to means for extracting facial features, voice features, and language patterns of the deceased from the collected data.

[1355] "Generative AI means" refers to means that use artificial intelligence technology to generate a digital clone of a deceased person based on extracted characteristics.

[1356] The "means for providing an interface" is a means for providing an operation screen for a user to interact with a digital clone.

[1357] "Means for displaying or audibly outputting the generated response to the user" refers to means for displaying or audibly playing the response generated by the generation AI means on the user's terminal.

[1358] The "virtual environment providing means" is a means for providing a virtual space or environment in which a user can enjoy a conversation with a deceased person in a virtual space.

[1359] This invention provides a system for generating a digital clone of a deceased person and allowing interaction with the deceased person in a virtual space. The system includes the following main components:

[1360] 1. Data Collection Methods

[1361] Users use a dedicated web portal or application to upload videos, photos, and chat data of the deceased, which is then sent to a server and stored.

[1362] 2. Data analysis methods

[1363] The server analyzes the collected data, specifically by using computer vision technology to extract facial features, speech recognition technology to extract voice features, and natural language processing technology to analyze language patterns.

[1364] 3. Generation AI means

[1365] Based on the analyzed features, the server uses a generative AI model to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and manner of speech of the deceased.

[1366] 4. User Interface Provisioning Method

[1367] Users can access and interact with their digital clone through a dedicated application or web portal, using text and voice input.

[1368] 5. A means to display and speak the generated response

[1369] The server receives the user's question and uses a generative AI model to generate an appropriate response, which is then displayed or played aloud on the user's device.

[1370] 6. Means of providing virtual environments

[1371] Users can enjoy interacting with the deceased in a virtual space, which is provided using a dedicated application and a head-mounted display.

[1372] Hardware and software used

[1373] Hardware: General web servers, user devices (PCs, smartphones, tablets)

[1374] Software: Flask (web application framework), DeepFake technology (deepfake_gen), generative AI technology (ai_response_gen)

[1375] Specific examples

[1376] For example, if a user wanted to interact with a digital clone of their deceased grandmother, they would use the system as follows: The user would upload videos, photos, and chat data from when the grandmother was alive to the system. The server would analyze this data and extract the grandmother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to generate a digital clone that reflected her speaking style and personality.

[1377] The user accesses the grandmother's digital clone through a dedicated application and asks, "How are you doing these days?" The server analyzes the question and generates an appropriate response using a generative AI model. As a result, the user receives the response, "I'm fine. I water the flowers every day," allowing them to enjoy a conversation with the deceased.

[1378] Prompt Sentence Examples

[1379] "How are you doing these days?"

[1380] "Tell me your memories."

[1381] "Tell me your favorite dish."

[1382] In this way, the user can relive memories of the deceased while remembering them in a virtual space through dialogue with the deceased.

[1383] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1384] Step 1:

[1385] Users upload videos, photos, and chat data of the deceased person using a dedicated web portal or application. Specifically, users click the upload button to send video files, photo files, and text chat history to the system. This data is sent to the server and stored in storage.

[1386] Input: Video, photos, chat data

[1387] Output: Data stored on the server

[1388] Step 2:

[1389] The server then begins the process of analyzing the collected data, first using computer vision technology to extract the deceased's facial features from the videos and photos, then using speech recognition technology to extract the deceased's voice features from the videos, and finally using natural language processing technology to analyze the deceased's language patterns from the chat data.

[1390] Input: Videos, photos, and chat data stored on the server

[1391] Output: facial features, voice features, and language patterns of the deceased

[1392] Step 3:

[1393] The server then uses augmented reality technology (such as DeepFake technology) to create a digital clone of the deceased based on the extracted features. This process combines facial and vocal features to realistically recreate the appearance and voice of the deceased, and uses a generative AI model to create a digital clone with a speaking style based on the deceased's language patterns.

[1394] Input: facial features, voice features, and language patterns of the deceased

[1395] Output: Digital clone of the deceased

[1396] Step 4:

[1397] The user accesses the generated digital clone through a dedicated application or web portal and begins interacting with it. Specifically, the user opens the application and asks the digital clone questions by text input or voice input. The user's input is sent from the device to the server.

[1398] Input: User question (text or voice)

[1399] Output: Query data sent to the server

[1400] Step 5:

[1401] The server receives the user's question and generates an appropriate response using a pre-trained generative AI model, which generates a specific answer to the user's question while recreating the deceased's language patterns.

[1402] Input: User question data

[1403] Output: Digital clone response data

[1404] Step 6:

[1405] Once the response data is generated, the server sends it back to the user's device, where it is displayed or played audibly, allowing the user to receive responses from the deceased's digital clone and enjoy a conversation.

[1406] Input: Digital clone response data

[1407] Output: The response that is displayed or played aloud on the user's device

[1408] Step 7:

[1409] To further deepen the interaction in the virtual space, users can continue to ask questions using specific prompts. Examples include "How are you doing these days?", "Tell me a memory of your life," and "Tell me your favorite dish." These questions can encourage a more emotional interaction with the deceased and deepen the experience in the virtual space.

[1410] Input: New prompt question

[1411] Output: Continuing dialogue and responses

[1412] In this way, users can relive memories through dialogue with the deceased and deepen their emotional connection while remembering them.

[1413] 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.

[1414] The present invention combines a system that generates a digital clone of a deceased person, allows a user to interact with the deceased in a digital space, and an emotion engine that recognizes the user's emotions. Specific embodiments of the system are described below.

[1415] System configuration

[1416] The system includes the following main components:

[1417] 1. Data collection method: Users use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The server receives this data and stores it in storage.

[1418] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased. Facial features and voice features are analyzed using computer vision and speech recognition technologies, and language patterns are extracted using natural language processing technology.

[1419] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[1420] 4. Emotion Engine: The server analyzes user input and speech and recognizes the user's emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning techniques to determine the user's emotional state.

[1421] 5. User interface provision means: Users can access and start interacting with the digital clone through a dedicated application or web portal. Users can ask questions to the digital clone using text input or voice input.

[1422] 6. Response generation means: The server analyzes the user's question and generates an appropriate response using the generative AI model. The generated response is displayed to the user through the user interface or played aloud. The response content can be adjusted according to the user's emotions recognized by the emotion engine.

[1423] 7. Database Construction Method: The user's emotion recognition results are stored in a database to create a personalized user profile. This profile is used for future interactions and to improve the user experience.

[1424] Program processing flow (overview)

[1425] When a user uploads the deceased's data to the system, the server begins analyzing the data. Once the analysis is complete, a generative AI means generates a digital clone of the deceased. When the user accesses the digital clone and asks a question, the server analyzes the question and generates a response using the generative AI model. In parallel, an emotion engine recognizes emotions from the user's input and speech and adjusts the response. The generated response is displayed to the user through the user interface or played aloud. The user's emotion recognition results are stored in a database, and a personalized user profile is constructed.

[1426] Specific examples

[1427] For example, if a user wanted to create a digital clone of their deceased mother, they would use the system as follows: The user would upload videos, photos, and chat logs of the mother from when she was alive to the system. The server would analyze this data and extract the mother's facial features, voice characteristics, and speech patterns. DeepFake technology would then be used to faithfully reproduce her appearance and voice, and a generative AI model would be used to create a digital clone that reflects the mother's speaking style and personality.

[1428] The user opens a dedicated application and asks their mother's digital clone, "Mom, how was the weather today?" The device sends this question to the server, which uses a generative AI model to generate a response such as, "It was a beautiful sunny day today. It was a perfect day for a walk." At the same time, the emotion engine analyzes the user's input and voice and recognizes that the user is expressing a happy emotion. Based on this information, the server adjusts the response content and generates a warmer one. This response is displayed on the user's device, allowing the user to enjoy a conversation with their mother.

[1429] The above is a specific embodiment of the digital clone generation system according to the present invention, which allows users to continue to connect with their deceased loved ones and receive emotional support through conversation.

[1430] The processing flow will be explained below.

[1431] Step 1:

[1432] Users: Upload videos, photos, chat data, etc. of the deceased using a dedicated web portal or application. Users can complete the upload by dragging and dropping files or using a file selection dialog.

[1433] Step 2:

[1434] Server: Stores the uploaded data in storage. Records file metadata (file name, size, upload date and time, etc.) in a database.

[1435] Step 3:

[1436] Server: Analyzes the stored data. Extracts facial features of the deceased from videos and photos, and voice features from audio files. Using computer vision and voice recognition technologies, facial recognition algorithms identify facial landmarks, and voice analysis algorithms extract voice tone and pitch.

[1437] Step 4:

[1438] Server: Analyzes chat data and text data using natural language processing (NLP) techniques to extract the deceased's language patterns and unique expressions. Keyword frequency analysis and language models are used to identify commonly used phrases and vocabulary.

[1439] Step 5:

[1440] Server: Using DeepFake technology, a digital clone of the deceased is created based on the extracted facial and vocal features. A video generation model is used to realistically recreate the facial expressions of the deceased, and an audio reproduction model is used to faithfully recreate the voice.

[1441] Step 6:

[1442] Server: A generative AI model is used to learn the behavioral patterns and personality of the deceased. This model is trained to mimic the deceased's response patterns and thought process based on the analyzed chat data.

[1443] Step 7:

[1444] User: Accesses the digital clone through a dedicated application or web portal. The user can talk to the digital clone using text or voice chat. For example, they can type "Mom, how was the weather today?" into the chat box within the app.

[1445] Step 8:

[1446] Terminal: Sends user input to the server. Captures text questions and voice data in real time and transfers them to the server.

[1447] Step 9:

[1448] Server: Analyzes the user's question and generates an appropriate response using a generative AI model, such as "Today was a beautiful sunny day, perfect for a walk."

[1449] Step 10:

[1450] Server: In parallel, the emotion engine analyzes the user's input and speech, recognizing emotions from voice tone, facial expressions, and text input. The recognized emotions are used as information to tailor the response. For example, if the user has a happy expression, a warmer response will be generated accordingly.

[1451] Step 11:

[1452] Server: Sends the responses generated by the generative AI model and emotion engine to the device in text or voice format.

[1453] Step 12:

[1454] Terminal: Displays the received response to the user or plays it aloud. Shows the text on the display and uses the audio playback function to play the response in the voice of the deceased.

[1455] Step 13:

[1456] Server: Stores the user's emotion recognition results in a database and creates a personalized user profile that is used for future interactions and to improve the user experience.

[1457] Step 14:

[1458] User: Enjoys interacting with the digital clone. The user can re-enter questions or continue the conversation. This process repeats, continuing the interaction with the user.

[1459] The above are the specific processing steps of the digital clone generation system according to the present invention, which allows users to obtain emotional support through conversations with the deceased.

[1460] Example 2

[1461] 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."

[1462] In recent years, digital approaches to remembering the deceased have been gaining attention, but existing systems cannot faithfully reproduce the facial expressions, voice, and language of the deceased, and they cannot adjust the dialogue to match the user's emotions, making it difficult to realize effective dialogue to provide emotional support.In addition, systems that can personalize and optimize the user experience are not well developed.

[1463] 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 videos, photos, and chat data of the deceased; analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data; generation AI means for generating a digital clone of the deceased based on the extracted features; means for providing an interface through which the user interacts with the digital clone; emotion recognition means for recognizing emotions from the user's input and speech; means for adjusting responses generated by the generation AI means according to the user's emotional state; means for displaying or outputting the responses generated by the generation AI means to the user as audio; and means for storing the user's emotion recognition results in a database and creating a personalized user profile. This allows the appearance, voice, and speaking style of the deceased to be faithfully reproduced and dialogue tailored to the user's emotions can be provided, thereby personalizing and optimizing the user experience.

[1464] "Means of collection" refers to a function that allows users to upload videos, photos, and chat data of the deceased and send that data to a server.

[1465] "Analysis means" refers to a function that extracts the facial features, voice features, and language patterns of the deceased from the collected data, and includes computer vision technology, voice recognition technology, and natural language processing technology.

[1466] "Generative AI means" is a function that generates a digital clone based on extracted characteristics of the deceased, and utilizes DeepFake technology and generative AI technology to faithfully reproduce the appearance, voice, and speaking style of the deceased.

[1467] "Means for providing an interface" refers to the ability to provide an input and output interface for a user to interact with the digital clone, such as a dedicated application or a web portal.

[1468] The "emotion recognition means" is a function that uses deep learning technology to recognize emotions by analyzing voice tone, facial expressions, and text input from user input and speech.

[1469] The "means for adjusting a response" is a function for appropriately adjusting the response generated by the generation AI means in accordance with the emotional state of the user recognized by the emotion recognition means.

[1470] The "means for displaying or outputting by voice" is a function for visually displaying the generated response to the user or playing it back as voice.

[1471] The "means for saving in a database and creating a personalized user profile" is a function for saving the user's emotion recognition results in a database and building a personalized user profile based on the results, which will be used in future interactions.

[1472] As an example of a mode for implementing the invention, a system will be described that combines a system that generates a digital clone of a deceased person and allows a user to interact with the deceased in a digital space with an emotion engine that recognizes the user's emotions.

[1473] The system includes the following main components:

[1474] 1. Data Collection Methods

[1475] Users use a dedicated web portal or application to upload videos, photos, chat data, etc. of the deceased. The device sends this data to a server, which then stores it in storage. The hardware used in this process is the user's device (e.g., a PC or smartphone), and the software includes a web portal or mobile app.

[1476] 2. Data analysis methods

[1477] The server analyzes the collected data and extracts the facial features, voice characteristics, and language patterns of the deceased using computer vision, speech recognition, and natural language processing technologies, such as OpenCV (computer vision), Google Cloud Speech-to-Text API (speech recognition), and SpaCy (natural language processing).

[1478] 3. Generation AI means

[1479] Based on the analyzed features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased person. This digital clone faithfully reproduces the appearance, voice, and speaking style of the deceased, and generative AI models such as DeepFaceLab and GPT-3 are used.

[1480] 4. Emotion recognition means

[1481] The server analyzes user input and speech and recognizes the user's emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning technology, including TensorFlow and PyTorch.

[1482] 5. User Interface Provisioning Method

[1483] Users can access the digital clone through a dedicated application or web portal and begin interacting with it by asking questions using text or voice input.

[1484] 6. Response Coordination

[1485] The server analyzes the questions and utterances entered by the user and generates appropriate responses using a generative AI model. It also adjusts the response content according to the user's emotional state, as recognized by emotion recognition means.

[1486] 7. Display or audio output means

[1487] The generated response is displayed to the user through a user interface or played aloud.

[1488] 8. Means of storing in a database and creating individualized user profiles

[1489] The server stores the user's emotion recognition results in a database and builds an individualized user profile, which enables personalized interactions to enhance the user experience.

[1490] Specific examples

[1491] For example, if a user wants to create a digital clone of their deceased mother, they would use the system as follows: The program processing of this system is as follows.

[1492] 1. The user uploads videos, photos, and chat logs of their mother before she died to the system. The device sends this data to the server, which then stores it.

[1493] 2. The server analyzes the stored data and extracts the mother's facial features, voice characteristics, and phrasing using OpenCV, Google Cloud Speech-to-Text API, and SpaCy.

[1494] 3. Based on the extraction results, the server uses DeepFake technology to recreate the mother's face and voice, and uses a generative AI model to create a digital clone that reflects the mother's speaking style and personality.

[1495] 4. The user opens a dedicated application and asks the digital clone of their mother, "Mom, how was the weather today?" The prompt can be entered via text or voice.

[1496] 5. The device sends the question to the server, which analyzes it and uses a generative AI model to generate a response: "It was a beautiful sunny day today. A perfect day for a walk."

[1497] 6. The emotion recognition means analyzes the user's input and voice and recognizes that the user is feeling happy. As a result, the server adjusts the response content to be warmer.

[1498] 7. The adjusted response will be displayed on the device and played back in the mother's voice.

[1499] This system allows users to continue connecting with the deceased and receive emotional support through conversations. Furthermore, the system stores the user's emotion recognition results in a database, allowing for more personalized conversations and a better user experience.

[1500] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1501] Step 1:

[1502] Users use a dedicated web portal or application to upload videos, photos and chat data of the deceased.

[1503] Input: Videos, photos, and chat data of the deceased

[1504] Output: Data sent to the server

[1505] Specific operation: A user accesses the portal, clicks the upload button, selects a file from a local folder, and uploads it. The device sends the uploaded data to the server and stores it.

[1506] Step 2:

[1507] The server analyzes the collected data and extracts the facial features, voice characteristics, and language patterns of the deceased.

[1508] Input: Videos, photos, and chat data stored on the server

[1509] Output: Extracted facial features, voice features, and language patterns

[1510] How it works: The server uses OpenCV to extract facial features from video, Google Cloud Speech-to-Text API to convert audio data to text, and SpaCy to analyze language patterns from chat data.

[1511] Step 3:

[1512] Based on the analyzed characteristics, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased person.

[1513] Input: extracted facial features, voice features, language patterns

[1514] Output: Generated digital clone

[1515] How it works: The server uses DeepFaceLab to recreate the face and voice of the deceased, and then uses GPT-3 to generate a text-based dialogue model that reflects the deceased's speaking style and personality.

[1516] Step 4:

[1517] Users access and interact with their digital clone through a dedicated application or web portal.

[1518] Input: User text or voice input

[1519] Output: The user's question sent to the server

[1520] How it works: The user opens the application, clicks on the digital clone icon, and enters a question via text or voice. The device then sends the question to the server.

[1521] Step 5:

[1522] The server analyzes the user's question and uses a generative AI model to generate an appropriate response.

[1523] Input: User question

[1524] Output: The generated response

[1525] What it does: The server uses GPT-3 to analyze the user's input and generates a response like, "It was a beautiful sunny day today. A perfect day for a walk."

[1526] Step 6:

[1527] The server recognizes emotions from the user's input and speech and performs adaptive processing.

[1528] Input: User input (text or voice)

[1529] Output: User's emotional state

[1530] Specific operation: The server uses TensorFlow to analyze the user's voice tone and text, and recognizes emotions such as whether the user is happy.

[1531] Step 7:

[1532] The server adjusts the response content according to the emotion recognition results.

[1533] Input: Initial generated response, user's emotional state

[1534] Output: Adjusted response

[1535] What it does: The server adjusts the initial response, "It was a beautiful sunny day today," to something like "It was a beautiful sunny day today. A perfect day for a walk, did you enjoy it?" depending on the emotion.

[1536] Step 8:

[1537] The generated response is displayed to the user through a user interface or played aloud.

[1538] Input: Adjusted response

[1539] Output: Display to user or play audio

[1540] Specific behavior: The device receives the response from the server and displays it on the screen as text or plays it back in the mother's voice.

[1541] Step 9:

[1542] The server stores the user's emotion recognition results in a database and builds a personalized user profile.

[1543] Input: User emotion recognition results

[1544] Output: Updated user profile

[1545] Specific operation: The server stores the emotion recognition results in a database and updates the personalized profile for the next interaction.

[1546] (Application example 2)

[1547] 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."

[1548] In factories and production lines, there is a challenge in efficiently transferring the knowledge and skills of experts to new employees and automated machines. Furthermore, when experts retire or pass away, there is a risk that their valuable skills and know-how will be lost. Furthermore, when new employees receive training from experts, it is difficult to provide appropriate guidance that takes into account their emotions and level of understanding.

[1549] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1550] In this invention, the server includes a means for collecting videos, photos, and chat data of the deceased, an analysis means for extracting facial features, voice features, and language patterns of the deceased from the collected data, and a generation AI means for generating a digital clone of the deceased based on the extracted features, which makes it possible to educate and train new employees and automated machines in the knowledge and skills of an expert through the digital clone.

[1551] A "deceased person" is someone who was active in a certain workplace or society during their lifetime, but who is now deceased.

[1552] A "video" is a video file that records the actions and statements of the deceased.

[1553] A "photograph" is still image data that captures the face or figure of the deceased.

[1554] "Chat data" is a record of text-based conversations that the deceased had while alive.

[1555] "Analysis means" refers to technology or equipment for extracting and analyzing specific information from collected data.

[1556] "Facial features" are the external facial characteristics and identifying information of the deceased.

[1557] "Voice features" are characteristics or patterns related to the deceased person's voice.

[1558] A "language pattern" is the particular language or way of expressing oneself that the deceased used.

[1559] "Generative AI means" refers to a means for generating a digital clone based on information about a deceased person using artificial intelligence technology.

[1560] An "interface providing means" is a technique or device that provides a way for a user to interact with a digital clone.

[1561] "Means for displaying or audibly outputting the generated response to the user" refers to technology or devices for visually or audibly conveying the response generated by the digital clone to the user.

[1562] A "digital clone" is a digital representation that recreates the appearance, voice, and manner of speaking of a deceased person.

[1563] An "expert" is someone who has advanced knowledge and skills in a particular workplace or field.

[1564] A "new employee" is someone who has recently joined a workplace or organization and needs to learn the knowledge and skills of an expert.

[1565] An "automatic machine" is a mechanical device that automatically performs a specific task or process.

[1566] This invention relates to a digital coaching system that aims to transfer the knowledge and skills of experts to new employees and automated machines.

[1567] System configuration

[1568] The system includes the following main components:

[1569] 1. Data collection method: The server collects videos, photos, and chat data of the deceased and stores them in storage. Users upload these data using a dedicated web portal or application.

[1570] 2. Data analysis method: The server analyzes the collected data and extracts the facial features, voice features, and language patterns of the deceased person. Facial features and voice features are extracted using computer vision and voice recognition technologies, and language patterns are extracted using natural language processing technology.

[1571] 3. Generative AI method: Based on the extracted features, the server uses DeepFake technology and generative AI technology to create a digital clone of the deceased, which faithfully reproduces the appearance, voice, and speaking style of the deceased.

[1572] 4. Emotion Engine: The server analyzes user and automated machine input and speech and recognizes emotions from voice tone, facial expressions, and text input. The emotion engine uses deep learning techniques to determine emotional states.

[1573] 5. User interface provision means: Users or automated machines can access and initiate a dialogue with the digital clone through a dedicated application or head-mounted display (HMD). Users can ask questions or give instructions to the digital clone using text input or voice input.

[1574] 6. Response generation means: The server analyzes the questions posed by the user or automated machine and generates an appropriate response using the generative AI model. The generated response is displayed or output as voice through the user interface. The response content can be adjusted according to the emotions recognized by the emotion engine.

[1575] 7. Database construction method: Emotion recognition results and dialogue data from users and automated machines are stored in a database to create personalized user profiles and work logs. These profiles are used for future dialogues and work assistance, improving the user experience.

[1576] Usage example

[1577] For example, if a new employee wants to learn how to maintain a new machine in a factory, they can use the system as follows: The user uploads a video of the expert working, along with audio commentary and instructions. The server analyzes this data and extracts the expert's facial features, vocal characteristics, and language patterns. DeepFake technology is then used to faithfully reproduce the expert's appearance and voice, and a generative AI model is used to create a digital clone that reflects the expert's speaking style and teaching style.

[1578] A user or automated machine wears an HMD and asks a digital clone of an expert, "How do I maintain this machine?" The server analyzes the question and uses a generative AI model to generate a response such as, "First, loosen this nut, then remove the filter." At the same time, an emotion engine analyzes the user's voice and movements to recognize confusion. Based on this information, the server adjusts the response, adding more detailed instructions and diagrams. The response is displayed on the HMD display, allowing the user to proceed with the task with confidence while receiving visual guidance.

[1579] The above is a specific embodiment of the digital coaching system based on the present invention. This system enables the knowledge and skills of experts to be efficiently passed on to the next generation, and improves the productivity of factories and production lines through education and guidance.

[1580] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1581] Step 1:

[1582] The server collects videos, photos, and chat data of the deceased uploaded by users using a dedicated web portal or application. The input data are the deceased's video files, photo files, and chat text data. The server stores these files in storage and prepares them for analysis. The output is that this data is centrally managed on the server.

[1583] Step 2:

[1584] The server analyzes the collected video, photo, and chat data. It uses computer vision technology to extract facial features and speech recognition technology to identify voice features. It also uses natural language processing technology to extract language patterns from the chat data. The input data is the video, photo, and chat data collected in Step 1. Data processing involves facial feature mapping, speech spectrum analysis, text tokenization, and pattern recognition. The output is facial, voice, and language feature data of the deceased.

[1585] Step 3:

[1586] The server uses deepfake technology and generative AI technology to generate a digital clone based on the facial features, voice features, and language patterns of the deceased. The input data is the feature data extracted in step 2. Data calculations involve generating a face and voice based on an AI model and integrating language patterns. The output is a digital clone that faithfully reproduces the deceased.

[1587] Step 4:

[1588] The server accepts input from users or automated machines. In particular, users or robots access it through dedicated applications or head-mounted displays (HMDs) and begin interacting with their digital clones. The input in this step is a question or instruction from the user or robot. The server recognizes this and prepares to proceed to the next step. The output is a confirmation that the question or instruction was received.

[1589] Step 5:

[1590] The server analyzes questions and instructions from users or automated machines and generates appropriate responses using a generative AI model. In parallel, an emotion engine recognizes emotions from the user's voice tone, facial expressions, and text input. The input is the question or instruction and emotion data received in step 4. Data calculations involve question analysis using natural language processing, emotion analysis using the emotion engine, and response generation using an AI model. The output is an appropriate response text or voice data according to the emotion.

[1591] Step 6:

[1592] The server displays or outputs the generated response to the user or automated machine. The input is the output of step 5. The specific operation is to display text or video on the HMD display and play audio using the audio output device. The output is the response received by the user or automated machine.

[1593] Step 7:

[1594] The server stores the emotion recognition results and dialogue data of the user and the automated machine in a database. The input data is the output of steps 5 and 6. As data storage, personalized user profiles and work logs are recorded in the database. The output is an updated database, which can be used for future dialogue and guidance.

[1595] 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.

[1596] 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.

[1597] 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.

[1598] 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.

[1599] 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.

[1600] 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.

[1601] 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).

[1602] 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.

[1603] 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."

[1604] 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.

[1605] 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).

[1606] 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.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] 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.

[1612] 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.

[1613] 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.

[1614] 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.

[1615] 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.

[1616] The following is further disclosed regarding the above embodiment.

[1617] (Claim 1)

[1618] Means of collecting videos, photos, and chat data of the deceased;

[1619] analytical means for extracting facial features, vocal features, and language patterns of the deceased from the collected data;

[1620] a generative AI means for generating a digital clone of the deceased person based on the extracted features; and

[1621] means for providing an interface for a user to interact with the digital clone;

[1622] A system including a means for displaying or outputting by voice the response generated by the generation AI means to the user.

[1623] (Claim 2)

[1624] 10. The system of claim 1, comprising a generation means for recreating the face and voice of a deceased person using DeepFake technology.

[1625] (Claim 3)

[1626] The system according to claim 1, further comprising an analysis means for analyzing the deceased's use of language and unique expressions using natural language processing technology.

[1627] "Example 1"

[1628] (Claim 1)

[1629] A means for collecting image data, audio data, and text data of the deceased;

[1630] an analytical means for extracting facial features, vocal features, and language patterns of the deceased from the collected data;

[1631] a generative artificial intelligence means for generating a digital clone of the deceased person based on the extracted features;

[1632] means for providing a user interface for a user to interact with the digital clone;

[1633] The system includes means for displaying or audibly outputting to the user the response generated by the generating artificial intelligence means.

[1634] (Claim 2)

[1635] 10. The system of claim 1, further comprising a generating means for recreating the face and voice of a deceased person using deepfake technology.

[1636] (Claim 3)

[1637] The system according to claim 1, further comprising an analysis means for analyzing the language characteristics and unique expressions of the deceased person using natural language processing technology.

[1638] "Application Example 1"

[1639] (Claim 1)

[1640] Means of collecting videos, photos, and chat data of the deceased;

[1641] analytical means for extracting facial features, vocal features, and language patterns of the deceased from the collected data;

[1642] a generative AI means for generating a digital clone of the deceased person based on the extracted features; and

[1643] means for providing an interface for a user to interact with the digital clone;

[1644] a means for displaying or outputting the response generated by the generating AI means to the user by voice;

[1645] A system including a virtual environment providing means for a user to enjoy conversation with a deceased person in a virtual space.

[1646] (Claim 2)

[1647] 10. The system of claim 1, comprising a generation means for recreating the face and voice of a deceased person using DeepFake technology.

[1648] (Claim 3)

[1649] The system according to claim 1, further comprising an analysis means for analyzing the deceased's use of language and unique expressions using natural language processing technology.

[1650] "Example 2: Combining Emotion Engines"

[1651] (Claim 1)

[1652] Means of collecting videos, photos, and chat data of the deceased;

[1653] analytical means for extracting facial features, vocal features, and language patterns of the deceased from the collected data;

[1654] a generative AI means for generating a digital clone of the deceased person based on the extracted features; and

[1655] means for providing an interface for a user to interact with the digital clone;

[1656] an emotion recognition means for recognizing emotions from a user's input or speech;

[1657] a means for adjusting the response generated by the generating AI means in accordance with the emotional state of the user;

[1658] a means for displaying or outputting the response generated by the generating AI means to the user by voice;

[1659] The system includes a means for storing the user's emotion recognition results in a database and creating a personalized user profile.

[1660] (Claim 2)

[1661] 10. The system of claim 1, comprising a generation means for recreating the face and voice of a deceased person using DeepFake technology.

[1662] (Claim 3)

[1663] The system according to claim 1, further comprising an analysis means for analyzing the deceased's use of language and unique expressions using natural language processing technology.

[1664] "Application example 2 when combining emotion engines"

[1665] (Claim 1)

[1666] Means of collecting videos, photos, and chat data of the deceased;

[1667] analytical means for extracting facial features, vocal features, and language patterns of the deceased from the collected data;

[1668] a generative AI means for generating a digital clone of the deceased person based on the extracted features; and

[1669] means for providing an interface for a user to interact with the digital clone;

[1670] a means for displaying or outputting the response generated by the generating AI means to the user by voice;

[1671] A system that includes a means of using digital clones to teach and instruct new employees and automated machines in the knowledge and skills of experts.

[1672] (Claim 2)

[1673] 10. The system of claim 1, comprising a generation means for recreating the face and voice of a deceased person using DeepFake technology.

[1674] (Claim 3)

[1675] The system according to claim 1, further comprising an analysis means for analyzing the deceased's use of language and unique expressions using natural language processing technology. [Explanation of symbols]

[1676] 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. Means of collecting videos, photos, and chat data of the deceased; analytical means for extracting facial features, vocal features, and language patterns of the deceased from the collected data; a generative AI means for generating a digital clone of the deceased person based on the extracted features; and means for providing an interface for a user to interact with the digital clone; A system including a means for displaying or outputting by voice the response generated by the generation AI means to the user.

2. The system of claim 1, comprising a generation means for recreating the face and voice of a deceased person using DeepFake technology.

3. The system according to claim 1, further comprising an analysis means for analyzing the deceased's use of language and idiosyncratic expressions using natural language processing technology.

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