System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving and storing data, such as daily utterances, conversations, thoughts, pictures, videos, and the like; means for pre-processing the stored data to convert audio data to text and remove noise; means for training a personality replication model using the pre-processed data; means for receiving a request from a user and generating a response with the trained personality replication model; and means for presenting the generated response 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] Conventional technology does not allow people to talk or consult with deceased or great figures, making it difficult to alleviate the feelings of loneliness and anxiety that result from this. Furthermore, there is a lack of efficient means to meet the demands of training and mental health care for individuals and companies. As a result, it is not possible to provide the peace of mind of knowing that someone is always nearby to consult with, hindering the improvement of people's quality of life. [Means for solving the problem]

[0005] To solve this problem, the present invention provides a system including means for receiving and saving data such as everyday remarks, conversations, thoughts, photos, and videos, means for preprocessing the saved data, converting voice data into text, and removing noise, means for training a personality reproduction model using the preprocessed data, means for receiving requests from a user and generating responses using the trained personality reproduction model, and means for presenting the generated responses to the user. Furthermore, by including means for performing virus scans and integrity checks on data provided by the user, and means for providing the generated responses to the user as voice through voice synthesis, a safer and more versatile system is realized.

[0006] "Data" refers to information provided in the form of everyday statements, conversations, thoughts, photographs, videos, etc., or a collection of such information.

[0007] "Receiving means" refers to a device or software that has the function of receiving data provided by a user and incorporating it into the system.

[0008] "Storage means" refers to a database or storage system that safely stores received data and allows it to be retrieved later.

[0009] "Preprocessing" refers to a series of operations that convert received data into a format suitable for training an AI model, including converting voice data into text, removing noise, and classifying data.

[0010] A "personality model" is an algorithm or computer program that allows a trained AI to mimic the language and thought patterns of a specific person and generate responses based on that person.

[0011] "Training" is the process of training an AI model using preprocessed data, with the goal of optimizing the model's parameters.

[0012] A "request" is information or a question that a user inputs to inquire or consult with the system.

[0013] "Generation means" is the function by which the AI ​​model generates an appropriate response based on the received request.

[0014] The "response presentation means" refers to an interface or device for providing the generated response to the user, and includes screen display and voice synthesis.

[0015] A "virus scan" is a security check to detect and remove harmful software lurking in data provided by users.

[0016] "Integrity checking" is the process of verifying that received data is uncorrupted and complete.

[0017] "Speech synthesis" is a technology that converts text data into speech that sounds like a human voice. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The system of the present invention allows users to converse with or consult with great or deceased people using ordinary remarks, conversations, thoughts, photographs, videos, etc. An embodiment of this system will be described below.

[0040] System configuration

[0041] The system consists of three main components: a server, a terminal, and a user.

[0042] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, and generate responses.

[0043] Terminal: Provides a user interface, collects data from the user, inputs requests, displays generated responses, and plays audio.

[0044] User: Uses the system to provide data and enter requests for conversations or consultations.

[0045] Program processing flow

[0046] 1. Data collection phase:

[0047] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and upload them to the server via their devices.

[0048] The terminal verifies and uploads the data.

[0049] 2. Data preprocessing phase:

[0050] The server stores the received data and performs virus scans and integrity checks.

[0051] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[0052] 3. Model training phase:

[0053] The server uses the preprocessed data to train a personality model, which learns the phrasing and thought patterns of a specific person.

[0054] The server adjusts the model parameters, performs optimization, evaluates the model's accuracy, and retrains it with additional data if necessary.

[0055] 4. Generation phase:

[0056] The user inputs a request for conversation or consultation into the terminal.

[0057] The terminal converts the input into a request format and sends it to the server.

[0058] The server receives the request and generates a response using the personality reproduction model.

[0059] The generated response is formatted in a way that is easy for the user to understand.

[0060] 5. Response Presentation Phase:

[0061] The server sends the formatted response to the terminal.

[0062] The terminal displays the received response and, if necessary, provides a voice response using speech synthesis.

[0063] Specific examples

[0064] 1. Questions for the deceased:

[0065] Suppose a user wants to ask a deceased family member for advice about a household problem.

[0066] The user inputs into the terminal, "There have been a lot of disagreements in my family recently. How should we deal with them?"

[0067] The terminal sends this request to the server.

[0068] Using a personality model of the deceased, the server generates a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[0069] The terminal displays the generated response on the screen and also plays it aloud.

[0070] 2. Advice from great people:

[0071] Suppose a user asks a historical figure for career advice.

[0072] The user types into the terminal, "How should I adjust to my new workplace?"

[0073] The terminal sends this request to the server.

[0074] Using a model of a great person's personality, the server generates a response: "It's important to observe the first few weeks and actively ask questions of your colleagues."

[0075] The terminal displays the generated response on the screen and, if necessary, provides the response via voice synthesis.

[0076] In this way, the system of the invention is designed to enable users to receive appropriate advice from great and deceased people in various situations, providing a sense of security and improving quality of life.

[0077] The processing flow will be explained below.

[0078] Step 1: Data collection phase

[0079] 1-1. User:

[0080] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and prepare this data on their device.

[0081] Check the data content and start uploading through the system interface.

[0082] 1-2. Device:

[0083] Receive data provided by a user.

[0084] When data is uploaded, the format and size are checked and it is prepared for sending to the server.

[0085] 1-3. Server:

[0086] Save the data received from the device.

[0087] After the initial save, the data is scanned for viruses and integrity checked.

[0088] Once it is deemed safe, the data is moved to a long-term storage database.

[0089] Step 2: Data preprocessing phase

[0090] 2-1. Server:

[0091] Extract the stored data and classify it into formats such as text, audio, images, and videos.

[0092] 2-2. Server:

[0093] The voice data is converted into text using voice recognition technology.

[0094] Natural language analysis is applied to the text data to perform semantic analysis and grammar checks.

[0095] Remove unnecessary information and noise.

[0096] 2-3. Server:

[0097] Image recognition technology is used to extract important scenes and text information from image and video data.

[0098] Associate the extracted information with other data.

[0099] 2-4. Server:

[0100] After preprocessing, the data is converted into a format suitable for AI learning and saved as a training dataset.

[0101] Step 3: Model training phase

[0102] 3-1. Server:

[0103] Input the training dataset into the AI ​​training module.

[0104] Train the data using a pre-built AI model (e.g., GPT-3).

[0105] 3-2. Server:

[0106] During the training process, the model parameters are adjusted and optimized.

[0107] Evaluate the performance of the trained model.

[0108] If necessary, retrain using additional data.

[0109] Step 4: Generate Phase

[0110] 4-1. User:

[0111] A user who wishes to have a conversation or consultation inputs a request through the terminal interface.

[0112] 4-2. Terminal:

[0113] Receives requests from users and converts them into the appropriate request format.

[0114] Send the request to the server.

[0115] 4-3. Server:

[0116] Receives requests and inputs them into the relevant personality representation model.

[0117] The AI ​​model generates the appropriate response.

[0118] 4-4. Server:

[0119] Format the generated response in a user-friendly format.

[0120] Sends the response to the terminal.

[0121] Step 5: Response Presentation Phase

[0122] 5-1. Terminal:

[0123] The response received from the server is parsed and displayed in the user interface.

[0124] If necessary, speech synthesis is performed to provide a voice response.

[0125] 5-2. User:

[0126] Review the proposed response and decide on the next action (e.g., ask again, arrange another consultation).

[0127] Through these steps, users can have conversations and consult with great people and deceased people, and can gain comfort and useful advice.

[0128] Example 1

[0129] 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."

[0130] In modern times, it is practically impossible to converse or consult with great or deceased figures. However, if we could simulate this, it would be possible to provide users with psychological support and advice. However, this requires efficient collection of past data and the generation of accurate responses based on a reliable model. To realize such a system, many technical challenges must be overcome.

[0131] 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.

[0132] In this invention, the server includes means for receiving and storing data such as everyday speech, conversation, thoughts, photos, and videos; means for preprocessing the stored data, converting voice data to text, and removing noise; and means for training a personality reproduction model using the preprocessed data. This makes it possible to effectively handle data of any format provided by the user and faithfully reproduce the vocabulary and thought patterns of a specific person. The server also includes means for performing virus scans and integrity checks on the data provided by the user, ensuring the security and reliability of the entire system. Furthermore, a means for providing the generated responses to the user as voice through speech synthesis can be provided, providing a more natural conversational experience.

[0133] "Means for receiving and storing" refers to the function of receiving data such as statements, conversations, thoughts, photos, and videos provided by users and storing them within the system.

[0134] "Preprocessing means" is a function that analyzes the stored data, performs necessary conversions and filtering, and prepares the data in a format suitable for subsequent processing.

[0135] "Means for converting voice data into text" is a function for converting voice data into text information.

[0136] "Means for removing noise" refers to a function that removes unnecessary noise and interference in the data to improve the quality of the data.

[0137] The "means for training a personality reproduction model" is a function that trains a machine learning model based on preprocessed data to generate responses that mimic a specific person.

[0138] The "means for receiving requests" is a function that allows a user to input questions or inquiries into the system and receives them for processing.

[0139] The "means for generating a response" is a function that uses a trained personality reproduction model to generate an appropriate response to a user request.

[0140] The "means for presenting to the user" is a function for displaying or reproducing the generated response in a format that is easy for the user to understand.

[0141] "Means for virus scanning and integrity checking" refers to a function that checks whether there are any security issues with data provided by users and verifies the integrity of the data.

[0142] "Means for providing speech by speech synthesis" is a function for converting a response in text format into speech and letting the user hear it.

[0143] MODE FOR CARRYING OUT THE INVENTION

[0144] The present invention enables users to have conversations and consultations with great people and deceased people using ordinary remarks, conversations, thoughts, photographs, videos, etc. The following describes in detail the mode for implementing this system.

[0145] This system consists of three main components: a server, a terminal, and a user.

[0146] server

[0147] The server is the heart of the system and is responsible for:

[0148] Receives, stores, scans for viruses and checks for integrity of data.

[0149] Preprocessing of received data is performed, converting voice data to text and removing noise.

[0150] Extract and associate important scenes and text information from photos and videos.

[0151] The preprocessed data is used to train a personality reconstruction model.

[0152] A request from a user is received and a response is generated using a personality reproduction model.

[0153] Formats the generated response and sends it to the terminal.

[0154] The server uses Python and TensorFlow to build and train machine learning models, the OpenCV library to analyze data, and the Google Cloud Speech-to-Text API to convert audio data.

[0155] Terminal

[0156] The terminal provides the user interface and is responsible for:

[0157] Data is collected from users, verified, and then uploaded to the server.

[0158] It provides an interface for users to input requests and send them to the server.

[0159] The generated response is received and displayed on the screen.

[0160] If necessary, speech synthesis is performed to provide a response to the user as speech.

[0161] The device uses common electronic devices such as smartphones and PCs, and these functions are realized through application software. For voice synthesis, the Google Text-to-Speech API is used.

[0162] User

[0163] Users use the system to:

[0164] Upload your data to seek advice on family issues or careers.

[0165] Input your request for dialogue or consultation into the terminal.

[0166] The generated response is received and used by display or audio.

[0167] Users can easily input their requests using the terminal interface and receive appropriate advice from the system.

[0168] Specific examples

[0169] 1. Questions for the deceased

[0170] When a user seeks advice from the deceased regarding a domestic problem, the user inputs into the terminal, "There have been many disagreements in the family recently. How should we deal with this?"

[0171] The terminal sends this request to the server.

[0172] Using a personality model of the deceased, the server generates a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[0173] The terminal displays the generated response on the screen and also plays it aloud.

[0174] Example prompt sentence:

[0175] "There have been a lot of disagreements in the family recently. How should I handle them?"

[0176] 2. Advice from great people

[0177] To seek career advice from a historical figure, a user would type "How should I adjust to a new job?" into the device.

[0178] The terminal sends this request to the server.

[0179] Using a model of a great person's personality, the server generates a response such as, "It's important to observe the first few weeks and actively ask questions of your colleagues."

[0180] The terminal displays the generated response on the screen and, if necessary, provides the response via voice synthesis.

[0181] Example prompt sentence:

[0182] How do I adjust to a new workplace?

[0183] In this way, this system allows users to improve their quality of life and receive spiritual support by receiving advice from great people and deceased people.

[0184] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0185] Step 1:

[0186] Data Collection Phase

[0187] Input: Data such as statements, conversations, thoughts, photos, videos, etc.

[0188] Process: The user collects data related to deceased or important people and uploads this data to the server using the device.

[0189] How it works: Users use their smartphones or computers to record speech, enter text from conversations, take photos and videos, and save them to cloud storage. The device then sends the collected data to a server via Wi-Fi.

[0190] Output: The data received by the server.

[0191] Step 2:

[0192] Data Preprocessing Phase

[0193] Input: Data such as received statements, conversations, thoughts, photos, videos, etc.

[0194] Processing: The server pre-processes the received data, which includes virus scanning and integrity checking, converting audio data to text, removing noise, and extracting important scenes and text information from photos and videos.

[0195] Specific operation: The server scans the data using virus scanning software such as Trend Micro, converts the audio data into text using the Google Cloud Speech-to-Text API, and extracts important scenes using the OpenCV library.

[0196] Output: Preprocessed data.

[0197] Step 3:

[0198] Model training phase

[0199] Input: Preprocessed data.

[0200] Processing: The server uses the preprocessed data to train a personality reproduction model, extracting features from the data and learning the language and thought patterns of a particular person.

[0201] How it works: The server uses Python and TensorFlow to build and train deep learning models, adjust hyperparameters using techniques such as grid search, and evaluate the accuracy of the models by performing cross-validation.

[0202] Output: A trained personality reproduction model.

[0203] Step 4:

[0204] Request Processing Phase

[0205] Input: User request (in text format).

[0206] Processing: The user inputs a request for conversation or consultation into the terminal. The terminal converts the input into a request format and sends it to the server.

[0207] What it does: A user opens the application on their smartphone and types "How can I adjust to a new job?" into the text box. The device converts this request into JSON format and sends it to the server.

[0208] Output: The request sent to the server.

[0209] Step 5:

[0210] Response Generation Phase

[0211] Input: The request sent to the server.

[0212] Processing: The server receives the request and uses the personality model to generate a response, which is then formatted in a way that is easy for the user to understand.

[0213] What it does: The server uses a generative AI model to generate an appropriate response based on the request, and the response is formatted in JSON.

[0214] Output: The formatted response.

[0215] Step 6:

[0216] Response presentation phase

[0217] Input: The formatted response.

[0218] Processing: The server sends the formatted response to the device, which displays the received response and plays it back as audio using speech synthesis if necessary.

[0219] Specific operation: The server sends the generated response as an HTTPS response. The device receives the response and displays the message "It's important to observe the first few weeks and actively ask questions of your colleagues." The device then uses the Google Text-to-Speech API to synthesize speech and play the response aloud.

[0220] Output: The response presented to the user (in text and audio format).

[0221] (Application example 1)

[0222] 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."

[0223] Today, there is a demand for technology that allows users to converse with deceased figures and historical figures. Conventional technologies require cumbersome data collection and processing to recreate the personalities of deceased figures and historical figures, and provide responses to users without a sense of realism. Furthermore, the generated responses are poorly synthesized, resulting in a lack of real-time performance. The present invention aims to solve these problems and provide a more realistic and immersive dialogue experience.

[0224] 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.

[0225] In this invention, the server includes means for receiving and storing data such as everyday remarks, conversations, thoughts, photos, and videos, means for preprocessing the stored data, converting voice data into text, and removing noise, means for training a personality reproduction model using the preprocessed data, means for receiving requests from users and generating responses using the trained personality reproduction model, means for presenting the generated responses to the users, and means for converting the generated responses into voice and playing them back, thereby enabling users to enjoy real-time conversations with deceased or great people.

[0226] "Data" refers to information such as statements, conversations, thoughts, photos, and videos provided by users.

[0227] "Preprocessing" refers to the process of analyzing the stored data, converting the audio data to text, and removing noise.

[0228] A "personality reproduction model" refers to a generative AI model that learns the language and thought patterns of a specific person based on preprocessed data.

[0229] A "request" refers to the content of a question or inquiry that a user inputs into the system.

[0230] "Response" refers to a reply generated by the personality reproduction model based on a user request.

[0231] "Speech synthesis" refers to the technology of converting text data into voice data.

[0232] "Virus scanning" refers to the process of checking whether data provided by a user contains viruses.

[0233] "Integrity check" refers to the process of verifying the consistency and completeness of data provided by a user.

[0234] The system of the present invention allows users to enjoy conversations and consultations with deceased or great figures, and a specific implementation method for this purpose will be described below. The main components of the system are a server, a terminal, and a user.

[0235] Server Roles

[0236] The server performs the following functions:

[0237] 1. Receiving and storing data: Receive data such as statements, conversations, thoughts, photos, and videos provided by users and store them in a database.

[0238] 2. Data preprocessing: Converting audio to text and removing noise from the stored data, as well as extracting and correlating important scenes and text information from photos and videos.

[0239] 3. Model training: Using the preprocessed data, we train a personality model. The model learns the characteristics and language of the specified deceased or great person.

[0240] 4. Request processing and response generation: Receives a request from the user and generates a response using the trained personality model.

[0241] 5. Send Response: The generated response is formatted for presentation to the user and sent to the terminal.

[0242] Device Role

[0243] The terminal provides the user interface and is responsible for:

[0244] 1. Data collection: Collect user statements, conversations, photos, and videos and upload them to the server.

[0245] 2. Request Input: Provide an interface that allows users to input requests for conversation or consultation.

[0246] 3. Displaying responses and playing them back: Displays the responses sent from the server and, if necessary, plays them back as audio using speech synthesis technology (such as gTTS).

[0247] Hardware and software used

[0248] Hardware: smartphone, head-mounted display (HMD), server.

[0249] Software: SpeechRecognition, gTTS (Google Text-to-Speech), Transformers library (using GPT-2 model).

[0250] Specific examples

[0251] Here are some concrete usage examples:

[0252] Example 1: Questions for the deceased

[0253] If a user seeks advice from a deceased family member about a domestic issue:

[0254] 1. The user types into the terminal, "There have been a lot of disagreements in my family recently. How should we deal with them?"

[0255] 2. The device sends this request to the server.

[0256] 3. The server uses a replica model of the deceased person's personality to generate a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[0257] 4. The device displays the generated response on the screen and plays it aloud using speech synthesis technology.

[0258] Example 2: Advice from great people

[0259] If a user asks a historical figure for career advice:

[0260] 1. The user types into the terminal, "How can I adjust to a new workplace?"

[0261] 2. The device sends this request to the server.

[0262] 3. Using a model of a great person's personality, the server generates a response: "It's important to observe the first few weeks and actively ask questions of your colleagues."

[0263] 4. The device displays the generated response on the screen and plays it aloud using speech synthesis technology.

[0264] In this way, users can enjoy a real-time interactive experience with deceased or great people.

[0265] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0266] Step 1:

[0267] Data collection and storage

[0268] Input: Users provide statements, conversations, thoughts, photos, videos, etc. to the device.

[0269] How it works: The device receives this data and uploads it to the server.

[0270] Output: Data stored on the server.

[0271] Step 2:

[0272] Data Preprocessing

[0273] Input: User data stored on the server.

[0274] How it works: The server converts audio data into text, removes noise, and extracts and correlates important scenes and text information from photos and videos.

[0275] Output: Preprocessed text data and related information.

[0276] Step 3:

[0277] Training the model

[0278] Input: Preprocessed data.

[0279] How it works: The server trains a personality model using the preprocessed data. It uses a generative AI model (e.g., GPT-2).

[0280] Output: A trained personality reproduction model.

[0281] Step 4:

[0282] Accepting user requests

[0283] Input: The user inputs a request (e.g., "How can I adjust to a new job?") through the terminal.

[0284] Operation: The terminal receives a request from the user and sends it to the server.

[0285] Output: The request sent to the server.

[0286] Step 5:

[0287] Generating a response

[0288] Input: User requests sent to the server and the trained personality model.

[0289] How it works: The server analyzes the request and uses its personality model to generate an appropriate response.

[0290] Output: The generated response sentence.

[0291] Step 6:

[0292] Presenting a response

[0293] Input: The generated response sentence.

[0294] What it does: The server formats the generated response into a user-friendly format and sends it to the device.

[0295] Output: The response sent to the terminal.

[0296] Step 7:

[0297] Speech synthesis and playback

[0298] Input: The response sent to the terminal.

[0299] Operation: The device uses speech synthesis technology (e.g., gTTS) to convert the response sentence into audio data and play it back.

[0300] Output: The user's on-screen response and the spoken response.

[0301] In this way, the input data is processed at each step, and finally a response is provided to the user in real time.

[0302] 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.

[0303] The present invention is a system that enables users to converse with or consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to make the responses provided more human-like and appropriate to the situation. The following describes an embodiment of this system.

[0304] System configuration

[0305] The system consists of the following main components: a server, a terminal, a user, and an emotion engine.

[0306] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, generate responses, and recognize emotions.

[0307] Terminal: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays or plays audio generated responses.

[0308] User: Uses the system to provide data and enter requests for conversations or consultations.

[0309] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server.

[0310] Program processing flow

[0311] 1. Data collection phase:

[0312] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and upload them to the server via their devices.

[0313] Example: A user uploads a video message or letter from a deceased person to the device.

[0314] 2. Data preprocessing phase:

[0315] The server stores the received data and performs virus scans and integrity checks.

[0316] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[0317] 3. Model training phase:

[0318] The server uses the preprocessed data to train a personality model, which learns the phrasing and thought patterns of a specific person.

[0319] Example: An AI model learns from the words and letters of the deceased person as training data.

[0320] 4. Emotion Recognition Phase:

[0321] The emotion engine analyzes emotions from the user's voice and text, and the analyzed emotion information is sent to the server.

[0322] Example: If a user says, "I've been feeling stressed at work lately," the emotion engine will recognize emotions like "stress" and "anxiety."

[0323] 5. Generation phase:

[0324] The user inputs a request for conversation or consultation into the terminal.

[0325] The terminal sends this request to the server.

[0326] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[0327] Example: Reflecting the user's feeling of "stress," the response generated is "First, take a deep breath and take some time to relax."

[0328] 6. Response Presentation Phase:

[0329] The server formats the generated response in a form that is easy for the user to understand.

[0330] The terminal displays the received response and, if necessary, provides a voice response using speech synthesis.

[0331] Example: The device displays the text "First, take a deep breath and take some time to relax" on the screen and simultaneously plays the audio.

[0332] This embodiment allows users to converse with and consult with great or deceased figures, and the emotion engine adjusts responses based on the user's emotions, resulting in more human-like responses. This allows users to feel more at ease and receive specific advice and support. Implementation of this system can support a wide range of applications, including mental health, education, and career counseling for individuals and companies.

[0333] The processing flow will be explained below.

[0334] The present invention is a system that allows users to converse and consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system provides responses that are more human-like and appropriate to the situation. The processing flow of this system is explained below step by step.

[0335] Program processing flow

[0336] Step 1: Data collection phase

[0337] 1-1. User:

[0338] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[0339] Prepare the collected data for uploading through the device interface.

[0340] 1-2. Device:

[0341] Receive data provided by a user.

[0342] The format and size of the data are checked and prepared for sending to the server.

[0343] 1-3. Server:

[0344] Receives data sent from the device and temporarily stores it.

[0345] Run a virus scan and check the integrity of your data.

[0346] Data that has been confirmed as secure is stored in a long-term database.

[0347] Step 2: Data preprocessing phase

[0348] 2-1. Server:

[0349] The stored data is extracted and classified into text data, audio data, image data, and video data.

[0350] 2-2. Server:

[0351] The voice data is converted into text using a voice recognition system.

[0352] Natural language processing is applied to text data to perform semantic analysis and grammar checks.

[0353] Remove noise and unnecessary information.

[0354] 2-3. Server:

[0355] Image recognition technology is used to extract important scenes and text information from image and video data.

[0356] Associate the extracted information with other data.

[0357] 2-4. Server:

[0358] After preprocessing, the data is converted into a format suitable for AI learning and saved as a training dataset.

[0359] Step 3: Model training phase

[0360] 3-1. Server:

[0361] The preprocessed data is input into the AI ​​training module.

[0362] Use an AI model (e.g., GPT-3) to train the data.

[0363] 3-2. Server:

[0364] During the training process, the parameters of the model are adjusted and optimized.

[0365] Evaluate the performance of the trained model and retrain it with additional data if necessary.

[0366] Step 4: Emotion Recognition Phase

[0367] 4-1. Terminal:

[0368] The user's voice and input text are sent to the emotion engine.

[0369] 4-2. Emotion Engine:

[0370] Analyze emotions from user voice and text.

[0371] The analyzed emotion information is sent to the server.

[0372] 4-3. Server:

[0373] Emotion information is received and stored in a database.

[0374] Step 5: Generate Phase

[0375] 5-1. User:

[0376] A user who wishes to have a conversation or consultation inputs a request through the terminal interface.

[0377] 5-2. Terminal:

[0378] Receives a user request and converts it into a request format.

[0379] Send the request to the server.

[0380] 5-3. Server:

[0381] Requests and emotional information are integrated and input into a personality representation model.

[0382] The AI ​​model generates the appropriate response.

[0383] 5-4. Server:

[0384] Format the generated response in a user-friendly format.

[0385] Sends the response to the terminal.

[0386] Step 6: Response Presentation Phase

[0387] 6-1. Terminal:

[0388] The response received from the server is parsed and displayed in the user interface.

[0389] If necessary, speech synthesis is performed to provide a response in voice.

[0390] 6-2. User:

[0391] Review the suggested responses.

[0392] Determine next actions or further requests as needed.

[0393] Examples:

[0394] 1. Family advice:

[0395] The user inputs into the terminal, "There have been a lot of disagreements in the family recently. How should we deal with them?"

[0396] The emotion engine analyzes the user's emotions, such as anxiety or confusion, and transmits them to the server.

[0397] The server uses a replica model of the deceased person's personality to generate a response such as, "Differences of opinion should be seen as opportunities for growth. Let's respect each other's opinions," and provides it in a warm, emotionally appropriate format.

[0398] The terminal displays the generated response on the screen and also plays it aloud.

[0399] This embodiment allows users to have conversations and consultations with great or deceased figures and deceased figures that are tailored based on their emotions, allowing them to receive more human-like responses, which gives users a sense of security and allows them to receive specific advice and mental support.

[0400] Example 2

[0401] 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."

[0402] Conventional conversation systems have had difficulty responding appropriately to the diversity of data provided by users and the fluctuations in their emotions, and generating human-like responses. Furthermore, to realize a dialogue with a deceased or great figure, it is necessary to reproduce the person's language and thought patterns and provide responses that adapt to the user's emotions, but there has been a lack of effective means to achieve this.

[0403] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and storing data such as everyday remarks, conversations, thoughts, photos, and videos, means for preprocessing the stored data, converting voice data into text, and removing noise, means for training a personality reproduction model using the preprocessed data, means for analyzing emotions from the user's voice and text, means for receiving a request from the user based on the emotion analysis result and generating a response using the trained personality reproduction model, and means for presenting the generated response to the user. This generates a human-like response according to the user's emotions, making it possible to have a more realistic conversation with deceased or great people.

[0404] "Receiving" means that a terminal or server obtains data or requests provided by a user.

[0405] "Storage" means to hold the received data in a storage device or database.

[0406] "Preprocessing" refers to analyzing and processing the received data and converting it into a format suitable for subsequent processing.

[0407] "Converting voice data to text" means extracting text information from a voice signal using voice recognition technology.

[0408] "Noise reduction" refers to removing unwanted noise and unnecessary information from audio data or other data.

[0409] A "personality reproduction model" is an artificial intelligence model that learns the language and thought patterns of a specific person and generates responses based on that.

[0410] "Emotion analysis" refers to identifying a user's mood or emotional state from their voice or text.

[0411] "Receiving a request" means acquiring a question or inquiry from a user.

[0412] "Generating a response" means creating an appropriate reply based on the received request and the results of sentiment analysis.

[0413] "Presenting" refers to visually or audibly indicating to the user the generated response.

[0414] "Virus scanning" means inspecting stored data for the presence of malware or viruses.

[0415] "Integrity checking" is the process of verifying that data is accurate and complete.

[0416] "Speech synthesis" is a technology that generates human speech from text data.

[0417] The present invention is a system that allows users to converse with or consult with great or deceased figures using data such as past statements, conversations, thoughts, photographs, and videos. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the responses provided become more human-like and appropriate to the situation. A specific embodiment for implementing this system is described below.

[0418] System configuration

[0419] The system consists of the following main components: a server, a terminal, a user, and an emotion engine.

[0420] Server: Plays a central role in receiving, storing, and preprocessing data, training the personality model, processing requests, generating responses, and recognizing emotions. It requires a high-performance server for hardware, and uses a database management system and machine learning libraries (e.g., TensorFlow and PyTorch) for training the AI ​​model for software.

[0421] Device: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays and plays generated responses. Examples include smartphones and PCs.

[0422] Users: use the system to provide data and enter requests for conversations or consultations. Users use dedicated applications.

[0423] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server. This uses natural language processing libraries for emotion recognition (e.g., NLTK and SpaCy) and voice analysis tools (e.g., Google Speech-to-Text API).

[0424] System Operation

[0425] 1. Data Collection:

[0426] Users use devices such as smartphones and PCs to upload data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[0427] Example: A user uploads a video message or letter from a deceased person to the device.

[0428] Sample prompt: The user says, "I'm uploading a message from a deceased loved one."

[0429] 2. Data Preprocessing:

[0430] The server stores the received data, performs virus scans and integrity checks, then converts the audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[0431] Example: A server uses OCR technology to extract text from a photo.

[0432] 3. Model training:

[0433] The server uses the preprocessed data to train a personality model, which then replicates the speech patterns and thought patterns of a specific person.

[0434] Example: The AI ​​model learns from the statements and letters of the deceased person as training data.

[0435] 4. Emotion recognition:

[0436] The emotion engine analyzes emotions from the user's voice and text and provides the results to the server.

[0437] Example: When a user says, "I've been feeling stressed at work lately," the emotion engine recognizes the emotions "stress" and "anxiety."

[0438] 5. Response Generation:

[0439] A user inputs a request for conversation or consultation into a terminal, which then transmits the request to a server.

[0440] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[0441] Example: Generate a response that reflects the user's emotion of "stress": "First, take a deep breath and take some time to relax."

[0442] 6. Response suggestions:

[0443] The server formats the generated response in a format that is easy for the user to understand, and the terminal displays the received response and, if necessary, performs speech synthesis to provide an audible response.

[0444] Example: The device displays the text "Take a moment to relax and take a deep breath" and plays it aloud at the same time.

[0445] This system allows users to converse and consult with deceased or great figures, and an emotion engine adjusts responses to match the user's emotions, resulting in a more human-like interaction.

[0446] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0447] Step 1:

[0448] Data collection:

[0449] Users use devices such as smartphones and PCs to upload data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[0450] Specific operation: The user launches the smartphone app, taps the "Data Upload" button, selects photos and videos of the deceased person from the gallery, and begins uploading.

[0451] Input: Media such as photos, videos, audio data, and text.

[0452] Output: Data stored on the device.

[0453] Step 2:

[0454] Data transmission:

[0455] The terminal transmits the received data to the server.

[0456] Specific operation: The device checks the network connection and sends the saved data to the server.

[0457] Input: Data stored on the device.

[0458] Output: The server receives the data.

[0459] Step 3:

[0460] Data preprocessing:

[0461] The server stores the received data and performs virus scans and integrity checks.

[0462] Specific operation: The server runs a virus scan software on the data received to check its integrity.

[0463] Input: Received photos, videos, audio data, and text.

[0464] Output: Pre-processed data that has been verified as safe.

[0465] Step 4:

[0466] Data Analysis:

[0467] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[0468] How it works: The server uses speech recognition software to convert speech to text, applies noise filtering algorithms, uses image recognition to extract key scenes from photos, and uses OCR technology to extract text.

[0469] Input: Pre-processed data that has been verified as safe.

[0470] Output: Parsed text data, clean audio data, extracted information.

[0471] Step 5:

[0472] Model training:

[0473] The server trains a personality reproduction model using the preprocessed data.

[0474] What it does: The server uses a machine learning library (e.g., TensorFlow or PyTorch) to train an AI model and iterate over the dataset.

[0475] Input: Analyzed text data and audio data.

[0476] Output: A trained personality reproduction model.

[0477] Step 6:

[0478] Emotion recognition:

[0479] The emotion engine analyzes emotions from the user's voice and text and provides the results to the server.

[0480] Specific operation: When a user says to the device, "I've been feeling stressed at work lately," the emotion engine analyzes the voice data and recognizes emotions such as "stress" and "anxiety."

[0481] Input: User's voice and text data.

[0482] Output: Parsed emotion information.

[0483] Step 7:

[0484] Request processing and response generation:

[0485] A user inputs a request for conversation or consultation into a terminal, which then transmits the request to a server.

[0486] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[0487] Specific operation: The user types "How can I reduce stress?" into the chat window, and the device sends the request to the server. The server generates a response using the personality reproduction model and emotional information.

[0488] Input: User request, emotion information, trained personality model.

[0489] Output: The generated response.

[0490] Step 8:

[0491] Response prompt:

[0492] The server formats the generated response in a user-friendly format and sends it to the device, which displays the received response and, if necessary, provides a spoken response using speech synthesis.

[0493] Specific operation: The server sends the text and audio data "First, take a deep breath and take some time to relax" to the device, which then displays and plays it back.

[0494] Input: The generated response data.

[0495] Output: The response (text and audio) presented to the user.

[0496] Step 9:

[0497] User Feedback:

[0498] Users can enter feedback on the responses they provide, which can be used to improve the system.

[0499] What happens: The user sends feedback saying "This response was helpful."

[0500] Input: User feedback.

[0501] Output: The feedback data is stored on the server and used for future improvements.

[0502] (Application example 2)

[0503] 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."

[0504] Conventional counseling support systems have difficulty generating responses that take into account the user's emotional state, making it difficult to provide advice that is appropriate for each individual user. Providing appropriate support that takes into account the client's past data and emotion analysis results is also a challenge. As a result, these systems are not sufficiently effective in improving the user's mental health or reducing stress.

[0505] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and saving data such as everyday remarks, conversations, thoughts, photos, and videos; means for preprocessing the saved data, converting voice data into text, and removing noise; means for training a personality reproduction model using the preprocessed data; means for analyzing requests from users with an emotion engine and acquiring emotional information; means for generating prompts based on the acquired emotional information and adjusting the generated responses; means for providing the generated responses to the user as voice through speech synthesis; and means for providing appropriate advice and support during counseling sessions based on the client's past data and emotion analysis results. This makes it possible to provide more personalized advice and support that takes the user's emotional state into consideration.

[0506] "Data" refers to information such as statements, conversations, thoughts, photographs, and videos obtained from a variety of sources.

[0507] "Preprocessing" refers to the process of analyzing the received data, converting the voice data into text, and removing noise.

[0508] A "personality reproduction model" is a machine learning model that reproduces the language and thought patterns of a specific person based on preprocessed data.

[0509] A "request" is an input of a question or conversation that a user makes to the system.

[0510] The "emotion engine" is a system component that analyzes emotions from the user's voice and text and obtains the results.

[0511] A "prompt sentence" is an input sentence that adjusts the response to be generated based on the emotional information analyzed by the emotion engine.

[0512] A "response" is a response from the system generated in response to a user request.

[0513] "Speech synthesis" is a technique that provides a generated response to a user as speech.

[0514] A "counseling session" is a time or opportunity for a client to consult or meet with a counselor.

[0515] "Past data" refers to records of information such as utterances, conversations, and emotional states that users have provided to the system in the past.

[0516] "Appropriate advice and support" refers to the most effective advice and support measures based on the user's emotional state and past data.

[0517] The present invention is a system that allows users to converse with or consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, by combining it with an emotion engine, the responses provided are more human-like and appropriate to the situation. Specific embodiments of this system are described below.

[0518] System configuration

[0519] The system consists of the following main components:

[0520] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, generate responses, and recognize emotions.

[0521] Terminal: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays or plays audio generated responses.

[0522] User: Uses the system to provide data and enter requests for conversations or consultations.

[0523] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server.

[0524] Specific processing flow

[0525] 1. Data collection and preprocessing:

[0526] Users upload data such as statements, thoughts, photos, and videos related to deceased or great people to the server via their devices.

[0527] The server stores and pre-processes the received data, which includes converting the audio data to text, removing noise, and extracting important information from photos and videos.

[0528] 2. Training the personality model:

[0529] Using the pre-processed data, the server trains a personality model that can learn the phrasing and thought patterns of a particular person.

[0530] 3. Emotion analysis:

[0531] The emotion engine analyzes the user's input data and recognizes the user's emotional state. This emotion information is sent to the server and used to generate appropriate responses.

[0532] 4. Generate and present the response:

[0533] When a user inputs a request, the server integrates the request with emotional information from the emotion engine and generates an appropriate response using a personality reproduction model.

[0534] The generated response is presented to the user as text and speech.

[0535] Hardware and software used

[0536] Hardware: Smartphone

[0537] software:

[0538] OpenAI API: Generate responses using generative AI models.

[0539] Emotional Analysis: Analyzes user emotions using an emotion engine.

[0540] Data Processor: Preprocesses and formats user data.

[0541] TextToSpeech: Text-to-speech technology to provide a generated response audibly.

[0542] Specific examples

[0543] As a concrete example, entering the following prompt sentence will cause the system to generate an appropriate response:

[0544] text

[0545] The user is experiencing the emotion of stress. Generate an appropriate response to the following request: I've been feeling stressed at work lately. What should I do?

[0546] This allows the server to generate a response such as "First, take a deep breath and take some time to relax," which is then provided to the user via voice synthesis technology.This system allows users to receive emotionally sensitive advice without meeting a counselor in person.

[0547] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0548] Step 1:

[0549] Users upload data such as statements, thoughts, photos, and videos related to deceased or great people to a server via their device. At this time, the device accepts data selection and upload operations through a user interface. Input data includes audio data, text data, image data, etc., and is sent to the server. As an output, a data file in the format saved on the server is generated.

[0550] Step 2:

[0551] The server stores the received data and performs preprocessing. Specifically, it scans the stored data for viruses and checks its integrity. It then converts the audio data into text and removes noise. For image and video data, it extracts important scenes and text information. The input is the data received in step 1, and the output is a clean, preprocessed dataset.

[0552] Step 3:

[0553] The server uses the preprocessed data to train a personality reproduction model. This process involves feeding the clean dataset into an AI training algorithm to learn the linguistic and thought patterns of a specific person. Specifically, the model is trained using the OpenAI API. The input is the preprocessed dataset, and the output is a trained personality reproduction model.

[0554] Step 4:

[0555] The emotion engine analyzes the user's input data and recognizes emotional information. The user inputs their consultation details via text or voice through the device. The device sends this input data to the server, which then activates the emotion engine to analyze the emotions. Emotional information is output as "stress," "anxiety," "joy," etc. and sent to the server.

[0556] Step 5:

[0557] The user inputs a request for conversation or consultation into the terminal. The terminal sends this request to the server. The server integrates the request with the emotional information sent from the emotion engine and generates a prompt. The input is the user's request and emotional information, and the output is the generated prompt.

[0558] Step 6:

[0559] The server uses the prompt sentence to generate an appropriate response using a personality reproduction model. The server inputs the prompt sentence into the generative AI model (OpenAI API) and generates a response. The input is the generated prompt sentence, and the output is an appropriate response text. As a concrete example, the following prompt sentence is used:

[0560] text

[0561] The user is experiencing the emotion of stress. Generate an appropriate response to the following request: I've been feeling stressed at work lately. What should I do?

[0562] Step 7:

[0563] The generated response is presented to the user. The terminal receives the response from the server, displays it as text, and plays it back as audio using speech synthesis technology. Specifically, it uses the TextToSpeech module to convert the generated text into speech. The input is the response text from the server, and the output is a response in audio and text format that the user can understand.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] [Second embodiment]

[0568] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0569] 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.

[0570] 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).

[0571] 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.

[0572] 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.

[0573] 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).

[0574] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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."

[0580] The system of the present invention allows users to converse with or consult with great or deceased people using ordinary remarks, conversations, thoughts, photographs, videos, etc. An embodiment of this system will be described below.

[0581] System configuration

[0582] The system consists of three main components: a server, a terminal, and a user.

[0583] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, and generate responses.

[0584] Terminal: Provides a user interface, collects data from the user, inputs requests, displays generated responses, and plays audio.

[0585] User: Uses the system to provide data and enter requests for conversations or consultations.

[0586] Program processing flow

[0587] 1. Data collection phase:

[0588] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and upload them to the server via their devices.

[0589] The terminal verifies and uploads the data.

[0590] 2. Data preprocessing phase:

[0591] The server stores the received data and performs virus scans and integrity checks.

[0592] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[0593] 3. Model training phase:

[0594] The server uses the preprocessed data to train a personality model, which learns the phrasing and thought patterns of a specific person.

[0595] The server adjusts the model parameters, performs optimization, evaluates the model's accuracy, and retrains it with additional data if necessary.

[0596] 4. Generation phase:

[0597] The user inputs a request for conversation or consultation into the terminal.

[0598] The terminal converts the input into a request format and sends it to the server.

[0599] The server receives the request and generates a response using the personality reproduction model.

[0600] The generated response is formatted in a way that is easy for the user to understand.

[0601] 5. Response Presentation Phase:

[0602] The server sends the formatted response to the terminal.

[0603] The terminal displays the received response and, if necessary, provides a voice response using speech synthesis.

[0604] Specific examples

[0605] 1. Questions for the deceased:

[0606] Suppose a user wants to ask a deceased family member for advice about a household problem.

[0607] The user inputs into the terminal, "There have been a lot of disagreements in my family recently. How should we deal with them?"

[0608] The terminal sends this request to the server.

[0609] Using a personality model of the deceased, the server generates a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[0610] The terminal displays the generated response on the screen and also plays it aloud.

[0611] 2. Advice from great people:

[0612] Suppose a user asks a historical figure for career advice.

[0613] The user types into the terminal, "How should I adjust to my new workplace?"

[0614] The terminal sends this request to the server.

[0615] Using a model of a great person's personality, the server generates a response: "It's important to observe the first few weeks and actively ask questions of your colleagues."

[0616] The terminal displays the generated response on the screen and, if necessary, provides the response via voice synthesis.

[0617] In this way, the system of the invention is designed to enable users to receive appropriate advice from great and deceased people in various situations, providing a sense of security and improving quality of life.

[0618] The processing flow will be explained below.

[0619] Step 1: Data collection phase

[0620] 1-1. User:

[0621] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and prepare this data on their device.

[0622] Check the data content and start uploading through the system interface.

[0623] 1-2. Device:

[0624] Receive data provided by a user.

[0625] When data is uploaded, the format and size are checked and it is prepared for sending to the server.

[0626] 1-3. Server:

[0627] Save the data received from the device.

[0628] After the initial save, the data is scanned for viruses and integrity checked.

[0629] Once it is deemed safe, the data is moved to a long-term storage database.

[0630] Step 2: Data preprocessing phase

[0631] 2-1. Server:

[0632] Extract the stored data and classify it into formats such as text, audio, images, and videos.

[0633] 2-2. Server:

[0634] The voice data is converted into text using voice recognition technology.

[0635] Natural language analysis is applied to the text data to perform semantic analysis and grammar checks.

[0636] Remove unnecessary information and noise.

[0637] 2-3. Server:

[0638] Image recognition technology is used to extract important scenes and text information from image and video data.

[0639] Associate the extracted information with other data.

[0640] 2-4. Server:

[0641] After preprocessing, the data is converted into a format suitable for AI learning and saved as a training dataset.

[0642] Step 3: Model training phase

[0643] 3-1. Server:

[0644] Input the training dataset into the AI ​​training module.

[0645] Train the data using a pre-built AI model (e.g., GPT-3).

[0646] 3-2. Server:

[0647] During the training process, the model parameters are adjusted and optimized.

[0648] Evaluate the performance of the trained model.

[0649] If necessary, retrain using additional data.

[0650] Step 4: Generate Phase

[0651] 4-1. User:

[0652] A user who wishes to have a conversation or consultation inputs a request through the terminal interface.

[0653] 4-2. Terminal:

[0654] Receives requests from users and converts them into the appropriate request format.

[0655] Send the request to the server.

[0656] 4-3. Server:

[0657] Receives requests and inputs them into the relevant personality representation model.

[0658] The AI ​​model generates the appropriate response.

[0659] 4-4. Server:

[0660] Format the generated response in a user-friendly format.

[0661] Sends the response to the terminal.

[0662] Step 5: Response Presentation Phase

[0663] 5-1. Terminal:

[0664] The response received from the server is parsed and displayed in the user interface.

[0665] If necessary, speech synthesis is performed to provide a voice response.

[0666] 5-2. User:

[0667] Review the proposed response and decide on the next action (e.g., ask again, arrange another consultation).

[0668] Through these steps, users can have conversations and consult with great people and deceased people, and can gain comfort and useful advice.

[0669] Example 1

[0670] 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."

[0671] In modern times, it is practically impossible to converse or consult with great or deceased figures. However, if we could simulate this, it would be possible to provide users with psychological support and advice. However, this requires efficient collection of past data and the generation of accurate responses based on a reliable model. To realize such a system, many technical challenges must be overcome.

[0672] 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.

[0673] In this invention, the server includes means for receiving and storing data such as everyday speech, conversation, thoughts, photos, and videos; means for preprocessing the stored data, converting voice data to text, and removing noise; and means for training a personality reproduction model using the preprocessed data. This makes it possible to effectively handle data of any format provided by the user and faithfully reproduce the vocabulary and thought patterns of a specific person. The server also includes means for performing virus scans and integrity checks on the data provided by the user, ensuring the security and reliability of the entire system. Furthermore, a means for providing the generated responses to the user as voice through speech synthesis can be provided, providing a more natural conversational experience.

[0674] "Means for receiving and storing" refers to the function of receiving data such as statements, conversations, thoughts, photos, and videos provided by users and storing them within the system.

[0675] "Preprocessing means" is a function that analyzes the stored data, performs necessary conversions and filtering, and prepares the data in a format suitable for subsequent processing.

[0676] "Means for converting voice data into text" is a function for converting voice data into text information.

[0677] "Means for removing noise" refers to a function that removes unnecessary noise and interference in the data to improve the quality of the data.

[0678] The "means for training a personality reproduction model" is a function that trains a machine learning model based on preprocessed data to generate responses that mimic a specific person.

[0679] The "means for receiving requests" is a function that allows a user to input questions or inquiries into the system and receives them for processing.

[0680] The "means for generating a response" is a function that uses a trained personality reproduction model to generate an appropriate response to a user request.

[0681] The "means for presenting to the user" is a function for displaying or reproducing the generated response in a format that is easy for the user to understand.

[0682] "Means for virus scanning and integrity checking" refers to a function that checks whether there are any security issues with data provided by users and verifies the integrity of the data.

[0683] "Means for providing speech by speech synthesis" is a function for converting a response in text format into speech and letting the user hear it.

[0684] MODE FOR CARRYING OUT THE INVENTION

[0685] The present invention enables users to have conversations and consultations with great people and deceased people using ordinary remarks, conversations, thoughts, photographs, videos, etc. The following describes in detail the mode for implementing this system.

[0686] This system consists of three main components: a server, a terminal, and a user.

[0687] server

[0688] The server is the heart of the system and is responsible for:

[0689] Receives, stores, scans for viruses and checks for integrity of data.

[0690] Preprocessing of received data is performed, converting voice data to text and removing noise.

[0691] Extract and associate important scenes and text information from photos and videos.

[0692] The preprocessed data is used to train a personality reconstruction model.

[0693] A request from a user is received and a response is generated using a personality reproduction model.

[0694] Formats the generated response and sends it to the terminal.

[0695] The server uses Python and TensorFlow to build and train machine learning models, the OpenCV library to analyze data, and the Google Cloud Speech-to-Text API to convert audio data.

[0696] Terminal

[0697] The terminal provides the user interface and is responsible for:

[0698] Data is collected from users, verified, and then uploaded to the server.

[0699] It provides an interface for users to input requests and send them to the server.

[0700] The generated response is received and displayed on the screen.

[0701] If necessary, speech synthesis is performed to provide a response to the user as speech.

[0702] The device uses common electronic devices such as smartphones and PCs, and these functions are realized through application software. For voice synthesis, the Google Text-to-Speech API is used.

[0703] User

[0704] Users use the system to:

[0705] Upload your data to seek advice on family issues or careers.

[0706] Input your request for dialogue or consultation into the terminal.

[0707] The generated response is received and used by display or audio.

[0708] Users can easily input their requests using the terminal interface and receive appropriate advice from the system.

[0709] Specific examples

[0710] 1. Questions for the deceased

[0711] When a user seeks advice from the deceased regarding a domestic problem, the user inputs into the terminal, "There have been many disagreements in the family recently. How should we deal with this?"

[0712] The terminal sends this request to the server.

[0713] Using a personality model of the deceased, the server generates a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[0714] The terminal displays the generated response on the screen and also plays it aloud.

[0715] Example prompt sentence:

[0716] "There have been a lot of disagreements in the family recently. How should I handle them?"

[0717] 2. Advice from great people

[0718] To seek career advice from a historical figure, a user would type "How should I adjust to a new job?" into the device.

[0719] The terminal sends this request to the server.

[0720] Using a model of a great person's personality, the server generates a response such as, "It's important to observe the first few weeks and actively ask questions of your colleagues."

[0721] The terminal displays the generated response on the screen and, if necessary, provides the response via voice synthesis.

[0722] Example prompt sentence:

[0723] How do I adjust to a new workplace?

[0724] In this way, this system allows users to improve their quality of life and receive spiritual support by receiving advice from great people and deceased people.

[0725] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0726] Step 1:

[0727] Data Collection Phase

[0728] Input: Data such as statements, conversations, thoughts, photos, videos, etc.

[0729] Process: The user collects data related to deceased or important people and uploads this data to the server using the device.

[0730] How it works: Users use their smartphones or computers to record speech, enter text from conversations, take photos and videos, and save them to cloud storage. The device then sends the collected data to a server via Wi-Fi.

[0731] Output: The data received by the server.

[0732] Step 2:

[0733] Data Preprocessing Phase

[0734] Input: Data such as received statements, conversations, thoughts, photos, videos, etc.

[0735] Processing: The server pre-processes the received data, which includes virus scanning and integrity checking, converting audio data to text, removing noise, and extracting important scenes and text information from photos and videos.

[0736] Specific operation: The server scans the data using virus scanning software such as Trend Micro, converts the audio data into text using the Google Cloud Speech-to-Text API, and extracts important scenes using the OpenCV library.

[0737] Output: Preprocessed data.

[0738] Step 3:

[0739] Model training phase

[0740] Input: Preprocessed data.

[0741] Processing: The server uses the preprocessed data to train a personality reproduction model, extracting features from the data and learning the language and thought patterns of a particular person.

[0742] How it works: The server uses Python and TensorFlow to build and train deep learning models, adjust hyperparameters using techniques such as grid search, and evaluate the accuracy of the models by performing cross-validation.

[0743] Output: A trained personality reproduction model.

[0744] Step 4:

[0745] Request Processing Phase

[0746] Input: User request (in text format).

[0747] Processing: The user inputs a request for conversation or consultation into the terminal. The terminal converts the input into a request format and sends it to the server.

[0748] What it does: A user opens the application on their smartphone and types "How can I adjust to a new job?" into the text box. The device converts this request into JSON format and sends it to the server.

[0749] Output: The request sent to the server.

[0750] Step 5:

[0751] Response Generation Phase

[0752] Input: The request sent to the server.

[0753] Processing: The server receives the request and uses the personality model to generate a response, which is then formatted in a way that is easy for the user to understand.

[0754] What it does: The server uses a generative AI model to generate an appropriate response based on the request, and the response is formatted in JSON.

[0755] Output: The formatted response.

[0756] Step 6:

[0757] Response presentation phase

[0758] Input: The formatted response.

[0759] Processing: The server sends the formatted response to the device, which displays the received response and plays it back as audio using speech synthesis if necessary.

[0760] Specific operation: The server sends the generated response as an HTTPS response. The device receives the response and displays the message "It's important to observe the first few weeks and actively ask questions of your colleagues." The device then uses the Google Text-to-Speech API to synthesize speech and play the response aloud.

[0761] Output: The response presented to the user (in text and audio format).

[0762] (Application example 1)

[0763] 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."

[0764] Today, there is a demand for technology that allows users to converse with deceased figures and historical figures. Conventional technologies require cumbersome data collection and processing to recreate the personalities of deceased figures and historical figures, and provide responses to users without a sense of realism. Furthermore, the generated responses are poorly synthesized, resulting in a lack of real-time performance. The present invention aims to solve these problems and provide a more realistic and immersive dialogue experience.

[0765] 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.

[0766] In this invention, the server includes means for receiving and storing data such as everyday remarks, conversations, thoughts, photos, and videos, means for preprocessing the stored data, converting voice data into text, and removing noise, means for training a personality reproduction model using the preprocessed data, means for receiving requests from users and generating responses using the trained personality reproduction model, means for presenting the generated responses to the users, and means for converting the generated responses into voice and playing them back, thereby enabling users to enjoy real-time conversations with deceased or great people.

[0767] "Data" refers to information such as statements, conversations, thoughts, photos, and videos provided by users.

[0768] "Preprocessing" refers to the process of analyzing the stored data, converting the audio data to text, and removing noise.

[0769] A "personality reproduction model" refers to a generative AI model that learns the language and thought patterns of a specific person based on preprocessed data.

[0770] A "request" refers to the content of a question or inquiry that a user inputs into the system.

[0771] "Response" refers to a reply generated by the personality reproduction model based on a user request.

[0772] "Speech synthesis" refers to the technology of converting text data into voice data.

[0773] "Virus scanning" refers to the process of checking whether data provided by a user contains viruses.

[0774] "Integrity check" refers to the process of verifying the consistency and completeness of data provided by a user.

[0775] The system of the present invention allows users to enjoy conversations and consultations with deceased or great figures, and a specific implementation method for this purpose will be described below. The main components of the system are a server, a terminal, and a user.

[0776] Server Roles

[0777] The server performs the following functions:

[0778] 1. Receiving and storing data: Receive data such as statements, conversations, thoughts, photos, and videos provided by users and store them in a database.

[0779] 2. Data preprocessing: Converting audio to text and removing noise from the stored data, as well as extracting and correlating important scenes and text information from photos and videos.

[0780] 3. Model training: Using the preprocessed data, we train a personality model. The model learns the characteristics and language of the specified deceased or great person.

[0781] 4. Request processing and response generation: Receives a request from the user and generates a response using the trained personality model.

[0782] 5. Send Response: The generated response is formatted for presentation to the user and sent to the terminal.

[0783] Device Role

[0784] The terminal provides the user interface and is responsible for:

[0785] 1. Data collection: Collect user statements, conversations, photos, and videos and upload them to the server.

[0786] 2. Request Input: Provide an interface that allows users to input requests for conversation or consultation.

[0787] 3. Displaying responses and playing them back: Displays the responses sent from the server and, if necessary, plays them back as audio using speech synthesis technology (such as gTTS).

[0788] Hardware and software used

[0789] Hardware: smartphone, head-mounted display (HMD), server.

[0790] Software: SpeechRecognition, gTTS (Google Text-to-Speech), Transformers library (using GPT-2 model).

[0791] Specific examples

[0792] Here are some concrete usage examples:

[0793] Example 1: Questions for the deceased

[0794] If a user seeks advice from a deceased family member about a domestic issue:

[0795] 1. The user types into the terminal, "There have been a lot of disagreements in my family recently. How should we deal with them?"

[0796] 2. The device sends this request to the server.

[0797] 3. The server uses a replica model of the deceased person's personality to generate a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[0798] 4. The device displays the generated response on the screen and plays it aloud using speech synthesis technology.

[0799] Example 2: Advice from great people

[0800] If a user asks a historical figure for career advice:

[0801] 1. The user types into the terminal, "How can I adjust to a new workplace?"

[0802] 2. The device sends this request to the server.

[0803] 3. Using a model of a great person's personality, the server generates a response: "It's important to observe the first few weeks and actively ask questions of your colleagues."

[0804] 4. The device displays the generated response on the screen and plays it aloud using speech synthesis technology.

[0805] In this way, users can enjoy a real-time interactive experience with deceased or great people.

[0806] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0807] Step 1:

[0808] Data collection and storage

[0809] Input: Users provide statements, conversations, thoughts, photos, videos, etc. to the device.

[0810] How it works: The device receives this data and uploads it to the server.

[0811] Output: Data stored on the server.

[0812] Step 2:

[0813] Data Preprocessing

[0814] Input: User data stored on the server.

[0815] How it works: The server converts audio data into text, removes noise, and extracts and correlates important scenes and text information from photos and videos.

[0816] Output: Preprocessed text data and related information.

[0817] Step 3:

[0818] Training the model

[0819] Input: Preprocessed data.

[0820] How it works: The server trains a personality model using the preprocessed data. It uses a generative AI model (e.g., GPT-2).

[0821] Output: A trained personality reproduction model.

[0822] Step 4:

[0823] Accepting user requests

[0824] Input: The user inputs a request (e.g., "How can I adjust to a new job?") through the terminal.

[0825] Operation: The terminal receives a request from the user and sends it to the server.

[0826] Output: The request sent to the server.

[0827] Step 5:

[0828] Generating a response

[0829] Input: User requests sent to the server and the trained personality model.

[0830] How it works: The server analyzes the request and uses its personality model to generate an appropriate response.

[0831] Output: The generated response sentence.

[0832] Step 6:

[0833] Presenting a response

[0834] Input: The generated response sentence.

[0835] What it does: The server formats the generated response into a user-friendly format and sends it to the device.

[0836] Output: The response sent to the terminal.

[0837] Step 7:

[0838] Speech synthesis and playback

[0839] Input: The response sent to the terminal.

[0840] Operation: The device uses speech synthesis technology (e.g., gTTS) to convert the response sentence into audio data and play it back.

[0841] Output: The user's on-screen response and the spoken response.

[0842] In this way, the input data is processed at each step, and finally a response is provided to the user in real time.

[0843] 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.

[0844] The present invention is a system that enables users to converse with or consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to make the responses provided more human-like and appropriate to the situation. The following describes an embodiment of this system.

[0845] System configuration

[0846] The system consists of the following main components: a server, a terminal, a user, and an emotion engine.

[0847] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, generate responses, and recognize emotions.

[0848] Terminal: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays or plays audio generated responses.

[0849] User: Uses the system to provide data and enter requests for conversations or consultations.

[0850] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server.

[0851] Program processing flow

[0852] 1. Data collection phase:

[0853] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and upload them to the server via their devices.

[0854] Example: A user uploads a video message or letter from a deceased person to the device.

[0855] 2. Data preprocessing phase:

[0856] The server stores the received data and performs virus scans and integrity checks.

[0857] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[0858] 3. Model training phase:

[0859] The server uses the preprocessed data to train a personality model, which learns the phrasing and thought patterns of a specific person.

[0860] Example: An AI model learns from the words and letters of the deceased person as training data.

[0861] 4. Emotion Recognition Phase:

[0862] The emotion engine analyzes emotions from the user's voice and text, and the analyzed emotion information is sent to the server.

[0863] Example: If a user says, "I've been feeling stressed at work lately," the emotion engine will recognize emotions like "stress" and "anxiety."

[0864] 5. Generation phase:

[0865] The user inputs a request for conversation or consultation into the terminal.

[0866] The terminal sends this request to the server.

[0867] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[0868] Example: Reflecting the user's feeling of "stress," the response generated is "First, take a deep breath and take some time to relax."

[0869] 6. Response Presentation Phase:

[0870] The server formats the generated response in a form that is easy for the user to understand.

[0871] The terminal displays the received response and, if necessary, provides a voice response using speech synthesis.

[0872] Example: The device displays the text "First, take a deep breath and take some time to relax" on the screen and simultaneously plays the audio.

[0873] This embodiment allows users to converse with and consult with great or deceased figures, and the emotion engine adjusts responses based on the user's emotions, resulting in more human-like responses. This allows users to feel more at ease and receive specific advice and support. Implementation of this system can support a wide range of applications, including mental health, education, and career counseling for individuals and companies.

[0874] The processing flow will be explained below.

[0875] The present invention is a system that allows users to converse and consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system provides responses that are more human-like and appropriate to the situation. The processing flow of this system is explained below step by step.

[0876] Program processing flow

[0877] Step 1: Data collection phase

[0878] 1-1. User:

[0879] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[0880] Prepare the collected data for uploading through the device interface.

[0881] 1-2. Device:

[0882] Receive data provided by a user.

[0883] The format and size of the data are checked and prepared for sending to the server.

[0884] 1-3. Server:

[0885] Receives data sent from the device and temporarily stores it.

[0886] Run a virus scan and check the integrity of your data.

[0887] Data that has been confirmed as secure is stored in a long-term database.

[0888] Step 2: Data preprocessing phase

[0889] 2-1. Server:

[0890] The stored data is extracted and classified into text data, audio data, image data, and video data.

[0891] 2-2. Server:

[0892] The voice data is converted into text using a voice recognition system.

[0893] Natural language processing is applied to text data to perform semantic analysis and grammar checks.

[0894] Remove noise and unnecessary information.

[0895] 2-3. Server:

[0896] Image recognition technology is used to extract important scenes and text information from image and video data.

[0897] Associate the extracted information with other data.

[0898] 2-4. Server:

[0899] After preprocessing, the data is converted into a format suitable for AI learning and saved as a training dataset.

[0900] Step 3: Model training phase

[0901] 3-1. Server:

[0902] The preprocessed data is input into the AI ​​training module.

[0903] Use an AI model (e.g., GPT-3) to train the data.

[0904] 3-2. Server:

[0905] During the training process, the parameters of the model are adjusted and optimized.

[0906] Evaluate the performance of the trained model and retrain it with additional data if necessary.

[0907] Step 4: Emotion Recognition Phase

[0908] 4-1. Terminal:

[0909] The user's voice and input text are sent to the emotion engine.

[0910] 4-2. Emotion Engine:

[0911] Analyze emotions from user voice and text.

[0912] The analyzed emotion information is sent to the server.

[0913] 4-3. Server:

[0914] Emotion information is received and stored in a database.

[0915] Step 5: Generate Phase

[0916] 5-1. User:

[0917] A user who wishes to have a conversation or consultation inputs a request through the terminal interface.

[0918] 5-2. Terminal:

[0919] Receives a user request and converts it into a request format.

[0920] Send the request to the server.

[0921] 5-3. Server:

[0922] Requests and emotional information are integrated and input into a personality representation model.

[0923] The AI ​​model generates the appropriate response.

[0924] 5-4. Server:

[0925] Format the generated response in a user-friendly format.

[0926] Sends the response to the terminal.

[0927] Step 6: Response Presentation Phase

[0928] 6-1. Terminal:

[0929] The response received from the server is parsed and displayed in the user interface.

[0930] If necessary, speech synthesis is performed to provide a response in voice.

[0931] 6-2. User:

[0932] Review the suggested responses.

[0933] Determine next actions or further requests as needed.

[0934] Examples:

[0935] 1. Family advice:

[0936] The user inputs into the terminal, "There have been a lot of disagreements in the family recently. How should we deal with them?"

[0937] The emotion engine analyzes the user's emotions, such as anxiety or confusion, and transmits them to the server.

[0938] The server uses a replica model of the deceased person's personality to generate a response such as, "Differences of opinion should be seen as opportunities for growth. Let's respect each other's opinions," and provides it in a warm, emotionally appropriate format.

[0939] The terminal displays the generated response on the screen and also plays it aloud.

[0940] This embodiment allows users to have conversations and consultations with great or deceased figures and deceased figures that are tailored based on their emotions, allowing them to receive more human-like responses, which gives users a sense of security and allows them to receive specific advice and mental support.

[0941] Example 2

[0942] 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."

[0943] Conventional conversation systems have had difficulty responding appropriately to the diversity of data provided by users and the fluctuations in their emotions, and generating human-like responses. Furthermore, to realize a dialogue with a deceased or great figure, it is necessary to reproduce the person's language and thought patterns and provide responses that adapt to the user's emotions, but there has been a lack of effective means to achieve this.

[0944] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and storing data such as everyday remarks, conversations, thoughts, photos, and videos, means for preprocessing the stored data, converting voice data into text, and removing noise, means for training a personality reproduction model using the preprocessed data, means for analyzing emotions from the user's voice and text, means for receiving a request from the user based on the emotion analysis result and generating a response using the trained personality reproduction model, and means for presenting the generated response to the user. This generates a human-like response according to the user's emotions, making it possible to have a more realistic conversation with deceased or great people.

[0945] "Receiving" means that a terminal or server obtains data or requests provided by a user.

[0946] "Storage" means to hold the received data in a storage device or database.

[0947] "Preprocessing" refers to analyzing and processing the received data and converting it into a format suitable for subsequent processing.

[0948] "Converting voice data to text" means extracting text information from a voice signal using voice recognition technology.

[0949] "Noise reduction" refers to removing unwanted noise and unnecessary information from audio data or other data.

[0950] A "personality reproduction model" is an artificial intelligence model that learns the language and thought patterns of a specific person and generates responses based on that.

[0951] "Emotion analysis" refers to identifying a user's mood or emotional state from their voice or text.

[0952] "Receiving a request" means acquiring a question or inquiry from a user.

[0953] "Generating a response" means creating an appropriate reply based on the received request and the results of sentiment analysis.

[0954] "Presenting" refers to visually or audibly indicating to the user the generated response.

[0955] "Virus scanning" means inspecting stored data for the presence of malware or viruses.

[0956] "Integrity checking" is the process of verifying that data is accurate and complete.

[0957] "Speech synthesis" is a technology that generates human speech from text data.

[0958] The present invention is a system that allows users to converse with or consult with great or deceased figures using data such as past statements, conversations, thoughts, photographs, and videos. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the responses provided become more human-like and appropriate to the situation. A specific embodiment for implementing this system is described below.

[0959] System configuration

[0960] The system consists of the following main components: a server, a terminal, a user, and an emotion engine.

[0961] Server: Plays a central role in receiving, storing, and preprocessing data, training the personality model, processing requests, generating responses, and recognizing emotions. It requires a high-performance server for hardware, and uses a database management system and machine learning libraries (e.g., TensorFlow and PyTorch) for training the AI ​​model for software.

[0962] Device: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays and plays generated responses. Examples include smartphones and PCs.

[0963] Users: use the system to provide data and enter requests for conversations or consultations. Users use dedicated applications.

[0964] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server. This uses natural language processing libraries for emotion recognition (e.g., NLTK and SpaCy) and voice analysis tools (e.g., Google Speech-to-Text API).

[0965] System Operation

[0966] 1. Data Collection:

[0967] Users use devices such as smartphones and PCs to upload data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[0968] Example: A user uploads a video message or letter from a deceased person to the device.

[0969] Sample prompt: The user says, "I'm uploading a message from a deceased loved one."

[0970] 2. Data Preprocessing:

[0971] The server stores the received data, performs virus scans and integrity checks, then converts the audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[0972] Example: A server uses OCR technology to extract text from a photo.

[0973] 3. Model training:

[0974] The server uses the preprocessed data to train a personality model, which then replicates the speech patterns and thought patterns of a specific person.

[0975] Example: The AI ​​model learns from the statements and letters of the deceased person as training data.

[0976] 4. Emotion recognition:

[0977] The emotion engine analyzes emotions from the user's voice and text and provides the results to the server.

[0978] Example: When a user says, "I've been feeling stressed at work lately," the emotion engine recognizes the emotions "stress" and "anxiety."

[0979] 5. Response Generation:

[0980] A user inputs a request for conversation or consultation into a terminal, which then transmits the request to a server.

[0981] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[0982] Example: Generate a response that reflects the user's emotion of "stress": "First, take a deep breath and take some time to relax."

[0983] 6. Response suggestions:

[0984] The server formats the generated response in a format that is easy for the user to understand, and the terminal displays the received response and, if necessary, performs speech synthesis to provide an audible response.

[0985] Example: The device displays the text "Take a moment to relax and take a deep breath" and plays it aloud at the same time.

[0986] This system allows users to converse and consult with deceased or great figures, and an emotion engine adjusts responses to match the user's emotions, resulting in a more human-like interaction.

[0987] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0988] Step 1:

[0989] Data collection:

[0990] Users use devices such as smartphones and PCs to upload data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[0991] Specific operation: The user launches the smartphone app, taps the "Data Upload" button, selects photos and videos of the deceased person from the gallery, and begins uploading.

[0992] Input: Media such as photos, videos, audio data, and text.

[0993] Output: Data stored on the device.

[0994] Step 2:

[0995] Data transmission:

[0996] The terminal transmits the received data to the server.

[0997] Specific operation: The device checks the network connection and sends the saved data to the server.

[0998] Input: Data stored on the device.

[0999] Output: The server receives the data.

[1000] Step 3:

[1001] Data preprocessing:

[1002] The server stores the received data and performs virus scans and integrity checks.

[1003] Specific operation: The server runs a virus scan software on the data received to check its integrity.

[1004] Input: Received photos, videos, audio data, and text.

[1005] Output: Pre-processed data that has been verified as safe.

[1006] Step 4:

[1007] Data Analysis:

[1008] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[1009] How it works: The server uses speech recognition software to convert speech to text, applies noise filtering algorithms, uses image recognition to extract key scenes from photos, and uses OCR technology to extract text.

[1010] Input: Pre-processed data that has been verified as safe.

[1011] Output: Parsed text data, clean audio data, extracted information.

[1012] Step 5:

[1013] Model training:

[1014] The server trains a personality reproduction model using the preprocessed data.

[1015] What it does: The server uses a machine learning library (e.g., TensorFlow or PyTorch) to train an AI model and iterate over the dataset.

[1016] Input: Analyzed text data and audio data.

[1017] Output: A trained personality reproduction model.

[1018] Step 6:

[1019] Emotion recognition:

[1020] The emotion engine analyzes emotions from the user's voice and text and provides the results to the server.

[1021] Specific operation: When a user says to the device, "I've been feeling stressed at work lately," the emotion engine analyzes the voice data and recognizes emotions such as "stress" and "anxiety."

[1022] Input: User's voice and text data.

[1023] Output: Parsed emotion information.

[1024] Step 7:

[1025] Request processing and response generation:

[1026] A user inputs a request for conversation or consultation into a terminal, which then transmits the request to a server.

[1027] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[1028] Specific operation: The user types "How can I reduce stress?" into the chat window, and the device sends the request to the server. The server generates a response using the personality reproduction model and emotional information.

[1029] Input: User request, emotion information, trained personality model.

[1030] Output: The generated response.

[1031] Step 8:

[1032] Response prompt:

[1033] The server formats the generated response in a user-friendly format and sends it to the device, which displays the received response and, if necessary, provides a spoken response using speech synthesis.

[1034] Specific operation: The server sends the text and audio data "First, take a deep breath and take some time to relax" to the device, which then displays and plays it back.

[1035] Input: The generated response data.

[1036] Output: The response (text and audio) presented to the user.

[1037] Step 9:

[1038] User Feedback:

[1039] Users can enter feedback on the responses they provide, which can be used to improve the system.

[1040] What happens: The user sends feedback saying "This response was helpful."

[1041] Input: User feedback.

[1042] Output: The feedback data is stored on the server and used for future improvements.

[1043] (Application example 2)

[1044] 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."

[1045] Conventional counseling support systems have difficulty generating responses that take into account the user's emotional state, making it difficult to provide advice that is appropriate for each individual user. Providing appropriate support that takes into account the client's past data and emotion analysis results is also a challenge. As a result, these systems are not sufficiently effective in improving the user's mental health or reducing stress.

[1046] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and saving data such as everyday remarks, conversations, thoughts, photos, and videos; means for preprocessing the saved data, converting voice data into text, and removing noise; means for training a personality reproduction model using the preprocessed data; means for analyzing requests from users with an emotion engine and acquiring emotional information; means for generating prompts based on the acquired emotional information and adjusting the generated responses; means for providing the generated responses to the user as voice through speech synthesis; and means for providing appropriate advice and support during counseling sessions based on the client's past data and emotion analysis results. This makes it possible to provide more personalized advice and support that takes the user's emotional state into consideration.

[1047] "Data" refers to information such as statements, conversations, thoughts, photographs, and videos obtained from a variety of sources.

[1048] "Preprocessing" refers to the process of analyzing the received data, converting the voice data into text, and removing noise.

[1049] A "personality reproduction model" is a machine learning model that reproduces the language and thought patterns of a specific person based on preprocessed data.

[1050] A "request" is an input of a question or conversation that a user makes to the system.

[1051] The "emotion engine" is a system component that analyzes emotions from the user's voice and text and obtains the results.

[1052] A "prompt sentence" is an input sentence that adjusts the response to be generated based on the emotional information analyzed by the emotion engine.

[1053] A "response" is a response from the system generated in response to a user request.

[1054] "Speech synthesis" is a technique that provides a generated response to a user as speech.

[1055] A "counseling session" is a time or opportunity for a client to consult or meet with a counselor.

[1056] "Past data" refers to records of information such as utterances, conversations, and emotional states that users have provided to the system in the past.

[1057] "Appropriate advice and support" refers to the most effective advice and support measures based on the user's emotional state and past data.

[1058] The present invention is a system that allows users to converse with or consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, by combining it with an emotion engine, the responses provided are more human-like and appropriate to the situation. Specific embodiments of this system are described below.

[1059] System configuration

[1060] The system consists of the following main components:

[1061] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, generate responses, and recognize emotions.

[1062] Terminal: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays or plays audio generated responses.

[1063] User: Uses the system to provide data and enter requests for conversations or consultations.

[1064] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server.

[1065] Specific processing flow

[1066] 1. Data collection and preprocessing:

[1067] Users upload data such as statements, thoughts, photos, and videos related to deceased or great people to the server via their devices.

[1068] The server stores and pre-processes the received data, which includes converting the audio data to text, removing noise, and extracting important information from photos and videos.

[1069] 2. Training the personality model:

[1070] Using the pre-processed data, the server trains a personality model that can learn the phrasing and thought patterns of a particular person.

[1071] 3. Emotion analysis:

[1072] The emotion engine analyzes the user's input data and recognizes the user's emotional state. This emotion information is sent to the server and used to generate appropriate responses.

[1073] 4. Generate and present the response:

[1074] When a user inputs a request, the server integrates the request with emotional information from the emotion engine and generates an appropriate response using a personality reproduction model.

[1075] The generated response is presented to the user as text and speech.

[1076] Hardware and software used

[1077] Hardware: Smartphone

[1078] software:

[1079] OpenAI API: Generate responses using generative AI models.

[1080] Emotional Analysis: Analyzes user emotions using an emotion engine.

[1081] Data Processor: Preprocesses and formats user data.

[1082] TextToSpeech: Text-to-speech technology to provide a generated response audibly.

[1083] Specific examples

[1084] As a concrete example, entering the following prompt sentence will cause the system to generate an appropriate response:

[1085] text

[1086] The user is experiencing the emotion of stress. Generate an appropriate response to the following request: I've been feeling stressed at work lately. What should I do?

[1087] This allows the server to generate a response such as "First, take a deep breath and take some time to relax," which is then provided to the user via voice synthesis technology.This system allows users to receive emotionally sensitive advice without meeting a counselor in person.

[1088] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1089] Step 1:

[1090] Users upload data such as statements, thoughts, photos, and videos related to deceased or great people to a server via their device. At this time, the device accepts data selection and upload operations through a user interface. Input data includes audio data, text data, image data, etc., and is sent to the server. As an output, a data file in the format saved on the server is generated.

[1091] Step 2:

[1092] The server stores the received data and performs preprocessing. Specifically, it scans the stored data for viruses and checks its integrity. It then converts the audio data into text and removes noise. For image and video data, it extracts important scenes and text information. The input is the data received in step 1, and the output is a clean, preprocessed dataset.

[1093] Step 3:

[1094] The server uses the preprocessed data to train a personality reproduction model. This process involves feeding the clean dataset into an AI training algorithm to learn the linguistic and thought patterns of a specific person. Specifically, the model is trained using the OpenAI API. The input is the preprocessed dataset, and the output is a trained personality reproduction model.

[1095] Step 4:

[1096] The emotion engine analyzes the user's input data and recognizes emotional information. The user inputs their consultation details via text or voice through the device. The device sends this input data to the server, which then activates the emotion engine to analyze the emotions. Emotional information is output as "stress," "anxiety," "joy," etc. and sent to the server.

[1097] Step 5:

[1098] The user inputs a request for conversation or consultation into the terminal. The terminal sends this request to the server. The server integrates the request with the emotional information sent from the emotion engine and generates a prompt. The input is the user's request and emotional information, and the output is the generated prompt.

[1099] Step 6:

[1100] The server uses the prompt sentence to generate an appropriate response using a personality reproduction model. The server inputs the prompt sentence into the generative AI model (OpenAI API) and generates a response. The input is the generated prompt sentence, and the output is an appropriate response text. As a concrete example, the following prompt sentence is used:

[1101] text

[1102] The user is experiencing the emotion of stress. Generate an appropriate response to the following request: I've been feeling stressed at work lately. What should I do?

[1103] Step 7:

[1104] The generated response is presented to the user. The terminal receives the response from the server, displays it as text, and plays it back as audio using speech synthesis technology. Specifically, it uses the TextToSpeech module to convert the generated text into speech. The input is the response text from the server, and the output is a response in audio and text format that the user can understand.

[1105] 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.

[1106] 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.

[1107] 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.

[1108] [Third embodiment]

[1109] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1110] 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.

[1111] 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).

[1112] 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.

[1113] 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.

[1114] 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).

[1115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] 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.

[1120] 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."

[1121] The system of the present invention allows users to converse with or consult with great or deceased people using ordinary remarks, conversations, thoughts, photographs, videos, etc. An embodiment of this system will be described below.

[1122] System configuration

[1123] The system consists of three main components: a server, a terminal, and a user.

[1124] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, and generate responses.

[1125] Terminal: Provides a user interface, collects data from the user, inputs requests, displays generated responses, and plays audio.

[1126] User: Uses the system to provide data and enter requests for conversations or consultations.

[1127] Program processing flow

[1128] 1. Data collection phase:

[1129] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and upload them to the server via their devices.

[1130] The terminal verifies and uploads the data.

[1131] 2. Data preprocessing phase:

[1132] The server stores the received data and performs virus scans and integrity checks.

[1133] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[1134] 3. Model training phase:

[1135] The server uses the preprocessed data to train a personality model, which learns the phrasing and thought patterns of a specific person.

[1136] The server adjusts the model parameters, performs optimization, evaluates the model's accuracy, and retrains it with additional data if necessary.

[1137] 4. Generation phase:

[1138] The user inputs a request for conversation or consultation into the terminal.

[1139] The terminal converts the input into a request format and sends it to the server.

[1140] The server receives the request and generates a response using the personality reproduction model.

[1141] The generated response is formatted in a way that is easy for the user to understand.

[1142] 5. Response Presentation Phase:

[1143] The server sends the formatted response to the terminal.

[1144] The terminal displays the received response and, if necessary, provides a voice response using speech synthesis.

[1145] Specific examples

[1146] 1. Questions for the deceased:

[1147] Suppose a user wants to ask a deceased family member for advice about a household problem.

[1148] The user inputs into the terminal, "There have been a lot of disagreements in my family recently. How should we deal with them?"

[1149] The terminal sends this request to the server.

[1150] Using a personality model of the deceased, the server generates a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[1151] The terminal displays the generated response on the screen and also plays it aloud.

[1152] 2. Advice from great people:

[1153] Suppose a user asks a historical figure for career advice.

[1154] The user types into the terminal, "How should I adjust to my new workplace?"

[1155] The terminal sends this request to the server.

[1156] Using a model of a great person's personality, the server generates a response: "It's important to observe the first few weeks and actively ask questions of your colleagues."

[1157] The terminal displays the generated response on the screen and, if necessary, provides the response via voice synthesis.

[1158] In this way, the system of the invention is designed to enable users to receive appropriate advice from great and deceased people in various situations, providing a sense of security and improving quality of life.

[1159] The processing flow will be explained below.

[1160] Step 1: Data collection phase

[1161] 1-1. User:

[1162] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and prepare this data on their device.

[1163] Check the data content and start uploading through the system interface.

[1164] 1-2. Device:

[1165] Receive data provided by a user.

[1166] When data is uploaded, the format and size are checked and it is prepared for sending to the server.

[1167] 1-3. Server:

[1168] Save the data received from the device.

[1169] After the initial save, the data is scanned for viruses and integrity checked.

[1170] Once it is deemed safe, the data is moved to a long-term storage database.

[1171] Step 2: Data preprocessing phase

[1172] 2-1. Server:

[1173] Extract the stored data and classify it into formats such as text, audio, images, and videos.

[1174] 2-2. Server:

[1175] The voice data is converted into text using voice recognition technology.

[1176] Natural language analysis is applied to the text data to perform semantic analysis and grammar checks.

[1177] Remove unnecessary information and noise.

[1178] 2-3. Server:

[1179] Image recognition technology is used to extract important scenes and text information from image and video data.

[1180] Associate the extracted information with other data.

[1181] 2-4. Server:

[1182] After preprocessing, the data is converted into a format suitable for AI learning and saved as a training dataset.

[1183] Step 3: Model training phase

[1184] 3-1. Server:

[1185] Input the training dataset into the AI ​​training module.

[1186] Train the data using a pre-built AI model (e.g., GPT-3).

[1187] 3-2. Server:

[1188] During the training process, the model parameters are adjusted and optimized.

[1189] Evaluate the performance of the trained model.

[1190] If necessary, retrain using additional data.

[1191] Step 4: Generate Phase

[1192] 4-1. User:

[1193] A user who wishes to have a conversation or consultation inputs a request through the terminal interface.

[1194] 4-2. Terminal:

[1195] Receives requests from users and converts them into the appropriate request format.

[1196] Send the request to the server.

[1197] 4-3. Server:

[1198] Receives requests and inputs them into the relevant personality representation model.

[1199] The AI ​​model generates the appropriate response.

[1200] 4-4. Server:

[1201] Format the generated response in a user-friendly format.

[1202] Sends the response to the terminal.

[1203] Step 5: Response Presentation Phase

[1204] 5-1. Terminal:

[1205] The response received from the server is parsed and displayed in the user interface.

[1206] If necessary, speech synthesis is performed to provide a voice response.

[1207] 5-2. User:

[1208] Review the proposed response and decide on the next action (e.g., ask again, arrange another consultation).

[1209] Through these steps, users can have conversations and consult with great people and deceased people, and can gain comfort and useful advice.

[1210] Example 1

[1211] 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."

[1212] In modern times, it is practically impossible to converse or consult with great or deceased figures. However, if we could simulate this, it would be possible to provide users with psychological support and advice. However, this requires efficient collection of past data and the generation of accurate responses based on a reliable model. To realize such a system, many technical challenges must be overcome.

[1213] 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.

[1214] In this invention, the server includes means for receiving and storing data such as everyday speech, conversation, thoughts, photos, and videos; means for preprocessing the stored data, converting voice data to text, and removing noise; and means for training a personality reproduction model using the preprocessed data. This makes it possible to effectively handle data of any format provided by the user and faithfully reproduce the vocabulary and thought patterns of a specific person. The server also includes means for performing virus scans and integrity checks on the data provided by the user, ensuring the security and reliability of the entire system. Furthermore, a means for providing the generated responses to the user as voice through speech synthesis can be provided, providing a more natural conversational experience.

[1215] "Means for receiving and storing" refers to the function of receiving data such as statements, conversations, thoughts, photos, and videos provided by users and storing them within the system.

[1216] "Preprocessing means" is a function that analyzes the stored data, performs necessary conversions and filtering, and prepares the data in a format suitable for subsequent processing.

[1217] "Means for converting voice data into text" is a function for converting voice data into text information.

[1218] "Means for removing noise" refers to a function that removes unnecessary noise and interference in the data to improve the quality of the data.

[1219] The "means for training a personality reproduction model" is a function that trains a machine learning model based on preprocessed data to generate responses that mimic a specific person.

[1220] The "means for receiving requests" is a function that allows a user to input questions or inquiries into the system and receives them for processing.

[1221] The "means for generating a response" is a function that uses a trained personality reproduction model to generate an appropriate response to a user request.

[1222] The "means for presenting to the user" is a function for displaying or reproducing the generated response in a format that is easy for the user to understand.

[1223] "Means for virus scanning and integrity checking" refers to a function that checks whether there are any security issues with data provided by users and verifies the integrity of the data.

[1224] "Means for providing speech by speech synthesis" is a function for converting a response in text format into speech and letting the user hear it.

[1225] MODE FOR CARRYING OUT THE INVENTION

[1226] The present invention enables users to have conversations and consultations with great people and deceased people using ordinary remarks, conversations, thoughts, photographs, videos, etc. The following describes in detail the mode for implementing this system.

[1227] This system consists of three main components: a server, a terminal, and a user.

[1228] server

[1229] The server is the heart of the system and is responsible for:

[1230] Receives, stores, scans for viruses and checks for integrity of data.

[1231] Preprocessing of received data is performed, converting voice data to text and removing noise.

[1232] Extract and associate important scenes and text information from photos and videos.

[1233] The preprocessed data is used to train a personality reconstruction model.

[1234] A request from a user is received and a response is generated using a personality reproduction model.

[1235] Formats the generated response and sends it to the terminal.

[1236] The server uses Python and TensorFlow to build and train machine learning models, the OpenCV library to analyze data, and the Google Cloud Speech-to-Text API to convert audio data.

[1237] Terminal

[1238] The terminal provides the user interface and is responsible for:

[1239] Data is collected from users, verified, and then uploaded to the server.

[1240] It provides an interface for users to input requests and send them to the server.

[1241] The generated response is received and displayed on the screen.

[1242] If necessary, speech synthesis is performed to provide a response to the user as speech.

[1243] The device uses common electronic devices such as smartphones and PCs, and these functions are realized through application software. For voice synthesis, the Google Text-to-Speech API is used.

[1244] User

[1245] Users use the system to:

[1246] Upload your data to seek advice on family issues or careers.

[1247] Input your request for dialogue or consultation into the terminal.

[1248] The generated response is received and used by display or audio.

[1249] Users can easily input their requests using the terminal interface and receive appropriate advice from the system.

[1250] Specific examples

[1251] 1. Questions for the deceased

[1252] When a user seeks advice from the deceased regarding a domestic problem, the user inputs into the terminal, "There have been many disagreements in the family recently. How should we deal with this?"

[1253] The terminal sends this request to the server.

[1254] Using a personality model of the deceased, the server generates a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[1255] The terminal displays the generated response on the screen and also plays it aloud.

[1256] Example prompt sentence:

[1257] "There have been a lot of disagreements in the family recently. How should I handle them?"

[1258] 2. Advice from great people

[1259] To seek career advice from a historical figure, a user would type "How should I adjust to a new job?" into the device.

[1260] The terminal sends this request to the server.

[1261] Using a model of a great person's personality, the server generates a response such as, "It's important to observe the first few weeks and actively ask questions of your colleagues."

[1262] The terminal displays the generated response on the screen and, if necessary, provides the response via voice synthesis.

[1263] Example prompt sentence:

[1264] How do I adjust to a new workplace?

[1265] In this way, this system allows users to improve their quality of life and receive spiritual support by receiving advice from great people and deceased people.

[1266] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1267] Step 1:

[1268] Data Collection Phase

[1269] Input: Data such as statements, conversations, thoughts, photos, videos, etc.

[1270] Process: The user collects data related to deceased or important people and uploads this data to the server using the device.

[1271] How it works: Users use their smartphones or computers to record speech, enter text from conversations, take photos and videos, and save them to cloud storage. The device then sends the collected data to a server via Wi-Fi.

[1272] Output: The data received by the server.

[1273] Step 2:

[1274] Data Preprocessing Phase

[1275] Input: Data such as received statements, conversations, thoughts, photos, videos, etc.

[1276] Processing: The server pre-processes the received data, which includes virus scanning and integrity checking, converting audio data to text, removing noise, and extracting important scenes and text information from photos and videos.

[1277] Specific operation: The server scans the data using virus scanning software such as Trend Micro, converts the audio data into text using the Google Cloud Speech-to-Text API, and extracts important scenes using the OpenCV library.

[1278] Output: Preprocessed data.

[1279] Step 3:

[1280] Model training phase

[1281] Input: Preprocessed data.

[1282] Processing: The server uses the preprocessed data to train a personality reproduction model, extracting features from the data and learning the language and thought patterns of a particular person.

[1283] How it works: The server uses Python and TensorFlow to build and train deep learning models, adjust hyperparameters using techniques such as grid search, and evaluate the accuracy of the models by performing cross-validation.

[1284] Output: A trained personality reproduction model.

[1285] Step 4:

[1286] Request Processing Phase

[1287] Input: User request (in text format).

[1288] Processing: The user inputs a request for conversation or consultation into the terminal. The terminal converts the input into a request format and sends it to the server.

[1289] What it does: A user opens the application on their smartphone and types "How can I adjust to a new job?" into the text box. The device converts this request into JSON format and sends it to the server.

[1290] Output: The request sent to the server.

[1291] Step 5:

[1292] Response Generation Phase

[1293] Input: The request sent to the server.

[1294] Processing: The server receives the request and uses the personality model to generate a response, which is then formatted in a way that is easy for the user to understand.

[1295] What it does: The server uses a generative AI model to generate an appropriate response based on the request, and the response is formatted in JSON.

[1296] Output: The formatted response.

[1297] Step 6:

[1298] Response presentation phase

[1299] Input: The formatted response.

[1300] Processing: The server sends the formatted response to the device, which displays the received response and plays it back as audio using speech synthesis if necessary.

[1301] Specific operation: The server sends the generated response as an HTTPS response. The device receives the response and displays the message "It's important to observe the first few weeks and actively ask questions of your colleagues." The device then uses the Google Text-to-Speech API to synthesize speech and play the response aloud.

[1302] Output: The response presented to the user (in text and audio format).

[1303] (Application example 1)

[1304] 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."

[1305] Today, there is a demand for technology that allows users to converse with deceased figures and historical figures. Conventional technologies require cumbersome data collection and processing to recreate the personalities of deceased figures and historical figures, and provide responses to users without a sense of realism. Furthermore, the generated responses are poorly synthesized, resulting in a lack of real-time performance. The present invention aims to solve these problems and provide a more realistic and immersive dialogue experience.

[1306] 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.

[1307] In this invention, the server includes means for receiving and storing data such as everyday remarks, conversations, thoughts, photos, and videos, means for preprocessing the stored data, converting voice data into text, and removing noise, means for training a personality reproduction model using the preprocessed data, means for receiving requests from users and generating responses using the trained personality reproduction model, means for presenting the generated responses to the users, and means for converting the generated responses into voice and playing them back, thereby enabling users to enjoy real-time conversations with deceased or great people.

[1308] "Data" refers to information such as statements, conversations, thoughts, photos, and videos provided by users.

[1309] "Preprocessing" refers to the process of analyzing the stored data, converting the audio data to text, and removing noise.

[1310] A "personality reproduction model" refers to a generative AI model that learns the language and thought patterns of a specific person based on preprocessed data.

[1311] A "request" refers to the content of a question or inquiry that a user inputs into the system.

[1312] "Response" refers to a reply generated by the personality reproduction model based on a user request.

[1313] "Speech synthesis" refers to the technology of converting text data into voice data.

[1314] "Virus scanning" refers to the process of checking whether data provided by a user contains viruses.

[1315] "Integrity check" refers to the process of verifying the consistency and completeness of data provided by a user.

[1316] The system of the present invention allows users to enjoy conversations and consultations with deceased or great figures, and a specific implementation method for this purpose will be described below. The main components of the system are a server, a terminal, and a user.

[1317] Server Roles

[1318] The server performs the following functions:

[1319] 1. Receiving and storing data: Receive data such as statements, conversations, thoughts, photos, and videos provided by users and store them in a database.

[1320] 2. Data preprocessing: Converting audio to text and removing noise from the stored data, as well as extracting and correlating important scenes and text information from photos and videos.

[1321] 3. Model training: Using the preprocessed data, we train a personality model. The model learns the characteristics and language of the specified deceased or great person.

[1322] 4. Request processing and response generation: Receives a request from the user and generates a response using the trained personality model.

[1323] 5. Send Response: The generated response is formatted for presentation to the user and sent to the terminal.

[1324] Device Role

[1325] The terminal provides the user interface and is responsible for:

[1326] 1. Data collection: Collect user statements, conversations, photos, and videos and upload them to the server.

[1327] 2. Request Input: Provide an interface that allows users to input requests for conversation or consultation.

[1328] 3. Displaying responses and playing them back: Displays the responses sent from the server and, if necessary, plays them back as audio using speech synthesis technology (such as gTTS).

[1329] Hardware and software used

[1330] Hardware: smartphone, head-mounted display (HMD), server.

[1331] Software: SpeechRecognition, gTTS (Google Text-to-Speech), Transformers library (using GPT-2 model).

[1332] Specific examples

[1333] Here are some concrete usage examples:

[1334] Example 1: Questions for the deceased

[1335] If a user seeks advice from a deceased family member about a domestic issue:

[1336] 1. The user types into the terminal, "There have been a lot of disagreements in my family recently. How should we deal with them?"

[1337] 2. The device sends this request to the server.

[1338] 3. The server uses a replica model of the deceased person's personality to generate a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[1339] 4. The device displays the generated response on the screen and plays it aloud using speech synthesis technology.

[1340] Example 2: Advice from great people

[1341] If a user asks a historical figure for career advice:

[1342] 1. The user types into the terminal, "How can I adjust to a new workplace?"

[1343] 2. The device sends this request to the server.

[1344] 3. Using a model of a great person's personality, the server generates a response: "It's important to observe the first few weeks and actively ask questions of your colleagues."

[1345] 4. The device displays the generated response on the screen and plays it aloud using speech synthesis technology.

[1346] In this way, users can enjoy a real-time interactive experience with deceased or great people.

[1347] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1348] Step 1:

[1349] Data collection and storage

[1350] Input: Users provide statements, conversations, thoughts, photos, videos, etc. to the device.

[1351] How it works: The device receives this data and uploads it to the server.

[1352] Output: Data stored on the server.

[1353] Step 2:

[1354] Data Preprocessing

[1355] Input: User data stored on the server.

[1356] How it works: The server converts audio data into text, removes noise, and extracts and correlates important scenes and text information from photos and videos.

[1357] Output: Preprocessed text data and related information.

[1358] Step 3:

[1359] Training the model

[1360] Input: Preprocessed data.

[1361] How it works: The server trains a personality model using the preprocessed data. It uses a generative AI model (e.g., GPT-2).

[1362] Output: A trained personality reproduction model.

[1363] Step 4:

[1364] Accepting user requests

[1365] Input: The user inputs a request (e.g., "How can I adjust to a new job?") through the terminal.

[1366] Operation: The terminal receives a request from the user and sends it to the server.

[1367] Output: The request sent to the server.

[1368] Step 5:

[1369] Generating a response

[1370] Input: User requests sent to the server and the trained personality model.

[1371] How it works: The server analyzes the request and uses its personality model to generate an appropriate response.

[1372] Output: The generated response sentence.

[1373] Step 6:

[1374] Presenting a response

[1375] Input: The generated response sentence.

[1376] What it does: The server formats the generated response into a user-friendly format and sends it to the device.

[1377] Output: The response sent to the terminal.

[1378] Step 7:

[1379] Speech synthesis and playback

[1380] Input: The response sent to the terminal.

[1381] Operation: The device uses speech synthesis technology (e.g., gTTS) to convert the response sentence into audio data and play it back.

[1382] Output: The user's on-screen response and the spoken response.

[1383] In this way, the input data is processed at each step, and finally a response is provided to the user in real time.

[1384] 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.

[1385] The present invention is a system that enables users to converse with or consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to make the responses provided more human-like and appropriate to the situation. The following describes an embodiment of this system.

[1386] System configuration

[1387] The system consists of the following main components: a server, a terminal, a user, and an emotion engine.

[1388] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, generate responses, and recognize emotions.

[1389] Terminal: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays or plays audio generated responses.

[1390] User: Uses the system to provide data and enter requests for conversations or consultations.

[1391] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server.

[1392] Program processing flow

[1393] 1. Data collection phase:

[1394] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and upload them to the server via their devices.

[1395] Example: A user uploads a video message or letter from a deceased person to the device.

[1396] 2. Data preprocessing phase:

[1397] The server stores the received data and performs virus scans and integrity checks.

[1398] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[1399] 3. Model training phase:

[1400] The server uses the preprocessed data to train a personality model, which learns the phrasing and thought patterns of a specific person.

[1401] Example: An AI model learns from the words and letters of the deceased person as training data.

[1402] 4. Emotion Recognition Phase:

[1403] The emotion engine analyzes emotions from the user's voice and text, and the analyzed emotion information is sent to the server.

[1404] Example: If a user says, "I've been feeling stressed at work lately," the emotion engine will recognize emotions like "stress" and "anxiety."

[1405] 5. Generation phase:

[1406] The user inputs a request for conversation or consultation into the terminal.

[1407] The terminal sends this request to the server.

[1408] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[1409] Example: Reflecting the user's feeling of "stress," the response generated is "First, take a deep breath and take some time to relax."

[1410] 6. Response Presentation Phase:

[1411] The server formats the generated response in a form that is easy for the user to understand.

[1412] The terminal displays the received response and, if necessary, provides a voice response using speech synthesis.

[1413] Example: The device displays the text "First, take a deep breath and take some time to relax" on the screen and simultaneously plays the audio.

[1414] This embodiment allows users to converse with and consult with great or deceased figures, and the emotion engine adjusts responses based on the user's emotions, resulting in more human-like responses. This allows users to feel more at ease and receive specific advice and support. Implementation of this system can support a wide range of applications, including mental health, education, and career counseling for individuals and companies.

[1415] The processing flow will be explained below.

[1416] The present invention is a system that allows users to converse and consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system provides responses that are more human-like and appropriate to the situation. The processing flow of this system is explained below step by step.

[1417] Program processing flow

[1418] Step 1: Data collection phase

[1419] 1-1. User:

[1420] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[1421] Prepare the collected data for uploading through the device interface.

[1422] 1-2. Device:

[1423] Receive data provided by a user.

[1424] The format and size of the data are checked and prepared for sending to the server.

[1425] 1-3. Server:

[1426] Receives data sent from the device and temporarily stores it.

[1427] Run a virus scan and check the integrity of your data.

[1428] Data that has been confirmed as secure is stored in a long-term database.

[1429] Step 2: Data preprocessing phase

[1430] 2-1. Server:

[1431] The stored data is extracted and classified into text data, audio data, image data, and video data.

[1432] 2-2. Server:

[1433] The voice data is converted into text using a voice recognition system.

[1434] Natural language processing is applied to text data to perform semantic analysis and grammar checks.

[1435] Remove noise and unnecessary information.

[1436] 2-3. Server:

[1437] Image recognition technology is used to extract important scenes and text information from image and video data.

[1438] Associate the extracted information with other data.

[1439] 2-4. Server:

[1440] After preprocessing, the data is converted into a format suitable for AI learning and saved as a training dataset.

[1441] Step 3: Model training phase

[1442] 3-1. Server:

[1443] The preprocessed data is input into the AI ​​training module.

[1444] Use an AI model (e.g., GPT-3) to train the data.

[1445] 3-2. Server:

[1446] During the training process, the parameters of the model are adjusted and optimized.

[1447] Evaluate the performance of the trained model and retrain it with additional data if necessary.

[1448] Step 4: Emotion Recognition Phase

[1449] 4-1. Terminal:

[1450] The user's voice and input text are sent to the emotion engine.

[1451] 4-2. Emotion Engine:

[1452] Analyze emotions from user voice and text.

[1453] The analyzed emotion information is sent to the server.

[1454] 4-3. Server:

[1455] Emotion information is received and stored in a database.

[1456] Step 5: Generate Phase

[1457] 5-1. User:

[1458] A user who wishes to have a conversation or consultation inputs a request through the terminal interface.

[1459] 5-2. Terminal:

[1460] Receives a user request and converts it into a request format.

[1461] Send the request to the server.

[1462] 5-3. Server:

[1463] Requests and emotional information are integrated and input into a personality representation model.

[1464] The AI ​​model generates the appropriate response.

[1465] 5-4. Server:

[1466] Format the generated response in a user-friendly format.

[1467] Sends the response to the terminal.

[1468] Step 6: Response Presentation Phase

[1469] 6-1. Terminal:

[1470] The response received from the server is parsed and displayed in the user interface.

[1471] If necessary, speech synthesis is performed to provide a response in voice.

[1472] 6-2. User:

[1473] Review the suggested responses.

[1474] Determine next actions or further requests as needed.

[1475] Examples:

[1476] 1. Family advice:

[1477] The user inputs into the terminal, "There have been a lot of disagreements in the family recently. How should we deal with them?"

[1478] The emotion engine analyzes the user's emotions, such as anxiety or confusion, and transmits them to the server.

[1479] The server uses a replica model of the deceased person's personality to generate a response such as, "Differences of opinion should be seen as opportunities for growth. Let's respect each other's opinions," and provides it in a warm, emotionally appropriate format.

[1480] The terminal displays the generated response on the screen and also plays it aloud.

[1481] This embodiment allows users to have conversations and consultations with great or deceased figures and deceased figures that are tailored based on their emotions, allowing them to receive more human-like responses, which gives users a sense of security and allows them to receive specific advice and mental support.

[1482] Example 2

[1483] 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."

[1484] Conventional conversation systems have had difficulty responding appropriately to the diversity of data provided by users and the fluctuations in their emotions, and generating human-like responses. Furthermore, to realize a dialogue with a deceased or great figure, it is necessary to reproduce the person's language and thought patterns and provide responses that adapt to the user's emotions, but there has been a lack of effective means to achieve this.

[1485] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and storing data such as everyday remarks, conversations, thoughts, photos, and videos, means for preprocessing the stored data, converting voice data into text, and removing noise, means for training a personality reproduction model using the preprocessed data, means for analyzing emotions from the user's voice and text, means for receiving a request from the user based on the emotion analysis result and generating a response using the trained personality reproduction model, and means for presenting the generated response to the user. This generates a human-like response according to the user's emotions, making it possible to have a more realistic conversation with deceased or great people.

[1486] "Receiving" means that a terminal or server obtains data or requests provided by a user.

[1487] "Storage" means to hold the received data in a storage device or database.

[1488] "Preprocessing" refers to analyzing and processing the received data and converting it into a format suitable for subsequent processing.

[1489] "Converting voice data to text" means extracting text information from a voice signal using voice recognition technology.

[1490] "Noise reduction" refers to removing unwanted noise and unnecessary information from audio data or other data.

[1491] A "personality reproduction model" is an artificial intelligence model that learns the language and thought patterns of a specific person and generates responses based on that.

[1492] "Emotion analysis" refers to identifying a user's mood or emotional state from their voice or text.

[1493] "Receiving a request" means acquiring a question or inquiry from a user.

[1494] "Generating a response" means creating an appropriate reply based on the received request and the results of sentiment analysis.

[1495] "Presenting" refers to visually or audibly indicating to the user the generated response.

[1496] "Virus scanning" means inspecting stored data for the presence of malware or viruses.

[1497] "Integrity checking" is the process of verifying that data is accurate and complete.

[1498] "Speech synthesis" is a technology that generates human speech from text data.

[1499] The present invention is a system that allows users to converse with or consult with great or deceased figures using data such as past statements, conversations, thoughts, photographs, and videos. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the responses provided become more human-like and appropriate to the situation. A specific embodiment for implementing this system is described below.

[1500] System configuration

[1501] The system consists of the following main components: a server, a terminal, a user, and an emotion engine.

[1502] Server: Plays a central role in receiving, storing, and preprocessing data, training the personality model, processing requests, generating responses, and recognizing emotions. It requires a high-performance server for hardware, and uses a database management system and machine learning libraries (e.g., TensorFlow and PyTorch) for training the AI ​​model for software.

[1503] Device: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays and plays generated responses. Examples include smartphones and PCs.

[1504] Users: use the system to provide data and enter requests for conversations or consultations. Users use dedicated applications.

[1505] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server. This uses natural language processing libraries for emotion recognition (e.g., NLTK and SpaCy) and voice analysis tools (e.g., Google Speech-to-Text API).

[1506] System Operation

[1507] 1. Data Collection:

[1508] Users use devices such as smartphones and PCs to upload data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[1509] Example: A user uploads a video message or letter from a deceased person to the device.

[1510] Sample prompt: The user says, "I'm uploading a message from a deceased loved one."

[1511] 2. Data Preprocessing:

[1512] The server stores the received data, performs virus scans and integrity checks, then converts the audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[1513] Example: A server uses OCR technology to extract text from a photo.

[1514] 3. Model training:

[1515] The server uses the preprocessed data to train a personality model, which then replicates the speech patterns and thought patterns of a specific person.

[1516] Example: The AI ​​model learns from the statements and letters of the deceased person as training data.

[1517] 4. Emotion recognition:

[1518] The emotion engine analyzes emotions from the user's voice and text and provides the results to the server.

[1519] Example: When a user says, "I've been feeling stressed at work lately," the emotion engine recognizes the emotions "stress" and "anxiety."

[1520] 5. Response Generation:

[1521] A user inputs a request for conversation or consultation into a terminal, which then transmits the request to a server.

[1522] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[1523] Example: Generate a response that reflects the user's emotion of "stress": "First, take a deep breath and take some time to relax."

[1524] 6. Response suggestions:

[1525] The server formats the generated response in a format that is easy for the user to understand, and the terminal displays the received response and, if necessary, performs speech synthesis to provide an audible response.

[1526] Example: The device displays the text "Take a moment to relax and take a deep breath" and plays it aloud at the same time.

[1527] This system allows users to converse and consult with deceased or great figures, and an emotion engine adjusts responses to match the user's emotions, resulting in a more human-like interaction.

[1528] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1529] Step 1:

[1530] Data collection:

[1531] Users use devices such as smartphones and PCs to upload data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[1532] Specific operation: The user launches the smartphone app, taps the "Data Upload" button, selects photos and videos of the deceased person from the gallery, and begins uploading.

[1533] Input: Media such as photos, videos, audio data, and text.

[1534] Output: Data stored on the device.

[1535] Step 2:

[1536] Data transmission:

[1537] The terminal transmits the received data to the server.

[1538] Specific operation: The device checks the network connection and sends the saved data to the server.

[1539] Input: Data stored on the device.

[1540] Output: The server receives the data.

[1541] Step 3:

[1542] Data preprocessing:

[1543] The server stores the received data and performs virus scans and integrity checks.

[1544] Specific operation: The server runs a virus scan software on the data received to check its integrity.

[1545] Input: Received photos, videos, audio data, and text.

[1546] Output: Pre-processed data that has been verified as safe.

[1547] Step 4:

[1548] Data Analysis:

[1549] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[1550] How it works: The server uses speech recognition software to convert speech to text, applies noise filtering algorithms, uses image recognition to extract key scenes from photos, and uses OCR technology to extract text.

[1551] Input: Pre-processed data that has been verified as safe.

[1552] Output: Parsed text data, clean audio data, extracted information.

[1553] Step 5:

[1554] Model training:

[1555] The server trains a personality reproduction model using the preprocessed data.

[1556] What it does: The server uses a machine learning library (e.g., TensorFlow or PyTorch) to train an AI model and iterate over the dataset.

[1557] Input: Analyzed text data and audio data.

[1558] Output: A trained personality reproduction model.

[1559] Step 6:

[1560] Emotion recognition:

[1561] The emotion engine analyzes emotions from the user's voice and text and provides the results to the server.

[1562] Specific operation: When a user says to the device, "I've been feeling stressed at work lately," the emotion engine analyzes the voice data and recognizes emotions such as "stress" and "anxiety."

[1563] Input: User's voice and text data.

[1564] Output: Parsed emotion information.

[1565] Step 7:

[1566] Request processing and response generation:

[1567] A user inputs a request for conversation or consultation into a terminal, which then transmits the request to a server.

[1568] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[1569] Specific operation: The user types "How can I reduce stress?" into the chat window, and the device sends the request to the server. The server generates a response using the personality reproduction model and emotional information.

[1570] Input: User request, emotion information, trained personality model.

[1571] Output: The generated response.

[1572] Step 8:

[1573] Response prompt:

[1574] The server formats the generated response in a user-friendly format and sends it to the device, which displays the received response and, if necessary, provides a spoken response using speech synthesis.

[1575] Specific operation: The server sends the text and audio data "First, take a deep breath and take some time to relax" to the device, which then displays and plays it back.

[1576] Input: The generated response data.

[1577] Output: The response (text and audio) presented to the user.

[1578] Step 9:

[1579] User Feedback:

[1580] Users can enter feedback on the responses they provide, which can be used to improve the system.

[1581] What happens: The user sends feedback saying "This response was helpful."

[1582] Input: User feedback.

[1583] Output: The feedback data is stored on the server and used for future improvements.

[1584] (Application example 2)

[1585] 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."

[1586] Conventional counseling support systems have difficulty generating responses that take into account the user's emotional state, making it difficult to provide advice that is appropriate for each individual user. Providing appropriate support that takes into account the client's past data and emotion analysis results is also a challenge. As a result, these systems are not sufficiently effective in improving the user's mental health or reducing stress.

[1587] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and saving data such as everyday remarks, conversations, thoughts, photos, and videos; means for preprocessing the saved data, converting voice data into text, and removing noise; means for training a personality reproduction model using the preprocessed data; means for analyzing requests from users with an emotion engine and acquiring emotional information; means for generating prompts based on the acquired emotional information and adjusting the generated responses; means for providing the generated responses to the user as voice through speech synthesis; and means for providing appropriate advice and support during counseling sessions based on the client's past data and emotion analysis results. This makes it possible to provide more personalized advice and support that takes the user's emotional state into consideration.

[1588] "Data" refers to information such as statements, conversations, thoughts, photographs, and videos obtained from a variety of sources.

[1589] "Preprocessing" refers to the process of analyzing the received data, converting the voice data into text, and removing noise.

[1590] A "personality reproduction model" is a machine learning model that reproduces the language and thought patterns of a specific person based on preprocessed data.

[1591] A "request" is an input of a question or conversation that a user makes to the system.

[1592] The "emotion engine" is a system component that analyzes emotions from the user's voice and text and obtains the results.

[1593] A "prompt sentence" is an input sentence that adjusts the response to be generated based on the emotional information analyzed by the emotion engine.

[1594] A "response" is a response from the system generated in response to a user request.

[1595] "Speech synthesis" is a technique that provides a generated response to a user as speech.

[1596] A "counseling session" is a time or opportunity for a client to consult or meet with a counselor.

[1597] "Past data" refers to records of information such as utterances, conversations, and emotional states that users have provided to the system in the past.

[1598] "Appropriate advice and support" refers to the most effective advice and support measures based on the user's emotional state and past data.

[1599] The present invention is a system that allows users to converse with or consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, by combining it with an emotion engine, the responses provided are more human-like and appropriate to the situation. Specific embodiments of this system are described below.

[1600] System configuration

[1601] The system consists of the following main components:

[1602] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, generate responses, and recognize emotions.

[1603] Terminal: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays or plays audio generated responses.

[1604] User: Uses the system to provide data and enter requests for conversations or consultations.

[1605] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server.

[1606] Specific processing flow

[1607] 1. Data collection and preprocessing:

[1608] Users upload data such as statements, thoughts, photos, and videos related to deceased or great people to the server via their devices.

[1609] The server stores and pre-processes the received data, which includes converting the audio data to text, removing noise, and extracting important information from photos and videos.

[1610] 2. Training the personality model:

[1611] Using the pre-processed data, the server trains a personality model that can learn the phrasing and thought patterns of a particular person.

[1612] 3. Emotion analysis:

[1613] The emotion engine analyzes the user's input data and recognizes the user's emotional state. This emotion information is sent to the server and used to generate appropriate responses.

[1614] 4. Generate and present the response:

[1615] When a user inputs a request, the server integrates the request with emotional information from the emotion engine and generates an appropriate response using a personality reproduction model.

[1616] The generated response is presented to the user as text and speech.

[1617] Hardware and software used

[1618] Hardware: Smartphone

[1619] software:

[1620] OpenAI API: Generate responses using generative AI models.

[1621] Emotional Analysis: Analyzes user emotions using an emotion engine.

[1622] Data Processor: Preprocesses and formats user data.

[1623] TextToSpeech: Text-to-speech technology to provide a generated response audibly.

[1624] Specific examples

[1625] As a concrete example, entering the following prompt sentence will cause the system to generate an appropriate response:

[1626] text

[1627] The user is experiencing the emotion of stress. Generate an appropriate response to the following request: I've been feeling stressed at work lately. What should I do?

[1628] This allows the server to generate a response such as "First, take a deep breath and take some time to relax," which is then provided to the user via voice synthesis technology.This system allows users to receive emotionally sensitive advice without meeting a counselor in person.

[1629] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1630] Step 1:

[1631] Users upload data such as statements, thoughts, photos, and videos related to deceased or great people to a server via their device. At this time, the device accepts data selection and upload operations through a user interface. Input data includes audio data, text data, image data, etc., and is sent to the server. As an output, a data file in the format saved on the server is generated.

[1632] Step 2:

[1633] The server stores the received data and performs preprocessing. Specifically, it scans the stored data for viruses and checks its integrity. It then converts the audio data into text and removes noise. For image and video data, it extracts important scenes and text information. The input is the data received in step 1, and the output is a clean, preprocessed dataset.

[1634] Step 3:

[1635] The server uses the preprocessed data to train a personality reproduction model. This process involves feeding the clean dataset into an AI training algorithm to learn the linguistic and thought patterns of a specific person. Specifically, the model is trained using the OpenAI API. The input is the preprocessed dataset, and the output is a trained personality reproduction model.

[1636] Step 4:

[1637] The emotion engine analyzes the user's input data and recognizes emotional information. The user inputs their consultation details via text or voice through the device. The device sends this input data to the server, which then activates the emotion engine to analyze the emotions. Emotional information is output as "stress," "anxiety," "joy," etc. and sent to the server.

[1638] Step 5:

[1639] The user inputs a request for conversation or consultation into the terminal. The terminal sends this request to the server. The server integrates the request with the emotional information sent from the emotion engine and generates a prompt. The input is the user's request and emotional information, and the output is the generated prompt.

[1640] Step 6:

[1641] The server uses the prompt sentence to generate an appropriate response using a personality reproduction model. The server inputs the prompt sentence into the generative AI model (OpenAI API) and generates a response. The input is the generated prompt sentence, and the output is an appropriate response text. As a concrete example, the following prompt sentence is used:

[1642] text

[1643] The user is experiencing the emotion of stress. Generate an appropriate response to the following request: I've been feeling stressed at work lately. What should I do?

[1644] Step 7:

[1645] The generated response is presented to the user. The terminal receives the response from the server, displays it as text, and plays it back as audio using speech synthesis technology. Specifically, it uses the TextToSpeech module to convert the generated text into speech. The input is the response text from the server, and the output is a response in audio and text format that the user can understand.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] [Fourth embodiment]

[1650] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1651] 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.

[1652] 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).

[1653] 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.

[1654] 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.

[1655] 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).

[1656] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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."

[1663] The system of the present invention allows users to converse with or consult with great or deceased people using ordinary remarks, conversations, thoughts, photographs, videos, etc. An embodiment of this system will be described below.

[1664] System configuration

[1665] The system consists of three main components: a server, a terminal, and a user.

[1666] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, and generate responses.

[1667] Terminal: Provides a user interface, collects data from the user, inputs requests, displays generated responses, and plays audio.

[1668] User: Uses the system to provide data and enter requests for conversations or consultations.

[1669] Program processing flow

[1670] 1. Data collection phase:

[1671] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and upload them to the server via their devices.

[1672] The terminal verifies and uploads the data.

[1673] 2. Data preprocessing phase:

[1674] The server stores the received data and performs virus scans and integrity checks.

[1675] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[1676] 3. Model training phase:

[1677] The server uses the preprocessed data to train a personality model, which learns the phrasing and thought patterns of a specific person.

[1678] The server adjusts the model parameters, performs optimization, evaluates the model's accuracy, and retrains it with additional data if necessary.

[1679] 4. Generation phase:

[1680] The user inputs a request for conversation or consultation into the terminal.

[1681] The terminal converts the input into a request format and sends it to the server.

[1682] The server receives the request and generates a response using the personality reproduction model.

[1683] The generated response is formatted in a way that is easy for the user to understand.

[1684] 5. Response Presentation Phase:

[1685] The server sends the formatted response to the terminal.

[1686] The terminal displays the received response and, if necessary, provides a voice response using speech synthesis.

[1687] Specific examples

[1688] 1. Questions for the deceased:

[1689] Suppose a user wants to ask a deceased family member for advice about a household problem.

[1690] The user inputs into the terminal, "There have been a lot of disagreements in my family recently. How should we deal with them?"

[1691] The terminal sends this request to the server.

[1692] Using a personality model of the deceased, the server generates a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[1693] The terminal displays the generated response on the screen and also plays it aloud.

[1694] 2. Advice from great people:

[1695] Suppose a user asks a historical figure for career advice.

[1696] The user types into the terminal, "How should I adjust to my new workplace?"

[1697] The terminal sends this request to the server.

[1698] Using a model of a great person's personality, the server generates a response: "It's important to observe the first few weeks and actively ask questions of your colleagues."

[1699] The terminal displays the generated response on the screen and, if necessary, provides the response via voice synthesis.

[1700] In this way, the system of the invention is designed to enable users to receive appropriate advice from great and deceased people in various situations, providing a sense of security and improving quality of life.

[1701] The processing flow will be explained below.

[1702] Step 1: Data collection phase

[1703] 1-1. User:

[1704] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and prepare this data on their device.

[1705] Check the data content and start uploading through the system interface.

[1706] 1-2. Device:

[1707] Receive data provided by a user.

[1708] When data is uploaded, the format and size are checked and it is prepared for sending to the server.

[1709] 1-3. Server:

[1710] Save the data received from the device.

[1711] After the initial save, the data is scanned for viruses and integrity checked.

[1712] Once it is deemed safe, the data is moved to a long-term storage database.

[1713] Step 2: Data preprocessing phase

[1714] 2-1. Server:

[1715] Extract the stored data and classify it into formats such as text, audio, images, and videos.

[1716] 2-2. Server:

[1717] The voice data is converted into text using voice recognition technology.

[1718] Natural language analysis is applied to the text data to perform semantic analysis and grammar checks.

[1719] Remove unnecessary information and noise.

[1720] 2-3. Server:

[1721] Image recognition technology is used to extract important scenes and text information from image and video data.

[1722] Associate the extracted information with other data.

[1723] 2-4. Server:

[1724] After preprocessing, the data is converted into a format suitable for AI learning and saved as a training dataset.

[1725] Step 3: Model training phase

[1726] 3-1. Server:

[1727] Input the training dataset into the AI ​​training module.

[1728] Train the data using a pre-built AI model (e.g., GPT-3).

[1729] 3-2. Server:

[1730] During the training process, the model parameters are adjusted and optimized.

[1731] Evaluate the performance of the trained model.

[1732] If necessary, retrain using additional data.

[1733] Step 4: Generate Phase

[1734] 4-1. User:

[1735] A user who wishes to have a conversation or consultation inputs a request through the terminal interface.

[1736] 4-2. Terminal:

[1737] Receives requests from users and converts them into the appropriate request format.

[1738] Send the request to the server.

[1739] 4-3. Server:

[1740] Receives requests and inputs them into the relevant personality representation model.

[1741] The AI ​​model generates the appropriate response.

[1742] 4-4. Server:

[1743] Format the generated response in a user-friendly format.

[1744] Sends the response to the terminal.

[1745] Step 5: Response Presentation Phase

[1746] 5-1. Terminal:

[1747] The response received from the server is parsed and displayed in the user interface.

[1748] If necessary, speech synthesis is performed to provide a voice response.

[1749] 5-2. User:

[1750] Review the proposed response and decide on the next action (e.g., ask again, arrange another consultation).

[1751] Through these steps, users can have conversations and consult with great people and deceased people, and can gain comfort and useful advice.

[1752] Example 1

[1753] 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."

[1754] In modern times, it is practically impossible to converse or consult with great or deceased figures. However, if we could simulate this, it would be possible to provide users with psychological support and advice. However, this requires efficient collection of past data and the generation of accurate responses based on a reliable model. To realize such a system, many technical challenges must be overcome.

[1755] 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.

[1756] In this invention, the server includes means for receiving and storing data such as everyday speech, conversation, thoughts, photos, and videos; means for preprocessing the stored data, converting voice data to text, and removing noise; and means for training a personality reproduction model using the preprocessed data. This makes it possible to effectively handle data of any format provided by the user and faithfully reproduce the vocabulary and thought patterns of a specific person. The server also includes means for performing virus scans and integrity checks on the data provided by the user, ensuring the security and reliability of the entire system. Furthermore, a means for providing the generated responses to the user as voice through speech synthesis can be provided, providing a more natural conversational experience.

[1757] "Means for receiving and storing" refers to the function of receiving data such as statements, conversations, thoughts, photos, and videos provided by users and storing them within the system.

[1758] "Preprocessing means" is a function that analyzes the stored data, performs necessary conversions and filtering, and prepares the data in a format suitable for subsequent processing.

[1759] "Means for converting voice data into text" is a function for converting voice data into text information.

[1760] "Means for removing noise" refers to a function that removes unnecessary noise and interference in the data to improve the quality of the data.

[1761] The "means for training a personality reproduction model" is a function that trains a machine learning model based on preprocessed data to generate responses that mimic a specific person.

[1762] The "means for receiving requests" is a function that allows a user to input questions or inquiries into the system and receives them for processing.

[1763] The "means for generating a response" is a function that uses a trained personality reproduction model to generate an appropriate response to a user request.

[1764] The "means for presenting to the user" is a function for displaying or reproducing the generated response in a format that is easy for the user to understand.

[1765] "Means for virus scanning and integrity checking" refers to a function that checks whether there are any security issues with data provided by users and verifies the integrity of the data.

[1766] "Means for providing speech by speech synthesis" is a function for converting a response in text format into speech and letting the user hear it.

[1767] MODE FOR CARRYING OUT THE INVENTION

[1768] The present invention enables users to have conversations and consultations with great people and deceased people using ordinary remarks, conversations, thoughts, photographs, videos, etc. The following describes in detail the mode for implementing this system.

[1769] This system consists of three main components: a server, a terminal, and a user.

[1770] server

[1771] The server is the heart of the system and is responsible for:

[1772] Receives, stores, scans for viruses and checks for integrity of data.

[1773] Preprocessing of received data is performed, converting voice data to text and removing noise.

[1774] Extract and associate important scenes and text information from photos and videos.

[1775] The preprocessed data is used to train a personality reconstruction model.

[1776] A request from a user is received and a response is generated using a personality reproduction model.

[1777] Formats the generated response and sends it to the terminal.

[1778] The server uses Python and TensorFlow to build and train machine learning models, the OpenCV library to analyze data, and the Google Cloud Speech-to-Text API to convert audio data.

[1779] Terminal

[1780] The terminal provides the user interface and is responsible for:

[1781] Data is collected from users, verified, and then uploaded to the server.

[1782] It provides an interface for users to input requests and send them to the server.

[1783] The generated response is received and displayed on the screen.

[1784] If necessary, speech synthesis is performed to provide a response to the user as speech.

[1785] The device uses common electronic devices such as smartphones and PCs, and these functions are realized through application software. For voice synthesis, the Google Text-to-Speech API is used.

[1786] User

[1787] Users use the system to:

[1788] Upload your data to seek advice on family issues or careers.

[1789] Input your request for dialogue or consultation into the terminal.

[1790] The generated response is received and used by display or audio.

[1791] Users can easily input their requests using the terminal interface and receive appropriate advice from the system.

[1792] Specific examples

[1793] 1. Questions for the deceased

[1794] When a user seeks advice from the deceased regarding a domestic problem, the user inputs into the terminal, "There have been many disagreements in the family recently. How should we deal with this?"

[1795] The terminal sends this request to the server.

[1796] Using a personality model of the deceased, the server generates a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[1797] The terminal displays the generated response on the screen and also plays it aloud.

[1798] Example prompt sentence:

[1799] "There have been a lot of disagreements in the family recently. How should I handle them?"

[1800] 2. Advice from great people

[1801] To seek career advice from a historical figure, a user would type "How should I adjust to a new job?" into the device.

[1802] The terminal sends this request to the server.

[1803] Using a model of a great person's personality, the server generates a response such as, "It's important to observe the first few weeks and actively ask questions of your colleagues."

[1804] The terminal displays the generated response on the screen and, if necessary, provides the response via voice synthesis.

[1805] Example prompt sentence:

[1806] How do I adjust to a new workplace?

[1807] In this way, this system allows users to improve their quality of life and receive spiritual support by receiving advice from great people and deceased people.

[1808] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1809] Step 1:

[1810] Data Collection Phase

[1811] Input: Data such as statements, conversations, thoughts, photos, videos, etc.

[1812] Process: The user collects data related to deceased or important people and uploads this data to the server using the device.

[1813] How it works: Users use their smartphones or computers to record speech, enter text from conversations, take photos and videos, and save them to cloud storage. The device then sends the collected data to a server via Wi-Fi.

[1814] Output: The data received by the server.

[1815] Step 2:

[1816] Data Preprocessing Phase

[1817] Input: Data such as received statements, conversations, thoughts, photos, videos, etc.

[1818] Processing: The server pre-processes the received data, which includes virus scanning and integrity checking, converting audio data to text, removing noise, and extracting important scenes and text information from photos and videos.

[1819] Specific operation: The server scans the data using virus scanning software such as Trend Micro, converts the audio data into text using the Google Cloud Speech-to-Text API, and extracts important scenes using the OpenCV library.

[1820] Output: Preprocessed data.

[1821] Step 3:

[1822] Model training phase

[1823] Input: Preprocessed data.

[1824] Processing: The server uses the preprocessed data to train a personality reproduction model, extracting features from the data and learning the language and thought patterns of a particular person.

[1825] How it works: The server uses Python and TensorFlow to build and train deep learning models, adjust hyperparameters using techniques such as grid search, and evaluate the accuracy of the models by performing cross-validation.

[1826] Output: A trained personality reproduction model.

[1827] Step 4:

[1828] Request Processing Phase

[1829] Input: User request (in text format).

[1830] Processing: The user inputs a request for conversation or consultation into the terminal. The terminal converts the input into a request format and sends it to the server.

[1831] What it does: A user opens the application on their smartphone and types "How can I adjust to a new job?" into the text box. The device converts this request into JSON format and sends it to the server.

[1832] Output: The request sent to the server.

[1833] Step 5:

[1834] Response Generation Phase

[1835] Input: The request sent to the server.

[1836] Processing: The server receives the request and uses the personality model to generate a response, which is then formatted in a way that is easy for the user to understand.

[1837] What it does: The server uses a generative AI model to generate an appropriate response based on the request, and the response is formatted in JSON.

[1838] Output: The formatted response.

[1839] Step 6:

[1840] Response presentation phase

[1841] Input: The formatted response.

[1842] Processing: The server sends the formatted response to the device, which displays the received response and plays it back as audio using speech synthesis if necessary.

[1843] Specific operation: The server sends the generated response as an HTTPS response. The device receives the response and displays the message "It's important to observe the first few weeks and actively ask questions of your colleagues." The device then uses the Google Text-to-Speech API to synthesize speech and play the response aloud.

[1844] Output: The response presented to the user (in text and audio format).

[1845] (Application example 1)

[1846] 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."

[1847] Today, there is a demand for technology that allows users to converse with deceased figures and historical figures. Conventional technologies require cumbersome data collection and processing to recreate the personalities of deceased figures and historical figures, and provide responses to users without a sense of realism. Furthermore, the generated responses are poorly synthesized, resulting in a lack of real-time performance. The present invention aims to solve these problems and provide a more realistic and immersive dialogue experience.

[1848] 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.

[1849] In this invention, the server includes means for receiving and storing data such as everyday remarks, conversations, thoughts, photos, and videos, means for preprocessing the stored data, converting voice data into text, and removing noise, means for training a personality reproduction model using the preprocessed data, means for receiving requests from users and generating responses using the trained personality reproduction model, means for presenting the generated responses to the users, and means for converting the generated responses into voice and playing them back, thereby enabling users to enjoy real-time conversations with deceased or great people.

[1850] "Data" refers to information such as statements, conversations, thoughts, photos, and videos provided by users.

[1851] "Preprocessing" refers to the process of analyzing the stored data, converting the audio data to text, and removing noise.

[1852] A "personality reproduction model" refers to a generative AI model that learns the language and thought patterns of a specific person based on preprocessed data.

[1853] A "request" refers to the content of a question or inquiry that a user inputs into the system.

[1854] "Response" refers to a reply generated by the personality reproduction model based on a user request.

[1855] "Speech synthesis" refers to the technology of converting text data into voice data.

[1856] "Virus scanning" refers to the process of checking whether data provided by a user contains viruses.

[1857] "Integrity check" refers to the process of verifying the consistency and completeness of data provided by a user.

[1858] The system of the present invention allows users to enjoy conversations and consultations with deceased or great figures, and a specific implementation method for this purpose will be described below. The main components of the system are a server, a terminal, and a user.

[1859] Server Roles

[1860] The server performs the following functions:

[1861] 1. Receiving and storing data: Receive data such as statements, conversations, thoughts, photos, and videos provided by users and store them in a database.

[1862] 2. Data preprocessing: Converting audio to text and removing noise from the stored data, as well as extracting and correlating important scenes and text information from photos and videos.

[1863] 3. Model training: Using the preprocessed data, we train a personality model. The model learns the characteristics and language of the specified deceased or great person.

[1864] 4. Request processing and response generation: Receives a request from the user and generates a response using the trained personality model.

[1865] 5. Send Response: The generated response is formatted for presentation to the user and sent to the terminal.

[1866] Device Role

[1867] The terminal provides the user interface and is responsible for:

[1868] 1. Data collection: Collect user statements, conversations, photos, and videos and upload them to the server.

[1869] 2. Request Input: Provide an interface that allows users to input requests for conversation or consultation.

[1870] 3. Displaying responses and playing them back: Displays the responses sent from the server and, if necessary, plays them back as audio using speech synthesis technology (such as gTTS).

[1871] Hardware and software used

[1872] Hardware: smartphone, head-mounted display (HMD), server.

[1873] Software: SpeechRecognition, gTTS (Google Text-to-Speech), Transformers library (using GPT-2 model).

[1874] Specific examples

[1875] Here are some concrete usage examples:

[1876] Example 1: Questions for the deceased

[1877] If a user seeks advice from a deceased family member about a domestic issue:

[1878] 1. The user types into the terminal, "There have been a lot of disagreements in my family recently. How should we deal with them?"

[1879] 2. The device sends this request to the server.

[1880] 3. The server uses a replica model of the deceased person's personality to generate a response that reads, "Differences of opinion should be seen as opportunities for growth. It's important to respect each other's opinions and find common ground."

[1881] 4. The device displays the generated response on the screen and plays it aloud using speech synthesis technology.

[1882] Example 2: Advice from great people

[1883] If a user asks a historical figure for career advice:

[1884] 1. The user types into the terminal, "How can I adjust to a new workplace?"

[1885] 2. The device sends this request to the server.

[1886] 3. Using a model of a great person's personality, the server generates a response: "It's important to observe the first few weeks and actively ask questions of your colleagues."

[1887] 4. The device displays the generated response on the screen and plays it aloud using speech synthesis technology.

[1888] In this way, users can enjoy a real-time interactive experience with deceased or great people.

[1889] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1890] Step 1:

[1891] Data collection and storage

[1892] Input: Users provide statements, conversations, thoughts, photos, videos, etc. to the device.

[1893] How it works: The device receives this data and uploads it to the server.

[1894] Output: Data stored on the server.

[1895] Step 2:

[1896] Data Preprocessing

[1897] Input: User data stored on the server.

[1898] How it works: The server converts audio data into text, removes noise, and extracts and correlates important scenes and text information from photos and videos.

[1899] Output: Preprocessed text data and related information.

[1900] Step 3:

[1901] Training the model

[1902] Input: Preprocessed data.

[1903] How it works: The server trains a personality model using the preprocessed data. It uses a generative AI model (e.g., GPT-2).

[1904] Output: A trained personality reproduction model.

[1905] Step 4:

[1906] Accepting user requests

[1907] Input: The user inputs a request (e.g., "How can I adjust to a new job?") through the terminal.

[1908] Operation: The terminal receives a request from the user and sends it to the server.

[1909] Output: The request sent to the server.

[1910] Step 5:

[1911] Generating a response

[1912] Input: User requests sent to the server and the trained personality model.

[1913] How it works: The server analyzes the request and uses its personality model to generate an appropriate response.

[1914] Output: The generated response sentence.

[1915] Step 6:

[1916] Presenting a response

[1917] Input: The generated response sentence.

[1918] What it does: The server formats the generated response into a user-friendly format and sends it to the device.

[1919] Output: The response sent to the terminal.

[1920] Step 7:

[1921] Speech synthesis and playback

[1922] Input: The response sent to the terminal.

[1923] Operation: The device uses speech synthesis technology (e.g., gTTS) to convert the response sentence into audio data and play it back.

[1924] Output: The user's on-screen response and the spoken response.

[1925] In this way, the input data is processed at each step, and finally a response is provided to the user in real time.

[1926] 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.

[1927] The present invention is a system that enables users to converse with or consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to make the responses provided more human-like and appropriate to the situation. The following describes an embodiment of this system.

[1928] System configuration

[1929] The system consists of the following main components: a server, a terminal, a user, and an emotion engine.

[1930] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, generate responses, and recognize emotions.

[1931] Terminal: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays or plays audio generated responses.

[1932] User: Uses the system to provide data and enter requests for conversations or consultations.

[1933] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server.

[1934] Program processing flow

[1935] 1. Data collection phase:

[1936] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people, and upload them to the server via their devices.

[1937] Example: A user uploads a video message or letter from a deceased person to the device.

[1938] 2. Data preprocessing phase:

[1939] The server stores the received data and performs virus scans and integrity checks.

[1940] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[1941] 3. Model training phase:

[1942] The server uses the preprocessed data to train a personality model, which learns the phrasing and thought patterns of a specific person.

[1943] Example: An AI model learns from the words and letters of the deceased person as training data.

[1944] 4. Emotion Recognition Phase:

[1945] The emotion engine analyzes emotions from the user's voice and text, and the analyzed emotion information is sent to the server.

[1946] Example: If a user says, "I've been feeling stressed at work lately," the emotion engine will recognize emotions like "stress" and "anxiety."

[1947] 5. Generation phase:

[1948] The user inputs a request for conversation or consultation into the terminal.

[1949] The terminal sends this request to the server.

[1950] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[1951] Example: Reflecting the user's feeling of "stress," the response generated is "First, take a deep breath and take some time to relax."

[1952] 6. Response Presentation Phase:

[1953] The server formats the generated response in a form that is easy for the user to understand.

[1954] The terminal displays the received response and, if necessary, provides a voice response using speech synthesis.

[1955] Example: The device displays the text "First, take a deep breath and take some time to relax" on the screen and simultaneously plays the audio.

[1956] This embodiment allows users to converse with and consult with great or deceased figures, and the emotion engine adjusts responses based on the user's emotions, resulting in more human-like responses. This allows users to feel more at ease and receive specific advice and support. Implementation of this system can support a wide range of applications, including mental health, education, and career counseling for individuals and companies.

[1957] The processing flow will be explained below.

[1958] The present invention is a system that allows users to converse and consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system provides responses that are more human-like and appropriate to the situation. The processing flow of this system is explained below step by step.

[1959] Program processing flow

[1960] Step 1: Data collection phase

[1961] 1-1. User:

[1962] Users collect data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[1963] Prepare the collected data for uploading through the device interface.

[1964] 1-2. Device:

[1965] Receive data provided by a user.

[1966] The format and size of the data are checked and prepared for sending to the server.

[1967] 1-3. Server:

[1968] Receives data sent from the device and temporarily stores it.

[1969] Run a virus scan and check the integrity of your data.

[1970] Data that has been confirmed as secure is stored in a long-term database.

[1971] Step 2: Data preprocessing phase

[1972] 2-1. Server:

[1973] The stored data is extracted and classified into text data, audio data, image data, and video data.

[1974] 2-2. Server:

[1975] The voice data is converted into text using a voice recognition system.

[1976] Natural language processing is applied to text data to perform semantic analysis and grammar checks.

[1977] Remove noise and unnecessary information.

[1978] 2-3. Server:

[1979] Image recognition technology is used to extract important scenes and text information from image and video data.

[1980] Associate the extracted information with other data.

[1981] 2-4. Server:

[1982] After preprocessing, the data is converted into a format suitable for AI learning and saved as a training dataset.

[1983] Step 3: Model training phase

[1984] 3-1. Server:

[1985] The preprocessed data is input into the AI ​​training module.

[1986] Use an AI model (e.g., GPT-3) to train the data.

[1987] 3-2. Server:

[1988] During the training process, the parameters of the model are adjusted and optimized.

[1989] Evaluate the performance of the trained model and retrain it with additional data if necessary.

[1990] Step 4: Emotion Recognition Phase

[1991] 4-1. Terminal:

[1992] The user's voice and input text are sent to the emotion engine.

[1993] 4-2. Emotion Engine:

[1994] Analyze emotions from user voice and text.

[1995] The analyzed emotion information is sent to the server.

[1996] 4-3. Server:

[1997] Emotion information is received and stored in a database.

[1998] Step 5: Generate Phase

[1999] 5-1. User:

[2000] A user who wishes to have a conversation or consultation inputs a request through the terminal interface.

[2001] 5-2. Terminal:

[2002] Receives a user request and converts it into a request format.

[2003] Send the request to the server.

[2004] 5-3. Server:

[2005] Requests and emotional information are integrated and input into a personality representation model.

[2006] The AI ​​model generates the appropriate response.

[2007] 5-4. Server:

[2008] Format the generated response in a user-friendly format.

[2009] Sends the response to the terminal.

[2010] Step 6: Response Presentation Phase

[2011] 6-1. Terminal:

[2012] The response received from the server is parsed and displayed in the user interface.

[2013] If necessary, speech synthesis is performed to provide a response in voice.

[2014] 6-2. User:

[2015] Review the suggested responses.

[2016] Determine next actions or further requests as needed.

[2017] Examples:

[2018] 1. Family advice:

[2019] The user inputs into the terminal, "There have been a lot of disagreements in the family recently. How should we deal with them?"

[2020] The emotion engine analyzes the user's emotions, such as anxiety or confusion, and transmits them to the server.

[2021] The server uses a replica model of the deceased person's personality to generate a response such as, "Differences of opinion should be seen as opportunities for growth. Let's respect each other's opinions," and provides it in a warm, emotionally appropriate format.

[2022] The terminal displays the generated response on the screen and also plays it aloud.

[2023] This embodiment allows users to have conversations and consultations with great or deceased figures and deceased figures that are tailored based on their emotions, allowing them to receive more human-like responses, which gives users a sense of security and allows them to receive specific advice and mental support.

[2024] Example 2

[2025] 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."

[2026] Conventional conversation systems have had difficulty responding appropriately to the diversity of data provided by users and the fluctuations in their emotions, and generating human-like responses. Furthermore, to realize a dialogue with a deceased or great figure, it is necessary to reproduce the person's language and thought patterns and provide responses that adapt to the user's emotions, but there has been a lack of effective means to achieve this.

[2027] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and storing data such as everyday remarks, conversations, thoughts, photos, and videos, means for preprocessing the stored data, converting voice data into text, and removing noise, means for training a personality reproduction model using the preprocessed data, means for analyzing emotions from the user's voice and text, means for receiving a request from the user based on the emotion analysis result and generating a response using the trained personality reproduction model, and means for presenting the generated response to the user. This generates a human-like response according to the user's emotions, making it possible to have a more realistic conversation with deceased or great people.

[2028] "Receiving" means that a terminal or server obtains data or requests provided by a user.

[2029] "Storage" means to hold the received data in a storage device or database.

[2030] "Preprocessing" refers to analyzing and processing the received data and converting it into a format suitable for subsequent processing.

[2031] "Converting voice data to text" means extracting text information from a voice signal using voice recognition technology.

[2032] "Noise reduction" refers to removing unwanted noise and unnecessary information from audio data or other data.

[2033] A "personality reproduction model" is an artificial intelligence model that learns the language and thought patterns of a specific person and generates responses based on that.

[2034] "Emotion analysis" refers to identifying a user's mood or emotional state from their voice or text.

[2035] "Receiving a request" means acquiring a question or inquiry from a user.

[2036] "Generating a response" means creating an appropriate reply based on the received request and the results of sentiment analysis.

[2037] "Presenting" refers to visually or audibly indicating to the user the generated response.

[2038] "Virus scanning" means inspecting stored data for the presence of malware or viruses.

[2039] "Integrity checking" is the process of verifying that data is accurate and complete.

[2040] "Speech synthesis" is a technology that generates human speech from text data.

[2041] The present invention is a system that allows users to converse with or consult with great or deceased figures using data such as past statements, conversations, thoughts, photographs, and videos. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the responses provided become more human-like and appropriate to the situation. A specific embodiment for implementing this system is described below.

[2042] System configuration

[2043] The system consists of the following main components: a server, a terminal, a user, and an emotion engine.

[2044] Server: Plays a central role in receiving, storing, and preprocessing data, training the personality model, processing requests, generating responses, and recognizing emotions. It requires a high-performance server for hardware, and uses a database management system and machine learning libraries (e.g., TensorFlow and PyTorch) for training the AI ​​model for software.

[2045] Device: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays and plays generated responses. Examples include smartphones and PCs.

[2046] Users: use the system to provide data and enter requests for conversations or consultations. Users use dedicated applications.

[2047] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server. This uses natural language processing libraries for emotion recognition (e.g., NLTK and SpaCy) and voice analysis tools (e.g., Google Speech-to-Text API).

[2048] System Operation

[2049] 1. Data Collection:

[2050] Users use devices such as smartphones and PCs to upload data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[2051] Example: A user uploads a video message or letter from a deceased person to the device.

[2052] Sample prompt: The user says, "I'm uploading a message from a deceased loved one."

[2053] 2. Data Preprocessing:

[2054] The server stores the received data, performs virus scans and integrity checks, then converts the audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[2055] Example: A server uses OCR technology to extract text from a photo.

[2056] 3. Model training:

[2057] The server uses the preprocessed data to train a personality model, which then replicates the speech patterns and thought patterns of a specific person.

[2058] Example: The AI ​​model learns from the statements and letters of the deceased person as training data.

[2059] 4. Emotion recognition:

[2060] The emotion engine analyzes emotions from the user's voice and text and provides the results to the server.

[2061] Example: When a user says, "I've been feeling stressed at work lately," the emotion engine recognizes the emotions "stress" and "anxiety."

[2062] 5. Response Generation:

[2063] A user inputs a request for conversation or consultation into a terminal, which then transmits the request to a server.

[2064] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[2065] Example: Generate a response that reflects the user's emotion of "stress": "First, take a deep breath and take some time to relax."

[2066] 6. Response suggestions:

[2067] The server formats the generated response in a format that is easy for the user to understand, and the terminal displays the received response and, if necessary, performs speech synthesis to provide an audible response.

[2068] Example: The device displays the text "Take a moment to relax and take a deep breath" and plays it aloud at the same time.

[2069] This system allows users to converse and consult with deceased or great figures, and an emotion engine adjusts responses to match the user's emotions, resulting in a more human-like interaction.

[2070] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2071] Step 1:

[2072] Data collection:

[2073] Users use devices such as smartphones and PCs to upload data such as statements, conversations, thoughts, photos, and videos related to deceased or great people.

[2074] Specific operation: The user launches the smartphone app, taps the "Data Upload" button, selects photos and videos of the deceased person from the gallery, and begins uploading.

[2075] Input: Media such as photos, videos, audio data, and text.

[2076] Output: Data stored on the device.

[2077] Step 2:

[2078] Data transmission:

[2079] The terminal transmits the received data to the server.

[2080] Specific operation: The device checks the network connection and sends the saved data to the server.

[2081] Input: Data stored on the device.

[2082] Output: The server receives the data.

[2083] Step 3:

[2084] Data preprocessing:

[2085] The server stores the received data and performs virus scans and integrity checks.

[2086] Specific operation: The server runs a virus scan software on the data received to check its integrity.

[2087] Input: Received photos, videos, audio data, and text.

[2088] Output: Pre-processed data that has been verified as safe.

[2089] Step 4:

[2090] Data Analysis:

[2091] The server analyzes the stored data, converts audio data into text, removes noise, and extracts and associates important scenes and text information from photos and videos.

[2092] How it works: The server uses speech recognition software to convert speech to text, applies noise filtering algorithms, uses image recognition to extract key scenes from photos, and uses OCR technology to extract text.

[2093] Input: Pre-processed data that has been verified as safe.

[2094] Output: Parsed text data, clean audio data, extracted information.

[2095] Step 5:

[2096] Model training:

[2097] The server trains a personality reproduction model using the preprocessed data.

[2098] What it does: The server uses a machine learning library (e.g., TensorFlow or PyTorch) to train an AI model and iterate over the dataset.

[2099] Input: Analyzed text data and audio data.

[2100] Output: A trained personality reproduction model.

[2101] Step 6:

[2102] Emotion recognition:

[2103] The emotion engine analyzes emotions from the user's voice and text and provides the results to the server.

[2104] Specific operation: When a user says to the device, "I've been feeling stressed at work lately," the emotion engine analyzes the voice data and recognizes emotions such as "stress" and "anxiety."

[2105] Input: User's voice and text data.

[2106] Output: Parsed emotion information.

[2107] Step 7:

[2108] Request processing and response generation:

[2109] A user inputs a request for conversation or consultation into a terminal, which then transmits the request to a server.

[2110] The server integrates the request and the emotional information sent from the emotion engine and generates an appropriate response using a personality reproduction model.

[2111] Specific operation: The user types "How can I reduce stress?" into the chat window, and the device sends the request to the server. The server generates a response using the personality reproduction model and emotional information.

[2112] Input: User request, emotion information, trained personality model.

[2113] Output: The generated response.

[2114] Step 8:

[2115] Response prompt:

[2116] The server formats the generated response in a user-friendly format and sends it to the device, which displays the received response and, if necessary, provides a spoken response using speech synthesis.

[2117] Specific operation: The server sends the text and audio data "First, take a deep breath and take some time to relax" to the device, which then displays and plays it back.

[2118] Input: The generated response data.

[2119] Output: The response (text and audio) presented to the user.

[2120] Step 9:

[2121] User Feedback:

[2122] Users can enter feedback on the responses they provide, which can be used to improve the system.

[2123] What happens: The user sends feedback saying "This response was helpful."

[2124] Input: User feedback.

[2125] Output: The feedback data is stored on the server and used for future improvements.

[2126] (Application example 2)

[2127] 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."

[2128] Conventional counseling support systems have difficulty generating responses that take into account the user's emotional state, making it difficult to provide advice that is appropriate for each individual user. Providing appropriate support that takes into account the client's past data and emotion analysis results is also a challenge. As a result, these systems are not sufficiently effective in improving the user's mental health or reducing stress.

[2129] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving and saving data such as everyday remarks, conversations, thoughts, photos, and videos; means for preprocessing the saved data, converting voice data into text, and removing noise; means for training a personality reproduction model using the preprocessed data; means for analyzing requests from users with an emotion engine and acquiring emotional information; means for generating prompts based on the acquired emotional information and adjusting the generated responses; means for providing the generated responses to the user as voice through speech synthesis; and means for providing appropriate advice and support during counseling sessions based on the client's past data and emotion analysis results. This makes it possible to provide more personalized advice and support that takes the user's emotional state into consideration.

[2130] "Data" refers to information such as statements, conversations, thoughts, photographs, and videos obtained from a variety of sources.

[2131] "Preprocessing" refers to the process of analyzing the received data, converting the voice data into text, and removing noise.

[2132] A "personality reproduction model" is a machine learning model that reproduces the language and thought patterns of a specific person based on preprocessed data.

[2133] A "request" is an input of a question or conversation that a user makes to the system.

[2134] The "emotion engine" is a system component that analyzes emotions from the user's voice and text and obtains the results.

[2135] A "prompt sentence" is an input sentence that adjusts the response to be generated based on the emotional information analyzed by the emotion engine.

[2136] A "response" is a response from the system generated in response to a user request.

[2137] "Speech synthesis" is a technique that provides a generated response to a user as speech.

[2138] A "counseling session" is a time or opportunity for a client to consult or meet with a counselor.

[2139] "Past data" refers to records of information such as utterances, conversations, and emotional states that users have provided to the system in the past.

[2140] "Appropriate advice and support" refers to the most effective advice and support measures based on the user's emotional state and past data.

[2141] The present invention is a system that allows users to converse with or consult with great or deceased figures using everyday speech, conversation, thoughts, photos, videos, etc. Furthermore, by combining it with an emotion engine, the responses provided are more human-like and appropriate to the situation. Specific embodiments of this system are described below.

[2142] System configuration

[2143] The system consists of the following main components:

[2144] Server: The central role is to receive, store, and preprocess data, train the personality model, process requests, generate responses, and recognize emotions.

[2145] Terminal: Provides the user interface, collects data from the user, inputs requests, monitors emotions, and displays or plays audio generated responses.

[2146] User: Uses the system to provide data and enter requests for conversations or consultations.

[2147] Emotion engine: Analyzes emotions from the user's voice and text and provides the results to the server.

[2148] Specific processing flow

[2149] 1. Data collection and preprocessing:

[2150] Users upload data such as statements, thoughts, photos, and videos related to deceased or great people to the server via their devices.

[2151] The server stores and pre-processes the received data, which includes converting the audio data to text, removing noise, and extracting important information from photos and videos.

[2152] 2. Training the personality model:

[2153] Using the pre-processed data, the server trains a personality model that can learn the phrasing and thought patterns of a particular person.

[2154] 3. Emotion analysis:

[2155] The emotion engine analyzes the user's input data and recognizes the user's emotional state. This emotion information is sent to the server and used to generate appropriate responses.

[2156] 4. Generate and present the response:

[2157] When a user inputs a request, the server integrates the request with emotional information from the emotion engine and generates an appropriate response using a personality reproduction model.

[2158] The generated response is presented to the user as text and speech.

[2159] Hardware and software used

[2160] Hardware: Smartphone

[2161] software:

[2162] OpenAI API: Generate responses using generative AI models.

[2163] Emotional Analysis: Analyzes user emotions using an emotion engine.

[2164] Data Processor: Preprocesses and formats user data.

[2165] TextToSpeech: Text-to-speech technology to provide a generated response audibly.

[2166] Specific examples

[2167] As a concrete example, entering the following prompt sentence will cause the system to generate an appropriate response:

[2168] text

[2169] The user is experiencing the emotion of stress. Generate an appropriate response to the following request: I've been feeling stressed at work lately. What should I do?

[2170] This allows the server to generate a response such as "First, take a deep breath and take some time to relax," which is then provided to the user via voice synthesis technology.This system allows users to receive emotionally sensitive advice without meeting a counselor in person.

[2171] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2172] Step 1:

[2173] Users upload data such as statements, thoughts, photos, and videos related to deceased or great people to a server via their device. At this time, the device accepts data selection and upload operations through a user interface. Input data includes audio data, text data, image data, etc., and is sent to the server. As an output, a data file in the format saved on the server is generated.

[2174] Step 2:

[2175] The server stores the received data and performs preprocessing. Specifically, it scans the stored data for viruses and checks its integrity. It then converts the audio data into text and removes noise. For image and video data, it extracts important scenes and text information. The input is the data received in step 1, and the output is a clean, preprocessed dataset.

[2176] Step 3:

[2177] The server uses the preprocessed data to train a personality reproduction model. This process involves feeding the clean dataset into an AI training algorithm to learn the linguistic and thought patterns of a specific person. Specifically, the model is trained using the OpenAI API. The input is the preprocessed dataset, and the output is a trained personality reproduction model.

[2178] Step 4:

[2179] The emotion engine analyzes the user's input data and recognizes emotional information. The user inputs their consultation details via text or voice through the device. The device sends this input data to the server, which then activates the emotion engine to analyze the emotions. Emotional information is output as "stress," "anxiety," "joy," etc. and sent to the server.

[2180] Step 5:

[2181] The user inputs a request for conversation or consultation into the terminal. The terminal sends this request to the server. The server integrates the request with the emotional information sent from the emotion engine and generates a prompt. The input is the user's request and emotional information, and the output is the generated prompt.

[2182] Step 6:

[2183] The server uses the prompt sentence to generate an appropriate response using a personality reproduction model. The server inputs the prompt sentence into the generative AI model (OpenAI API) and generates a response. The input is the generated prompt sentence, and the output is an appropriate response text. As a concrete example, the following prompt sentence is used:

[2184] text

[2185] The user is experiencing the emotion of stress. Generate an appropriate response to the following request: I've been feeling stressed at work lately. What should I do?

[2186] Step 7:

[2187] The generated response is presented to the user. The terminal receives the response from the server, displays it as text, and plays it back as audio using speech synthesis technology. Specifically, it uses the TextToSpeech module to convert the generated text into speech. The input is the response text from the server, and the output is a response in audio and text format that the user can understand.

[2188] 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.

[2189] 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.

[2190] 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.

[2191] 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.

[2192] 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.

[2193] 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.

[2194] 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).

[2195] 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.

[2196] 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."

[2197] 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.

[2198] 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).

[2199] 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.

[2200] 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.

[2201] 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.

[2202] 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.

[2203] 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.

[2204] 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.

[2205] 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.

[2206] 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.

[2207] 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.

[2208] 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.

[2209] The following is further disclosed regarding the above embodiment.

[2210] (Claim 1)

[2211] A means to receive and store data such as everyday statements, conversations, thoughts, photos, and videos,

[2212] means for preprocessing the stored data, converting speech data to text, and removing noise;

[2213] means for training a personality reconstruction model using the preprocessed data;

[2214] means for receiving a request from a user and generating a response using the trained personality reproduction model;

[2215] means for presenting the generated response to a user;

[2216] A system including:

[2217] (Claim 2)

[2218] 10. The system of claim 1, further comprising means for performing virus scanning and integrity checking of user-provided data.

[2219] (Claim 3)

[2220] 10. The system of claim 1, further comprising means for providing the generated response audibly to the user by speech synthesis.

[2221] "Example 1"

[2222] (Claim 1)

[2223] A means to receive and store data such as everyday statements, conversations, thoughts, photos, and videos,

[2224] means for preprocessing the stored data, converting speech data to text, and removing noise;

[2225] means for training a personality reconstruction model using the preprocessed data;

[2226] means for receiving a request from a user and generating a response using the trained personality reproduction model;

[2227] means for presenting the generated response to a user;

[2228] A system including:

[2229] (Claim 2)

[2230] 10. The system of claim 1, further comprising means for performing virus scanning and integrity checking of user-provided data.

[2231] (Claim 3)

[2232] 10. The system of claim 1, further comprising means for providing the generated response audibly to the user by speech synthesis.

[2233] "Application Example 1"

[2234] (Claim 1)

[2235] A means to receive and store data such as everyday statements, conversations, thoughts, photos, and videos,

[2236] means for preprocessing the stored data, converting speech data to text, and removing noise;

[2237] means for training a personality reconstruction model using the preprocessed data;

[2238] means for receiving a request from a user and generating a response using the trained personality reproduction model;

[2239] means for presenting the generated response to a user;

[2240] means for converting the generated response into speech and playing it back;

[2241] A system including:

[2242] (Claim 2)

[2243] 10. The system of claim 1, further comprising means for performing virus scanning and integrity checking of user-provided data.

[2244] (Claim 3)

[2245] The system according to claim 1, which uses a personality reproduction model that reproduces the characteristics of a deceased person or a great person.

[2246] "Example 2: Combining Emotion Engines"

[2247] (Claim 1)

[2248] A means to receive and store data such as everyday statements, conversations, thoughts, photos, and videos,

[2249] means for preprocessing the stored data, converting speech data to text, and removing noise;

[2250] means for training a personality reconstruction model using the preprocessed data;

[2251] A means of analyzing emotions from the user's voice and text;

[2252] means for receiving a request from a user based on the emotion analysis result and generating a response using the trained personality reproduction model;

[2253] means for presenting the generated response to a user;

[2254] A system including:

[2255] (Claim 2)

[2256] 10. The system of claim 1, further comprising means for performing virus scanning and integrity checking of user-provided data.

[2257] (Claim 3)

[2258] 10. The system of claim 1, further comprising means for providing the generated response audibly to the user by speech synthesis.

[2259] "Application example 2 when combining emotion engines"

[2260] (Claim 1)

[2261] A means to receive and store data such as everyday statements, conversations, thoughts, photos, and videos,

[2262] means for preprocessing the stored data, converting speech data to text, and removing noise;

[2263] means for training a personality reconstruction model using the preprocessed data;

[2264] means for receiving a request from a user and generating a response using the trained personality reproduction model;

[2265] means for presenting the generated response to a user;

[2266] A means for analyzing a request from a user using an emotion engine and acquiring emotion information;

[2267] a means for generating a prompt sentence based on the acquired emotional information and adjusting the generated response;

[2268] means for providing the generated response to the user as a voice by speech synthesis;

[2269] A system including:

[2270] (Claim 2)

[2271] 10. The system of claim 1, further comprising means for performing virus scanning and integrity checking of user-provided data.

[2272] (Claim 3)

[2273] 10. The system according to claim 1, further comprising means for providing appropriate advice and support during a counseling session based on the client's past data and emotion analysis results. [Explanation of symbols]

[2274] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to receive and store data such as everyday statements, conversations, thoughts, photos, and videos, means for preprocessing the stored data, converting speech data to text, and removing noise; means for training a personality reconstruction model using the preprocessed data; means for receiving a request from a user and generating a response using the trained personality reproduction model; means for presenting the generated response to a user; A system including:

2. 10. The system of claim 1, further comprising means for performing virus scanning and integrity checking of user-provided data.

3. The system of claim 1 further comprising means for providing the generated response audibly to the user by speech synthesis.

Images