system

Abstract

Provide a system. [Solution] A means for inputting personal thoughts and feelings in blog format; means for saving the input text data; means for analyzing the stored text data and extracting emotions and psychological states; a means for learning an individual's interaction style based on the extracted emotions and psychological states; means for generating a personalized artificial intelligence model based on the learned interaction style; A system including a means for interacting with a specific user using the generated artificial intelligence model.

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] When a person dies prematurely or unexpectedly, their surviving family members often lose psychological support and suffer from deep grief and loneliness. In particular, surviving children may lose the opportunity to inherit their parents' teachings and experiences, resulting in a lack of growth and mental support. Furthermore, technology for recreating the deceased's memories and conversations in the real world has not yet been sufficiently developed, creating a need for a digital dialogue system that preserves the deceased's personality. The present invention aims to solve these problems by providing a system that preserves the personality, emotions, and knowledge of a deceased individual in digital form, providing psychological comfort to their family members. [Means for solving the problem]

[0005] The present invention provides the following means: a means for inputting an individual's thoughts and feelings in blog format, a means for saving the input text data, and a means for analyzing the saved text data and extracting emotions and psychological states; a means for learning an individual's dialogue style based on the extracted emotions and psychological states, and a means for generating an individual's artificial intelligence model based on the learned dialogue style; and a means for using the generated artificial intelligence model to engage in dialogue with a specific user. By including a means for managing access rights for specific users and a means for presenting answers generated using the artificial intelligence model to specific users, it is possible to recreate the personality of the deceased while protecting privacy and providing psychological support to family members.

[0006] An "individual" refers to a person who inputs their own thoughts and feelings into the system, and who primarily functions as a user.

[0007] "Thoughts and feelings" refers to the expression of an individual's inner thoughts and feelings arising from their daily lives and experiences, and in this system refers to those entered in blog format.

[0008] "Blog format" refers to a method in which an individual records their thoughts and feelings, like a diary or blog, and is entered as text data.

[0009] "Text data" refers to all textual information entered by individuals, and refers to information that is stored by the system and used for analysis.

[0010] "Storage" refers to the act of recording input text data in a database or other recording medium for later processing.

[0011] "Analysis" refers to the process of processing stored text data and extracting emotions, psychological states, dialogue patterns, etc. from it.

[0012] "Emotions and psychological state" refers to information that indicates an individual's emotional reactions and mental state, extracted through analysis from an individual's input data.

[0013] "Dialogue style" refers to the unique language patterns and ways of expression that individuals have in dialogue, and is what the system learns.

[0014] "Learning" refers to the process by which the system builds an artificial intelligence model that mimics an individual's interaction style based on collected text data and analysis results.

[0015] "Artificial intelligence model" refers to an artificial intelligence program that reproduces a learned conversation style and enables conversation with family members or specific users.

[0016] "Generation" refers to the act of creating an artificial intelligence model based on an individual's interaction style, a process applied through the implementation of a computer program.

[0017] "Specific users" refer to users who are authorized to use the system, such as an individual's family, and who have the authority to interact with the artificial intelligence model.

[0018] "Dialogue" refers to verbal exchanges between a particular user and an artificial intelligence model, including responses based on a memorized dialogue style.

[0019] "Access authority" refers to the permission given to a specific user to use a system, and refers to the function of managing access to a system while protecting personal privacy.

[0020] "Presenting" refers to the act of displaying a response generated by an artificial intelligence model to a particular user, and refers to providing information to the user visually or audibly. [Brief explanation of the drawings]

[0021] [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 illustrating 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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The present invention is a system in which individuals input their thoughts and feelings in blog format, and then use an artificial intelligence model generated based on this information to have a dialogue with a specific user (for example, a family member).

[0043] System configuration

[0044] The system consists of the following main modules:

[0045] 1. Data Collection Module

[0046] 2. Data storage module

[0047] 3. Psychological Analysis Module

[0048] 4. AI Learning Module

[0049] 5. AI dialogue module

[0050] 6. Access Management Module

[0051] System Operation

[0052] Data Collection Module

[0053] Users enter their daily thoughts and feelings in the form of a blog. This entry is usually done through a device such as a computer or smartphone. For example, a user might enter, "Today I played in the park with my kids and had a great time." This text data is sent to a server in real time.

[0054] Data Storage Module

[0055] The server receives the text data sent from the device and stores it in a database. The stored data includes metadata such as the input text, input date and time, and user ID. This allows past input data to be accumulated and used for future analysis and learning.

[0056] Psychological Analysis Module

[0057] The server periodically analyzes the text data stored in the database. Using natural language processing (NLP) technology, it extracts an individual's emotions and psychological state from the text data. For example, the expression "I had fun" is tagged with a positive emotion tag. The analysis results are then stored back in the database.

[0058] AI Learning Module

[0059] The server uses the stored text data and psychological analysis results to learn an individual's conversation style and expression. During this learning process, machine learning algorithms are used to extract the main keywords, phrases, and emotional patterns used by the user, and an artificial intelligence model is generated based on this information. This model is stored in digital form and used for future interactions.

[0060] AI dialogue module

[0061] A specific user (such as a family member) can access the system and interact with an AI model of the deceased person. The server receives the specific user's input and uses the AI ​​model to generate a response to this input. For example, if the user inputs, "Good things happened at school today," the AI ​​will respond, "That's great. Your happiness makes me happy too."

[0062] Access Management Module

[0063] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[0064] Specific examples

[0065] User A left the following diary entry before his death:

[0066] I watched a movie with my family today and had a great time.

[0067] The server stored this text data and tagged it with "fun" and "family love" as the results of the sentiment analysis.

[0068] A few years later, User A's child accesses the system and types, "I got good marks at school today."

[0069] The server's AI model responds, "That's great! Your hard work has paid off."

[0070] As described above, this system digitally preserves personal memories and emotions, and allows for dialogue that can provide psychological support to family members. This technology not only allows family members to feel connected to the deceased, but also allows them to receive encouragement and advice. This system will provide peace of mind to family members and help ease their grief.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] Users can write their thoughts and feelings in a blog format. For example, a user can write an entry such as "I went to the park with my kids today and had a great time." This is done using devices such as smartphones and computers.

[0074] Step 2:

[0075] The device transmits the text data entered by the user to the server in real time, including the entry text, user ID, input date and time, and other metadata.

[0076] Step 3:

[0077] The server stores the received text data in a database, which accumulates all previous entries made by the user and makes them available for future processing.

[0078] Step 4:

[0079] The server periodically retrieves new text data stored in the database and performs a psychological analysis. Natural language processing (NLP) techniques are used to extract emotions and psychological states from the text data. For example, a positive emotion can be extracted from the expression "I had fun."

[0080] Step 5:

[0081] The server then stores the extracted emotions and psychological states in a database, allowing the user's emotional fluctuations and psychological states to be tracked over time.

[0082] Step 6:

[0083] The server uses the accumulated text data and psychological analysis results to learn the user's conversation style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[0084] Step 7:

[0085] The server digitally stores the AI ​​model for each user, and this model is then used to interact with that specific user (e.g., a family member).

[0086] Step 8:

[0087] When a particular user attempts to access the system, the server checks the access rights and goes through an authentication process to verify that the user is the correct user. If authentication is successful, the user is granted access.

[0088] Step 9:

[0089] When a specific user initiates a dialogue with the AI ​​model, the server receives the user's input and generates a response. In this process, the AI ​​model generates natural responses based on the input, and the dialogue takes place. For example, if a user types, "I got good grades at school today," the AI ​​will respond, "That's great, congratulations."

[0090] Step 10:

[0091] The server then presents the generated response to the particular user, who receives the response on their terminal and the dialogue continues.

[0092] In this way, the system digitally preserves and recreates the memories and emotions of the deceased, providing psychological comfort to the family. This process consists of a series of steps that accumulate the user's emotions and thoughts, and then generates and responds to them using a dialogue model.

[0093] Example 1

[0094] 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."

[0095] In modern society, there is no system that can digitally preserve the memories and emotions of the deceased and allow them to communicate with their families and loved ones. Furthermore, there is a lack of privacy protection features that protect the words and emotions of the deceased and make them available only to specific users. These issues need to be addressed.

[0096] 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.

[0097] In this invention, the server includes means for inputting personal thoughts and feelings in blog format, means for saving the input text data, means for analyzing the saved text data and extracting emotions and psychological states, means for learning the personal conversation style based on the extracted emotions and psychological states, means for generating a personal AI model based on the learned conversation style, means for engaging in conversation with a specific user using the generated AI model, means for accepting a conversation request from a specific user and generating a response using the AI ​​model, and means for managing access rights to ensure that only specific users can use the server. This allows family and loved ones to share memories and feelings of the deceased and engage in conversation, while also ensuring full privacy protection.

[0098] The "means for inputting personal thoughts and feelings in blog format" is a means for a user to input his or her daily thoughts and feelings in text format and send them to the system.

[0099] The "means for saving input text data" refers to a means for storing the text data input by the user in an information storage device such as a database.

[0100] The "means for extracting emotions and psychological states" refers to a means that uses natural language processing technology to analyze text data and extract the user's emotions and psychological states from the data.

[0101] The "means for learning an individual's conversational style" refers to a means using a machine learning algorithm that learns an individual's language usage and conversational patterns based on stored text data and the results of its sentiment analysis.

[0102] "Means for generating a personalized artificial intelligence model" means means for creating an AI model dedicated to an individual based on the learned interaction style.

[0103] The "means for having a dialogue with a specific user" is a means for having a virtual dialogue with a specified user using the generated artificial intelligence model.

[0104] "Means for accepting a dialogue request from a specific user and generating a response using an artificial intelligence model" refers to a means by which a system receives a dialogue request from a specific user and generates an appropriate response to that request using an AI model.

[0105] "Means for managing access rights and allowing only specific users to use the system" refers to means for authenticating users and managing their rights in order to ensure privacy protection of the system.

[0106] MODE FOR CARRYING OUT THE INVENTION

[0107] The present invention is a system that allows users to input their thoughts and feelings in a blog format, generates an AI model based on this information, and engages in a dialogue with a specific user. In one embodiment, the system operates as follows:

[0108] Data collection

[0109] Users use devices such as computers and smartphones to input their daily thoughts and feelings in blog format. For example, a user might use a smartphone application to input, "Today I played at the park with my kids. It was so much fun." This text data is sent to a server in real time via the device.

[0110] Data storage

[0111] The server processes the received text data, adds metadata such as the input date and time and user ID, and stores the data in a database. Database management uses software such as MySQL (registered trademark) or PostgreSQL.

[0112] psychological analysis

[0113] The server periodically analyzes the text data stored in the database. It uses natural language processing (NLP) technology to extract emotions and psychological states from the text data. For example, it assigns a positive emotion tag to the phrase "I had fun." This process uses Python NLP libraries (such as NLTK and SpaCy).

[0114] Training an AI model

[0115] The server learns an individual's conversation style and expression method based on the stored text data and psychological analysis results. This learning is done using machine learning libraries such as TENSORFLOW (registered trademark) and PyTorch. The personalized AI model generated through the learning process is saved in digital format.

[0116] User interaction

[0117] A specific user (e.g., a family member) can access the system and interact with the deceased person's AI model. For example, if a specific user inputs "Something good happened at school today," the server receives this request and uses a pre-trained AI model to generate and provide a response such as "That's good to hear, I'm happy for you."

[0118] Access Management

[0119] The server manages access permissions and ensures privacy protection for the system. It uses authentication technologies such as OAuth2.0 and JWT (JSON Web Token) to ensure that only specific users can access the system after going through the appropriate authentication process.

[0120] Specific examples

[0121] For example, if user A wrote in his diary, "Today I watched a movie with my family and had a great time," the server will save this text data and tag it with the emotion tags "fun" and "family love." A few years later, if user A's child writes, "I got good marks at school today," the server's AI model will respond, "That's great. Your hard work has paid off."

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] The user inputs their thoughts and feelings.

[0125] The input content is blog-style text data, and is sent to the server in real time using a device (e.g., a smartphone or computer).

[0126] Specifically, the user opens the smartphone application and enters "I played in the park with my kids today, it was a lot of fun." The input data (prompt text) is converted to JSON format and sent to the server via the API.

[0127] Step 2:

[0128] The server stores the received text data in a database.

[0129] Input: Text data sent by the user, input date and time, user ID, etc.

[0130] Output: Text data stored in a database.

[0131] Specifically, use database management software (e.g., MySQL or PostgreSQL) to execute the following SQL query: "INSERT INTO blog_entries (user_id, entry_date, text) VALUES (1, '2023-10-03 14:00:00', 'Today I played at the park with my kids. It was so much fun');"

[0132] Step 3:

[0133] The server analyzes the stored text data and extracts emotions and psychological states.

[0134] Input: Text data stored in a database.

[0135] Output: Extracted emotion tags and mental state data.

[0136] Specifically, it performs sentiment analysis using Python NLP libraries (such as NLTK and SpaCy) and assigns a positive emotion tag to the expression "it was fun."

[0137] Step 4:

[0138] The server learns the individual's interaction style based on the extracted emotions and psychological states.

[0139] Input: Psychological analysis results and text data.

[0140] Output: A learned personal interaction style model.

[0141] Specifically, it uses machine learning libraries (TensorFlow and PyTorch) to learn user interaction patterns, using collected keywords and phrases to train the model.

[0142] Step 5:

[0143] A specific user makes an interaction request.

[0144] Input: Text input from a particular user.

[0145] Output: The request data sent to the server.

[0146] Specifically, a dialogue request (for example, "Something good happened at school today") is sent from the terminal to the server.

[0147] Step 6:

[0148] The server accepts interaction requests and generates responses using artificial intelligence models.

[0149] Input: An AI model with text input and training data from a specific user.

[0150] Output: The generated response text.

[0151] Specifically, the trained AI model is used to generate an appropriate response (e.g., "That's great, I'm happy you're happy").

[0152] Step 7:

[0153] The server then sends the generated response to the particular user.

[0154] Input: The generated response text.

[0155] Output: The response message that is displayed on the user's terminal.

[0156] As a specific operation, the response text is sent to the user's terminal and displayed in the application or browser.

[0157] Step 8:

[0158] The server manages access privileges and allows only specific users to access the system.

[0159] Input: User credentials.

[0160] Output: The access allowed or denied decision.

[0161] Specifically, it uses authentication technologies such as OAuth2.0 and JWT (JSON Web Token) to perform appropriate user authentication.

[0162] (Application example 1)

[0163] 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."

[0164] In modern society, there are limited means to maintain connections and memories with the deceased, which places an immeasurable psychological burden on family and loved ones, especially when emotional support is needed. There is a need for a system that not only preserves the thoughts and feelings of the deceased, but also utilizes them to enable interaction as if they were alive. Furthermore, data privacy and secure management are essential.

[0165] 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.

[0166] In this invention, the server includes: means for inputting personal thoughts and feelings in blog format; means for saving the input text data; means for analyzing the saved text data and extracting emotions and psychological states; means for learning the personal conversation style based on the extracted emotions and psychological states; means for generating a personal AI model based on the learned conversation style; means for engaging in conversation with a specific user using the generated AI model; encryption means for securely saving and managing the input text data and conversation style; authentication means for allowing a specific user to securely access the system; and means for the generated AI model to present optimal responses to the specific user. This allows family members and close friends to feel an emotional connection with the deceased, safely manage data while protecting their privacy, and receive emotional support.

[0167] "Means for inputting personal thoughts and feelings in blog format" is an interface that allows users to input their daily thoughts and feelings in text format.

[0168] The "means for saving input text data" is a function for safely saving input text data in a storage device such as a database.

[0169] The "means for analyzing stored text data and extracting emotions and psychological states" is a function that analyzes stored text data using natural language processing technology and automatically extracts the user's emotions and psychological states from the data.

[0170] The "means for learning an individual's interaction style based on extracted emotions and psychological states" is a process of learning a user's interaction patterns using a machine learning algorithm based on extracted emotional and psychological state data.

[0171] The "means for generating a personal AI model based on the learned dialogue style" is a function for generating an AI model that imitates the user's dialogue style based on the learned dialogue patterns.

[0172] "Means for using the generated artificial intelligence model to have a dialogue with a specific user" is a system for using the generated artificial intelligence model to have a dialogue with a specific user.

[0173] "Encryption methods for securely storing and managing input text data and interaction styles" refers to methods for encrypting stored text data and machine learning models to protect them from unauthorized access.

[0174] "Authentication means that allows a specific user to access the system safely" is an authentication function that verifies that the user accessing the system is a legitimate user.

[0175] "Means for the generated artificial intelligence model to present the optimal response to a specific user" refers to the function by which the generated AI model generates an appropriate response to the user's input and presents it to the user.

[0176] This invention relates to a system that allows users to input their thoughts and feelings in blog format, generate an AI of the deceased based on the input, and allow specific users to interact with the AI. The system is composed of multiple modules, including data privacy protection and secure management.

[0177] System Configuration

[0178] The system consists of the following main modules:

[0179] 1. Data Collection Module

[0180] 2. Data storage module

[0181] 3. Psychological Analysis Module

[0182] 4. AI Learning Module

[0183] 5. AI dialogue module

[0184] 6. Encryption methods

[0185] 7. Authentication Methods

[0186] Data Collection Module

[0187] Users can input their daily thoughts and feelings in blog format on their devices. This input data can be done using an app on their smartphone. For example, if a user inputs "I had a great time at the park today," the text data is sent to the server in real time.

[0188] Data Storage Module

[0189] The server receives the text data sent from the device and stores it in a database (e.g., MongoDB). Data is stored using encryption technology (AES encryption) to protect the privacy of the data.

[0190] Psychological Analysis Module

[0191] The server analyzes the stored text data using a Python library (e.g., SpaCy or NLTK), extracts the emotions and psychological states contained in the text from the analysis results, and stores the information back in the database.

[0192] AI Learning Module

[0193] The server uses the stored text data and the results of the psychology analysis to learn the individual's interaction style. This learning process involves using machine learning algorithms (e.g., TensorFlow or PyTorch) to build a generative AI model that reflects the user's interaction style.

[0194] AI dialogue module

[0195] A specific user (e.g., a family member) can access the system and interact with the AI ​​model of the deceased. This interaction module responds to the user's input using a generative AI model stored on the server. For example, if the user types, "I got good marks in school today," the AI ​​will respond, "That's great! Your hard work has paid off."

[0196] Encryption method

[0197] The server applies AES encryption to all data transmitted and stored to prevent unauthorized access to the data.

[0198] Authentication Method

[0199] The server uses OAuth 2.0 to authenticate users so that only specific users can access the system. Therefore, the authentication system protects user privacy and manages appropriate access rights.

[0200] Specific examples

[0201] User A writes in his diary: "I felt good today, I went to lunch with a friend."

[0202] 1. Data collection: This data is sent from the smartphone to a server.

[0203] 2. Data storage: Data is stored in MongoDB and AES encrypted.

[0204] 3. Psychological analysis: NLP technology is used to assign a positive tag based on "feeling good."

[0205] 4. AI Learning: Learn positive emotion patterns from past data.

[0206] 5. AI dialogue: When User B types into the app, "I received an award in class today," the AI ​​responds, "That's great, that's a really nice thing to happen."

[0207] 6. Authentication and encryption: Authentication is provided to ensure access is restricted to family members only.

[0208] Prompt Sentence Examples

[0209] User: "I got recognized in class today."

[0210] AI response: "Amazing, that's a really good thing."

[0211] This system allows families and loved ones to feel an emotional connection with the deceased, safely manage their data, and receive emotional support.

[0212] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0213] Step 1:

[0214] Users input their daily thoughts and feelings in blog format from their devices. Input data is done using an app on their smartphone. For example, if they input "I had a great time at the park today," the input text data is generated and sent to the server. The input is the user's text data, and the output is the transmission of the text data to the server.

[0215] Step 2:

[0216] The server receives the text data sent from the terminal and stores it in a database (for example, MongoDB). At this time, AES encryption is applied to the data storage to protect the privacy of the data. The input is the sent text data, and the output is stored in the database in encrypted form.

[0217] Step 3:

[0218] The server analyzes the stored text data using a Python library (e.g., SpaCy or NLTK). As a result of the analysis, emotions and psychological states contained in the text are extracted, and this information is stored in the database again. For example, positive emotions are extracted from the keyword "fun." The input is the stored text data, and the output is data on emotions and psychological states.

[0219] Step 4:

[0220] The server uses the stored text data and the results of the psychology analysis to learn the individual's interaction style. This learning process uses machine learning algorithms (e.g., TensorFlow or PyTorch). A generative AI model that reflects the user's interaction style is constructed. The input is past text data and emotion data, and the output is the trained AI model.

[0221] Step 5:

[0222] When a specific user accesses the server, authentication is performed using OAuth 2.0. This process ensures that only specific users can access the system. The input is the user's authentication information, and the output is the result of authentication success or failure.

[0223] Step 6:

[0224] A specific user (e.g., a family member) accesses the system and interacts with the AI ​​model of the deceased person. This interaction process is carried out using a generative AI model stored on the server. For example, if the user inputs, "I got good marks in school today," the AI ​​responds, "That's great! Your hard work paid off." The input is the user's text input, and the output is the AI's response.

[0225] Step 7:

[0226] The server continuously stores all text data and dialogue logs in a database so that they can be reused for analysis and learning as needed. This data is also encrypted to protect it from unauthorized access. The input is dialogue log data, and the output is encrypted log data stored in a database.

[0227] 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.

[0228] The present invention is a system in which individuals input their thoughts and feelings in blog format, analyze this data in real time using an emotion engine, generate an artificial intelligence model, and engage in dialogue with specific users.

[0229] System configuration

[0230] The system consists of the following main modules:

[0231] 1. Data Collection Module

[0232] 2. Data storage module

[0233] 3. Psychological Analysis Module

[0234] 4. Emotion Engine

[0235] 5. AI Learning Module

[0236] 6. AI dialogue module

[0237] 7. Access Management Module

[0238] System Operation

[0239] Data Collection Module

[0240] A user writes his or her thoughts and feelings in a blog format. For example, a user might write, "I had a great time with my friends today." The device then sends this text data to the server in real time.

[0241] Data Storage Module

[0242] The server receives the text data sent from the terminal and stores it in a database, which accumulates all the entries the user has previously entered and makes them available for future processing.

[0243] Psychological Analysis Module

[0244] The server periodically retrieves the text data stored in the database and analyzes the emotions in real time using an emotion engine. Natural language processing (NLP) techniques are used to extract an individual's emotions and psychological state from the text data. For example, a positive emotion can be extracted from the expression "It was fun."

[0245] Emotion Engine

[0246] The server uses an emotion engine to extract emotions from the user's input data in real time. The emotion engine passes the analyzed emotion data to a psychology analysis module, which stores the emotions and psychological state in a database. This makes it possible to track the user's emotional fluctuations and psychological state over time.

[0247] AI Learning Module

[0248] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[0249] AI dialogue module

[0250] A specific user (for example, a family member) can access this system and interact with the user's artificial intelligence model. The server receives the specific user's input and uses the artificial intelligence model to generate a response to this input. The input data is analyzed using an emotion engine, and a natural response is generated according to the user's emotions. For example, if the user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[0251] Access Management Module

[0252] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[0253] Specific examples

[0254] User A left the following diary entry before his death:

[0255] I enjoyed dinner with my family today and felt very happy.

[0256] The server stored this text data and extracted the positive emotion of "happiness" using an emotion engine. This data was then trained by an AI learning module to model User A's conversation style.

[0257] A few years later, User A's child accesses the system and types, "I'm a little nervous about my final exam today."

[0258] The server's AI model uses an emotion engine to analyze the input data in real time and respond with something like, "You seem nervous. Why don't you plan something fun to help you relax after the exam?"

[0259] As described above, this system digitally preserves and reproduces the memories and emotions of the deceased, and realizes a dialogue that provides psychological comfort to family members. This process consists of a series of steps that accumulate the user's emotions and thoughts, and then generate and respond to a dialogue model based on them.

[0260] The processing flow will be explained below.

[0261] Step 1:

[0262] A user inputs his / her thoughts and feelings into the terminal in a blog format, for example, "I had a great time with my friends today."

[0263] Step 2:

[0264] The device sends the entered text data to the server in real time, including the entry text, user ID, input date and time, and other metadata.

[0265] Step 3:

[0266] The server receives the text data and stores it in a database, including the text data and metadata.

[0267] Step 4:

[0268] The server periodically retrieves the stored text data and analyzes the emotions in real time using an emotion engine. Natural language processing (NLP) techniques are used to extract the user's emotions and psychological state from the text data. For example, a positive emotion is extracted from the expression "It was fun."

[0269] Step 5:

[0270] The emotion engine passes the extracted emotion data to the psychology analysis module, and stores the emotion and psychological state in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time.

[0271] Step 6:

[0272] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses.

[0273] Step 7:

[0274] The server then builds an artificial intelligence model based on the learning results and stores this model digitally for each user, which can then be used to interact with specific users (such as family members).

[0275] Step 8:

[0276] When a particular user attempts to access a system, the server checks their access rights, goes through an authentication process to verify they are the right user, and then allows or denies access.

[0277] Step 9:

[0278] When a specific user initiates a dialogue with the AI ​​model, the server receives the user's input and analyzes the user's emotions in real time using the emotion engine. Based on this analysis, the AI ​​model generates a response to the user's input.

[0279] Step 10:

[0280] The server presents the generated response to the specific user, and in some cases, the emotion engine adjusts the response content to make it appear more natural and emotionally appropriate.

[0281] Specific examples

[0282] For example, if User A enters "I enjoyed dinner with my family today and felt very happy," the emotion engine will extract the positive emotion of "happiness" and store it in the database. A few years later, if User A's child accesses the system and enters "I'm a little nervous today because of my final exam," the AI ​​model on the server will use the emotion engine to analyze the input data in real time and respond with, "You seem nervous. Why don't you plan something fun to help you relax after the exam is over?"

[0283] In this way, the system digitally preserves and recreates the memories and emotions of the deceased, enabling interaction that provides psychological comfort to family members.

[0284] Example 2

[0285] 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."

[0286] In modern society, systems that effectively store individual thoughts and emotions and use them to engage in conversations using artificial intelligence are still in the early stages of development. In particular, there are few systems that analyze user input data in real time and generate responses based on emotions and psychological states, and only a limited number of systems also provide privacy protection. This makes it difficult for users to deeply understand their own emotions or to have emotionally rich conversations with specific users. Furthermore, there are security issues due to the lack of access permission management.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0288] In this invention, the server includes a means for inputting personal thoughts and feelings in blog format, a means for saving the input text data, a means for analyzing emotions and psychological states, a means for engaging in dialogue with specific users using the generated AI model, and a means for managing access rights, which enables real-time analysis of input data, tracking of emotions and psychological states, generation of dialogue models and presentation of responses, and access management with enhanced security.

[0289] "Personal thoughts and feelings" refers to the thoughts and feelings that a user has internally.

[0290] "Means for inputting in blog format" refers to a function that allows users to input their thoughts and feelings as text into the device and send that data to the server.

[0291] "Means for saving input text data" refers to a function that saves the text data entered by the user in a database so that it can be used for subsequent processing or analysis.

[0292] "Means for extracting emotions and psychological states" refers to technology for analyzing stored text data and identifying a user's emotions and psychological states.

[0293] "Means for learning individual conversational styles" refers to a function for learning a user's unique language patterns and conversational styles based on extracted emotional and psychological state data.

[0294] "Means for generating an artificial intelligence model" refers to a technology for generating an artificial intelligence model for engaging in natural dialogue with a user based on a learned dialogue style.

[0295] "Means for interacting with a specific user" refers to a function for using the generated artificial intelligence model to interact with a specific user and provide an appropriate response.

[0296] "Emotion engine" refers to technology for analyzing and extracting user emotions from input data.

[0297] "Means of storing in a database" refers to the storage technology that allows the server to retain the data received and make it accessible in the future.

[0298] "Means for managing access rights" refers to technology that authenticates and verifies permissions to ensure that only specific users can access the system.

[0299] "Means for sending in real time" refers to a function that allows text data entered by the user to be sent immediately to the server.

[0300] "Means for generating and presenting a response" refers to technology that uses a generated artificial intelligence model to generate a natural response based on the emotion of a specific user in response to input from that user, and then presents that response.

[0301] The present invention is a system in which individuals input their thoughts and feelings in blog format, analyze this data in real time using an emotion engine, generate an artificial intelligence model, and engage in dialogue with specific users. Next, a description will be given of an embodiment of the present invention with specific examples.

[0302] The system consists of the following main modules: data collection module, data storage module, psychology analysis module, emotion engine, AI learning module, AI dialogue module, and access management module.

[0303] Data Collection Module

[0304] A user writes his or her thoughts and feelings in a blog format. For example, a user might write, "I had a great time with my friends today." The device then sends this text data to the server in real time.

[0305] (Example prompt) "I had a great time with my friends today."

[0306] Data Storage Module

[0307] The server receives the text data sent from the terminal and stores it in a database, which accumulates all the entries the user has previously entered and makes them available for future processing.

[0308] Psychological Analysis Module

[0309] The server periodically retrieves the text data stored in the database and analyzes the emotions in real time using an emotion engine. Specifically, it uses natural language processing (NLP) technology to extract an individual's emotions and psychological state from the text data. For example, a positive emotion can be extracted from the expression "It was fun."

[0310] Emotion Engine

[0311] The server uses an emotion engine to extract emotions from the user's input data in real time. The emotion engine passes the analyzed emotion data to a psychology analysis module, which stores the emotions and psychological state in a database. This makes it possible to track the user's emotional fluctuations and psychological state over time.

[0312] AI Learning Module

[0313] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[0314] AI dialogue module

[0315] A specific user (e.g., a family member) can access this system and interact with the user's artificial intelligence model. The server receives the specific user's input and uses the artificial intelligence model to generate a response to this input. An emotion engine is used to analyze the input data and generate a natural response based on the user's emotions. For example, if a user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[0316] (Example prompt) "I feel a little tired today."

[0317] Access Management Module

[0318] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[0319] Examples:

[0320] User A left the following diary entry before his death: "Today I enjoyed dinner with my family and felt very happy."

[0321] The server stored this text data and extracted the emotion "happiness" using the emotion engine. This data was then trained by the AI ​​learning module to model User A's conversation style.

[0322] A few years later, if User A's child accesses the system and enters, "I'm a little nervous today because of my final exams," the server's AI model will use its emotion engine to analyze the input data and respond, "You're feeling nervous. Why don't you plan something fun to help you relax after the exams are over?"

[0323] In this way, the system accumulates the user's emotions and thoughts, and realizes natural and emotional dialogue based on the dialogue model.

[0324] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0325] Step 1:

[0326] Users input their thoughts and feelings into the device in a blog format. For example, they can write an entry such as, "I had a great time with my friends today." The input text data is sent from the device to the server in real time.

[0327] Specifically, when a user inputs a blog entry and presses the "Post" button, the terminal generates an HTTP request and sends the input data to the server.

[0328] Input: User-entered text data

[0329] Output: Sending text data from the terminal to the server

[0330] Step 2:

[0331] The server receives the text data sent from the terminal and stores it in a database. This save operation accumulates all entries that the user has previously entered.

[0332] Specifically, the server receives an HTTP request, extracts text data from the request body, and inserts it into a database.

[0333] Input: Text data received from the device

[0334] Output: Text data stored in the database

[0335] Step 3:

[0336] The server periodically retrieves text data from the database and passes it to the emotion engine for real-time emotion analysis. Natural language processing (NLP) techniques are used to extract an individual's emotions and psychological state from the text data.

[0337] Specifically, the server retrieves new text entries from the database as a scheduled task, inputs them into the emotion engine, and obtains the analysis results.

[0338] Input: Text data in a database

[0339] Output: Analysis results by the emotion engine (emotion data)

[0340] Step 4:

[0341] The server uses the emotion engine to extract emotion data and stores it in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time.

[0342] Specifically, the server receives the emotion data returned from the emotion engine and stores it in a database.

[0343] Input: Emotion engine analysis results (emotion data)

[0344] Output: Emotion data stored in a database

[0345] Step 5:

[0346] The server uses the stored text and emotion data to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns and emotional responses and build an artificial intelligence model.

[0347] Specifically, the server periodically retrieves text data and emotion data from the database, inputs them into a machine learning algorithm to train a model, and saves the generated model.

[0348] Input: Text data and emotion data

[0349] Output: A trained artificial intelligence model

[0350] Step 6:

[0351] A specific user can access the system and interact with the AI ​​model. The server receives the specific user's input and uses the AI ​​model to generate a response. The emotion engine is used to analyze the input data and generate a natural response.

[0352] Specifically, when a specific user enters a sentence into the dialogue interface and presses the "send" button, the server receives the input data, analyzes it with the emotion engine, and the AI ​​model generates a response and sends it back to the specific user.

[0353] Input: Data entered by a specific user

[0354] Output: The response generated from the artificial intelligence model

[0355] Step 7:

[0356] The server manages access privileges and allows only specific users to access the system. This is for the purpose of system security and privacy protection, and verifies specific users through an authentication process and grants or denies access privileges.

[0357] Specifically, the server checks the authentication information when a user logs in and assigns appropriate access privileges.

[0358] Input: User credentials

[0359] Output: Access granted or denied

[0360] (Application example 2)

[0361] 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."

[0362] As individuals record their daily experiences and emotions, there is a need for a system that can suggest appropriate content based on this information and provide support and empathy through dialogue. However, existing systems are limited in their ability to effectively utilize individuals' emotional data to suggest personalized content and provide dialogue. As a result, users are often left with low satisfaction and find it difficult to receive psychological support or empathy.

[0363] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0364] In this invention, the server includes means for inputting personal thoughts and feelings in blog format, means for saving the input text data, means for analyzing the saved text data and extracting emotions and psychological states, means for learning the personal interaction style based on the extracted emotions and psychological states, means for generating a personal AI model based on the learned interaction style, means for suggesting content such as movies, music, and books based on the emotions and psychological states input by the user, means for engaging in dialogue with a specific user using the generated AI model, and means installed on a smartphone that allows the user to input emotions in diary format. This enables personalized content suggestions and dialogue based on the individual's emotions and psychological states, thereby improving support and empathy for the user.

[0365] "Means for inputting personal thoughts and feelings in blog format" refers to a means by which a user inputs everyday experiences and feelings in text format and records them as a blog.

[0366] "Means for saving input text data" refers to means for recording the text data entered by the user in a database or storage and saving it permanently or for the long term.

[0367] The "means for analyzing stored text data and extracting emotions and psychological states" refers to a means for analyzing stored text data and identifying the emotions and psychological states of a user using natural language processing technology.

[0368] The "means for learning individual conversational styles" is a means for learning a user's unique language patterns and emotional responses based on the extracted emotions and psychological states.

[0369] The "means for generating a personal artificial intelligence model" is a means for creating an artificial intelligence model based on the learned dialogue style and conducting dialogue through this model.

[0370] The "means for suggesting content" is a means for recommending appropriate media content such as movies, music, books, etc. to a user based on the results of analyzing the user's emotions and psychological state.

[0371] The "means for interacting with a specific user" is a means for using the generated artificial intelligence model to interact with a specified user.

[0372] "A means that is installed on a smartphone and allows users to input emotions in diary format" is a means that is provided as a smartphone app and allows users to input daily events and emotions in diary format.

[0373] The present invention is a system that allows users to input their thoughts and feelings in blog format, analyzes the data in real time using an emotion engine, generates an artificial intelligence model, and engages in a dialogue with a specific user. This system is realized mainly using the following hardware and software:

[0374] Hardware and software used

[0375] Smartphone: A device used by users to enter their emotions in diary format.

[0376] Server: A machine that stores data, analyzes it, generates artificial intelligence models, and processes interactions.

[0377] Flask: A web framework for implementing server-side APIs.

[0378] spaCy: A library for performing natural language processing (NLP).

[0379] Program processing overview

[0380] Data collection and storage

[0381] Users use a smartphone application to input their daily experiences and feelings in blog format. This input text data is sent to a server in real time. The server stores the received text data in a database. For example, if a user inputs "I had a great time with my friends today," this text data is sent as is to the server and stored there.

[0382] Emotion analysis

[0383] The server periodically retrieves the stored text data and analyzes the emotions and psychological state in real time using the spaCy library. For example, positive emotions are extracted from the text "I had a good time." This emotional data is stored in a database and tracked over time.

[0384] Content Suggestion

[0385] The server then suggests appropriate content, such as movies, music, and books, to the user based on the analyzed emotions and psychological state. For example, if a user inputs "I feel a little tired today," the server will suggest relaxing music and movies.

[0386] Conducting dialogue

[0387] The server learns the user's interaction style based on the extracted emotions and psychological state, and then uses the generated AI model to converse with the specific user. For example, if a user inputs "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[0388] Specific examples

[0389] When User A opens the smartphone app and types, "I had a good time with my friends today," the server analyzes the emotion as positive and stores it. If User A then types, "I'm a little tired today," the server responds, "That must have been tough. It's important to take time to relax and rest."

[0390] Prompt Sentence Examples

[0391] If a user types, "I had a great time with my friends today," the system responds, "That's great. What do you want to do next?"

[0392] In this way, the system of the present invention can propose and interact with personalized content based on an individual's emotions and psychological state, thereby improving support and empathy for the user.

[0393] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0394] Step 1:

[0395] The user starts a smartphone application and enters their emotions in a diary format. This entry is in text format, such as "I had a great time with my friends today." The input data is sent from the smartphone to the server in real time.

[0396] Input: Text data entered by the user

[0397] Output: Text data sent to the server

[0398] Step 2:

[0399] The server stores the received text data in a database, which accumulates all user-entered entries and makes them available for later analysis and learning.

[0400] Input: Text data sent from the smartphone device

[0401] Output: Text entry stored in the database

[0402] Step 3:

[0403] The server periodically retrieves the stored text data and analyzes it in real time using an emotion engine, which uses natural language processing (NLP) technology to extract positive emotions from expressions such as "I enjoyed it."

[0404] Input: Text data stored in a database

[0405] Output: Parsed sentiment data (positive, negative, etc.)

[0406] Step 4:

[0407] The extracted emotion data is then stored in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time, revealing the user's emotional patterns at different times.

[0408] Input: Parsed emotion data

[0409] Output: Emotion data stored in a database

[0410] Step 5:

[0411] Based on the analyzed emotions and psychological state, the server learns the user's interaction style and generates an artificial intelligence model that learns the user's unique language patterns and emotional responses for use in future interactions.

[0412] Input: Emotion data and text data stored in the database

[0413] Output: An artificial intelligence model that reflects the user's interaction style and emotions

[0414] Step 6:

[0415] The server then suggests content such as movies, music, and books to the user based on the analyzed emotions and psychological state. For example, if a user inputs "I feel a little tired today," relaxing music and movies will be recommended.

[0416] Input: Analyzed emotion data, artificial intelligence model

[0417] Output: A list of suggested content for the user

[0418] Step 7:

[0419] A specific user (e.g., a family member) can interact with the system using the stored text data and the generated AI model. The server receives input from the specific user and generates a response to this input. For example, if the user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[0420] Input: Text input by a specific user, artificial intelligence model

[0421] Output: The generated dialogue response

[0422] Step 8:

[0423] The server stores the content list presented to the user and the response log of the generated dialogue in a database, which is used for future analysis and to improve the quality of the dialogue. This improves the accuracy of the entire system and continuously improves the user experience.

[0424] Input: Content suggestion list, generated dialogue response

[0425] Output: Content suggestions and dialogue response logs stored in a database

[0426] 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.

[0427] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0428] 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.

[0429] [Second embodiment]

[0430] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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."

[0442] The present invention is a system in which individuals input their thoughts and feelings in blog format, and then use an artificial intelligence model generated based on this information to have a dialogue with a specific user (for example, a family member).

[0443] System configuration

[0444] The system consists of the following main modules:

[0445] 1. Data Collection Module

[0446] 2. Data storage module

[0447] 3. Psychological Analysis Module

[0448] 4. AI Learning Module

[0449] 5. AI dialogue module

[0450] 6. Access Management Module

[0451] System Operation

[0452] Data Collection Module

[0453] Users enter their daily thoughts and feelings in the form of a blog. This entry is usually done through a device such as a computer or smartphone. For example, a user might enter, "Today I played in the park with my kids and had a great time." This text data is sent to a server in real time.

[0454] Data Storage Module

[0455] The server receives the text data sent from the device and stores it in a database. The stored data includes metadata such as the input text, input date and time, and user ID. This allows past input data to be accumulated and used for future analysis and learning.

[0456] Psychological Analysis Module

[0457] The server periodically analyzes the text data stored in the database. Using natural language processing (NLP) technology, it extracts an individual's emotions and psychological state from the text data. For example, the expression "I had fun" is tagged with a positive emotion tag. The analysis results are then stored back in the database.

[0458] AI Learning Module

[0459] The server uses the stored text data and psychological analysis results to learn an individual's conversation style and expression. During this learning process, machine learning algorithms are used to extract the main keywords, phrases, and emotional patterns used by the user, and an artificial intelligence model is generated based on this information. This model is stored in digital form and used for future interactions.

[0460] AI dialogue module

[0461] A specific user (such as a family member) can access the system and interact with an AI model of the deceased person. The server receives the specific user's input and uses the AI ​​model to generate a response to this input. For example, if the user inputs, "Good things happened at school today," the AI ​​will respond, "That's great. Your happiness makes me happy too."

[0462] Access Management Module

[0463] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[0464] Specific examples

[0465] User A left the following diary entry before his death:

[0466] I watched a movie with my family today and had a great time.

[0467] The server stored this text data and tagged it with "fun" and "family love" as the results of the sentiment analysis.

[0468] A few years later, User A's child accesses the system and types, "I got good marks at school today."

[0469] The server's AI model responds, "That's great! Your hard work has paid off."

[0470] As described above, this system digitally preserves personal memories and emotions, and allows for dialogue that can provide psychological support to family members. This technology not only allows family members to feel connected to the deceased, but also allows them to receive encouragement and advice. This system will provide peace of mind to family members and help ease their grief.

[0471] The processing flow will be explained below.

[0472] Step 1:

[0473] Users can write their thoughts and feelings in a blog format. For example, a user can write an entry such as "I went to the park with my kids today and had a great time." This is done using devices such as smartphones and computers.

[0474] Step 2:

[0475] The device transmits the text data entered by the user to the server in real time, including the entry text, user ID, input date and time, and other metadata.

[0476] Step 3:

[0477] The server stores the received text data in a database, which accumulates all previous entries made by the user and makes them available for future processing.

[0478] Step 4:

[0479] The server periodically retrieves new text data stored in the database and performs a psychological analysis. Natural language processing (NLP) techniques are used to extract emotions and psychological states from the text data. For example, a positive emotion can be extracted from the expression "I had fun."

[0480] Step 5:

[0481] The server then stores the extracted emotions and psychological states in a database, allowing the user's emotional fluctuations and psychological states to be tracked over time.

[0482] Step 6:

[0483] The server uses the accumulated text data and psychological analysis results to learn the user's conversation style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[0484] Step 7:

[0485] The server digitally stores the AI ​​model for each user, and this model is then used to interact with that specific user (e.g., a family member).

[0486] Step 8:

[0487] When a particular user attempts to access the system, the server checks the access rights and goes through an authentication process to verify that the user is the correct user. If authentication is successful, the user is granted access.

[0488] Step 9:

[0489] When a specific user initiates a dialogue with the AI ​​model, the server receives the user's input and generates a response. In this process, the AI ​​model generates natural responses based on the input, and the dialogue takes place. For example, if a user types, "I got good grades at school today," the AI ​​will respond, "That's great, congratulations."

[0490] Step 10:

[0491] The server then presents the generated response to the particular user, who receives the response on their terminal and the dialogue continues.

[0492] In this way, the system digitally preserves and recreates the memories and emotions of the deceased, providing psychological comfort to the family. This process consists of a series of steps that accumulate the user's emotions and thoughts, and then generates and responds to them using a dialogue model.

[0493] Example 1

[0494] 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."

[0495] In modern society, there is no system that can digitally preserve the memories and emotions of the deceased and allow them to communicate with their families and loved ones. Furthermore, there is a lack of privacy protection features that protect the words and emotions of the deceased and make them available only to specific users. These issues need to be addressed.

[0496] 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.

[0497] In this invention, the server includes means for inputting personal thoughts and feelings in blog format, means for saving the input text data, means for analyzing the saved text data and extracting emotions and psychological states, means for learning the personal conversation style based on the extracted emotions and psychological states, means for generating a personal AI model based on the learned conversation style, means for engaging in conversation with a specific user using the generated AI model, means for accepting a conversation request from a specific user and generating a response using the AI ​​model, and means for managing access rights to ensure that only specific users can use the server. This allows family and loved ones to share memories and feelings of the deceased and engage in conversation, while also ensuring full privacy protection.

[0498] The "means for inputting personal thoughts and feelings in blog format" is a means for a user to input his or her daily thoughts and feelings in text format and send them to the system.

[0499] The "means for saving input text data" refers to a means for storing the text data input by the user in an information storage device such as a database.

[0500] The "means for extracting emotions and psychological states" refers to a means that uses natural language processing technology to analyze text data and extract the user's emotions and psychological states from the data.

[0501] The "means for learning an individual's conversational style" refers to a means using a machine learning algorithm that learns an individual's language usage and conversational patterns based on stored text data and the results of its sentiment analysis.

[0502] "Means for generating a personalized artificial intelligence model" means means for creating an AI model dedicated to an individual based on the learned interaction style.

[0503] The "means for having a dialogue with a specific user" is a means for having a virtual dialogue with a specified user using the generated artificial intelligence model.

[0504] "Means for accepting a dialogue request from a specific user and generating a response using an artificial intelligence model" refers to a means by which a system receives a dialogue request from a specific user and generates an appropriate response to that request using an AI model.

[0505] "Means for managing access rights and allowing only specific users to use the system" refers to means for authenticating users and managing their rights in order to ensure privacy protection of the system.

[0506] MODE FOR CARRYING OUT THE INVENTION

[0507] The present invention is a system that allows users to input their thoughts and feelings in a blog format, generates an AI model based on this information, and engages in a dialogue with a specific user. In one embodiment, the system operates as follows:

[0508] Data collection

[0509] Users use devices such as computers and smartphones to input their daily thoughts and feelings in blog format. For example, a user might use a smartphone application to input, "Today I played at the park with my kids. It was so much fun." This text data is sent to a server in real time via the device.

[0510] Data storage

[0511] The server processes the received text data, adds metadata such as the input date and time and user ID, and stores the data in a database. Database management software such as MySQL or PostgreSQL is used.

[0512] psychological analysis

[0513] The server periodically analyzes the text data stored in the database. It uses natural language processing (NLP) technology to extract emotions and psychological states from the text data. For example, it assigns a positive emotion tag to the phrase "I had fun." This process uses Python NLP libraries (such as NLTK and SpaCy).

[0514] Training an AI model

[0515] The server learns an individual's conversation style and expression based on the stored text data and psychological analysis results. This learning is done using machine learning libraries such as TensorFlow and PyTorch. The resulting personalized AI model is stored digitally.

[0516] User interaction

[0517] A specific user (e.g., a family member) can access the system and interact with the deceased person's AI model. For example, if a specific user inputs "Something good happened at school today," the server receives this request and uses a pre-trained AI model to generate and provide a response such as "That's good to hear, I'm happy for you."

[0518] Access Management

[0519] The server manages access permissions and ensures privacy protection for the system. It uses authentication technologies such as OAuth2.0 and JWT (JSON Web Token) to ensure that only specific users can access the system after going through the appropriate authentication process.

[0520] Specific examples

[0521] For example, if user A wrote in his diary, "Today I watched a movie with my family and had a great time," the server will save this text data and tag it with the emotion tags "fun" and "family love." A few years later, if user A's child writes, "I got good marks at school today," the server's AI model will respond, "That's great. Your hard work has paid off."

[0522] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0523] Step 1:

[0524] The user inputs their thoughts and feelings.

[0525] The input content is blog-style text data, and is sent to the server in real time using a device (e.g., a smartphone or computer).

[0526] Specifically, the user opens the smartphone application and enters "I played in the park with my kids today, it was a lot of fun." The input data (prompt text) is converted to JSON format and sent to the server via the API.

[0527] Step 2:

[0528] The server stores the received text data in a database.

[0529] Input: Text data sent by the user, input date and time, user ID, etc.

[0530] Output: Text data stored in a database.

[0531] Specifically, use database management software (e.g., MySQL or PostgreSQL) to execute the following SQL query: "INSERT INTO blog_entries (user_id, entry_date, text) VALUES (1, '2023-10-03 14:00:00', 'Today I played at the park with my kids. It was so much fun');"

[0532] Step 3:

[0533] The server analyzes the stored text data and extracts emotions and psychological states.

[0534] Input: Text data stored in a database.

[0535] Output: Extracted emotion tags and mental state data.

[0536] Specifically, it performs sentiment analysis using Python NLP libraries (such as NLTK and SpaCy) and assigns a positive emotion tag to the expression "it was fun."

[0537] Step 4:

[0538] The server learns the individual's interaction style based on the extracted emotions and psychological states.

[0539] Input: Psychological analysis results and text data.

[0540] Output: A learned personal interaction style model.

[0541] Specifically, it uses machine learning libraries (TensorFlow and PyTorch) to learn user interaction patterns, using collected keywords and phrases to train the model.

[0542] Step 5:

[0543] A specific user makes an interaction request.

[0544] Input: Text input from a particular user.

[0545] Output: The request data sent to the server.

[0546] Specifically, a dialogue request (for example, "Something good happened at school today") is sent from the terminal to the server.

[0547] Step 6:

[0548] The server accepts interaction requests and generates responses using artificial intelligence models.

[0549] Input: An AI model with text input and training data from a specific user.

[0550] Output: The generated response text.

[0551] Specifically, the trained AI model is used to generate an appropriate response (e.g., "That's great, I'm happy you're happy").

[0552] Step 7:

[0553] The server then sends the generated response to the particular user.

[0554] Input: The generated response text.

[0555] Output: The response message that is displayed on the user's terminal.

[0556] As a specific operation, the response text is sent to the user's terminal and displayed in the application or browser.

[0557] Step 8:

[0558] The server manages access privileges and allows only specific users to access the system.

[0559] Input: User credentials.

[0560] Output: The access allowed or denied decision.

[0561] Specifically, it uses authentication technologies such as OAuth2.0 and JWT (JSON Web Token) to perform appropriate user authentication.

[0562] (Application example 1)

[0563] 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."

[0564] In modern society, there are limited means to maintain connections and memories with the deceased, which places an immeasurable psychological burden on family and loved ones, especially when emotional support is needed. There is a need for a system that not only preserves the thoughts and feelings of the deceased, but also utilizes them to enable interaction as if they were alive. Furthermore, data privacy and secure management are essential.

[0565] 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.

[0566] In this invention, the server includes: means for inputting personal thoughts and feelings in blog format; means for saving the input text data; means for analyzing the saved text data and extracting emotions and psychological states; means for learning the personal conversation style based on the extracted emotions and psychological states; means for generating a personal AI model based on the learned conversation style; means for engaging in conversation with a specific user using the generated AI model; encryption means for securely saving and managing the input text data and conversation style; authentication means for allowing a specific user to securely access the system; and means for the generated AI model to present optimal responses to the specific user. This allows family members and close friends to feel an emotional connection with the deceased, safely manage data while protecting their privacy, and receive emotional support.

[0567] "Means for inputting personal thoughts and feelings in blog format" is an interface that allows users to input their daily thoughts and feelings in text format.

[0568] The "means for saving input text data" is a function for safely saving input text data in a storage device such as a database.

[0569] The "means for analyzing stored text data and extracting emotions and psychological states" is a function that analyzes stored text data using natural language processing technology and automatically extracts the user's emotions and psychological states from the data.

[0570] The "means for learning an individual's interaction style based on extracted emotions and psychological states" is a process of learning a user's interaction patterns using a machine learning algorithm based on extracted emotional and psychological state data.

[0571] The "means for generating a personal AI model based on the learned dialogue style" is a function for generating an AI model that imitates the user's dialogue style based on the learned dialogue patterns.

[0572] "Means for using the generated artificial intelligence model to have a dialogue with a specific user" is a system for using the generated artificial intelligence model to have a dialogue with a specific user.

[0573] "Encryption methods for securely storing and managing input text data and interaction styles" refers to methods for encrypting stored text data and machine learning models to protect them from unauthorized access.

[0574] "Authentication means that allows a specific user to access the system safely" is an authentication function that verifies that the user accessing the system is a legitimate user.

[0575] "Means for the generated artificial intelligence model to present the optimal response to a specific user" refers to the function by which the generated AI model generates an appropriate response to the user's input and presents it to the user.

[0576] This invention relates to a system that allows users to input their thoughts and feelings in blog format, generate an AI of the deceased based on the input, and allow specific users to interact with the AI. The system is composed of multiple modules, including data privacy protection and secure management.

[0577] System Configuration

[0578] The system consists of the following main modules:

[0579] 1. Data Collection Module

[0580] 2. Data storage module

[0581] 3. Psychological Analysis Module

[0582] 4. AI Learning Module

[0583] 5. AI dialogue module

[0584] 6. Encryption methods

[0585] 7. Authentication Methods

[0586] Data Collection Module

[0587] Users can input their daily thoughts and feelings in blog format on their devices. This input data can be done using an app on their smartphone. For example, if a user inputs "I had a great time at the park today," the text data is sent to the server in real time.

[0588] Data Storage Module

[0589] The server receives the text data sent from the device and stores it in a database (e.g., MongoDB). Data is stored using encryption technology (AES encryption) to protect the privacy of the data.

[0590] Psychological Analysis Module

[0591] The server analyzes the stored text data using a Python library (e.g., SpaCy or NLTK), extracts the emotions and psychological states contained in the text from the analysis results, and stores the information back in the database.

[0592] AI Learning Module

[0593] The server uses the stored text data and the results of the psychology analysis to learn the individual's interaction style. This learning process involves using machine learning algorithms (e.g., TensorFlow or PyTorch) to build a generative AI model that reflects the user's interaction style.

[0594] AI dialogue module

[0595] A specific user (e.g., a family member) can access the system and interact with the AI ​​model of the deceased. This interaction module responds to the user's input using a generative AI model stored on the server. For example, if the user types, "I got good marks in school today," the AI ​​will respond, "That's great! Your hard work has paid off."

[0596] Encryption method

[0597] The server applies AES encryption to all data transmitted and stored to prevent unauthorized access to the data.

[0598] Authentication Method

[0599] The server uses OAuth 2.0 to authenticate users so that only specific users can access the system. Therefore, the authentication system protects user privacy and manages appropriate access rights.

[0600] Specific examples

[0601] User A writes in his diary: "I felt good today, I went to lunch with a friend."

[0602] 1. Data collection: This data is sent from the smartphone to a server.

[0603] 2. Data storage: Data is stored in MongoDB and AES encrypted.

[0604] 3. Psychological analysis: NLP technology is used to assign a positive tag based on "feeling good."

[0605] 4. AI Learning: Learn positive emotion patterns from past data.

[0606] 5. AI dialogue: When User B types into the app, "I received an award in class today," the AI ​​responds, "That's great, that's a really nice thing to happen."

[0607] 6. Authentication and encryption: Authentication is provided to ensure access is restricted to family members only.

[0608] Prompt Sentence Examples

[0609] User: "I got recognized in class today."

[0610] AI response: "Amazing, that's a really good thing."

[0611] This system allows families and loved ones to feel an emotional connection with the deceased, safely manage their data, and receive emotional support.

[0612] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0613] Step 1:

[0614] Users input their daily thoughts and feelings in blog format from their devices. Input data is done using an app on their smartphone. For example, if they input "I had a great time at the park today," the input text data is generated and sent to the server. The input is the user's text data, and the output is the transmission of the text data to the server.

[0615] Step 2:

[0616] The server receives the text data sent from the terminal and stores it in a database (for example, MongoDB). At this time, AES encryption is applied to the data storage to protect the privacy of the data. The input is the sent text data, and the output is stored in the database in encrypted form.

[0617] Step 3:

[0618] The server analyzes the stored text data using a Python library (e.g., SpaCy or NLTK). As a result of the analysis, emotions and psychological states contained in the text are extracted, and this information is stored in the database again. For example, positive emotions are extracted from the keyword "fun." The input is the stored text data, and the output is data on emotions and psychological states.

[0619] Step 4:

[0620] The server uses the stored text data and the results of the psychology analysis to learn the individual's interaction style. This learning process uses machine learning algorithms (e.g., TensorFlow or PyTorch). A generative AI model that reflects the user's interaction style is constructed. The input is past text data and emotion data, and the output is the trained AI model.

[0621] Step 5:

[0622] When a specific user accesses the server, authentication is performed using OAuth 2.0. This process ensures that only specific users can access the system. The input is the user's authentication information, and the output is the result of authentication success or failure.

[0623] Step 6:

[0624] A specific user (e.g., a family member) accesses the system and interacts with the AI ​​model of the deceased person. This interaction process is carried out using a generative AI model stored on the server. For example, if the user inputs, "I got good marks in school today," the AI ​​responds, "That's great! Your hard work paid off." The input is the user's text input, and the output is the AI's response.

[0625] Step 7:

[0626] The server continuously stores all text data and dialogue logs in a database so that they can be reused for analysis and learning as needed. This data is also encrypted to protect it from unauthorized access. The input is dialogue log data, and the output is encrypted log data stored in a database.

[0627] 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.

[0628] The present invention is a system in which individuals input their thoughts and feelings in blog format, analyze this data in real time using an emotion engine, generate an artificial intelligence model, and engage in dialogue with specific users.

[0629] System configuration

[0630] The system consists of the following main modules:

[0631] 1. Data Collection Module

[0632] 2. Data storage module

[0633] 3. Psychological Analysis Module

[0634] 4. Emotion Engine

[0635] 5. AI Learning Module

[0636] 6. AI dialogue module

[0637] 7. Access Management Module

[0638] System Operation

[0639] Data Collection Module

[0640] A user writes his or her thoughts and feelings in a blog format. For example, a user might write, "I had a great time with my friends today." The device then sends this text data to the server in real time.

[0641] Data Storage Module

[0642] The server receives the text data sent from the terminal and stores it in a database, which accumulates all the entries the user has previously entered and makes them available for future processing.

[0643] Psychological Analysis Module

[0644] The server periodically retrieves the text data stored in the database and analyzes the emotions in real time using an emotion engine. Natural language processing (NLP) techniques are used to extract an individual's emotions and psychological state from the text data. For example, a positive emotion can be extracted from the expression "It was fun."

[0645] Emotion Engine

[0646] The server uses an emotion engine to extract emotions from the user's input data in real time. The emotion engine passes the analyzed emotion data to a psychology analysis module, which stores the emotions and psychological state in a database. This makes it possible to track the user's emotional fluctuations and psychological state over time.

[0647] AI Learning Module

[0648] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[0649] AI dialogue module

[0650] A specific user (for example, a family member) can access this system and interact with the user's artificial intelligence model. The server receives the specific user's input and uses the artificial intelligence model to generate a response to this input. The input data is analyzed using an emotion engine, and a natural response is generated according to the user's emotions. For example, if the user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[0651] Access Management Module

[0652] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[0653] Specific examples

[0654] User A left the following diary entry before his death:

[0655] I enjoyed dinner with my family today and felt very happy.

[0656] The server stored this text data and extracted the positive emotion of "happiness" using an emotion engine. This data was then trained by an AI learning module to model User A's conversation style.

[0657] A few years later, User A's child accesses the system and types, "I'm a little nervous about my final exam today."

[0658] The server's AI model uses an emotion engine to analyze the input data in real time and respond with something like, "You seem nervous. Why don't you plan something fun to help you relax after the exam?"

[0659] As described above, this system digitally preserves and reproduces the memories and emotions of the deceased, and realizes a dialogue that provides psychological comfort to family members. This process consists of a series of steps that accumulate the user's emotions and thoughts, and then generate and respond to a dialogue model based on them.

[0660] The processing flow will be explained below.

[0661] Step 1:

[0662] A user inputs his / her thoughts and feelings into the terminal in a blog format, for example, "I had a great time with my friends today."

[0663] Step 2:

[0664] The device sends the entered text data to the server in real time, including the entry text, user ID, input date and time, and other metadata.

[0665] Step 3:

[0666] The server receives the text data and stores it in a database, including the text data and metadata.

[0667] Step 4:

[0668] The server periodically retrieves the stored text data and analyzes the emotions in real time using an emotion engine. Natural language processing (NLP) techniques are used to extract the user's emotions and psychological state from the text data. For example, a positive emotion is extracted from the expression "It was fun."

[0669] Step 5:

[0670] The emotion engine passes the extracted emotion data to the psychology analysis module, and stores the emotion and psychological state in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time.

[0671] Step 6:

[0672] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses.

[0673] Step 7:

[0674] The server then builds an artificial intelligence model based on the learning results and stores this model digitally for each user, which can then be used to interact with specific users (such as family members).

[0675] Step 8:

[0676] When a particular user attempts to access a system, the server checks their access rights, goes through an authentication process to verify they are the right user, and then allows or denies access.

[0677] Step 9:

[0678] When a specific user initiates a dialogue with the AI ​​model, the server receives the user's input and analyzes the user's emotions in real time using the emotion engine. Based on this analysis, the AI ​​model generates a response to the user's input.

[0679] Step 10:

[0680] The server presents the generated response to the specific user, and in some cases, the emotion engine adjusts the response content to make it appear more natural and emotionally appropriate.

[0681] Specific examples

[0682] For example, if User A enters "I enjoyed dinner with my family today and felt very happy," the emotion engine will extract the positive emotion of "happiness" and store it in the database. A few years later, if User A's child accesses the system and enters "I'm a little nervous today because of my final exam," the AI ​​model on the server will use the emotion engine to analyze the input data in real time and respond with, "You seem nervous. Why don't you plan something fun to help you relax after the exam is over?"

[0683] In this way, the system digitally preserves and recreates the memories and emotions of the deceased, enabling interaction that provides psychological comfort to family members.

[0684] Example 2

[0685] 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."

[0686] In modern society, systems that effectively store individual thoughts and emotions and use them to engage in conversations using artificial intelligence are still in the early stages of development. In particular, there are few systems that analyze user input data in real time and generate responses based on emotions and psychological states, and only a limited number of systems also provide privacy protection. This makes it difficult for users to deeply understand their own emotions or to have emotionally rich conversations with specific users. Furthermore, there are security issues due to the lack of access permission management.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0688] In this invention, the server includes a means for inputting personal thoughts and feelings in blog format, a means for saving the input text data, a means for analyzing emotions and psychological states, a means for engaging in dialogue with specific users using the generated AI model, and a means for managing access rights, which enables real-time analysis of input data, tracking of emotions and psychological states, generation of dialogue models and presentation of responses, and access management with enhanced security.

[0689] "Personal thoughts and feelings" refers to the thoughts and feelings that a user has internally.

[0690] "Means for inputting in blog format" refers to a function that allows users to input their thoughts and feelings as text into the device and send that data to the server.

[0691] "Means for saving input text data" refers to a function that saves the text data entered by the user in a database so that it can be used for subsequent processing or analysis.

[0692] "Means for extracting emotions and psychological states" refers to technology for analyzing stored text data and identifying a user's emotions and psychological states.

[0693] "Means for learning individual conversational styles" refers to a function for learning a user's unique language patterns and conversational styles based on extracted emotional and psychological state data.

[0694] "Means for generating an artificial intelligence model" refers to a technology for generating an artificial intelligence model for engaging in natural dialogue with a user based on a learned dialogue style.

[0695] "Means for interacting with a specific user" refers to a function for using the generated artificial intelligence model to interact with a specific user and provide an appropriate response.

[0696] "Emotion engine" refers to technology for analyzing and extracting user emotions from input data.

[0697] "Means of storing in a database" refers to the storage technology that allows the server to retain the data received and make it accessible in the future.

[0698] "Means for managing access rights" refers to technology that authenticates and verifies permissions to ensure that only specific users can access the system.

[0699] "Means for sending in real time" refers to a function that allows text data entered by the user to be sent immediately to the server.

[0700] "Means for generating and presenting a response" refers to technology that uses a generated artificial intelligence model to generate a natural response based on the emotion of a specific user in response to input from that user, and then presents that response.

[0701] The present invention is a system in which individuals input their thoughts and feelings in blog format, analyze this data in real time using an emotion engine, generate an artificial intelligence model, and engage in dialogue with specific users. Next, a description will be given of an embodiment of the present invention with specific examples.

[0702] The system consists of the following main modules: data collection module, data storage module, psychology analysis module, emotion engine, AI learning module, AI dialogue module, and access management module.

[0703] Data Collection Module

[0704] A user writes his or her thoughts and feelings in a blog format. For example, a user might write, "I had a great time with my friends today." The device then sends this text data to the server in real time.

[0705] (Example prompt) "I had a great time with my friends today."

[0706] Data Storage Module

[0707] The server receives the text data sent from the terminal and stores it in a database, which accumulates all the entries the user has previously entered and makes them available for future processing.

[0708] Psychological Analysis Module

[0709] The server periodically retrieves the text data stored in the database and analyzes the emotions in real time using an emotion engine. Specifically, it uses natural language processing (NLP) technology to extract an individual's emotions and psychological state from the text data. For example, a positive emotion can be extracted from the expression "It was fun."

[0710] Emotion Engine

[0711] The server uses an emotion engine to extract emotions from the user's input data in real time. The emotion engine passes the analyzed emotion data to a psychology analysis module, which stores the emotions and psychological state in a database. This makes it possible to track the user's emotional fluctuations and psychological state over time.

[0712] AI Learning Module

[0713] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[0714] AI dialogue module

[0715] A specific user (e.g., a family member) can access this system and interact with the user's artificial intelligence model. The server receives the specific user's input and uses the artificial intelligence model to generate a response to this input. An emotion engine is used to analyze the input data and generate a natural response based on the user's emotions. For example, if a user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[0716] (Example prompt) "I feel a little tired today."

[0717] Access Management Module

[0718] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[0719] Examples:

[0720] User A left the following diary entry before his death: "Today I enjoyed dinner with my family and felt very happy."

[0721] The server stored this text data and extracted the emotion "happiness" using the emotion engine. This data was then trained by the AI ​​learning module to model User A's conversation style.

[0722] A few years later, if User A's child accesses the system and enters, "I'm a little nervous today because of my final exams," the server's AI model will use its emotion engine to analyze the input data and respond, "You're feeling nervous. Why don't you plan something fun to help you relax after the exams are over?"

[0723] In this way, the system accumulates the user's emotions and thoughts, and realizes natural and emotional dialogue based on the dialogue model.

[0724] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0725] Step 1:

[0726] Users input their thoughts and feelings into the device in a blog format. For example, they can write an entry such as, "I had a great time with my friends today." The input text data is sent from the device to the server in real time.

[0727] Specifically, when a user inputs a blog entry and presses the "Post" button, the terminal generates an HTTP request and sends the input data to the server.

[0728] Input: User-entered text data

[0729] Output: Sending text data from the terminal to the server

[0730] Step 2:

[0731] The server receives the text data sent from the terminal and stores it in a database. This save operation accumulates all entries that the user has previously entered.

[0732] Specifically, the server receives an HTTP request, extracts text data from the request body, and inserts it into a database.

[0733] Input: Text data received from the device

[0734] Output: Text data stored in the database

[0735] Step 3:

[0736] The server periodically retrieves text data from the database and passes it to the emotion engine for real-time emotion analysis. Natural language processing (NLP) techniques are used to extract an individual's emotions and psychological state from the text data.

[0737] Specifically, the server retrieves new text entries from the database as a scheduled task, inputs them into the emotion engine, and obtains the analysis results.

[0738] Input: Text data in a database

[0739] Output: Analysis results by the emotion engine (emotion data)

[0740] Step 4:

[0741] The server uses the emotion engine to extract emotion data and stores it in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time.

[0742] Specifically, the server receives the emotion data returned from the emotion engine and stores it in a database.

[0743] Input: Emotion engine analysis results (emotion data)

[0744] Output: Emotion data stored in a database

[0745] Step 5:

[0746] The server uses the stored text and emotion data to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns and emotional responses and build an artificial intelligence model.

[0747] Specifically, the server periodically retrieves text data and emotion data from the database, inputs them into a machine learning algorithm to train a model, and saves the generated model.

[0748] Input: Text data and emotion data

[0749] Output: A trained artificial intelligence model

[0750] Step 6:

[0751] A specific user can access the system and interact with the AI ​​model. The server receives the specific user's input and uses the AI ​​model to generate a response. The emotion engine is used to analyze the input data and generate a natural response.

[0752] Specifically, when a specific user enters a sentence into the dialogue interface and presses the "send" button, the server receives the input data, analyzes it with the emotion engine, and the AI ​​model generates a response and sends it back to the specific user.

[0753] Input: Data entered by a specific user

[0754] Output: The response generated from the artificial intelligence model

[0755] Step 7:

[0756] The server manages access privileges and allows only specific users to access the system. This is for the purpose of system security and privacy protection, and verifies specific users through an authentication process and grants or denies access privileges.

[0757] Specifically, the server checks the authentication information when a user logs in and assigns appropriate access privileges.

[0758] Input: User credentials

[0759] Output: Access granted or denied

[0760] (Application example 2)

[0761] 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."

[0762] As individuals record their daily experiences and emotions, there is a need for a system that can suggest appropriate content based on this information and provide support and empathy through dialogue. However, existing systems are limited in their ability to effectively utilize individuals' emotional data to suggest personalized content and provide dialogue. As a result, users are often left with low satisfaction and find it difficult to receive psychological support or empathy.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0764] In this invention, the server includes means for inputting personal thoughts and feelings in blog format, means for saving the input text data, means for analyzing the saved text data and extracting emotions and psychological states, means for learning the personal interaction style based on the extracted emotions and psychological states, means for generating a personal AI model based on the learned interaction style, means for suggesting content such as movies, music, and books based on the emotions and psychological states input by the user, means for engaging in dialogue with a specific user using the generated AI model, and means installed on a smartphone that allows the user to input emotions in diary format. This enables personalized content suggestions and dialogue based on the individual's emotions and psychological states, thereby improving support and empathy for the user.

[0765] "Means for inputting personal thoughts and feelings in blog format" refers to a means by which a user inputs everyday experiences and feelings in text format and records them as a blog.

[0766] "Means for saving input text data" refers to means for recording the text data entered by the user in a database or storage and saving it permanently or for the long term.

[0767] The "means for analyzing stored text data and extracting emotions and psychological states" refers to a means for analyzing stored text data and identifying the emotions and psychological states of a user using natural language processing technology.

[0768] The "means for learning individual conversational styles" is a means for learning a user's unique language patterns and emotional responses based on the extracted emotions and psychological states.

[0769] The "means for generating a personal artificial intelligence model" is a means for creating an artificial intelligence model based on the learned dialogue style and conducting dialogue through this model.

[0770] The "means for suggesting content" is a means for recommending appropriate media content such as movies, music, books, etc. to a user based on the results of analyzing the user's emotions and psychological state.

[0771] The "means for interacting with a specific user" is a means for using the generated artificial intelligence model to interact with a specified user.

[0772] "A means that is installed on a smartphone and allows users to input emotions in diary format" is a means that is provided as a smartphone app and allows users to input daily events and emotions in diary format.

[0773] The present invention is a system that allows users to input their thoughts and feelings in blog format, analyzes the data in real time using an emotion engine, generates an artificial intelligence model, and engages in a dialogue with a specific user. This system is realized mainly using the following hardware and software:

[0774] Hardware and software used

[0775] Smartphone: A device used by users to enter their emotions in diary format.

[0776] Server: A machine that stores data, analyzes it, generates artificial intelligence models, and processes interactions.

[0777] Flask: A web framework for implementing server-side APIs.

[0778] spaCy: A library for performing natural language processing (NLP).

[0779] Program processing overview

[0780] Data collection and storage

[0781] Users use a smartphone application to input their daily experiences and feelings in blog format. This input text data is sent to a server in real time. The server stores the received text data in a database. For example, if a user inputs "I had a great time with my friends today," this text data is sent as is to the server and stored there.

[0782] Emotion analysis

[0783] The server periodically retrieves the stored text data and analyzes the emotions and psychological state in real time using the spaCy library. For example, positive emotions are extracted from the text "I had a good time." This emotional data is stored in a database and tracked over time.

[0784] Content Suggestion

[0785] The server then suggests appropriate content, such as movies, music, and books, to the user based on the analyzed emotions and psychological state. For example, if a user inputs "I feel a little tired today," the server will suggest relaxing music and movies.

[0786] Conducting dialogue

[0787] The server learns the user's interaction style based on the extracted emotions and psychological state, and then uses the generated AI model to converse with the specific user. For example, if a user inputs "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[0788] Specific examples

[0789] When User A opens the smartphone app and types, "I had a good time with my friends today," the server analyzes the emotion as positive and stores it. If User A then types, "I'm a little tired today," the server responds, "That must have been tough. It's important to take time to relax and rest."

[0790] Prompt Sentence Examples

[0791] If a user types, "I had a great time with my friends today," the system responds, "That's great. What do you want to do next?"

[0792] In this way, the system of the present invention can propose and interact with personalized content based on an individual's emotions and psychological state, thereby improving support and empathy for the user.

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

[0794] Step 1:

[0795] The user starts a smartphone application and enters their emotions in a diary format. This entry is in text format, such as "I had a great time with my friends today." The input data is sent from the smartphone to the server in real time.

[0796] Input: Text data entered by the user

[0797] Output: Text data sent to the server

[0798] Step 2:

[0799] The server stores the received text data in a database, which accumulates all user-entered entries and makes them available for later analysis and learning.

[0800] Input: Text data sent from the smartphone device

[0801] Output: Text entry stored in the database

[0802] Step 3:

[0803] The server periodically retrieves the stored text data and analyzes it in real time using an emotion engine, which uses natural language processing (NLP) technology to extract positive emotions from expressions such as "I enjoyed it."

[0804] Input: Text data stored in a database

[0805] Output: Parsed sentiment data (positive, negative, etc.)

[0806] Step 4:

[0807] The extracted emotion data is then stored in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time, revealing the user's emotional patterns at different times.

[0808] Input: Parsed emotion data

[0809] Output: Emotion data stored in a database

[0810] Step 5:

[0811] Based on the analyzed emotions and psychological state, the server learns the user's interaction style and generates an artificial intelligence model that learns the user's unique language patterns and emotional responses for use in future interactions.

[0812] Input: Emotion data and text data stored in the database

[0813] Output: An artificial intelligence model that reflects the user's interaction style and emotions

[0814] Step 6:

[0815] The server then suggests content such as movies, music, and books to the user based on the analyzed emotions and psychological state. For example, if a user inputs "I feel a little tired today," relaxing music and movies will be recommended.

[0816] Input: Analyzed emotion data, artificial intelligence model

[0817] Output: A list of suggested content for the user

[0818] Step 7:

[0819] A specific user (e.g., a family member) can interact with the system using the stored text data and the generated AI model. The server receives input from the specific user and generates a response to this input. For example, if the user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[0820] Input: Text input by a specific user, artificial intelligence model

[0821] Output: The generated dialogue response

[0822] Step 8:

[0823] The server stores the content list presented to the user and the response log of the generated dialogue in a database, which is used for future analysis and to improve the quality of the dialogue. This improves the accuracy of the entire system and continuously improves the user experience.

[0824] Input: Content suggestion list, generated dialogue response

[0825] Output: Content suggestions and dialogue response logs stored in a database

[0826] 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.

[0827] 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.

[0828] 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.

[0829] [Third embodiment]

[0830] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0831] 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.

[0832] 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).

[0833] 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.

[0834] 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.

[0835] 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).

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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."

[0842] The present invention is a system in which individuals input their thoughts and feelings in blog format, and then use an artificial intelligence model generated based on this information to have a dialogue with a specific user (for example, a family member).

[0843] System configuration

[0844] The system consists of the following main modules:

[0845] 1. Data Collection Module

[0846] 2. Data storage module

[0847] 3. Psychological Analysis Module

[0848] 4. AI Learning Module

[0849] 5. AI dialogue module

[0850] 6. Access Management Module

[0851] System Operation

[0852] Data Collection Module

[0853] Users enter their daily thoughts and feelings in the form of a blog. This entry is usually done through a device such as a computer or smartphone. For example, a user might enter, "Today I played in the park with my kids and had a great time." This text data is sent to a server in real time.

[0854] Data Storage Module

[0855] The server receives the text data sent from the device and stores it in a database. The stored data includes metadata such as the input text, input date and time, and user ID. This allows past input data to be accumulated and used for future analysis and learning.

[0856] Psychological Analysis Module

[0857] The server periodically analyzes the text data stored in the database. Using natural language processing (NLP) technology, it extracts an individual's emotions and psychological state from the text data. For example, the expression "I had fun" is tagged with a positive emotion tag. The analysis results are then stored back in the database.

[0858] AI Learning Module

[0859] The server uses the stored text data and psychological analysis results to learn an individual's conversation style and expression. During this learning process, machine learning algorithms are used to extract the main keywords, phrases, and emotional patterns used by the user, and an artificial intelligence model is generated based on this information. This model is stored in digital form and used for future interactions.

[0860] AI dialogue module

[0861] A specific user (such as a family member) can access the system and interact with an AI model of the deceased person. The server receives the specific user's input and uses the AI ​​model to generate a response to this input. For example, if the user inputs, "Good things happened at school today," the AI ​​will respond, "That's great. Your happiness makes me happy too."

[0862] Access Management Module

[0863] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[0864] Specific examples

[0865] User A left the following diary entry before his death:

[0866] I watched a movie with my family today and had a great time.

[0867] The server stored this text data and tagged it with "fun" and "family love" as the results of the sentiment analysis.

[0868] A few years later, User A's child accesses the system and types, "I got good marks at school today."

[0869] The server's AI model responds, "That's great! Your hard work has paid off."

[0870] As described above, this system digitally preserves personal memories and emotions, and allows for dialogue that can provide psychological support to family members. This technology not only allows family members to feel connected to the deceased, but also allows them to receive encouragement and advice. This system will provide peace of mind to family members and help ease their grief.

[0871] The processing flow will be explained below.

[0872] Step 1:

[0873] Users can write their thoughts and feelings in a blog format. For example, a user can write an entry such as "I went to the park with my kids today and had a great time." This is done using devices such as smartphones and computers.

[0874] Step 2:

[0875] The device transmits the text data entered by the user to the server in real time, including the entry text, user ID, input date and time, and other metadata.

[0876] Step 3:

[0877] The server stores the received text data in a database, which accumulates all previous entries made by the user and makes them available for future processing.

[0878] Step 4:

[0879] The server periodically retrieves new text data stored in the database and performs a psychological analysis. Natural language processing (NLP) techniques are used to extract emotions and psychological states from the text data. For example, a positive emotion can be extracted from the expression "I had fun."

[0880] Step 5:

[0881] The server then stores the extracted emotions and psychological states in a database, allowing the user's emotional fluctuations and psychological states to be tracked over time.

[0882] Step 6:

[0883] The server uses the accumulated text data and psychological analysis results to learn the user's conversation style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[0884] Step 7:

[0885] The server digitally stores the AI ​​model for each user, and this model is then used to interact with that specific user (e.g., a family member).

[0886] Step 8:

[0887] When a particular user attempts to access the system, the server checks the access rights and goes through an authentication process to verify that the user is the correct user. If authentication is successful, the user is granted access.

[0888] Step 9:

[0889] When a specific user initiates a dialogue with the AI ​​model, the server receives the user's input and generates a response. In this process, the AI ​​model generates natural responses based on the input, and the dialogue takes place. For example, if a user types, "I got good grades at school today," the AI ​​will respond, "That's great, congratulations."

[0890] Step 10:

[0891] The server then presents the generated response to the particular user, who receives the response on their terminal and the dialogue continues.

[0892] In this way, the system digitally preserves and recreates the memories and emotions of the deceased, providing psychological comfort to the family. This process consists of a series of steps that accumulate the user's emotions and thoughts, and then generates and responds to them using a dialogue model.

[0893] Example 1

[0894] 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."

[0895] In modern society, there is no system that can digitally preserve the memories and emotions of the deceased and allow them to communicate with their families and loved ones. Furthermore, there is a lack of privacy protection features that protect the words and emotions of the deceased and make them available only to specific users. These issues need to be addressed.

[0896] 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.

[0897] In this invention, the server includes means for inputting personal thoughts and feelings in blog format, means for saving the input text data, means for analyzing the saved text data and extracting emotions and psychological states, means for learning the personal conversation style based on the extracted emotions and psychological states, means for generating a personal AI model based on the learned conversation style, means for engaging in conversation with a specific user using the generated AI model, means for accepting a conversation request from a specific user and generating a response using the AI ​​model, and means for managing access rights to ensure that only specific users can use the server. This allows family and loved ones to share memories and feelings of the deceased and engage in conversation, while also ensuring full privacy protection.

[0898] The "means for inputting personal thoughts and feelings in blog format" is a means for a user to input his or her daily thoughts and feelings in text format and send them to the system.

[0899] The "means for saving input text data" refers to a means for storing the text data input by the user in an information storage device such as a database.

[0900] The "means for extracting emotions and psychological states" refers to a means that uses natural language processing technology to analyze text data and extract the user's emotions and psychological states from the data.

[0901] The "means for learning an individual's conversational style" refers to a means using a machine learning algorithm that learns an individual's language usage and conversational patterns based on stored text data and the results of its sentiment analysis.

[0902] "Means for generating a personalized artificial intelligence model" means means for creating an AI model dedicated to an individual based on the learned interaction style.

[0903] The "means for having a dialogue with a specific user" is a means for having a virtual dialogue with a specified user using the generated artificial intelligence model.

[0904] "Means for accepting a dialogue request from a specific user and generating a response using an artificial intelligence model" refers to a means by which a system receives a dialogue request from a specific user and generates an appropriate response to that request using an AI model.

[0905] "Means for managing access rights and allowing only specific users to use the system" refers to means for authenticating users and managing their rights in order to ensure privacy protection of the system.

[0906] MODE FOR CARRYING OUT THE INVENTION

[0907] The present invention is a system that allows users to input their thoughts and feelings in a blog format, generates an AI model based on this information, and engages in a dialogue with a specific user. In one embodiment, the system operates as follows:

[0908] Data collection

[0909] Users use devices such as computers and smartphones to input their daily thoughts and feelings in blog format. For example, a user might use a smartphone application to input, "Today I played at the park with my kids. It was so much fun." This text data is sent to a server in real time via the device.

[0910] Data storage

[0911] The server processes the received text data, adds metadata such as the input date and time and user ID, and stores the data in a database. Database management software such as MySQL or PostgreSQL is used.

[0912] psychological analysis

[0913] The server periodically analyzes the text data stored in the database. It uses natural language processing (NLP) technology to extract emotions and psychological states from the text data. For example, it assigns a positive emotion tag to the phrase "I had fun." This process uses Python NLP libraries (such as NLTK and SpaCy).

[0914] Training an AI model

[0915] The server learns an individual's conversation style and expression based on the stored text data and psychological analysis results. This learning is done using machine learning libraries such as TensorFlow and PyTorch. The resulting personalized AI model is stored digitally.

[0916] User interaction

[0917] A specific user (e.g., a family member) can access the system and interact with the deceased person's AI model. For example, if a specific user inputs "Something good happened at school today," the server receives this request and uses a pre-trained AI model to generate and provide a response such as "That's good to hear, I'm happy for you."

[0918] Access Management

[0919] The server manages access permissions and ensures privacy protection for the system. It uses authentication technologies such as OAuth2.0 and JWT (JSON Web Token) to ensure that only specific users can access the system after going through the appropriate authentication process.

[0920] Specific examples

[0921] For example, if user A wrote in his diary, "Today I watched a movie with my family and had a great time," the server will save this text data and tag it with the emotion tags "fun" and "family love." A few years later, if user A's child writes, "I got good marks at school today," the server's AI model will respond, "That's great. Your hard work has paid off."

[0922] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0923] Step 1:

[0924] The user inputs their thoughts and feelings.

[0925] The input content is blog-style text data, and is sent to the server in real time using a device (e.g., a smartphone or computer).

[0926] Specifically, the user opens the smartphone application and enters "I played in the park with my kids today, it was a lot of fun." The input data (prompt text) is converted to JSON format and sent to the server via the API.

[0927] Step 2:

[0928] The server stores the received text data in a database.

[0929] Input: Text data sent by the user, input date and time, user ID, etc.

[0930] Output: Text data stored in a database.

[0931] Specifically, use database management software (e.g., MySQL or PostgreSQL) to execute the following SQL query: "INSERT INTO blog_entries (user_id, entry_date, text) VALUES (1, '2023-10-03 14:00:00', 'Today I played at the park with my kids. It was so much fun');"

[0932] Step 3:

[0933] The server analyzes the stored text data and extracts emotions and psychological states.

[0934] Input: Text data stored in a database.

[0935] Output: Extracted emotion tags and mental state data.

[0936] Specifically, it performs sentiment analysis using Python NLP libraries (such as NLTK and SpaCy) and assigns a positive emotion tag to the expression "it was fun."

[0937] Step 4:

[0938] The server learns the individual's interaction style based on the extracted emotions and psychological states.

[0939] Input: Psychological analysis results and text data.

[0940] Output: A learned personal interaction style model.

[0941] Specifically, it uses machine learning libraries (TensorFlow and PyTorch) to learn user interaction patterns, using collected keywords and phrases to train the model.

[0942] Step 5:

[0943] A specific user makes an interaction request.

[0944] Input: Text input from a particular user.

[0945] Output: The request data sent to the server.

[0946] Specifically, a dialogue request (for example, "Something good happened at school today") is sent from the terminal to the server.

[0947] Step 6:

[0948] The server accepts interaction requests and generates responses using artificial intelligence models.

[0949] Input: An AI model with text input and training data from a specific user.

[0950] Output: The generated response text.

[0951] Specifically, the trained AI model is used to generate an appropriate response (e.g., "That's great, I'm happy you're happy").

[0952] Step 7:

[0953] The server then sends the generated response to the particular user.

[0954] Input: The generated response text.

[0955] Output: The response message that is displayed on the user's terminal.

[0956] As a specific operation, the response text is sent to the user's terminal and displayed in the application or browser.

[0957] Step 8:

[0958] The server manages access privileges and allows only specific users to access the system.

[0959] Input: User credentials.

[0960] Output: The access allowed or denied decision.

[0961] Specifically, it uses authentication technologies such as OAuth2.0 and JWT (JSON Web Token) to perform appropriate user authentication.

[0962] (Application example 1)

[0963] 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."

[0964] In modern society, there are limited means to maintain connections and memories with the deceased, which places an immeasurable psychological burden on family and loved ones, especially when emotional support is needed. There is a need for a system that not only preserves the thoughts and feelings of the deceased, but also utilizes them to enable interaction as if they were alive. Furthermore, data privacy and secure management are essential.

[0965] 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.

[0966] In this invention, the server includes: means for inputting personal thoughts and feelings in blog format; means for saving the input text data; means for analyzing the saved text data and extracting emotions and psychological states; means for learning the personal conversation style based on the extracted emotions and psychological states; means for generating a personal AI model based on the learned conversation style; means for engaging in conversation with a specific user using the generated AI model; encryption means for securely saving and managing the input text data and conversation style; authentication means for allowing a specific user to securely access the system; and means for the generated AI model to present optimal responses to the specific user. This allows family members and close friends to feel an emotional connection with the deceased, safely manage data while protecting their privacy, and receive emotional support.

[0967] "Means for inputting personal thoughts and feelings in blog format" is an interface that allows users to input their daily thoughts and feelings in text format.

[0968] The "means for saving input text data" is a function for safely saving input text data in a storage device such as a database.

[0969] The "means for analyzing stored text data and extracting emotions and psychological states" is a function that analyzes stored text data using natural language processing technology and automatically extracts the user's emotions and psychological states from the data.

[0970] The "means for learning an individual's interaction style based on extracted emotions and psychological states" is a process of learning a user's interaction patterns using a machine learning algorithm based on extracted emotional and psychological state data.

[0971] The "means for generating a personal AI model based on the learned dialogue style" is a function for generating an AI model that imitates the user's dialogue style based on the learned dialogue patterns.

[0972] "Means for using the generated artificial intelligence model to have a dialogue with a specific user" is a system for using the generated artificial intelligence model to have a dialogue with a specific user.

[0973] "Encryption methods for securely storing and managing input text data and interaction styles" refers to methods for encrypting stored text data and machine learning models to protect them from unauthorized access.

[0974] "Authentication means that allows a specific user to access the system safely" is an authentication function that verifies that the user accessing the system is a legitimate user.

[0975] "Means for the generated artificial intelligence model to present the optimal response to a specific user" refers to the function by which the generated AI model generates an appropriate response to the user's input and presents it to the user.

[0976] This invention relates to a system that allows users to input their thoughts and feelings in blog format, generate an AI of the deceased based on the input, and allow specific users to interact with the AI. The system is composed of multiple modules, including data privacy protection and secure management.

[0977] System Configuration

[0978] The system consists of the following main modules:

[0979] 1. Data Collection Module

[0980] 2. Data storage module

[0981] 3. Psychological Analysis Module

[0982] 4. AI Learning Module

[0983] 5. AI dialogue module

[0984] 6. Encryption methods

[0985] 7. Authentication Methods

[0986] Data Collection Module

[0987] Users can input their daily thoughts and feelings in blog format on their devices. This input data can be done using an app on their smartphone. For example, if a user inputs "I had a great time at the park today," the text data is sent to the server in real time.

[0988] Data Storage Module

[0989] The server receives the text data sent from the device and stores it in a database (e.g., MongoDB). Data is stored using encryption technology (AES encryption) to protect the privacy of the data.

[0990] Psychological Analysis Module

[0991] The server analyzes the stored text data using a Python library (e.g., SpaCy or NLTK), extracts the emotions and psychological states contained in the text from the analysis results, and stores the information back in the database.

[0992] AI Learning Module

[0993] The server uses the stored text data and the results of the psychology analysis to learn the individual's interaction style. This learning process involves using machine learning algorithms (e.g., TensorFlow or PyTorch) to build a generative AI model that reflects the user's interaction style.

[0994] AI dialogue module

[0995] A specific user (e.g., a family member) can access the system and interact with the AI ​​model of the deceased. This interaction module responds to the user's input using a generative AI model stored on the server. For example, if the user types, "I got good marks in school today," the AI ​​will respond, "That's great! Your hard work has paid off."

[0996] Encryption method

[0997] The server applies AES encryption to all data transmitted and stored to prevent unauthorized access to the data.

[0998] Authentication Method

[0999] The server uses OAuth 2.0 to authenticate users so that only specific users can access the system. Therefore, the authentication system protects user privacy and manages appropriate access rights.

[1000] Specific examples

[1001] User A writes in his diary: "I felt good today, I went to lunch with a friend."

[1002] 1. Data collection: This data is sent from the smartphone to a server.

[1003] 2. Data storage: Data is stored in MongoDB and AES encrypted.

[1004] 3. Psychological analysis: NLP technology is used to assign a positive tag based on "feeling good."

[1005] 4. AI Learning: Learn positive emotion patterns from past data.

[1006] 5. AI dialogue: When User B types into the app, "I received an award in class today," the AI ​​responds, "That's great, that's a really nice thing to happen."

[1007] 6. Authentication and encryption: Authentication is provided to ensure access is restricted to family members only.

[1008] Prompt Sentence Examples

[1009] User: "I got recognized in class today."

[1010] AI response: "Amazing, that's a really good thing."

[1011] This system allows families and loved ones to feel an emotional connection with the deceased, safely manage their data, and receive emotional support.

[1012] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1013] Step 1:

[1014] Users input their daily thoughts and feelings in blog format from their devices. Input data is done using an app on their smartphone. For example, if they input "I had a great time at the park today," the input text data is generated and sent to the server. The input is the user's text data, and the output is the transmission of the text data to the server.

[1015] Step 2:

[1016] The server receives the text data sent from the terminal and stores it in a database (for example, MongoDB). At this time, AES encryption is applied to the data storage to protect the privacy of the data. The input is the sent text data, and the output is stored in the database in encrypted form.

[1017] Step 3:

[1018] The server analyzes the stored text data using a Python library (e.g., SpaCy or NLTK). As a result of the analysis, emotions and psychological states contained in the text are extracted, and this information is stored in the database again. For example, positive emotions are extracted from the keyword "fun." The input is the stored text data, and the output is data on emotions and psychological states.

[1019] Step 4:

[1020] The server uses the stored text data and the results of the psychology analysis to learn the individual's interaction style. This learning process uses machine learning algorithms (e.g., TensorFlow or PyTorch). A generative AI model that reflects the user's interaction style is constructed. The input is past text data and emotion data, and the output is the trained AI model.

[1021] Step 5:

[1022] When a specific user accesses the server, authentication is performed using OAuth 2.0. This process ensures that only specific users can access the system. The input is the user's authentication information, and the output is the result of authentication success or failure.

[1023] Step 6:

[1024] A specific user (e.g., a family member) accesses the system and interacts with the AI ​​model of the deceased person. This interaction process is carried out using a generative AI model stored on the server. For example, if the user inputs, "I got good marks in school today," the AI ​​responds, "That's great! Your hard work paid off." The input is the user's text input, and the output is the AI's response.

[1025] Step 7:

[1026] The server continuously stores all text data and dialogue logs in a database so that they can be reused for analysis and learning as needed. This data is also encrypted to protect it from unauthorized access. The input is dialogue log data, and the output is encrypted log data stored in a database.

[1027] 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.

[1028] The present invention is a system in which individuals input their thoughts and feelings in blog format, analyze this data in real time using an emotion engine, generate an artificial intelligence model, and engage in dialogue with specific users.

[1029] System configuration

[1030] The system consists of the following main modules:

[1031] 1. Data Collection Module

[1032] 2. Data storage module

[1033] 3. Psychological Analysis Module

[1034] 4. Emotion Engine

[1035] 5. AI Learning Module

[1036] 6. AI dialogue module

[1037] 7. Access Management Module

[1038] System Operation

[1039] Data Collection Module

[1040] A user writes his or her thoughts and feelings in a blog format. For example, a user might write, "I had a great time with my friends today." The device then sends this text data to the server in real time.

[1041] Data Storage Module

[1042] The server receives the text data sent from the terminal and stores it in a database, which accumulates all the entries the user has previously entered and makes them available for future processing.

[1043] Psychological Analysis Module

[1044] The server periodically retrieves the text data stored in the database and analyzes the emotions in real time using an emotion engine. Natural language processing (NLP) techniques are used to extract an individual's emotions and psychological state from the text data. For example, a positive emotion can be extracted from the expression "It was fun."

[1045] Emotion Engine

[1046] The server uses an emotion engine to extract emotions from the user's input data in real time. The emotion engine passes the analyzed emotion data to a psychology analysis module, which stores the emotions and psychological state in a database. This makes it possible to track the user's emotional fluctuations and psychological state over time.

[1047] AI Learning Module

[1048] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[1049] AI dialogue module

[1050] A specific user (for example, a family member) can access this system and interact with the user's artificial intelligence model. The server receives the specific user's input and uses the artificial intelligence model to generate a response to this input. The input data is analyzed using an emotion engine, and a natural response is generated according to the user's emotions. For example, if the user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[1051] Access Management Module

[1052] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[1053] Specific examples

[1054] User A left the following diary entry before his death:

[1055] I enjoyed dinner with my family today and felt very happy.

[1056] The server stored this text data and extracted the positive emotion of "happiness" using an emotion engine. This data was then trained by an AI learning module to model User A's conversation style.

[1057] A few years later, User A's child accesses the system and types, "I'm a little nervous about my final exam today."

[1058] The server's AI model uses an emotion engine to analyze the input data in real time and respond with something like, "You seem nervous. Why don't you plan something fun to help you relax after the exam?"

[1059] As described above, this system digitally preserves and reproduces the memories and emotions of the deceased, and realizes a dialogue that provides psychological comfort to family members. This process consists of a series of steps that accumulate the user's emotions and thoughts, and then generate and respond to a dialogue model based on them.

[1060] The processing flow will be explained below.

[1061] Step 1:

[1062] A user inputs his / her thoughts and feelings into the terminal in a blog format, for example, "I had a great time with my friends today."

[1063] Step 2:

[1064] The device sends the entered text data to the server in real time, including the entry text, user ID, input date and time, and other metadata.

[1065] Step 3:

[1066] The server receives the text data and stores it in a database, including the text data and metadata.

[1067] Step 4:

[1068] The server periodically retrieves the stored text data and analyzes the emotions in real time using an emotion engine. Natural language processing (NLP) techniques are used to extract the user's emotions and psychological state from the text data. For example, a positive emotion is extracted from the expression "It was fun."

[1069] Step 5:

[1070] The emotion engine passes the extracted emotion data to the psychology analysis module, and stores the emotion and psychological state in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time.

[1071] Step 6:

[1072] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses.

[1073] Step 7:

[1074] The server then builds an artificial intelligence model based on the learning results and stores this model digitally for each user, which can then be used to interact with specific users (such as family members).

[1075] Step 8:

[1076] When a particular user attempts to access a system, the server checks their access rights, goes through an authentication process to verify they are the right user, and then allows or denies access.

[1077] Step 9:

[1078] When a specific user initiates a dialogue with the AI ​​model, the server receives the user's input and analyzes the user's emotions in real time using the emotion engine. Based on this analysis, the AI ​​model generates a response to the user's input.

[1079] Step 10:

[1080] The server presents the generated response to the specific user, and in some cases, the emotion engine adjusts the response content to make it appear more natural and emotionally appropriate.

[1081] Specific examples

[1082] For example, if User A enters "I enjoyed dinner with my family today and felt very happy," the emotion engine will extract the positive emotion of "happiness" and store it in the database. A few years later, if User A's child accesses the system and enters "I'm a little nervous today because of my final exam," the AI ​​model on the server will use the emotion engine to analyze the input data in real time and respond with, "You seem nervous. Why don't you plan something fun to help you relax after the exam is over?"

[1083] In this way, the system digitally preserves and recreates the memories and emotions of the deceased, enabling interaction that provides psychological comfort to family members.

[1084] Example 2

[1085] 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."

[1086] In modern society, systems that effectively store individual thoughts and emotions and use them to engage in conversations using artificial intelligence are still in the early stages of development. In particular, there are few systems that analyze user input data in real time and generate responses based on emotions and psychological states, and only a limited number of systems also provide privacy protection. This makes it difficult for users to deeply understand their own emotions or to have emotionally rich conversations with specific users. Furthermore, there are security issues due to the lack of access permission management.

[1087] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1088] In this invention, the server includes a means for inputting personal thoughts and feelings in blog format, a means for saving the input text data, a means for analyzing emotions and psychological states, a means for engaging in dialogue with specific users using the generated AI model, and a means for managing access rights, which enables real-time analysis of input data, tracking of emotions and psychological states, generation of dialogue models and presentation of responses, and access management with enhanced security.

[1089] "Personal thoughts and feelings" refers to the thoughts and feelings that a user has internally.

[1090] "Means for inputting in blog format" refers to a function that allows users to input their thoughts and feelings as text into the device and send that data to the server.

[1091] "Means for saving input text data" refers to a function that saves the text data entered by the user in a database so that it can be used for subsequent processing or analysis.

[1092] "Means for extracting emotions and psychological states" refers to technology for analyzing stored text data and identifying a user's emotions and psychological states.

[1093] "Means for learning individual conversational styles" refers to a function for learning a user's unique language patterns and conversational styles based on extracted emotional and psychological state data.

[1094] "Means for generating an artificial intelligence model" refers to a technology for generating an artificial intelligence model for engaging in natural dialogue with a user based on a learned dialogue style.

[1095] "Means for interacting with a specific user" refers to a function for using the generated artificial intelligence model to interact with a specific user and provide an appropriate response.

[1096] "Emotion engine" refers to technology for analyzing and extracting user emotions from input data.

[1097] "Means of storing in a database" refers to the storage technology that allows the server to retain the data received and make it accessible in the future.

[1098] "Means for managing access rights" refers to technology that authenticates and verifies permissions to ensure that only specific users can access the system.

[1099] "Means for sending in real time" refers to a function that allows text data entered by the user to be sent immediately to the server.

[1100] "Means for generating and presenting a response" refers to technology that uses a generated artificial intelligence model to generate a natural response based on the emotion of a specific user in response to input from that user, and then presents that response.

[1101] The present invention is a system in which individuals input their thoughts and feelings in blog format, analyze this data in real time using an emotion engine, generate an artificial intelligence model, and engage in dialogue with specific users. Next, a description will be given of an embodiment of the present invention with specific examples.

[1102] The system consists of the following main modules: data collection module, data storage module, psychology analysis module, emotion engine, AI learning module, AI dialogue module, and access management module.

[1103] Data Collection Module

[1104] A user writes his or her thoughts and feelings in a blog format. For example, a user might write, "I had a great time with my friends today." The device then sends this text data to the server in real time.

[1105] (Example prompt) "I had a great time with my friends today."

[1106] Data Storage Module

[1107] The server receives the text data sent from the terminal and stores it in a database, which accumulates all the entries the user has previously entered and makes them available for future processing.

[1108] Psychological Analysis Module

[1109] The server periodically retrieves the text data stored in the database and analyzes the emotions in real time using an emotion engine. Specifically, it uses natural language processing (NLP) technology to extract an individual's emotions and psychological state from the text data. For example, a positive emotion can be extracted from the expression "It was fun."

[1110] Emotion Engine

[1111] The server uses an emotion engine to extract emotions from the user's input data in real time. The emotion engine passes the analyzed emotion data to a psychology analysis module, which stores the emotions and psychological state in a database. This makes it possible to track the user's emotional fluctuations and psychological state over time.

[1112] AI Learning Module

[1113] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[1114] AI dialogue module

[1115] A specific user (e.g., a family member) can access this system and interact with the user's artificial intelligence model. The server receives the specific user's input and uses the artificial intelligence model to generate a response to this input. An emotion engine is used to analyze the input data and generate a natural response based on the user's emotions. For example, if a user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[1116] (Example prompt) "I feel a little tired today."

[1117] Access Management Module

[1118] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[1119] Examples:

[1120] User A left the following diary entry before his death: "Today I enjoyed dinner with my family and felt very happy."

[1121] The server stored this text data and extracted the emotion "happiness" using the emotion engine. This data was then trained by the AI ​​learning module to model User A's conversation style.

[1122] A few years later, if User A's child accesses the system and enters, "I'm a little nervous today because of my final exams," the server's AI model will use its emotion engine to analyze the input data and respond, "You're feeling nervous. Why don't you plan something fun to help you relax after the exams are over?"

[1123] In this way, the system accumulates the user's emotions and thoughts, and realizes natural and emotional dialogue based on the dialogue model.

[1124] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1125] Step 1:

[1126] Users input their thoughts and feelings into the device in a blog format. For example, they can write an entry such as, "I had a great time with my friends today." The input text data is sent from the device to the server in real time.

[1127] Specifically, when a user inputs a blog entry and presses the "Post" button, the terminal generates an HTTP request and sends the input data to the server.

[1128] Input: User-entered text data

[1129] Output: Sending text data from the terminal to the server

[1130] Step 2:

[1131] The server receives the text data sent from the terminal and stores it in a database. This save operation accumulates all entries that the user has previously entered.

[1132] Specifically, the server receives an HTTP request, extracts text data from the request body, and inserts it into a database.

[1133] Input: Text data received from the device

[1134] Output: Text data stored in the database

[1135] Step 3:

[1136] The server periodically retrieves text data from the database and passes it to the emotion engine for real-time emotion analysis. Natural language processing (NLP) techniques are used to extract an individual's emotions and psychological state from the text data.

[1137] Specifically, the server retrieves new text entries from the database as a scheduled task, inputs them into the emotion engine, and obtains the analysis results.

[1138] Input: Text data in a database

[1139] Output: Analysis results by the emotion engine (emotion data)

[1140] Step 4:

[1141] The server uses the emotion engine to extract emotion data and stores it in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time.

[1142] Specifically, the server receives the emotion data returned from the emotion engine and stores it in a database.

[1143] Input: Emotion engine analysis results (emotion data)

[1144] Output: Emotion data stored in a database

[1145] Step 5:

[1146] The server uses the stored text and emotion data to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns and emotional responses and build an artificial intelligence model.

[1147] Specifically, the server periodically retrieves text data and emotion data from the database, inputs them into a machine learning algorithm to train a model, and saves the generated model.

[1148] Input: Text data and emotion data

[1149] Output: A trained artificial intelligence model

[1150] Step 6:

[1151] A specific user can access the system and interact with the AI ​​model. The server receives the specific user's input and uses the AI ​​model to generate a response. The emotion engine is used to analyze the input data and generate a natural response.

[1152] Specifically, when a specific user enters a sentence into the dialogue interface and presses the "send" button, the server receives the input data, analyzes it with the emotion engine, and the AI ​​model generates a response and sends it back to the specific user.

[1153] Input: Data entered by a specific user

[1154] Output: The response generated from the artificial intelligence model

[1155] Step 7:

[1156] The server manages access privileges and allows only specific users to access the system. This is for the purpose of system security and privacy protection, and verifies specific users through an authentication process and grants or denies access privileges.

[1157] Specifically, the server checks the authentication information when a user logs in and assigns appropriate access privileges.

[1158] Input: User credentials

[1159] Output: Access granted or denied

[1160] (Application example 2)

[1161] 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."

[1162] As individuals record their daily experiences and emotions, there is a need for a system that can suggest appropriate content based on this information and provide support and empathy through dialogue. However, existing systems are limited in their ability to effectively utilize individuals' emotional data to suggest personalized content and provide dialogue. As a result, users are often left with low satisfaction and find it difficult to receive psychological support or empathy.

[1163] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1164] In this invention, the server includes means for inputting personal thoughts and feelings in blog format, means for saving the input text data, means for analyzing the saved text data and extracting emotions and psychological states, means for learning the personal interaction style based on the extracted emotions and psychological states, means for generating a personal AI model based on the learned interaction style, means for suggesting content such as movies, music, and books based on the emotions and psychological states input by the user, means for engaging in dialogue with a specific user using the generated AI model, and means installed on a smartphone that allows the user to input emotions in diary format. This enables personalized content suggestions and dialogue based on the individual's emotions and psychological states, thereby improving support and empathy for the user.

[1165] "Means for inputting personal thoughts and feelings in blog format" refers to a means by which a user inputs everyday experiences and feelings in text format and records them as a blog.

[1166] "Means for saving input text data" refers to means for recording the text data entered by the user in a database or storage and saving it permanently or for the long term.

[1167] The "means for analyzing stored text data and extracting emotions and psychological states" refers to a means for analyzing stored text data and identifying the emotions and psychological states of a user using natural language processing technology.

[1168] The "means for learning individual conversational styles" is a means for learning a user's unique language patterns and emotional responses based on the extracted emotions and psychological states.

[1169] The "means for generating a personal artificial intelligence model" is a means for creating an artificial intelligence model based on the learned dialogue style and conducting dialogue through this model.

[1170] The "means for suggesting content" is a means for recommending appropriate media content such as movies, music, books, etc. to a user based on the results of analyzing the user's emotions and psychological state.

[1171] The "means for interacting with a specific user" is a means for using the generated artificial intelligence model to interact with a specified user.

[1172] "A means that is installed on a smartphone and allows users to input emotions in diary format" is a means that is provided as a smartphone app and allows users to input daily events and emotions in diary format.

[1173] The present invention is a system that allows users to input their thoughts and feelings in blog format, analyzes the data in real time using an emotion engine, generates an artificial intelligence model, and engages in a dialogue with a specific user. This system is realized mainly using the following hardware and software:

[1174] Hardware and software used

[1175] Smartphone: A device used by users to enter their emotions in diary format.

[1176] Server: A machine that stores data, analyzes it, generates artificial intelligence models, and processes interactions.

[1177] Flask: A web framework for implementing server-side APIs.

[1178] spaCy: A library for performing natural language processing (NLP).

[1179] Program processing overview

[1180] Data collection and storage

[1181] Users use a smartphone application to input their daily experiences and feelings in blog format. This input text data is sent to a server in real time. The server stores the received text data in a database. For example, if a user inputs "I had a great time with my friends today," this text data is sent as is to the server and stored there.

[1182] Emotion analysis

[1183] The server periodically retrieves the stored text data and analyzes the emotions and psychological state in real time using the spaCy library. For example, positive emotions are extracted from the text "I had a good time." This emotional data is stored in a database and tracked over time.

[1184] Content Suggestion

[1185] The server then suggests appropriate content, such as movies, music, and books, to the user based on the analyzed emotions and psychological state. For example, if a user inputs "I feel a little tired today," the server will suggest relaxing music and movies.

[1186] Conducting dialogue

[1187] The server learns the user's interaction style based on the extracted emotions and psychological state, and then uses the generated AI model to converse with the specific user. For example, if a user inputs "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[1188] Specific examples

[1189] When User A opens the smartphone app and types, "I had a good time with my friends today," the server analyzes the emotion as positive and stores it. If User A then types, "I'm a little tired today," the server responds, "That must have been tough. It's important to take time to relax and rest."

[1190] Prompt Sentence Examples

[1191] If a user types, "I had a great time with my friends today," the system responds, "That's great. What do you want to do next?"

[1192] In this way, the system of the present invention can propose and interact with personalized content based on an individual's emotions and psychological state, thereby improving support and empathy for the user.

[1193] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1194] Step 1:

[1195] The user starts a smartphone application and enters their emotions in a diary format. This entry is in text format, such as "I had a great time with my friends today." The input data is sent from the smartphone to the server in real time.

[1196] Input: Text data entered by the user

[1197] Output: Text data sent to the server

[1198] Step 2:

[1199] The server stores the received text data in a database, which accumulates all user-entered entries and makes them available for later analysis and learning.

[1200] Input: Text data sent from the smartphone device

[1201] Output: Text entry stored in the database

[1202] Step 3:

[1203] The server periodically retrieves the stored text data and analyzes it in real time using an emotion engine, which uses natural language processing (NLP) technology to extract positive emotions from expressions such as "I enjoyed it."

[1204] Input: Text data stored in a database

[1205] Output: Parsed sentiment data (positive, negative, etc.)

[1206] Step 4:

[1207] The extracted emotion data is then stored in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time, revealing the user's emotional patterns at different times.

[1208] Input: Parsed emotion data

[1209] Output: Emotion data stored in a database

[1210] Step 5:

[1211] Based on the analyzed emotions and psychological state, the server learns the user's interaction style and generates an artificial intelligence model that learns the user's unique language patterns and emotional responses for use in future interactions.

[1212] Input: Emotion data and text data stored in the database

[1213] Output: An artificial intelligence model that reflects the user's interaction style and emotions

[1214] Step 6:

[1215] The server then suggests content such as movies, music, and books to the user based on the analyzed emotions and psychological state. For example, if a user inputs "I feel a little tired today," relaxing music and movies will be recommended.

[1216] Input: Analyzed emotion data, artificial intelligence model

[1217] Output: A list of suggested content for the user

[1218] Step 7:

[1219] A specific user (e.g., a family member) can interact with the system using the stored text data and the generated AI model. The server receives input from the specific user and generates a response to this input. For example, if the user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[1220] Input: Text input by a specific user, artificial intelligence model

[1221] Output: The generated dialogue response

[1222] Step 8:

[1223] The server stores the content list presented to the user and the response log of the generated dialogue in a database, which is used for future analysis and to improve the quality of the dialogue. This improves the accuracy of the entire system and continuously improves the user experience.

[1224] Input: Content suggestion list, generated dialogue response

[1225] Output: Content suggestions and dialogue response logs stored in a database

[1226] 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.

[1227] 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.

[1228] 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.

[1229] [Fourth embodiment]

[1230] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1231] 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.

[1232] 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).

[1233] 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.

[1234] 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.

[1235] 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).

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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.

[1241] 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.

[1242] 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."

[1243] The present invention is a system in which individuals input their thoughts and feelings in blog format, and then use an artificial intelligence model generated based on this information to have a dialogue with a specific user (for example, a family member).

[1244] System configuration

[1245] The system consists of the following main modules:

[1246] 1. Data Collection Module

[1247] 2. Data storage module

[1248] 3. Psychological Analysis Module

[1249] 4. AI Learning Module

[1250] 5. AI dialogue module

[1251] 6. Access Management Module

[1252] System Operation

[1253] Data Collection Module

[1254] Users enter their daily thoughts and feelings in the form of a blog. This entry is usually done through a device such as a computer or smartphone. For example, a user might enter, "Today I played in the park with my kids and had a great time." This text data is sent to a server in real time.

[1255] Data Storage Module

[1256] The server receives the text data sent from the device and stores it in a database. The stored data includes metadata such as the input text, input date and time, and user ID. This allows past input data to be accumulated and used for future analysis and learning.

[1257] Psychological Analysis Module

[1258] The server periodically analyzes the text data stored in the database. Using natural language processing (NLP) technology, it extracts an individual's emotions and psychological state from the text data. For example, the expression "I had fun" is tagged with a positive emotion tag. The analysis results are then stored back in the database.

[1259] AI Learning Module

[1260] The server uses the stored text data and psychological analysis results to learn an individual's conversation style and expression. During this learning process, machine learning algorithms are used to extract the main keywords, phrases, and emotional patterns used by the user, and an artificial intelligence model is generated based on this information. This model is stored in digital form and used for future interactions.

[1261] AI dialogue module

[1262] A specific user (such as a family member) can access the system and interact with an AI model of the deceased person. The server receives the specific user's input and uses the AI ​​model to generate a response to this input. For example, if the user inputs, "Good things happened at school today," the AI ​​will respond, "That's great. Your happiness makes me happy too."

[1263] Access Management Module

[1264] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[1265] Specific examples

[1266] User A left the following diary entry before his death:

[1267] I watched a movie with my family today and had a great time.

[1268] The server stored this text data and tagged it with "fun" and "family love" as the results of the sentiment analysis.

[1269] A few years later, User A's child accesses the system and types, "I got good marks at school today."

[1270] The server's AI model responds, "That's great! Your hard work has paid off."

[1271] As described above, this system digitally preserves personal memories and emotions, and allows for dialogue that can provide psychological support to family members. This technology not only allows family members to feel connected to the deceased, but also allows them to receive encouragement and advice. This system will provide peace of mind to family members and help ease their grief.

[1272] The processing flow will be explained below.

[1273] Step 1:

[1274] Users can write their thoughts and feelings in a blog format. For example, a user can write an entry such as "I went to the park with my kids today and had a great time." This is done using devices such as smartphones and computers.

[1275] Step 2:

[1276] The device transmits the text data entered by the user to the server in real time, including the entry text, user ID, input date and time, and other metadata.

[1277] Step 3:

[1278] The server stores the received text data in a database, which accumulates all previous entries made by the user and makes them available for future processing.

[1279] Step 4:

[1280] The server periodically retrieves new text data stored in the database and performs a psychological analysis. Natural language processing (NLP) techniques are used to extract emotions and psychological states from the text data. For example, a positive emotion can be extracted from the expression "I had fun."

[1281] Step 5:

[1282] The server then stores the extracted emotions and psychological states in a database, allowing the user's emotional fluctuations and psychological states to be tracked over time.

[1283] Step 6:

[1284] The server uses the accumulated text data and psychological analysis results to learn the user's conversation style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[1285] Step 7:

[1286] The server digitally stores the AI ​​model for each user, and this model is then used to interact with that specific user (e.g., a family member).

[1287] Step 8:

[1288] When a particular user attempts to access the system, the server checks the access rights and goes through an authentication process to verify that the user is the correct user. If authentication is successful, the user is granted access.

[1289] Step 9:

[1290] When a specific user initiates a dialogue with the AI ​​model, the server receives the user's input and generates a response. In this process, the AI ​​model generates natural responses based on the input, and the dialogue takes place. For example, if a user types, "I got good grades at school today," the AI ​​will respond, "That's great, congratulations."

[1291] Step 10:

[1292] The server then presents the generated response to the particular user, who receives the response on their terminal and the dialogue continues.

[1293] In this way, the system digitally preserves and recreates the memories and emotions of the deceased, providing psychological comfort to the family. This process consists of a series of steps that accumulate the user's emotions and thoughts, and then generates and responds to them using a dialogue model.

[1294] Example 1

[1295] 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."

[1296] In modern society, there is no system that can digitally preserve the memories and emotions of the deceased and allow them to communicate with their families and loved ones. Furthermore, there is a lack of privacy protection features that protect the words and emotions of the deceased and make them available only to specific users. These issues need to be addressed.

[1297] 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.

[1298] In this invention, the server includes means for inputting personal thoughts and feelings in blog format, means for saving the input text data, means for analyzing the saved text data and extracting emotions and psychological states, means for learning the personal conversation style based on the extracted emotions and psychological states, means for generating a personal AI model based on the learned conversation style, means for engaging in conversation with a specific user using the generated AI model, means for accepting a conversation request from a specific user and generating a response using the AI ​​model, and means for managing access rights to ensure that only specific users can use the server. This allows family and loved ones to share memories and feelings of the deceased and engage in conversation, while also ensuring full privacy protection.

[1299] The "means for inputting personal thoughts and feelings in blog format" is a means for a user to input his or her daily thoughts and feelings in text format and send them to the system.

[1300] The "means for saving input text data" refers to a means for storing the text data input by the user in an information storage device such as a database.

[1301] The "means for extracting emotions and psychological states" refers to a means that uses natural language processing technology to analyze text data and extract the user's emotions and psychological states from the data.

[1302] The "means for learning an individual's conversational style" refers to a means using a machine learning algorithm that learns an individual's language usage and conversational patterns based on stored text data and the results of its sentiment analysis.

[1303] "Means for generating a personalized artificial intelligence model" means means for creating an AI model dedicated to an individual based on the learned interaction style.

[1304] The "means for having a dialogue with a specific user" is a means for having a virtual dialogue with a specified user using the generated artificial intelligence model.

[1305] "Means for accepting a dialogue request from a specific user and generating a response using an artificial intelligence model" refers to a means by which a system receives a dialogue request from a specific user and generates an appropriate response to that request using an AI model.

[1306] "Means for managing access rights and allowing only specific users to use the system" refers to means for authenticating users and managing their rights in order to ensure privacy protection of the system.

[1307] MODE FOR CARRYING OUT THE INVENTION

[1308] The present invention is a system that allows users to input their thoughts and feelings in a blog format, generates an AI model based on this information, and engages in a dialogue with a specific user. In one embodiment, the system operates as follows:

[1309] Data collection

[1310] Users use devices such as computers and smartphones to input their daily thoughts and feelings in blog format. For example, a user might use a smartphone application to input, "Today I played at the park with my kids. It was so much fun." This text data is sent to a server in real time via the device.

[1311] Data storage

[1312] The server processes the received text data, adds metadata such as the input date and time and user ID, and stores the data in a database. Database management software such as MySQL or PostgreSQL is used.

[1313] psychological analysis

[1314] The server periodically analyzes the text data stored in the database. It uses natural language processing (NLP) technology to extract emotions and psychological states from the text data. For example, it assigns a positive emotion tag to the phrase "I had fun." This process uses Python NLP libraries (such as NLTK and SpaCy).

[1315] Training an AI model

[1316] The server learns an individual's conversation style and expression based on the stored text data and psychological analysis results. This learning is done using machine learning libraries such as TensorFlow and PyTorch. The resulting personalized AI model is stored digitally.

[1317] User interaction

[1318] A specific user (e.g., a family member) can access the system and interact with the deceased person's AI model. For example, if a specific user inputs "Something good happened at school today," the server receives this request and uses a pre-trained AI model to generate and provide a response such as "That's good to hear, I'm happy for you."

[1319] Access Management

[1320] The server manages access permissions and ensures privacy protection for the system. It uses authentication technologies such as OAuth2.0 and JWT (JSON Web Token) to ensure that only specific users can access the system after going through the appropriate authentication process.

[1321] Specific examples

[1322] For example, if user A wrote in his diary, "Today I watched a movie with my family and had a great time," the server will save this text data and tag it with the emotion tags "fun" and "family love." A few years later, if user A's child writes, "I got good marks at school today," the server's AI model will respond, "That's great. Your hard work has paid off."

[1323] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1324] Step 1:

[1325] The user inputs their thoughts and feelings.

[1326] The input content is blog-style text data, and is sent to the server in real time using a device (e.g., a smartphone or computer).

[1327] Specifically, the user opens the smartphone application and enters "I played in the park with my kids today, it was a lot of fun." The input data (prompt text) is converted to JSON format and sent to the server via the API.

[1328] Step 2:

[1329] The server stores the received text data in a database.

[1330] Input: Text data sent by the user, input date and time, user ID, etc.

[1331] Output: Text data stored in a database.

[1332] Specifically, use database management software (e.g., MySQL or PostgreSQL) to execute the following SQL query: "INSERT INTO blog_entries (user_id, entry_date, text) VALUES (1, '2023-10-03 14:00:00', 'Today I played at the park with my kids. It was so much fun');"

[1333] Step 3:

[1334] The server analyzes the stored text data and extracts emotions and psychological states.

[1335] Input: Text data stored in a database.

[1336] Output: Extracted emotion tags and mental state data.

[1337] Specifically, it performs sentiment analysis using Python NLP libraries (such as NLTK and SpaCy) and assigns a positive emotion tag to the expression "it was fun."

[1338] Step 4:

[1339] The server learns the individual's interaction style based on the extracted emotions and psychological states.

[1340] Input: Psychological analysis results and text data.

[1341] Output: A learned personal interaction style model.

[1342] Specifically, it uses machine learning libraries (TensorFlow and PyTorch) to learn user interaction patterns, using collected keywords and phrases to train the model.

[1343] Step 5:

[1344] A specific user makes an interaction request.

[1345] Input: Text input from a particular user.

[1346] Output: The request data sent to the server.

[1347] Specifically, a dialogue request (for example, "Something good happened at school today") is sent from the terminal to the server.

[1348] Step 6:

[1349] The server accepts interaction requests and generates responses using artificial intelligence models.

[1350] Input: An AI model with text input and training data from a specific user.

[1351] Output: The generated response text.

[1352] Specifically, the trained AI model is used to generate an appropriate response (e.g., "That's great, I'm happy you're happy").

[1353] Step 7:

[1354] The server then sends the generated response to the particular user.

[1355] Input: The generated response text.

[1356] Output: The response message that is displayed on the user's terminal.

[1357] As a specific operation, the response text is sent to the user's terminal and displayed in the application or browser.

[1358] Step 8:

[1359] The server manages access privileges and allows only specific users to access the system.

[1360] Input: User credentials.

[1361] Output: The access allowed or denied decision.

[1362] Specifically, it uses authentication technologies such as OAuth2.0 and JWT (JSON Web Token) to perform appropriate user authentication.

[1363] (Application example 1)

[1364] 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."

[1365] In modern society, there are limited means to maintain connections and memories with the deceased, which places an immeasurable psychological burden on family and loved ones, especially when emotional support is needed. There is a need for a system that not only preserves the thoughts and feelings of the deceased, but also utilizes them to enable interaction as if they were alive. Furthermore, data privacy and secure management are essential.

[1366] 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.

[1367] In this invention, the server includes: means for inputting personal thoughts and feelings in blog format; means for saving the input text data; means for analyzing the saved text data and extracting emotions and psychological states; means for learning the personal conversation style based on the extracted emotions and psychological states; means for generating a personal AI model based on the learned conversation style; means for engaging in conversation with a specific user using the generated AI model; encryption means for securely saving and managing the input text data and conversation style; authentication means for allowing a specific user to securely access the system; and means for the generated AI model to present optimal responses to the specific user. This allows family members and close friends to feel an emotional connection with the deceased, safely manage data while protecting their privacy, and receive emotional support.

[1368] "Means for inputting personal thoughts and feelings in blog format" is an interface that allows users to input their daily thoughts and feelings in text format.

[1369] The "means for saving input text data" is a function for safely saving input text data in a storage device such as a database.

[1370] The "means for analyzing stored text data and extracting emotions and psychological states" is a function that analyzes stored text data using natural language processing technology and automatically extracts the user's emotions and psychological states from the data.

[1371] The "means for learning an individual's interaction style based on extracted emotions and psychological states" is a process of learning a user's interaction patterns using a machine learning algorithm based on extracted emotional and psychological state data.

[1372] The "means for generating a personal AI model based on the learned dialogue style" is a function for generating an AI model that imitates the user's dialogue style based on the learned dialogue patterns.

[1373] "Means for using the generated artificial intelligence model to have a dialogue with a specific user" is a system for using the generated artificial intelligence model to have a dialogue with a specific user.

[1374] "Encryption methods for securely storing and managing input text data and interaction styles" refers to methods for encrypting stored text data and machine learning models to protect them from unauthorized access.

[1375] "Authentication means that allows a specific user to access the system safely" is an authentication function that verifies that the user accessing the system is a legitimate user.

[1376] "Means for the generated artificial intelligence model to present the optimal response to a specific user" refers to the function by which the generated AI model generates an appropriate response to the user's input and presents it to the user.

[1377] This invention relates to a system that allows users to input their thoughts and feelings in blog format, generate an AI of the deceased based on the input, and allow specific users to interact with the AI. The system is composed of multiple modules, including data privacy protection and secure management.

[1378] System Configuration

[1379] The system consists of the following main modules:

[1380] 1. Data Collection Module

[1381] 2. Data storage module

[1382] 3. Psychological Analysis Module

[1383] 4. AI Learning Module

[1384] 5. AI dialogue module

[1385] 6. Encryption methods

[1386] 7. Authentication Methods

[1387] Data Collection Module

[1388] Users can input their daily thoughts and feelings in blog format on their devices. This input data can be done using an app on their smartphone. For example, if a user inputs "I had a great time at the park today," the text data is sent to the server in real time.

[1389] Data Storage Module

[1390] The server receives the text data sent from the device and stores it in a database (e.g., MongoDB). Data is stored using encryption technology (AES encryption) to protect the privacy of the data.

[1391] Psychological Analysis Module

[1392] The server analyzes the stored text data using a Python library (e.g., SpaCy or NLTK), extracts the emotions and psychological states contained in the text from the analysis results, and stores the information back in the database.

[1393] AI Learning Module

[1394] The server uses the stored text data and the results of the psychology analysis to learn the individual's interaction style. This learning process involves using machine learning algorithms (e.g., TensorFlow or PyTorch) to build a generative AI model that reflects the user's interaction style.

[1395] AI dialogue module

[1396] A specific user (e.g., a family member) can access the system and interact with the AI ​​model of the deceased. This interaction module responds to the user's input using a generative AI model stored on the server. For example, if the user types, "I got good marks in school today," the AI ​​will respond, "That's great! Your hard work has paid off."

[1397] Encryption method

[1398] The server applies AES encryption to all data transmitted and stored to prevent unauthorized access to the data.

[1399] Authentication Method

[1400] The server uses OAuth 2.0 to authenticate users so that only specific users can access the system. Therefore, the authentication system protects user privacy and manages appropriate access rights.

[1401] Specific examples

[1402] User A writes in his diary: "I felt good today, I went to lunch with a friend."

[1403] 1. Data collection: This data is sent from the smartphone to a server.

[1404] 2. Data storage: Data is stored in MongoDB and AES encrypted.

[1405] 3. Psychological analysis: NLP technology is used to assign a positive tag based on "feeling good."

[1406] 4. AI Learning: Learn positive emotion patterns from past data.

[1407] 5. AI dialogue: When User B types into the app, "I received an award in class today," the AI ​​responds, "That's great, that's a really nice thing to happen."

[1408] 6. Authentication and encryption: Authentication is provided to ensure access is restricted to family members only.

[1409] Prompt Sentence Examples

[1410] User: "I got recognized in class today."

[1411] AI response: "Amazing, that's a really good thing."

[1412] This system allows families and loved ones to feel an emotional connection with the deceased, safely manage their data, and receive emotional support.

[1413] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1414] Step 1:

[1415] Users input their daily thoughts and feelings in blog format from their devices. Input data is done using an app on their smartphone. For example, if they input "I had a great time at the park today," the input text data is generated and sent to the server. The input is the user's text data, and the output is the transmission of the text data to the server.

[1416] Step 2:

[1417] The server receives the text data sent from the terminal and stores it in a database (for example, MongoDB). At this time, AES encryption is applied to the data storage to protect the privacy of the data. The input is the sent text data, and the output is stored in the database in encrypted form.

[1418] Step 3:

[1419] The server analyzes the stored text data using a Python library (e.g., SpaCy or NLTK). As a result of the analysis, emotions and psychological states contained in the text are extracted, and this information is stored in the database again. For example, positive emotions are extracted from the keyword "fun." The input is the stored text data, and the output is data on emotions and psychological states.

[1420] Step 4:

[1421] The server uses the stored text data and the results of the psychology analysis to learn the individual's interaction style. This learning process uses machine learning algorithms (e.g., TensorFlow or PyTorch). A generative AI model that reflects the user's interaction style is constructed. The input is past text data and emotion data, and the output is the trained AI model.

[1422] Step 5:

[1423] When a specific user accesses the server, authentication is performed using OAuth 2.0. This process ensures that only specific users can access the system. The input is the user's authentication information, and the output is the result of authentication success or failure.

[1424] Step 6:

[1425] A specific user (e.g., a family member) accesses the system and interacts with the AI ​​model of the deceased person. This interaction process is carried out using a generative AI model stored on the server. For example, if the user inputs, "I got good marks in school today," the AI ​​responds, "That's great! Your hard work paid off." The input is the user's text input, and the output is the AI's response.

[1426] Step 7:

[1427] The server continuously stores all text data and dialogue logs in a database so that they can be reused for analysis and learning as needed. This data is also encrypted to protect it from unauthorized access. The input is dialogue log data, and the output is encrypted log data stored in a database.

[1428] 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.

[1429] The present invention is a system in which individuals input their thoughts and feelings in blog format, analyze this data in real time using an emotion engine, generate an artificial intelligence model, and engage in dialogue with specific users.

[1430] System configuration

[1431] The system consists of the following main modules:

[1432] 1. Data Collection Module

[1433] 2. Data storage module

[1434] 3. Psychological Analysis Module

[1435] 4. Emotion Engine

[1436] 5. AI Learning Module

[1437] 6. AI dialogue module

[1438] 7. Access Management Module

[1439] System Operation

[1440] Data Collection Module

[1441] A user writes his or her thoughts and feelings in a blog format. For example, a user might write, "I had a great time with my friends today." The device then sends this text data to the server in real time.

[1442] Data Storage Module

[1443] The server receives the text data sent from the terminal and stores it in a database, which accumulates all the entries the user has previously entered and makes them available for future processing.

[1444] Psychological Analysis Module

[1445] The server periodically retrieves the text data stored in the database and analyzes the emotions in real time using an emotion engine. Natural language processing (NLP) techniques are used to extract an individual's emotions and psychological state from the text data. For example, a positive emotion can be extracted from the expression "It was fun."

[1446] Emotion Engine

[1447] The server uses an emotion engine to extract emotions from the user's input data in real time. The emotion engine passes the analyzed emotion data to a psychology analysis module, which stores the emotions and psychological state in a database. This makes it possible to track the user's emotional fluctuations and psychological state over time.

[1448] AI Learning Module

[1449] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[1450] AI dialogue module

[1451] A specific user (for example, a family member) can access this system and interact with the user's artificial intelligence model. The server receives the specific user's input and uses the artificial intelligence model to generate a response to this input. The input data is analyzed using an emotion engine, and a natural response is generated according to the user's emotions. For example, if the user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[1452] Access Management Module

[1453] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[1454] Specific examples

[1455] User A left the following diary entry before his death:

[1456] I enjoyed dinner with my family today and felt very happy.

[1457] The server stored this text data and extracted the positive emotion of "happiness" using an emotion engine. This data was then trained by an AI learning module to model User A's conversation style.

[1458] A few years later, User A's child accesses the system and types, "I'm a little nervous about my final exam today."

[1459] The server's AI model uses an emotion engine to analyze the input data in real time and respond with something like, "You seem nervous. Why don't you plan something fun to help you relax after the exam?"

[1460] As described above, this system digitally preserves and reproduces the memories and emotions of the deceased, and realizes a dialogue that provides psychological comfort to family members. This process consists of a series of steps that accumulate the user's emotions and thoughts, and then generate and respond to a dialogue model based on them.

[1461] The processing flow will be explained below.

[1462] Step 1:

[1463] A user inputs his / her thoughts and feelings into the terminal in a blog format, for example, "I had a great time with my friends today."

[1464] Step 2:

[1465] The device sends the entered text data to the server in real time, including the entry text, user ID, input date and time, and other metadata.

[1466] Step 3:

[1467] The server receives the text data and stores it in a database, including the text data and metadata.

[1468] Step 4:

[1469] The server periodically retrieves the stored text data and analyzes the emotions in real time using an emotion engine. Natural language processing (NLP) techniques are used to extract the user's emotions and psychological state from the text data. For example, a positive emotion is extracted from the expression "It was fun."

[1470] Step 5:

[1471] The emotion engine passes the extracted emotion data to the psychology analysis module, and stores the emotion and psychological state in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time.

[1472] Step 6:

[1473] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses.

[1474] Step 7:

[1475] The server then builds an artificial intelligence model based on the learning results and stores this model digitally for each user, which can then be used to interact with specific users (such as family members).

[1476] Step 8:

[1477] When a particular user attempts to access a system, the server checks their access rights, goes through an authentication process to verify they are the right user, and then allows or denies access.

[1478] Step 9:

[1479] When a specific user initiates a dialogue with the AI ​​model, the server receives the user's input and analyzes the user's emotions in real time using the emotion engine. Based on this analysis, the AI ​​model generates a response to the user's input.

[1480] Step 10:

[1481] The server presents the generated response to the specific user, and in some cases, the emotion engine adjusts the response content to make it appear more natural and emotionally appropriate.

[1482] Specific examples

[1483] For example, if User A enters "I enjoyed dinner with my family today and felt very happy," the emotion engine will extract the positive emotion of "happiness" and store it in the database. A few years later, if User A's child accesses the system and enters "I'm a little nervous today because of my final exam," the AI ​​model on the server will use the emotion engine to analyze the input data in real time and respond with, "You seem nervous. Why don't you plan something fun to help you relax after the exam is over?"

[1484] In this way, the system digitally preserves and recreates the memories and emotions of the deceased, enabling interaction that provides psychological comfort to family members.

[1485] Example 2

[1486] 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."

[1487] In modern society, systems that effectively store individual thoughts and emotions and use them to engage in conversations using artificial intelligence are still in the early stages of development. In particular, there are few systems that analyze user input data in real time and generate responses based on emotions and psychological states, and only a limited number of systems also provide privacy protection. This makes it difficult for users to deeply understand their own emotions or to have emotionally rich conversations with specific users. Furthermore, there are security issues due to the lack of access permission management.

[1488] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1489] In this invention, the server includes a means for inputting personal thoughts and feelings in blog format, a means for saving the input text data, a means for analyzing emotions and psychological states, a means for engaging in dialogue with specific users using the generated AI model, and a means for managing access rights, which enables real-time analysis of input data, tracking of emotions and psychological states, generation of dialogue models and presentation of responses, and access management with enhanced security.

[1490] "Personal thoughts and feelings" refers to the thoughts and feelings that a user has internally.

[1491] "Means for inputting in blog format" refers to a function that allows users to input their thoughts and feelings as text into the device and send that data to the server.

[1492] "Means for saving input text data" refers to a function that saves the text data entered by the user in a database so that it can be used for subsequent processing or analysis.

[1493] "Means for extracting emotions and psychological states" refers to technology for analyzing stored text data and identifying a user's emotions and psychological states.

[1494] "Means for learning individual conversational styles" refers to a function for learning a user's unique language patterns and conversational styles based on extracted emotional and psychological state data.

[1495] "Means for generating an artificial intelligence model" refers to a technology for generating an artificial intelligence model for engaging in natural dialogue with a user based on a learned dialogue style.

[1496] "Means for interacting with a specific user" refers to a function for using the generated artificial intelligence model to interact with a specific user and provide an appropriate response.

[1497] "Emotion engine" refers to technology for analyzing and extracting user emotions from input data.

[1498] "Means of storing in a database" refers to the storage technology that allows the server to retain the data received and make it accessible in the future.

[1499] "Means for managing access rights" refers to technology that authenticates and verifies permissions to ensure that only specific users can access the system.

[1500] "Means for sending in real time" refers to a function that allows text data entered by the user to be sent immediately to the server.

[1501] "Means for generating and presenting a response" refers to technology that uses a generated artificial intelligence model to generate a natural response based on the emotion of a specific user in response to input from that user, and then presents that response.

[1502] The present invention is a system in which individuals input their thoughts and feelings in blog format, analyze this data in real time using an emotion engine, generate an artificial intelligence model, and engage in dialogue with specific users. Next, a description will be given of an embodiment of the present invention with specific examples.

[1503] The system consists of the following main modules: data collection module, data storage module, psychology analysis module, emotion engine, AI learning module, AI dialogue module, and access management module.

[1504] Data Collection Module

[1505] A user writes his or her thoughts and feelings in a blog format. For example, a user might write, "I had a great time with my friends today." The device then sends this text data to the server in real time.

[1506] (Example prompt) "I had a great time with my friends today."

[1507] Data Storage Module

[1508] The server receives the text data sent from the terminal and stores it in a database, which accumulates all the entries the user has previously entered and makes them available for future processing.

[1509] Psychological Analysis Module

[1510] The server periodically retrieves the text data stored in the database and analyzes the emotions in real time using an emotion engine. Specifically, it uses natural language processing (NLP) technology to extract an individual's emotions and psychological state from the text data. For example, a positive emotion can be extracted from the expression "It was fun."

[1511] Emotion Engine

[1512] The server uses an emotion engine to extract emotions from the user's input data in real time. The emotion engine passes the analyzed emotion data to a psychology analysis module, which stores the emotions and psychological state in a database. This makes it possible to track the user's emotional fluctuations and psychological state over time.

[1513] AI Learning Module

[1514] The server uses the stored text data and the emotional data extracted by the emotion engine to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns, phrases, and emotional responses, and then build an artificial intelligence model based on them.

[1515] AI dialogue module

[1516] A specific user (e.g., a family member) can access this system and interact with the user's artificial intelligence model. The server receives the specific user's input and uses the artificial intelligence model to generate a response to this input. An emotion engine is used to analyze the input data and generate a natural response based on the user's emotions. For example, if a user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[1517] (Example prompt) "I feel a little tired today."

[1518] Access Management Module

[1519] The server manages access permissions, ensuring that only specific users can access the system, which is important for privacy reasons, and verifies specific users through an appropriate authentication process, allowing or denying access.

[1520] Examples:

[1521] User A left the following diary entry before his death: "Today I enjoyed dinner with my family and felt very happy."

[1522] The server stored this text data and extracted the emotion "happiness" using the emotion engine. This data was then trained by the AI ​​learning module to model User A's conversation style.

[1523] A few years later, if User A's child accesses the system and enters, "I'm a little nervous today because of my final exams," the server's AI model will use its emotion engine to analyze the input data and respond, "You're feeling nervous. Why don't you plan something fun to help you relax after the exams are over?"

[1524] In this way, the system accumulates the user's emotions and thoughts, and realizes natural and emotional dialogue based on the dialogue model.

[1525] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1526] Step 1:

[1527] Users input their thoughts and feelings into the device in a blog format. For example, they can write an entry such as, "I had a great time with my friends today." The input text data is sent from the device to the server in real time.

[1528] Specifically, when a user inputs a blog entry and presses the "Post" button, the terminal generates an HTTP request and sends the input data to the server.

[1529] Input: User-entered text data

[1530] Output: Sending text data from the terminal to the server

[1531] Step 2:

[1532] The server receives the text data sent from the terminal and stores it in a database. This save operation accumulates all entries that the user has previously entered.

[1533] Specifically, the server receives an HTTP request, extracts text data from the request body, and inserts it into a database.

[1534] Input: Text data received from the device

[1535] Output: Text data stored in the database

[1536] Step 3:

[1537] The server periodically retrieves text data from the database and passes it to the emotion engine for real-time emotion analysis. Natural language processing (NLP) techniques are used to extract an individual's emotions and psychological state from the text data.

[1538] Specifically, the server retrieves new text entries from the database as a scheduled task, inputs them into the emotion engine, and obtains the analysis results.

[1539] Input: Text data in a database

[1540] Output: Analysis results by the emotion engine (emotion data)

[1541] Step 4:

[1542] The server uses the emotion engine to extract emotion data and stores it in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time.

[1543] Specifically, the server receives the emotion data returned from the emotion engine and stores it in a database.

[1544] Input: Emotion engine analysis results (emotion data)

[1545] Output: Emotion data stored in a database

[1546] Step 5:

[1547] The server uses the stored text and emotion data to learn the user's interaction style. During this learning process, machine learning algorithms are used to extract the user's unique language patterns and emotional responses and build an artificial intelligence model.

[1548] Specifically, the server periodically retrieves text data and emotion data from the database, inputs them into a machine learning algorithm to train a model, and saves the generated model.

[1549] Input: Text data and emotion data

[1550] Output: A trained artificial intelligence model

[1551] Step 6:

[1552] A specific user can access the system and interact with the AI ​​model. The server receives the specific user's input and uses the AI ​​model to generate a response. The emotion engine is used to analyze the input data and generate a natural response.

[1553] Specifically, when a specific user enters a sentence into the dialogue interface and presses the "send" button, the server receives the input data, analyzes it with the emotion engine, and the AI ​​model generates a response and sends it back to the specific user.

[1554] Input: Data entered by a specific user

[1555] Output: The response generated from the artificial intelligence model

[1556] Step 7:

[1557] The server manages access privileges and allows only specific users to access the system. This is for the purpose of system security and privacy protection, and verifies specific users through an authentication process and grants or denies access privileges.

[1558] Specifically, the server checks the authentication information when a user logs in and assigns appropriate access privileges.

[1559] Input: User credentials

[1560] Output: Access granted or denied

[1561] (Application example 2)

[1562] 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."

[1563] As individuals record their daily experiences and emotions, there is a need for a system that can suggest appropriate content based on this information and provide support and empathy through dialogue. However, existing systems are limited in their ability to effectively utilize individuals' emotional data to suggest personalized content and provide dialogue. As a result, users are often left with low satisfaction and find it difficult to receive psychological support or empathy.

[1564] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1565] In this invention, the server includes means for inputting personal thoughts and feelings in blog format, means for saving the input text data, means for analyzing the saved text data and extracting emotions and psychological states, means for learning the personal interaction style based on the extracted emotions and psychological states, means for generating a personal AI model based on the learned interaction style, means for suggesting content such as movies, music, and books based on the emotions and psychological states input by the user, means for engaging in dialogue with a specific user using the generated AI model, and means installed on a smartphone that allows the user to input emotions in diary format. This enables personalized content suggestions and dialogue based on the individual's emotions and psychological states, thereby improving support and empathy for the user.

[1566] "Means for inputting personal thoughts and feelings in blog format" refers to a means by which a user inputs everyday experiences and feelings in text format and records them as a blog.

[1567] "Means for saving input text data" refers to means for recording the text data entered by the user in a database or storage and saving it permanently or for the long term.

[1568] The "means for analyzing stored text data and extracting emotions and psychological states" refers to a means for analyzing stored text data and identifying the emotions and psychological states of a user using natural language processing technology.

[1569] The "means for learning individual conversational styles" is a means for learning a user's unique language patterns and emotional responses based on the extracted emotions and psychological states.

[1570] The "means for generating a personal artificial intelligence model" is a means for creating an artificial intelligence model based on the learned dialogue style and conducting dialogue through this model.

[1571] The "means for suggesting content" is a means for recommending appropriate media content such as movies, music, books, etc. to a user based on the results of analyzing the user's emotions and psychological state.

[1572] The "means for interacting with a specific user" is a means for using the generated artificial intelligence model to interact with a specified user.

[1573] "A means that is installed on a smartphone and allows users to input emotions in diary format" is a means that is provided as a smartphone app and allows users to input daily events and emotions in diary format.

[1574] The present invention is a system that allows users to input their thoughts and feelings in blog format, analyzes the data in real time using an emotion engine, generates an artificial intelligence model, and engages in a dialogue with a specific user. This system is realized mainly using the following hardware and software:

[1575] Hardware and software used

[1576] Smartphone: A device used by users to enter their emotions in diary format.

[1577] Server: A machine that stores data, analyzes it, generates artificial intelligence models, and processes interactions.

[1578] Flask: A web framework for implementing server-side APIs.

[1579] spaCy: A library for performing natural language processing (NLP).

[1580] Program processing overview

[1581] Data collection and storage

[1582] Users use a smartphone application to input their daily experiences and feelings in blog format. This input text data is sent to a server in real time. The server stores the received text data in a database. For example, if a user inputs "I had a great time with my friends today," this text data is sent as is to the server and stored there.

[1583] Emotion analysis

[1584] The server periodically retrieves the stored text data and analyzes the emotions and psychological state in real time using the spaCy library. For example, positive emotions are extracted from the text "I had a good time." This emotional data is stored in a database and tracked over time.

[1585] Content Suggestion

[1586] The server then suggests appropriate content, such as movies, music, and books, to the user based on the analyzed emotions and psychological state. For example, if a user inputs "I feel a little tired today," the server will suggest relaxing music and movies.

[1587] Conducting dialogue

[1588] The server learns the user's interaction style based on the extracted emotions and psychological state, and then uses the generated AI model to converse with the specific user. For example, if a user inputs "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[1589] Specific examples

[1590] When User A opens the smartphone app and types, "I had a good time with my friends today," the server analyzes the emotion as positive and stores it. If User A then types, "I'm a little tired today," the server responds, "That must have been tough. It's important to take time to relax and rest."

[1591] Prompt Sentence Examples

[1592] If a user types, "I had a great time with my friends today," the system responds, "That's great. What do you want to do next?"

[1593] In this way, the system of the present invention can propose and interact with personalized content based on an individual's emotions and psychological state, thereby improving support and empathy for the user.

[1594] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1595] Step 1:

[1596] The user starts a smartphone application and enters their emotions in a diary format. This entry is in text format, such as "I had a great time with my friends today." The input data is sent from the smartphone to the server in real time.

[1597] Input: Text data entered by the user

[1598] Output: Text data sent to the server

[1599] Step 2:

[1600] The server stores the received text data in a database, which accumulates all user-entered entries and makes them available for later analysis and learning.

[1601] Input: Text data sent from the smartphone device

[1602] Output: Text entry stored in the database

[1603] Step 3:

[1604] The server periodically retrieves the stored text data and analyzes it in real time using an emotion engine, which uses natural language processing (NLP) technology to extract positive emotions from expressions such as "I enjoyed it."

[1605] Input: Text data stored in a database

[1606] Output: Parsed sentiment data (positive, negative, etc.)

[1607] Step 4:

[1608] The extracted emotion data is then stored in a database, allowing the user's emotional fluctuations and psychological state to be tracked over time, revealing the user's emotional patterns at different times.

[1609] Input: Parsed emotion data

[1610] Output: Emotion data stored in a database

[1611] Step 5:

[1612] Based on the analyzed emotions and psychological state, the server learns the user's interaction style and generates an artificial intelligence model that learns the user's unique language patterns and emotional responses for use in future interactions.

[1613] Input: Emotion data and text data stored in the database

[1614] Output: An artificial intelligence model that reflects the user's interaction style and emotions

[1615] Step 6:

[1616] The server then suggests content such as movies, music, and books to the user based on the analyzed emotions and psychological state. For example, if a user inputs "I feel a little tired today," relaxing music and movies will be recommended.

[1617] Input: Analyzed emotion data, artificial intelligence model

[1618] Output: A list of suggested content for the user

[1619] Step 7:

[1620] A specific user (e.g., a family member) can interact with the system using the stored text data and the generated AI model. The server receives input from the specific user and generates a response to this input. For example, if the user inputs, "I'm a little tired today," the AI ​​will respond, "That must have been tough. It's important to take time to relax and rest."

[1621] Input: Text input by a specific user, artificial intelligence model

[1622] Output: The generated dialogue response

[1623] Step 8:

[1624] The server stores the content list presented to the user and the response log of the generated dialogue in a database, which is used for future analysis and to improve the quality of the dialogue. This improves the accuracy of the entire system and continuously improves the user experience.

[1625] Input: Content suggestion list, generated dialogue response

[1626] Output: Content suggestions and dialogue response logs stored in a database

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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).

[1634] 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.

[1635] 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."

[1636] 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.

[1637] 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).

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] The following is further disclosed regarding the above embodiment.

[1649] (Claim 1)

[1650] A means of inputting personal thoughts and feelings in blog format;

[1651] means for saving the input text data;

[1652] means for analyzing the stored text data and extracting emotions and psychological states;

[1653] a means for learning an individual's interaction style based on the extracted emotions and psychological states;

[1654] means for generating a personalized artificial intelligence model based on the learned interaction style;

[1655] A system including a means for interacting with a specific user using the generated artificial intelligence model.

[1656] (Claim 2)

[1657] Further includes means for managing access rights by specific users.

[1658] 10. The system of claim 1.

[1659] (Claim 3)

[1660] and means for presenting the answer generated using the artificial intelligence model to the particular user.

[1661] 10. The system of claim 1.

[1662] "Example 1"

[1663] (Claim 1)

[1664] A means of inputting personal thoughts and feelings in blog format;

[1665] means for saving the input text data;

[1666] means for analyzing the stored text data and extracting emotions and psychological states;

[1667] a means for learning an individual's interaction style based on the extracted emotions and psychological states;

[1668] means for generating a personalized artificial intelligence model based on the learned interaction style;

[1669] A means for interacting with a specific user using the generated artificial intelligence model;

[1670] means for accepting a dialogue request from a particular user and generating a response using an artificial intelligence model;

[1671] A system that includes a means to manage access rights and ensure availability to only specific users.

[1672] (Claim 2)

[1673] 10. The system of claim 1, further comprising means for verifying and granting or denying access to specific users through an appropriate authentication process to protect privacy in the system.

[1674] (Claim 3)

[1675] 10. The system of claim 1, further comprising means for generating a response from the generated artificial intelligence model based on input from the specific user and presenting the response to the specific user.

[1676] "Application Example 1"

[1677] (Claim 1)

[1678] A means of inputting personal thoughts and feelings in blog format;

[1679] means for saving the input text data;

[1680] means for analyzing the stored text data and extracting emotions and psychological states;

[1681] a means for learning an individual's interaction style based on the extracted emotions and psychological states;

[1682] means for generating a personalized artificial intelligence model based on the learned interaction style;

[1683] A means for interacting with a specific user using the generated artificial intelligence model;

[1684] Cryptographic means for securely storing and managing input text data and interaction styles;

[1685] Authentication measures to securely allow specific users to access the system;

[1686] A system including a means for the generated artificial intelligence model to present optimal responses to a particular user.

[1687] (Claim 2)

[1688] Further includes means for managing access rights by specific users.

[1689] 10. The system of claim 1.

[1690] (Claim 3)

[1691] and means for presenting the answer generated using the artificial intelligence model to the particular user.

[1692] 10. The system of claim 1.

[1693] "Example 2: Combining Emotion Engines"

[1694] (Claim 1)

[1695] A means of inputting personal thoughts and feelings in blog format,

[1696] means for saving the input text data;

[1697] means for analyzing the stored text data and extracting emotions and psychological states;

[1698] a means for learning an individual's interaction style based on the extracted emotions and psychological states;

[1699] means for generating a personalized artificial intelligence model based on the learned interaction style;

[1700] A means for using the generated artificial intelligence model to have a dialogue with a specific user;

[1701] means for analyzing a particular user's input and generating a response using an emotion engine;

[1702] A means for storing the generated emotion data in a database and tracking the individual's emotional fluctuations and psychological state;

[1703] means for transmitting text data entered by a user to a server in real time;

[1704] a means for storing the received data in a database by the server;

[1705] A system that includes a means for generating and presenting responses to data entered by a particular user.

[1706] (Claim 2)

[1707] Further includes means for managing access rights

[1708] 10. The system of claim 1.

[1709] (Claim 3)

[1710] and means for presenting the response generated using the artificial intelligence model to the specific user.

[1711] 10. The system of claim 1.

[1712] "Application example 2 when combining emotion engines"

[1713] (Claim 1)

[1714] A means of inputting personal thoughts and feelings in blog format;

[1715] means for saving the input text data;

[1716] means for analyzing the stored text data and extracting emotions and psychological states;

[1717] a means for learning an individual's interaction style based on the extracted emotions and psychological states;

[1718] means for generating a personalized artificial intelligence model based on the learned interaction style;

[1719] A means for suggesting content such as movies, music, books, etc. based on the emotions and psychological state input by the user;

[1720] A means for interacting with a specific user using the generated artificial intelligence model;

[1721] The system is installed on a smartphone and includes a means for users to input their emotions in diary format.

[1722] (Claim 2)

[1723] Further includes means for managing access rights by specific users.

[1724] 10. The system of claim 1.

[1725] (Claim 3)

[1726] and means for presenting the answer generated using the artificial intelligence model to the particular user.

[1727] 10. The system of claim 1. [Explanation of symbols]

[1728] 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 of inputting personal thoughts and feelings in blog format; means for saving the input text data; means for analyzing the stored text data and extracting emotions and psychological states; a means for learning an individual's interaction style based on the extracted emotions and psychological states; means for generating a personalized artificial intelligence model based on the learned interaction style; A system including a means for interacting with a specific user using the generated artificial intelligence model.

2. Further includes means for managing access rights by specific users. The system of claim 1 .

3. and means for presenting the answer generated using the artificial intelligence model to the particular user. The system of claim 1 .

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