System

Abstract

A system is provided.SOLUTION: A system comprising means for inputting basic information of a target person from a user, means for collecting information about the target person from the Internet and a database, means for analyzing the collected information and training a generative AI model, means for generating a dialogue based on an input of the user using the trained generative AI model, and means for providing the generated dialogue to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Modern generative AI technology has great potential for supporting user interactions and creative activities. However, simulating interactions with specific deceased or historical figures requires collecting detailed information about the target person and training an advanced generative AI model based on that information. Previous technologies have struggled to accurately reproduce the target person's characteristics and personality and achieve sophisticated interactions that meet user expectations. Furthermore, if a user wishes to engage in conversations or creative activities based on a specific event, there has been no system that can easily generate such virtual events or scenarios. Furthermore, providing a user-friendly experience by supporting various input methods, including voice input, has been a challenge. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means. First, it includes a means for having a user input basic information about a target person. Next, it includes a means for collecting information about the target person from the Internet and a database. It also includes a means for analyzing the collected information and training a generative AI model. It further provides a means for generating a dialogue based on the user's input using the trained generative AI model. It also includes a means for providing the generated dialogue to the user. In addition, it provides a means for generating a virtual event based on a dialogue with the target person, enabling dialogue and creative activities based on a specific event. It also includes a means for receiving the user's input as voice data and converting the voice data into text data, thereby supporting a variety of input methods and improving user convenience. This realizes a system that allows users to enjoy high-quality dialogue and creative activities.

[0006] "User" refers to an individual who accesses the System and engages in interactive and creative activities.

[0007] A "target person" refers to a person, such as a deceased or historical figure, about whom the generative AI model has information to simulate a dialogue.

[0008] "Basic information" refers to basic data about the target person, such as name, date of birth, and career history.

[0009] "Means" refers to the functions and processes used to realize this system.

[0010] "Collecting" refers to the act of obtaining and integrating information from the internet and databases.

[0011] "Internet" refers to a globally connected network of computers.

[0012] A "database" refers to a collection of data that stores information systematically and allows efficient retrieval.

[0013] "Analyzing" refers to the act of structuring collected information through classification, tagging, trend analysis, etc.

[0014] A "generative AI model" refers to an artificial intelligence algorithm or program that processes natural language.

[0015] "Training" refers to the act of using collected data to train a generative AI model and improve its performance.

[0016] "Dialogue" refers to the exchange of information between a user and a generative AI model.

[0017] "Providing" refers to the act of delivering the generated dialogue to the user as a display or sound.

[0018] "Virtual event" refers to a virtual event or activity that is generated based on a specific scenario during an interaction.

[0019] "Various input methods" refers to different methods that users use, such as text input and voice input.

[0020] "Voice data" refers to digital data that is a recording of a user's speech.

[0021] "Text data" refers to data that represents written character information in digital form.

[0022] "Converting" refers to the act of changing data from one format to another. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] This invention relates to a generative AI system that simulates interactions with deceased or historical figures. To implement this invention, it is necessary to collect basic information about the target person from a user, obtain additional information from the internet or a database, and train a generative AI model based on that information. The following describes the system's operation step by step.

[0045] First, the user accesses the system and creates an account. After logging in, a screen appears where they can enter information about the person (such as name, date of birth, and biography). The device then sends this information to the management server. The server then uses the received information to collect additional information about the person from the Internet and databases. This collection process includes relevant literary works, photographs, audio recordings, and more.

[0046] The server then analyzes the collected information to extract the target person's characteristics and features. The results of this analysis are used as training data for a generative AI model. The server then trains the generative AI model, which can then converse with the target person based on their language, speaking style, and knowledge.

[0047] Once training is complete, the user can begin a conversation with the target person. For example, the user might type, "Tell me about the special theory of relativity." This input is sent by the device to the server. The server uses the generative AI model to generate an appropriate response based on the user's input and sends it back to the device. The device then displays the generated response to the user as text or audio.

[0048] Furthermore, to enhance the user experience, the server can generate specific virtual events based on the interaction content (e.g., an event to celebrate a target person's birthday or a reenactment of a specific historical event). When the user selects the event mode, interactions and activities based on a special scenario are provided.

[0049] In addition, when a user uses voice input, the device converts the voice data into text data and sends this text data to the server. The server generates a response based on this and sends it back to the device. The device plays it back as voice data and provides it to the user. For example, if a user asks a question by voice, such as "Tell me about the era in which he lived," the device converts this voice into text and sends the generated text data to the server. The server generates a response that explains how the target person experienced that era based on the understood context.

[0050] In this way, the system of the present invention provides users with the opportunity to gain a deeper understanding of the lives and achievements of deceased or historical figures and to build personal memorials through interactions and virtual events with them.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The user launches the application and the account registration screen appears. The user enters registration information such as name, email address, and password.

[0054] Step 2:

[0055] The terminal receives the input user information and transmits it to the server.

[0056] Step 3:

[0057] The server stores the received information in a database and creates a user account.

[0058] Step 4:

[0059] The user opens a screen for registering the target person and enters basic information such as the target person's name, date of birth, and career history.

[0060] Step 5:

[0061] The terminal receives the basic information of the person entered and sends it to the server.

[0062] Step 6:

[0063] Based on the target person information received by the server, information related to the target person (documents, photographs, audio data, etc.) is collected from the Internet and databases.

[0064] Step 7:

[0065] The server analyzes the collected data, tags and classifies it, and generates a training dataset for the generative AI model.

[0066] Step 8:

[0067] The server trains the generative AI model to learn the characteristics and features of the target person.

[0068] Step 9:

[0069] The user opens a chat window or a voice input screen to start a conversation with the target person.

[0070] Step 10:

[0071] The user inputs what they want to talk about using text or voice. The device receives the user's input and sends it to the server either as is if it is text or as text if it is voice.

[0072] Step 11:

[0073] The server receives the user's input and makes a request to the generative AI model to generate a dialogue.

[0074] Step 12:

[0075] The server uses a generative AI model to generate a response based on the user's input, taking into account the characteristics and background information of the target person.

[0076] Step 13:

[0077] The server sends the generated response to the terminal.

[0078] Step 14:

[0079] The device displays the response received from the server as text or plays it back as audio.

[0080] Step 15:

[0081] If the user has further questions or conversations to make, they enter the information again and repeat the same process.

[0082] Step 16:

[0083] If the user selects the virtual event mode, they enter details to set up interactions and events based on a specific scenario.

[0084] Step 17:

[0085] The server receives the details of the event and generates special scenarios and interactive activities for the target person.

[0086] Step 18:

[0087] The terminal displays the contents of the virtual event, allowing the user to experience the event.

[0088] The above are the specific processing steps of the present invention.

[0089] Example 1

[0090] 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."

[0091] This invention relates to a generative AI system that simulates interactions with deceased or historical figures, allowing users to obtain rich information about the target person and gain a deeper understanding. However, existing technologies lack efficient means for achieving such advanced interactions and information provision, and are inferior in features such as voice input and virtual event generation. As a result, the user experience is limited.

[0092] 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.

[0093] In this invention, the server includes a means for allowing a user to input basic information about the target person, a means for collecting information about the target person from the Internet and databases, and a means for analyzing the collected information and training a generative AI model. This allows the user to simulate conversations with deceased or historical figures and generate virtual events, allowing the user to gain a deeper understanding of the target person and enjoy a richer experience. Furthermore, by adding a means for converting voice input into text format and conversely outputting text as voice, the user can enjoy a more intuitive and natural conversation environment.

[0094] "User" means an individual or entity that accesses the system, inputs information about a person, and obtains information through interaction.

[0095] A "target person" is a deceased or historical figure about whom a user attempts to gather information or interact using the system.

[0096] "Basic information" refers to basic and core information about the target person, such as their name, date of birth, and career history.

[0097] The "Internet" is a global network for gathering and communicating information.

[0098] A "database" is a collection of data that is structured so that information can be collected, stored, and searched systematically.

[0099] "Collection means" refers to the function of searching for and obtaining information about a target person from the Internet or a database.

[0100] "Analysis tools" are data processing functions that make sense of the collected information and identify specific patterns or characteristics.

[0101] A "generative AI model" is an artificial intelligence that uses machine learning and deep learning techniques to generate language and information based on specific input.

[0102] "Training means" refers to the processing means used to train the generative AI model to learn information about the target person and improve its accuracy.

[0103] "Dialogue generation means" refers to a function that uses a generative AI model to create an appropriate response based on user input.

[0104] "Providing means" refers to a function for displaying or reproducing the generated dialogue or information to the user.

[0105] "Voice input means" refers to a function that allows a user to input data into the system by voice.

[0106] The "voice conversion means" refers to a function for converting voice input into text data or outputting text data as voice data.

[0107] A "virtual event" is an event designed to virtually recreate a specific event or scenario relevant to a target person through interaction and information provision.

[0108] This invention relates to a generative AI system that simulates interactions with deceased or historical figures. The following hardware and software are used to implement the invention:

[0109] Hardware:

[0110] User's PC or smartphone

[0111] Internet connection

[0112] Cloud Server

[0113] software:

[0114] Web browser or dedicated app

[0115] Form entry function

[0116] HTTPS protocol

[0117] Web crawling tools

[0118] Database APIs

[0119] Text analysis tools (e.g., Python, Pandas)

[0120] Generative AI models (e.g., GPT-3 (registered trademark))

[0121] Chat Interface

[0122] Voice Recognition Software

[0123] Text-to-speech software (TTS)

[0124] The specific steps will be explained below.

[0125] Users access the system using a web browser or a dedicated app and create an account. After logging in, the user is taken to a screen where they can enter basic information about the person (such as name, date of birth, and career history). This information is then sent from the device to the management server.

[0126] Based on the received information, the server collects supplemental information about the target person from the internet and databases. It uses web crawling tools and database APIs to retrieve and analyze the relevant information. A text analysis tool (e.g., NLTK, Pandas) is used for the analysis, and the analysis results are used as training data for a generative AI model (e.g., GPT-3).

[0127] The server trains the generative AI model, which then enables it to converse with the target person based on their vocabulary, speaking style, and knowledge. Once training is complete, the user initiates a dialogue with the target person through a dialogue interface. The user can enter questions via text or voice. In the case of voice input, the device converts the voice data into text data and sends this text data to the server.

[0128] The server uses a generative AI model to generate an appropriate response based on the user's input and sends it back to the device, which then displays or plays the generated response to the user as text or audio.

[0129] The server can also generate specific virtual events based on the dialogue. For example, if a user types, "Tell me about the time he lived in," the server can analyze the dialogue log and generate a corresponding virtual event.

[0130] For example, consider the following prompt:

[0131] "Tell me about Albert Einstein, his research and achievements."

[0132] "Please tell me in detail how Cleopatra rose to power."

[0133] "Tell me about John Lennon's experiences with the Beatles."

[0134] In this way, the system of the invention provides users with the opportunity to gain a deeper understanding of the lives and achievements of deceased or historical figures and to build personal memorials through interactions and virtual events with them.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1:

[0137] A user accesses the system and creates an account.

[0138] Input: User information (name, email address, password)

[0139] Output: Created account information

[0140] Operation:

[0141] 1. The user clicks the "New Registration" button in a web browser or dedicated app.

[0142] 2. The user enters the required information into the form and presses the "Register" button.

[0143] 3. The terminal sends the entered user information to the management server.

[0144] Step 2:

[0145] The user enters basic information about the person.

[0146] Input: Basic information of the target person (name, date of birth, career history)

[0147] Output: Information about the target person sent to the management server

[0148] Operation:

[0149] 1. After logging in, the user moves to the screen where they can enter information about the person in question.

[0150] 2. The user enters the target person's information into the form and presses the "Submit" button.

[0151] 3. The terminal sends the input information to the management server.

[0152] Step 3:

[0153] The server collects supplemental information about the target person based on the received information.

[0154] Input: Basic information of the target person

[0155] Output: Additional information collected about the person of interest

[0156] Operation:

[0157] 1. The server starts web crawling using the target person's name as a key.

[0158] 2. The server calls the database API to retrieve the relevant information.

[0159] 3. The server stores the collected supplemental information.

[0160] Step 4:

[0161] The server analyzes the collected information and trains a generative AI model.

[0162] Input: Supplementary information collected

[0163] Output: A trained generative AI model

[0164] Operation:

[0165] 1. The server tokenizes the collected information through a text analysis tool.

[0166] 2. The server builds a knowledge base and trains a generative AI model based on that knowledge.

[0167] Step 5:

[0168] The user accesses the dialogue interface and begins a dialogue with the target person.

[0169] Input: User question (text or voice)

[0170] Output: Send a query to the server

[0171] Operation:

[0172] 1. The user presses the "Start conversation" button and enters a question in the input field.

[0173] 2. The user submits the question by pressing the "Submit" button.

[0174] Step 6:

[0175] The terminal transmits the user's interactive input to the server.

[0176] Input: User question (text or voice data)

[0177] Output: User question sent to the management server

[0178] Operation:

[0179] 1. For voice input, the device uses voice recognition software to convert voice data into text data.

[0180] 2. Send the converted text data or text input data to the server.

[0181] Step 7:

[0182] The server uses a generative AI model to generate an appropriate response and sends it back to the device.

[0183] Input: User question data

[0184] Output: The generated response data

[0185] Operation:

[0186] 1. The server inputs the user's question data into the generative AI model and generates an appropriate response.

[0187] 2. Send the generated response back to the terminal.

[0188] Step 8:

[0189] The terminal displays or plays the received response to the user.

[0190] Input: Response data from the server

[0191] Output: what is displayed to the user or what is played as audio

[0192] Operation:

[0193] 1. The terminal displays the received text data in the display area.

[0194] 2. For audio output, convert the text into audio and play it back.

[0195] Step 9:

[0196] The server generates specific virtual events based on the content of the dialogue.

[0197] Input: Dialogue

[0198] Output: The generated virtual event

[0199] Operation:

[0200] 1. The server analyzes the interaction log and generates appropriate events using an event generation algorithm.

[0201] 2. Send details of the generated event to the user's device.

[0202] In this way, the system provides an opportunity to gain a deeper understanding of the lives and achievements of deceased and historical figures through dialogue and virtual events.

[0203] (Application example 1)

[0204] 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."

[0205] Previous systems lacked generative AI models for interacting with historical figures and deceased individuals, limiting the virtual experience users could have for gaining a deeper understanding of the subject. Furthermore, the lack of an effective means of converting voice data to text data limited the user experience. This made interactive learning difficult in historical reenactments and educational settings.

[0206] 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.

[0207] In this invention, the server includes means for having a user input basic information about the target person, means for collecting information about the target person from the Internet and a database, means for analyzing the collected information and training a generative AI model, means for generating a dialogue based on the user's input using the trained generative AI model, means for providing the generated dialogue to the user, means for displaying the dialogue using a device that reproduces a virtual space, means for converting voice data into text data, and means for converting the generated text data into voice data. This allows the user to interact with the target person through an immersive experience in a virtual space, enabling deep understanding and interactive learning.

[0208] The "means for allowing the user to input basic information about the target person" is a function that allows the user to input basic information such as the target person's name, date of birth, and career history into the system.

[0209] "Means for collecting information about the subject from the Internet and databases" refers to a function for obtaining additional details about the entered subject from online and offline databases.

[0210] "Means for analyzing collected information and training a generative AI model" refers to a function that analyzes acquired information and trains an AI model based on the results of that analysis.

[0211] "Means for generating dialogue based on user input using a trained generative AI model" refers to a function that uses a trained AI model to generate dialogue in response to a user's text input.

[0212] "Means for providing the generated dialogue to the user" is a function for providing the dialogue generated by the generative AI model to the user by display or audio.

[0213] "Means for displaying a dialogue using a device that recreates a virtual space" refers to a function for visually displaying the content of a dialogue using a device for recreating a virtual reality space (e.g., smart glasses or a head-mounted display).

[0214] The "means for converting voice data into text data" is a function for converting information input by voice by the user into text format.

[0215] The "means for converting generated text data into voice data" is a function for providing the generated text-based dialogue to the user in voice form.

[0216] The present invention relates to a system that allows users to simulate interactions with deceased or historical figures. To realize this system, the following specific components and process steps are required:

[0217] System Components

[0218] Hardware:

[0219] 1. Smart glasses or head-mounted displays:

[0220] It is used to allow users to enjoy experiences in virtual space.

[0221] 2. Server:

[0222] It plays a central role in processing data for the entire system.

[0223] 3. Terminal:

[0224] The device (smartphone, tablet, etc.) through which the user interfaces with the system.

[0225] software:

[0226] 1. Python:

[0227] Used as the main development language for programs.

[0228] 2. Transformers library (GPT-2):

[0229] A generative AI model for natural language processing.

[0230] 3. Requests library:

[0231] To obtain additional information from the Internet.

[0232] 4. pyttsx3 library:

[0233] To convert text to speech.

[0234] Data processing and calculation

[0235] Enter basic information:

[0236] The user uses a terminal to input basic information about the target person (such as name, date of birth, and career history) into the system. This input information is sent to the management server.

[0237] Collecting additional information:

[0238] The server uses the basic information entered to gather additional details about the person from the internet and databases, retrieving relevant documents, photographs, audio recordings, and more.

[0239] Training the AI ​​model:

[0240] The server analyzes the collected information and extracts the characteristics and features of the target person. The analysis results are used as training data for a generative AI model (GPT-2). Once training is complete, the model is able to converse with the target person based on their vocabulary, speaking style, and knowledge.

[0241] Dialogue generation:

[0242] When a user types a question or request into the device, the input is sent to the server, which uses a generative AI model to generate an appropriate response and sends it back to the device, which then provides the generated response to the user as text or audio.

[0243] Virtual display:

[0244] The generated dialogue content is displayed in a virtual space using smart glasses or a head-mounted display, allowing users to have a more immersive dialogue experience.

[0245] Audio data conversion:

[0246] When a user inputs something by voice, the device converts the voice data into text data and sends this text data to the server. The server generates an appropriate response based on the text data and sends it back to the device. The device then converts the generated text data into voice data and provides it to the user.

[0247] Examples and prompts

[0248] Examples:

[0249] The user asks, "Tell me about special relativity." This speech is converted to text and sent to a server. The server uses a generative AI model to generate a response: "Special relativity is a theory that explains the constant speed of light and the relativity of time and space." The device then converts this text into speech and provides it to the user.

[0250] Example prompt sentence:

[0251] Tell me about Einstein's life.

[0252] What is special relativity?

[0253] Please explain his historical background.

[0254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0255] Step 1:

[0256] input:

[0257] The user enters the target person's basic information (name, date of birth, career history, etc.) into the terminal.

[0258] Data processing:

[0259] The terminal collects the basic information entered and sends it to the management server.

[0260] output:

[0261] The management server receives basic information about the target person.

[0262] Step 2:

[0263] input:

[0264] The server generates requests to retrieve additional information from the Internet and databases based on the person's basic information.

[0265] Data processing:

[0266] The server uses an API to gather information such as relevant documents, photos, audio recordings, etc. This information is retrieved using the requests library.

[0267] output:

[0268] The server stores the obtained additional information in a database.

[0269] Step 3:

[0270] input:

[0271] The server parses the stored additional information.

[0272] Data processing:

[0273] The server uses natural language processing techniques to tokenize the information and extract the characteristics and features of the target person, then trains a generative AI model (GPT-2) using the transformers library.

[0274] output:

[0275] A trained generative AI model is generated.

[0276] Step 4:

[0277] input:

[0278] The user inputs a question or request through the terminal.

[0279] Data processing:

[0280] The terminal transmits the user's input as text data to the server.

[0281] output:

[0282] The server receives the user's input.

[0283] Step 5:

[0284] input:

[0285] The server uses the generative AI model to generate a response based on the user's input.

[0286] Data processing:

[0287] The server prompts the trained generative AI model with user input to generate an appropriate response.

[0288] output:

[0289] The generated response is obtained as text data.

[0290] Step 6:

[0291] input:

[0292] The server sends the generated response to the terminal.

[0293] Data processing:

[0294] The device displays the received text data on smart glasses or a head-mounted display.

[0295] output:

[0296] Users experience text or voice responses in a virtual space.

[0297] Step 7:

[0298] input:

[0299] When a user asks a question by voice, the terminal receives the input as voice data.

[0300] Data processing:

[0301] The device uses a speech recognition API to convert voice data into text data.

[0302] output:

[0303] The converted text data is sent to the server.

[0304] Step 8:

[0305] input:

[0306] The server receives the voice input and generates a response using a generative AI model.

[0307] Data processing:

[0308] The server generates a response using the text data and sends an instruction to the terminal to convert the generated text data into voice data.

[0309] output:

[0310] The terminal converts the text data into voice data and provides it to the user.

[0311] This allows the user to have a natural conversation with the target person in the virtual space, providing a rich experience.

[0312] 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.

[0313] This invention relates to a generative AI system that recognizes a user's emotions and provides appropriate dialogue and interaction based on those emotions. To implement this invention, the following steps are required: collecting basic information, acquiring additional information, training a generative AI model, integrating an emotion engine, and generating and providing dialogue with the user. The operation of the system is explained below step by step.

[0314] First, a user accesses the system and creates an account. Once the user logs in, a screen appears where they can enter information about the person (such as name, date of birth, and career history). The device then sends this information to the server. The server then uses the received information to collect additional information related to the person from the Internet and databases. This collection process involves searching and retrieving documents, photographs, audio recordings, and more.

[0315] The server then analyzes the collected information, tagging and classifying it to generate a training dataset for the generative AI model, which the server uses to train the generative AI model to learn the characteristics and traits of the target person.

[0316] When a user interacts with a target person, the user inputs text or voice. The input data is sent from the terminal to the server. In particular, in the case of voice data, the terminal converts the voice into text data and sends it to the server as text data.

[0317] Here, the present invention uses an emotion engine. The server applies the emotion engine to the user's input (text or voice) to analyze the user's emotional state. The emotion analysis results are fed back to the generative AI model and reflected in the dialogue response. For example, if the user expresses sadness, the generated response will be comforting.

[0318] As a concrete example, consider a case where a user voice-inputs, "I'm not feeling too good today." The device converts this speech into text and sends it to a server. The server passes the text data to an emotion engine for analysis, which detects that the user is sad. A generative AI model then takes this emotional state into account and generates a response such as, "Are you OK? Let me know if there's anything I can help you with," which is sent to the device. The device then provides this response to the user in text or voice.

[0319] The emotion engine also comes into play when users select the virtual event mode. When setting up an event, the system can take into account the user's current emotional state to customize the event content to be more personal and emotionally relevant. For example, if a user sets up an event to celebrate a special anniversary and the analysis shows that the emotion at that time is joy, the event content will be set to be positive and enjoyable.

[0320] In this way, the system according to the present invention can recognize the user's emotions and provide more intimate and emotional interactions through dialogues and virtual events based on the emotions.

[0321] The processing flow will be explained below.

[0322] Step 1:

[0323] The user launches the application and the account registration screen appears. The user enters registration information such as name, email address, and password.

[0324] Step 2:

[0325] The terminal receives the input user information and transmits it to the server.

[0326] Step 3:

[0327] The server stores the received information in a database and creates a user account.

[0328] Step 4:

[0329] The user opens a screen for registering the target person and enters basic information such as the target person's name, date of birth, and career history.

[0330] Step 5:

[0331] The terminal receives the basic information of the person entered and sends it to the server.

[0332] Step 6:

[0333] Based on the information received by the server, additional information about the target person (such as literature, photographs, and audio data) is collected from the Internet and databases.

[0334] Step 7:

[0335] The server analyzes the collected data, tags and classifies it, and generates a training dataset for the generative AI model.

[0336] Step 8:

[0337] The server uses this dataset to train a generative AI model, which learns to interact with people based on their characteristics, speaking style, and knowledge.

[0338] Step 9:

[0339] The user opens a chat window or a voice input screen to start a conversation with the target person.

[0340] Step 10:

[0341] The user inputs the dialogue content by text or voice. The device receives the user's input and sends it to the server either as is if it is text or as text if it is voice.

[0342] Step 11:

[0343] The server receives the user's input (text or voice) and asks the emotion engine to analyze it, which detects the user's emotional state.

[0344] Step 12:

[0345] The server provides the analysis results from the emotion engine to the generative AI model, which generates an appropriate response based on the user's emotional state.

[0346] Step 13:

[0347] The server sends the generated response to the terminal.

[0348] Step 14:

[0349] The device displays the response received from the server as text or plays it back as audio.

[0350] Step 15:

[0351] If the user has further questions or conversations to make, they enter the information again and repeat the same process.

[0352] Step 16:

[0353] If the user selects the virtual event mode, they enter details to set up an event based on a specific scenario.

[0354] Step 17:

[0355] The device sends the entered event details to the server, which uses an emotion engine to customize the event content, taking into account the user's emotional state.

[0356] Step 18:

[0357] The server generates a customized scenario for the virtual event and sends it to the terminal.

[0358] Step 19:

[0359] The terminal provides the content of the virtual event to the user as text or audio.

[0360] This allows a generative AI system with an embedded emotion engine to provide dialogue and interaction that responds to the user's emotional state, creating a personalized experience.

[0361] Example 2

[0362] 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."

[0363] Conventional conversational AI systems have difficulty generating responses that take the user's emotional state into account, which can result in poor dialogue quality. Furthermore, they lack the ability to customize the content of virtual events based on the user's emotions, resulting in a uniform user experience.

[0364] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for having a user input basic information about the target person, means for collecting information about the target person from a global information network and a data repository, means for analyzing the collected information and training a generative AI model, means for integrating an emotion engine into the generative AI model and analyzing the emotional state in response to the user's input, means for adjusting the response of the generative AI model based on the analyzed emotional state, and means for providing the generated dialogue to the user. This enables natural dialogue that takes the user's emotions into consideration, and makes it possible to provide a virtual event that is more emotionally sensitive.

[0365] "User" refers to an individual who accesses the System and participates in the Interactions and Virtual Events.

[0366] "Subject Person" means the individual about whom information is entered into the System by a User.

[0367] "Basic information" refers to the initial and primary information about the person, such as name, date of birth, and background.

[0368] "Means" refers to a method or device for achieving a specific function or purpose.

[0369] "Global information network" refers to an information network that can obtain data from all over the world, including the Internet.

[0370] "Data repository" means a system or location for collecting, storing, and managing data.

[0371] "Collecting information" refers to the act of locating and obtaining data related to a person from the internet or data repositories.

[0372] A "generative AI model" refers to an artificial intelligence model that generates dialogue based on collected and analyzed data.

[0373] "Emotion engine" refers to an algorithm or software for analyzing a user's emotional state from their speech.

[0374] "Generating a response" refers to the act of creating an appropriate reply based on user input.

[0375] "Virtual Event" means a simulated event presented online.

[0376] "Analysis" refers to the process of organizing collected data and understanding its meaning and relevance.

[0377] "Text data" refers to written data converted from voice input using voice recognition technology.

[0378] This invention relates to a generative AI system that recognizes a user's emotions and provides appropriate dialogue and interaction based on those emotions. To implement this invention, the system operates through multiple steps. The operation of the system is described in detail below.

[0379] First, a user accesses the system's website or application and creates an account. After logging in, the user enters basic information about the person (e.g., name, date of birth, career history, etc.). The device (e.g., smartphone, tablet, personal computer) then sends this information to the server using a secure communication protocol (e.g., HTTPS).

[0380] Based on the received basic information, the server searches and collects additional information related to the person from global information networks (the Internet) and data repositories (various databases). This collection process obtains data such as documents, photographs, and audio recordings.

[0381] The server then analyzes the collected information, typically tagging and categorizing it to generate a training dataset for the generative AI model. This analysis uses natural language processing techniques (e.g., BERT, GPT, etc.). The server then uses the generated dataset to train the generative AI model, learning the characteristics and traits of the target person. This training is performed using a deep learning framework (e.g., TENSORFLOW®, PyTorch).

[0382] When a user initiates a dialogue, they input text or voice. In the case of voice, the device uses voice recognition software (e.g., Google® Speech-to-Text API) to convert the speech into text and send it to the server. The server then passes the received text data to an emotion engine (e.g., IBM Watson®) to analyze the user's emotional state. The results of this analysis are fed back to the generative AI model, which then creates a response that takes the user's emotional state into account.

[0383] For example, if a user types "I'm not feeling too good today," the server inputs this text into the emotion engine and analyzes that the user is feeling "sad." The generative AI model takes this emotional state into account and generates a response such as "Are you OK? Let me know if there's anything I can help you with," which is then sent from the server to the device. The device then provides this response to the user in the form of a text display or voice playback using speech synthesis software (e.g., Amazon Polly).

[0384] Furthermore, when a user selects the virtual event mode, the server uses an emotion engine to analyze the user's current emotional state. Based on the analysis results, the server customizes the event content. For example, if a user sets up an event to celebrate a special anniversary and the analyzed emotion is "happy," the event content will be set to be positive and enjoyable.

[0385] Examples of prompts:

[0386] "When a user makes a statement that expresses an emotion, recognize that emotion and generate an appropriate response. For example, if the user says, 'I'm not feeling very well today,' generate a comforting response."

[0387] In this way, this generative AI system can recognize the user's emotions and provide natural dialogue and virtual events based on them.

[0388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0389] Program flow:

[0390] Step 1:

[0391] A user accesses the system's website or application and creates an account. Input information includes a username, email address, and password. Once this basic data is entered, the device sends it to the server, which then registers the received information in a database and completes the account creation.

[0392] Step 2:

[0393] The user logs in and enters basic information about the person (such as name, date of birth, and background information). The device sends this information to the server, which uses the received information to gather additional information from the internet and data repositories. This information gathering can be done using web scraping tools, APIs, etc.

[0394] input:

[0395] Basic information of the target person entered by the user

[0396] output:

[0397] Additional information about the subject (documents, photographs, audio recordings, etc.)

[0398] Step 3:

[0399] The server analyzes the collected information and performs tagging and classification, specifically using natural language processing techniques to extract meaningful keywords and phrases from the text data, which then creates a training dataset for the generative AI model.

[0400] input:

[0401] Additional Information Collected

[0402] output:

[0403] Training dataset (tagged data)

[0404] Step 4:

[0405] The server trains the generative AI model using deep learning frameworks (TensorFlow, PyTorch, etc.). Through training, the generative AI model learns the characteristics and features of the target person.

[0406] input:

[0407] Training dataset

[0408] output:

[0409] Pre-trained generative AI models

[0410] Step 5:

[0411] To initiate a dialogue, the user inputs text or voice. In the case of voice input, the device converts the speech into text using speech recognition software (e.g., Google Speech-to-Text API) and sends the text data to the server.

[0412] input:

[0413] User text or voice input

[0414] output:

[0415] Text data (in the case of voice input)

[0416] Step 6:

[0417] The server passes the received text data to an emotion engine, which analyzes the user's emotional state using an emotion analysis tool such as IBM Watson. The analysis results are fed back to the generative AI model.

[0418] input:

[0419] User text data

[0420] output:

[0421] Emotion analysis results

[0422] Step 7:

[0423] Based on the results of emotion analysis, the generative AI model generates an appropriate response based on the user's emotional state. The response is generated in text format and sent from the server to the device.

[0424] input:

[0425] Emotion analysis results

[0426] output:

[0427] Response text data

[0428] Step 8:

[0429] The device provides the generated response to the user, which can be displayed as text or played aloud using speech synthesis software (e.g., Amazon Polly).

[0430] input:

[0431] Response text data

[0432] output:

[0433] Text display or audio playback

[0434] Step 9:

[0435] When the user selects the virtual event mode, the server analyzes the user's current emotional state using an emotion engine and customizes the event content to include many elements that express positive emotions.

[0436] input:

[0437] The user's emotional state

[0438] output:

[0439] Customized Virtual Event Content

[0440] (Application example 2)

[0441] 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."

[0442] Traditional online shopping sites lack the ability to suggest products based on the user's emotions and psychological state, making it difficult for users to find the products that best suit their emotions. Furthermore, the lack of personalized interactions and suggestions based on user input can lead to a decrease in user satisfaction.

[0443] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having the user input basic information about the target person, means for collecting information about the target person from the Internet and a database, means for analyzing the collected information and training a generative AI model, means for generating a dialogue based on the user's input using the trained generative AI model, means for providing the generated dialogue to the user, means for analyzing the user's emotions, and means for suggesting products based on the user's emotions. This enables personalized product suggestions based on the user's emotions.

[0444] "Users" refers to people who use the system.

[0445] A "person of interest" refers to an individual about whom a user is interested and about whom the user enters basic information.

[0446] "Basic information" refers to initial data about the person, such as name, date of birth, and background.

[0447] "Internet" refers to a global network for collecting information.

[0448] A "database" refers to a structured collection of data that allows information about a person to be stored and retrieved.

[0449] A "generative AI model" is a model trained using machine learning or artificial intelligence techniques and used to generate dialogue and suggestions.

[0450] "Training" refers to the process of teaching a generative AI model using collected data.

[0451] "Emotion analysis" refers to the technology of analyzing a person's emotions from their input (text or voice).

[0452] "Dialogue" refers to communication between a user and a system.

[0453] "Product suggestion" refers to the act of recommending appropriate products and services based on the user's emotions and needs.

[0454] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a specific description of embodiments of the present invention.

[0455] System program and hardware / software configuration

[0456] The system for realizing this invention is an application for a shopping site where users can receive product suggestions based on their emotions using a smartphone. This system is composed of the following main hardware and software:

[0457] Smartphone: Provides the user interface and collects initial data.

[0458] Server: Stores data, analyzes it, and hosts generative AI models.

[0459] Sentiment analysis engine (e.g., sentiment analysis API): Analyzes user sentiment.

[0460] Natural language processing module (e.g., natural language parsing API): performs string analysis of user input.

[0461] Generative AI models (e.g., Generative AI Model APIs): Generate dialogue and suggestions.

[0462] Program processing flow and data calculation

[0463] 1. Providing the user interface:

[0464] An interface is displayed on the smartphone where the user can log in and input their emotional state (text / voice).

[0465] Example: A user types, "I'm a little tired today."

[0466] 2. Data conversion and transmission:

[0467] The smartphone converts the voice input into text (if it was entered by voice) and sends the data to the server.

[0468] 3. Emotion analysis:

[0469] The server uses an emotion analysis engine to analyze the user's input emotional state.

[0470] Example: Detecting the emotion "tired" from the input text "I'm a little tired today."

[0471] 4. Response generation using generative AI models:

[0472] The emotional information detected by the emotion analysis engine is passed to a generative AI model, which generates optimal product suggestions for the user.

[0473] Example: "Are you looking for relaxation products?"

[0474] 5. Search product database:

[0475] Based on the category and keywords of the suggested product, the server searches the database of the online shopping site for related products and retrieves a list.

[0476] 6. User Visibility:

[0477] The proposed products and their explanations are displayed on the smartphone and provided to the user.

[0478] Example: "How about this aromatherapy set? It's especially relaxing."

[0479] Examples of prompt statements

[0480] The following is an example of a prompt to input to a generative AI model (e.g., a generative AI model API):

[0481] A user types, "I'm feeling a bit tired today." The user's emotion is "tired." Suggest products related to relaxation and stress reduction.

[0482] In this way, the system according to the present invention can provide a more personalized shopping experience through interactions and product suggestions based on the user's emotions.

[0483] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0484] Step 1:

[0485] Input and Output:

[0486] The user inputs their emotional state (by text or voice) using a smartphone.

[0487] Specific behavior:

[0488] A user enters "I'm a little tired today" into the application, and this input data is saved on the smartphone.

[0489] Step 2:

[0490] Input and Output:

[0491] The smartphone converts the voice input into text and sends the text data to the server.

[0492] Specific behavior:

[0493] When a voice input is received, the device uses its voice recognition function to convert it into text data, which is then sent to the server.

[0494] Step 3:

[0495] Input and Output:

[0496] The server sends the received text data to the emotion analysis engine, analyzes the emotional state, and receives the emotion analysis results from the emotion analysis engine.

[0497] Specific behavior:

[0498] The server transfers the input text data, "I'm a little tired today," to an emotion analysis engine and detects the emotion of "tired."

[0499] Step 4:

[0500] Input and Output:

[0501] The sentiment analysis results and user input data are passed to the generative AI model to generate optimal product suggestions. The generated product suggestions are then received.

[0502] Specific behavior:

[0503] The server inputs the emotional information "I'm tired" and the user's text data into a generative AI model and generates a suggestion such as "Are you looking for relaxation products?"

[0504] Step 5:

[0505] Input and Output:

[0506] Based on the product suggestions generated by the generative AI model, the server searches for related products from the online shopping site's database and obtains a product list.

[0507] Specific behavior:

[0508] The server uses the product category and keywords to search a database to retrieve a list of related products (e.g., aromatherapy sets).

[0509] Step 6:

[0510] Input and Output:

[0511] The acquired product list and suggestions are sent to the smartphone and displayed to the user.

[0512] Specific behavior:

[0513] The server then sends the acquired product list and the suggestions generated by the generative AI model together to the smartphone. The device then visually displays this to the user. For example, it might display, "How about this aromatherapy set? It has a particularly relaxing effect."

[0514] In this way, product suggestions based on the user's emotions are made while clarifying the input data and output data at each processing step.

[0515] 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.

[0516] 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.

[0517] 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.

[0518] [Second embodiment]

[0519] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0520] 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.

[0521] 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).

[0522] 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.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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."

[0531] This invention relates to a generative AI system that simulates interactions with deceased or historical figures. To implement this invention, it is necessary to collect basic information about the target person from a user, obtain additional information from the internet or a database, and train a generative AI model based on that information. The following describes the system's operation step by step.

[0532] First, the user accesses the system and creates an account. After logging in, a screen appears where they can enter information about the person (such as name, date of birth, and biography). The device then sends this information to the management server. The server then uses the received information to collect additional information about the person from the Internet and databases. This collection process includes relevant literary works, photographs, audio recordings, and more.

[0533] The server then analyzes the collected information to extract the target person's characteristics and features. The results of this analysis are used as training data for a generative AI model. The server then trains the generative AI model, which can then converse with the target person based on their language, speaking style, and knowledge.

[0534] Once training is complete, the user can begin a conversation with the target person. For example, the user might type, "Tell me about the special theory of relativity." This input is sent by the device to the server. The server uses the generative AI model to generate an appropriate response based on the user's input and sends it back to the device. The device then displays the generated response to the user as text or audio.

[0535] Furthermore, to enhance the user experience, the server can generate specific virtual events based on the interaction content (e.g., an event to celebrate a target person's birthday or a reenactment of a specific historical event). When the user selects the event mode, interactions and activities based on a special scenario are provided.

[0536] In addition, when a user uses voice input, the device converts the voice data into text data and sends this text data to the server. The server generates a response based on this and sends it back to the device. The device plays it back as voice data and provides it to the user. For example, if a user asks a question by voice, such as "Tell me about the era in which he lived," the device converts this voice into text and sends the generated text data to the server. The server generates a response that explains how the target person experienced that era based on the understood context.

[0537] In this way, the system of the present invention provides users with the opportunity to gain a deeper understanding of the lives and achievements of deceased or historical figures and to build personal memorials through interactions and virtual events with them.

[0538] The processing flow will be explained below.

[0539] Step 1:

[0540] The user launches the application and the account registration screen appears. The user enters registration information such as name, email address, and password.

[0541] Step 2:

[0542] The terminal receives the input user information and transmits it to the server.

[0543] Step 3:

[0544] The server stores the received information in a database and creates a user account.

[0545] Step 4:

[0546] The user opens a screen for registering the target person and enters basic information such as the target person's name, date of birth, and career history.

[0547] Step 5:

[0548] The terminal receives the basic information of the person entered and sends it to the server.

[0549] Step 6:

[0550] Based on the target person information received by the server, information related to the target person (documents, photographs, audio data, etc.) is collected from the Internet and databases.

[0551] Step 7:

[0552] The server analyzes the collected data, tags and classifies it, and generates a training dataset for the generative AI model.

[0553] Step 8:

[0554] The server trains the generative AI model to learn the characteristics and features of the target person.

[0555] Step 9:

[0556] The user opens a chat window or a voice input screen to start a conversation with the target person.

[0557] Step 10:

[0558] The user inputs what they want to talk about using text or voice. The device receives the user's input and sends it to the server either as is if it is text or as text if it is voice.

[0559] Step 11:

[0560] The server receives the user's input and makes a request to the generative AI model to generate a dialogue.

[0561] Step 12:

[0562] The server uses a generative AI model to generate a response based on the user's input, taking into account the characteristics and background information of the target person.

[0563] Step 13:

[0564] The server sends the generated response to the terminal.

[0565] Step 14:

[0566] The device displays the response received from the server as text or plays it back as audio.

[0567] Step 15:

[0568] If the user has further questions or conversations to make, they enter the information again and repeat the same process.

[0569] Step 16:

[0570] If the user selects the virtual event mode, they enter details to set up interactions and events based on a specific scenario.

[0571] Step 17:

[0572] The server receives the details of the event and generates special scenarios and interactive activities for the target person.

[0573] Step 18:

[0574] The terminal displays the contents of the virtual event, allowing the user to experience the event.

[0575] The above are the specific processing steps of the present invention.

[0576] Example 1

[0577] 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."

[0578] This invention relates to a generative AI system that simulates interactions with deceased or historical figures, allowing users to obtain rich information about the target person and gain a deeper understanding. However, existing technologies lack efficient means for achieving such advanced interactions and information provision, and are inferior in features such as voice input and virtual event generation. As a result, the user experience is limited.

[0579] 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.

[0580] In this invention, the server includes a means for allowing a user to input basic information about the target person, a means for collecting information about the target person from the Internet and databases, and a means for analyzing the collected information and training a generative AI model. This allows the user to simulate conversations with deceased or historical figures and generate virtual events, allowing the user to gain a deeper understanding of the target person and enjoy a richer experience. Furthermore, by adding a means for converting voice input into text format and conversely outputting text as voice, the user can enjoy a more intuitive and natural conversation environment.

[0581] "User" means an individual or entity that accesses the system, inputs information about a person, and obtains information through interaction.

[0582] A "target person" is a deceased or historical figure about whom a user attempts to gather information or interact using the system.

[0583] "Basic information" refers to basic and core information about the target person, such as their name, date of birth, and career history.

[0584] The "Internet" is a global network for gathering and communicating information.

[0585] A "database" is a collection of data that is structured so that information can be collected, stored, and searched systematically.

[0586] "Collection means" refers to the function of searching for and obtaining information about a target person from the Internet or a database.

[0587] "Analysis tools" are data processing functions that make sense of the collected information and identify specific patterns or characteristics.

[0588] A "generative AI model" is an artificial intelligence that uses machine learning and deep learning techniques to generate language and information based on specific input.

[0589] "Training means" refers to the processing means used to train the generative AI model to learn information about the target person and improve its accuracy.

[0590] "Dialogue generation means" refers to a function that uses a generative AI model to create an appropriate response based on user input.

[0591] "Providing means" refers to a function for displaying or reproducing the generated dialogue or information to the user.

[0592] "Voice input means" refers to a function that allows a user to input data into the system by voice.

[0593] The "voice conversion means" refers to a function for converting voice input into text data or outputting text data as voice data.

[0594] A "virtual event" is an event designed to virtually recreate a specific event or scenario relevant to a target person through interaction and information provision.

[0595] This invention relates to a generative AI system that simulates interactions with deceased or historical figures. The following hardware and software are used to implement the invention:

[0596] Hardware:

[0597] User's PC or smartphone

[0598] Internet connection

[0599] Cloud Server

[0600] software:

[0601] Web browser or dedicated app

[0602] Form entry function

[0603] HTTPS protocol

[0604] Web crawling tools

[0605] Database APIs

[0606] Text analysis tools (e.g., Python, Pandas)

[0607] Generative AI models (e.g., GPT-3)

[0608] Chat Interface

[0609] Voice Recognition Software

[0610] Text-to-speech software (TTS)

[0611] The specific steps will be explained below.

[0612] Users access the system using a web browser or a dedicated app and create an account. After logging in, the user is taken to a screen where they can enter basic information about the person (such as name, date of birth, and career history). This information is then sent from the device to the management server.

[0613] Based on the received information, the server collects supplemental information about the target person from the internet and databases. It uses web crawling tools and database APIs to retrieve and analyze the relevant information. A text analysis tool (e.g., NLTK, Pandas) is used for the analysis, and the analysis results are used as training data for a generative AI model (e.g., GPT-3).

[0614] The server trains the generative AI model, which then enables it to converse with the target person based on their vocabulary, speaking style, and knowledge. Once training is complete, the user initiates a dialogue with the target person through a dialogue interface. The user can enter questions via text or voice. In the case of voice input, the device converts the voice data into text data and sends this text data to the server.

[0615] The server uses a generative AI model to generate an appropriate response based on the user's input and sends it back to the device, which then displays or plays the generated response to the user as text or audio.

[0616] The server can also generate specific virtual events based on the dialogue. For example, if a user types, "Tell me about the time he lived in," the server can analyze the dialogue log and generate a corresponding virtual event.

[0617] For example, consider the following prompt:

[0618] "Tell me about Albert Einstein, his research and achievements."

[0619] "Please tell me in detail how Cleopatra rose to power."

[0620] "Tell me about John Lennon's experiences with the Beatles."

[0621] In this way, the system of the invention provides users with the opportunity to gain a deeper understanding of the lives and achievements of deceased or historical figures and to build personal memorials through interactions and virtual events with them.

[0622] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0623] Step 1:

[0624] A user accesses the system and creates an account.

[0625] Input: User information (name, email address, password)

[0626] Output: Created account information

[0627] Operation:

[0628] 1. The user clicks the "New Registration" button in a web browser or dedicated app.

[0629] 2. The user enters the required information into the form and presses the "Register" button.

[0630] 3. The terminal sends the entered user information to the management server.

[0631] Step 2:

[0632] The user enters basic information about the person.

[0633] Input: Basic information of the target person (name, date of birth, career history)

[0634] Output: Information about the target person sent to the management server

[0635] Operation:

[0636] 1. After logging in, the user moves to the screen where they can enter information about the person in question.

[0637] 2. The user enters the target person's information into the form and presses the "Submit" button.

[0638] 3. The terminal sends the input information to the management server.

[0639] Step 3:

[0640] The server collects supplemental information about the target person based on the received information.

[0641] Input: Basic information of the target person

[0642] Output: Additional information collected about the person of interest

[0643] Operation:

[0644] 1. The server starts web crawling using the target person's name as a key.

[0645] 2. The server calls the database API to retrieve the relevant information.

[0646] 3. The server stores the collected supplemental information.

[0647] Step 4:

[0648] The server analyzes the collected information and trains a generative AI model.

[0649] Input: Supplementary information collected

[0650] Output: A trained generative AI model

[0651] Operation:

[0652] 1. The server tokenizes the collected information through a text analysis tool.

[0653] 2. The server builds a knowledge base and trains a generative AI model based on that knowledge.

[0654] Step 5:

[0655] The user accesses the dialogue interface and begins a dialogue with the target person.

[0656] Input: User question (text or voice)

[0657] Output: Send a query to the server

[0658] Operation:

[0659] 1. The user presses the "Start conversation" button and enters a question in the input field.

[0660] 2. The user submits the question by pressing the "Submit" button.

[0661] Step 6:

[0662] The terminal transmits the user's interactive input to the server.

[0663] Input: User question (text or voice data)

[0664] Output: User question sent to the management server

[0665] Operation:

[0666] 1. For voice input, the device uses voice recognition software to convert voice data into text data.

[0667] 2. Send the converted text data or text input data to the server.

[0668] Step 7:

[0669] The server uses a generative AI model to generate an appropriate response and sends it back to the device.

[0670] Input: User question data

[0671] Output: The generated response data

[0672] Operation:

[0673] 1. The server inputs the user's question data into the generative AI model and generates an appropriate response.

[0674] 2. Send the generated response back to the terminal.

[0675] Step 8:

[0676] The terminal displays or plays the received response to the user.

[0677] Input: Response data from the server

[0678] Output: what is displayed to the user or what is played as audio

[0679] Operation:

[0680] 1. The terminal displays the received text data in the display area.

[0681] 2. For audio output, convert the text into audio and play it back.

[0682] Step 9:

[0683] The server generates specific virtual events based on the content of the dialogue.

[0684] Input: Dialogue

[0685] Output: The generated virtual event

[0686] Operation:

[0687] 1. The server analyzes the interaction log and generates appropriate events using an event generation algorithm.

[0688] 2. Send details of the generated event to the user's device.

[0689] In this way, the system provides an opportunity to gain a deeper understanding of the lives and achievements of deceased and historical figures through dialogue and virtual events.

[0690] (Application example 1)

[0691] 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."

[0692] Previous systems lacked generative AI models for interacting with historical figures and deceased individuals, limiting the virtual experience users could have for gaining a deeper understanding of the subject. Furthermore, the lack of an effective means of converting voice data to text data limited the user experience. This made interactive learning difficult in historical reenactments and educational settings.

[0693] 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.

[0694] In this invention, the server includes means for having a user input basic information about the target person, means for collecting information about the target person from the Internet and a database, means for analyzing the collected information and training a generative AI model, means for generating a dialogue based on the user's input using the trained generative AI model, means for providing the generated dialogue to the user, means for displaying the dialogue using a device that reproduces a virtual space, means for converting voice data into text data, and means for converting the generated text data into voice data. This allows the user to interact with the target person through an immersive experience in a virtual space, enabling deep understanding and interactive learning.

[0695] The "means for allowing the user to input basic information about the target person" is a function that allows the user to input basic information such as the target person's name, date of birth, and career history into the system.

[0696] "Means for collecting information about the subject from the Internet and databases" refers to a function for obtaining additional details about the entered subject from online and offline databases.

[0697] "Means for analyzing collected information and training a generative AI model" refers to a function that analyzes acquired information and trains an AI model based on the results of that analysis.

[0698] "Means for generating dialogue based on user input using a trained generative AI model" refers to a function that uses a trained AI model to generate dialogue in response to a user's text input.

[0699] "Means for providing the generated dialogue to the user" is a function for providing the dialogue generated by the generative AI model to the user by display or audio.

[0700] "Means for displaying a dialogue using a device that recreates a virtual space" refers to a function for visually displaying the content of a dialogue using a device for recreating a virtual reality space (e.g., smart glasses or a head-mounted display).

[0701] The "means for converting voice data into text data" is a function for converting information input by voice by the user into text format.

[0702] The "means for converting generated text data into voice data" is a function for providing the generated text-based dialogue to the user in voice form.

[0703] The present invention relates to a system that allows users to simulate interactions with deceased or historical figures. To realize this system, the following specific components and process steps are required:

[0704] System Components

[0705] Hardware:

[0706] 1. Smart glasses or head-mounted displays:

[0707] It is used to allow users to enjoy experiences in virtual space.

[0708] 2. Server:

[0709] It plays a central role in processing data for the entire system.

[0710] 3. Terminal:

[0711] The device (smartphone, tablet, etc.) through which the user interfaces with the system.

[0712] software:

[0713] 1. Python:

[0714] Used as the main development language for programs.

[0715] 2. Transformers library (GPT-2):

[0716] A generative AI model for natural language processing.

[0717] 3. Requests library:

[0718] To obtain additional information from the Internet.

[0719] 4. pyttsx3 library:

[0720] To convert text to speech.

[0721] Data processing and calculation

[0722] Enter basic information:

[0723] The user uses a terminal to input basic information about the target person (such as name, date of birth, and career history) into the system. This input information is sent to the management server.

[0724] Collecting additional information:

[0725] The server uses the basic information entered to gather additional details about the person from the internet and databases, retrieving relevant documents, photographs, audio recordings, and more.

[0726] Training the AI ​​model:

[0727] The server analyzes the collected information and extracts the characteristics and features of the target person. The analysis results are used as training data for a generative AI model (GPT-2). Once training is complete, the model is able to converse with the target person based on their vocabulary, speaking style, and knowledge.

[0728] Dialogue generation:

[0729] When a user types a question or request into the device, the input is sent to the server, which uses a generative AI model to generate an appropriate response and sends it back to the device, which then provides the generated response to the user as text or audio.

[0730] Virtual display:

[0731] The generated dialogue content is displayed in a virtual space using smart glasses or a head-mounted display, allowing users to have a more immersive dialogue experience.

[0732] Audio data conversion:

[0733] When a user inputs something by voice, the device converts the voice data into text data and sends this text data to the server. The server generates an appropriate response based on the text data and sends it back to the device. The device then converts the generated text data into voice data and provides it to the user.

[0734] Examples and prompts

[0735] Examples:

[0736] The user asks, "Tell me about special relativity." This speech is converted to text and sent to a server. The server uses a generative AI model to generate a response: "Special relativity is a theory that explains the constant speed of light and the relativity of time and space." The device then converts this text into speech and provides it to the user.

[0737] Example prompt sentence:

[0738] Tell me about Einstein's life.

[0739] What is special relativity?

[0740] Please explain his historical background.

[0741] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0742] Step 1:

[0743] input:

[0744] The user enters the target person's basic information (name, date of birth, career history, etc.) into the terminal.

[0745] Data processing:

[0746] The terminal collects the basic information entered and sends it to the management server.

[0747] output:

[0748] The management server receives basic information about the target person.

[0749] Step 2:

[0750] input:

[0751] The server generates requests to retrieve additional information from the Internet and databases based on the person's basic information.

[0752] Data processing:

[0753] The server uses an API to gather information such as relevant documents, photos, audio recordings, etc. This information is retrieved using the requests library.

[0754] output:

[0755] The server stores the obtained additional information in a database.

[0756] Step 3:

[0757] input:

[0758] The server parses the stored additional information.

[0759] Data processing:

[0760] The server uses natural language processing techniques to tokenize the information and extract the characteristics and features of the target person, then trains a generative AI model (GPT-2) using the transformers library.

[0761] output:

[0762] A trained generative AI model is generated.

[0763] Step 4:

[0764] input:

[0765] The user inputs a question or request through the terminal.

[0766] Data processing:

[0767] The terminal transmits the user's input as text data to the server.

[0768] output:

[0769] The server receives the user's input.

[0770] Step 5:

[0771] input:

[0772] The server uses the generative AI model to generate a response based on the user's input.

[0773] Data processing:

[0774] The server prompts the trained generative AI model with user input to generate an appropriate response.

[0775] output:

[0776] The generated response is obtained as text data.

[0777] Step 6:

[0778] input:

[0779] The server sends the generated response to the terminal.

[0780] Data processing:

[0781] The device displays the received text data on smart glasses or a head-mounted display.

[0782] output:

[0783] Users experience text or voice responses in a virtual space.

[0784] Step 7:

[0785] input:

[0786] When a user asks a question by voice, the terminal receives the input as voice data.

[0787] Data processing:

[0788] The device uses a speech recognition API to convert voice data into text data.

[0789] output:

[0790] The converted text data is sent to the server.

[0791] Step 8:

[0792] input:

[0793] The server receives the voice input and generates a response using a generative AI model.

[0794] Data processing:

[0795] The server generates a response using the text data and sends an instruction to the terminal to convert the generated text data into voice data.

[0796] output:

[0797] The terminal converts the text data into voice data and provides it to the user.

[0798] This allows the user to have a natural conversation with the target person in the virtual space, providing a rich experience.

[0799] 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.

[0800] This invention relates to a generative AI system that recognizes a user's emotions and provides appropriate dialogue and interaction based on those emotions. To implement this invention, the following steps are required: collecting basic information, acquiring additional information, training a generative AI model, integrating an emotion engine, and generating and providing dialogue with the user. The operation of the system is explained below step by step.

[0801] First, a user accesses the system and creates an account. Once the user logs in, a screen appears where they can enter information about the person (such as name, date of birth, and career history). The device then sends this information to the server. The server then uses the received information to collect additional information related to the person from the Internet and databases. This collection process involves searching and retrieving documents, photographs, audio recordings, and more.

[0802] The server then analyzes the collected information, tagging and classifying it to generate a training dataset for the generative AI model, which the server uses to train the generative AI model to learn the characteristics and traits of the target person.

[0803] When a user interacts with a target person, the user inputs text or voice. The input data is sent from the terminal to the server. In particular, in the case of voice data, the terminal converts the voice into text data and sends it to the server as text data.

[0804] Here, the present invention uses an emotion engine. The server applies the emotion engine to the user's input (text or voice) to analyze the user's emotional state. The emotion analysis results are fed back to the generative AI model and reflected in the dialogue response. For example, if the user expresses sadness, the generated response will be comforting.

[0805] As a concrete example, consider a case where a user voice-inputs, "I'm not feeling too good today." The device converts this speech into text and sends it to a server. The server passes the text data to an emotion engine for analysis, which detects that the user is sad. A generative AI model then takes this emotional state into account and generates a response such as, "Are you OK? Let me know if there's anything I can help you with," which is sent to the device. The device then provides this response to the user in text or voice.

[0806] The emotion engine also comes into play when users select the virtual event mode. When setting up an event, the system can take into account the user's current emotional state to customize the event content to be more personal and emotionally relevant. For example, if a user sets up an event to celebrate a special anniversary and the analysis shows that the emotion at that time is joy, the event content will be set to be positive and enjoyable.

[0807] In this way, the system according to the present invention can recognize the user's emotions and provide more intimate and emotional interactions through dialogues and virtual events based on the emotions.

[0808] The processing flow will be explained below.

[0809] Step 1:

[0810] The user launches the application and the account registration screen appears. The user enters registration information such as name, email address, and password.

[0811] Step 2:

[0812] The terminal receives the input user information and transmits it to the server.

[0813] Step 3:

[0814] The server stores the received information in a database and creates a user account.

[0815] Step 4:

[0816] The user opens a screen for registering the target person and enters basic information such as the target person's name, date of birth, and career history.

[0817] Step 5:

[0818] The terminal receives the basic information of the person entered and sends it to the server.

[0819] Step 6:

[0820] Based on the information received by the server, additional information about the target person (such as literature, photographs, and audio data) is collected from the Internet and databases.

[0821] Step 7:

[0822] The server analyzes the collected data, tags and classifies it, and generates a training dataset for the generative AI model.

[0823] Step 8:

[0824] The server uses this dataset to train a generative AI model, which learns to interact with people based on their characteristics, speaking style, and knowledge.

[0825] Step 9:

[0826] The user opens a chat window or a voice input screen to start a conversation with the target person.

[0827] Step 10:

[0828] The user inputs the dialogue content by text or voice. The device receives the user's input and sends it to the server either as is if it is text or as text if it is voice.

[0829] Step 11:

[0830] The server receives the user's input (text or voice) and asks the emotion engine to analyze it, which detects the user's emotional state.

[0831] Step 12:

[0832] The server provides the analysis results from the emotion engine to the generative AI model, which generates an appropriate response based on the user's emotional state.

[0833] Step 13:

[0834] The server sends the generated response to the terminal.

[0835] Step 14:

[0836] The device displays the response received from the server as text or plays it back as audio.

[0837] Step 15:

[0838] If the user has further questions or conversations to make, they enter the information again and repeat the same process.

[0839] Step 16:

[0840] If the user selects the virtual event mode, they enter details to set up an event based on a specific scenario.

[0841] Step 17:

[0842] The device sends the entered event details to the server, which uses an emotion engine to customize the event content, taking into account the user's emotional state.

[0843] Step 18:

[0844] The server generates a customized scenario for the virtual event and sends it to the terminal.

[0845] Step 19:

[0846] The terminal provides the content of the virtual event to the user as text or audio.

[0847] This allows a generative AI system with an embedded emotion engine to provide dialogue and interaction that responds to the user's emotional state, creating a personalized experience.

[0848] Example 2

[0849] 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."

[0850] Conventional conversational AI systems have difficulty generating responses that take the user's emotional state into account, which can result in poor dialogue quality. Furthermore, they lack the ability to customize the content of virtual events based on the user's emotions, resulting in a uniform user experience.

[0851] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for having a user input basic information about the target person, means for collecting information about the target person from a global information network and a data repository, means for analyzing the collected information and training a generative AI model, means for integrating an emotion engine into the generative AI model and analyzing the emotional state in response to the user's input, means for adjusting the response of the generative AI model based on the analyzed emotional state, and means for providing the generated dialogue to the user. This enables natural dialogue that takes the user's emotions into consideration, and makes it possible to provide a virtual event that is more emotionally sensitive.

[0852] "User" refers to an individual who accesses the System and participates in the Interactions and Virtual Events.

[0853] "Subject Person" means the individual about whom information is entered into the System by a User.

[0854] "Basic information" refers to the initial and primary information about the person, such as name, date of birth, and background.

[0855] "Means" refers to a method or device for achieving a specific function or purpose.

[0856] "Global information network" refers to an information network that can obtain data from all over the world, including the Internet.

[0857] "Data repository" means a system or location for collecting, storing, and managing data.

[0858] "Collecting information" refers to the act of locating and obtaining data related to a person from the internet or data repositories.

[0859] A "generative AI model" refers to an artificial intelligence model that generates dialogue based on collected and analyzed data.

[0860] "Emotion engine" refers to an algorithm or software for analyzing a user's emotional state from their speech.

[0861] "Generating a response" refers to the act of creating an appropriate reply based on user input.

[0862] "Virtual Event" means a simulated event presented online.

[0863] "Analysis" refers to the process of organizing collected data and understanding its meaning and relevance.

[0864] "Text data" refers to written data converted from voice input using voice recognition technology.

[0865] This invention relates to a generative AI system that recognizes a user's emotions and provides appropriate dialogue and interaction based on those emotions. To implement this invention, the system operates through multiple steps. The operation of the system is described in detail below.

[0866] First, a user accesses the system's website or application and creates an account. After logging in, the user enters basic information about the person (e.g., name, date of birth, career history, etc.). The device (e.g., smartphone, tablet, personal computer) then sends this information to the server using a secure communication protocol (e.g., HTTPS).

[0867] Based on the received basic information, the server searches and collects additional information related to the person from global information networks (the Internet) and data repositories (various databases). This collection process obtains data such as documents, photographs, and audio recordings.

[0868] The server then analyzes the collected information, typically tagging and categorizing it to generate a training dataset for the generative AI model. This analysis uses natural language processing techniques (e.g., BERT, GPT, etc.). The server then uses the generated dataset to train the generative AI model, learning the characteristics and traits of the target person. This training is achieved using a deep learning framework (e.g., TensorFlow, PyTorch).

[0869] When a user initiates a dialogue, they input text or voice. In the case of voice, the device uses voice recognition software (e.g., Google Speech-to-Text API) to convert the speech into text and send it to the server. The server then passes the received text data to an emotion engine (e.g., IBM Watson) to analyze the user's emotional state. The results of this analysis are fed back to the generative AI model, which then creates a response that takes the user's emotional state into account.

[0870] For example, if a user types "I'm not feeling too good today," the server inputs this text into the emotion engine and analyzes that the user is feeling "sad." The generative AI model takes this emotional state into account and generates a response such as "Are you OK? Let me know if there's anything I can help you with," which is then sent from the server to the device. The device then provides this response to the user in the form of a text display or voice playback using speech synthesis software (e.g., Amazon Polly).

[0871] Furthermore, when a user selects the virtual event mode, the server uses an emotion engine to analyze the user's current emotional state. Based on the analysis results, the server customizes the event content. For example, if a user sets up an event to celebrate a special anniversary and the analyzed emotion is "happy," the event content will be set to be positive and enjoyable.

[0872] Examples of prompts:

[0873] "When a user makes a statement that expresses an emotion, recognize that emotion and generate an appropriate response. For example, if the user says, 'I'm not feeling very well today,' generate a comforting response."

[0874] In this way, this generative AI system can recognize the user's emotions and provide natural dialogue and virtual events based on them.

[0875] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0876] Program flow:

[0877] Step 1:

[0878] A user accesses the system's website or application and creates an account. Input information includes a username, email address, and password. Once this basic data is entered, the device sends it to the server, which then registers the received information in a database and completes the account creation.

[0879] Step 2:

[0880] The user logs in and enters basic information about the person (such as name, date of birth, and background information). The device sends this information to the server, which uses the received information to gather additional information from the internet and data repositories. This information gathering can be done using web scraping tools, APIs, etc.

[0881] input:

[0882] Basic information of the target person entered by the user

[0883] output:

[0884] Additional information about the subject (documents, photographs, audio recordings, etc.)

[0885] Step 3:

[0886] The server analyzes the collected information and performs tagging and classification, specifically using natural language processing techniques to extract meaningful keywords and phrases from the text data, which then creates a training dataset for the generative AI model.

[0887] input:

[0888] Additional Information Collected

[0889] output:

[0890] Training dataset (tagged data)

[0891] Step 4:

[0892] The server trains the generative AI model using deep learning frameworks (TensorFlow, PyTorch, etc.). Through training, the generative AI model learns the characteristics and features of the target person.

[0893] input:

[0894] Training dataset

[0895] output:

[0896] Pre-trained generative AI models

[0897] Step 5:

[0898] To initiate a dialogue, the user inputs text or voice. In the case of voice input, the device converts the speech into text using speech recognition software (e.g., Google Speech-to-Text API) and sends the text data to the server.

[0899] input:

[0900] User text or voice input

[0901] output:

[0902] Text data (in the case of voice input)

[0903] Step 6:

[0904] The server passes the received text data to an emotion engine, which analyzes the user's emotional state using an emotion analysis tool such as IBM Watson. The analysis results are fed back to the generative AI model.

[0905] input:

[0906] User text data

[0907] output:

[0908] Emotion analysis results

[0909] Step 7:

[0910] Based on the results of emotion analysis, the generative AI model generates an appropriate response based on the user's emotional state. The response is generated in text format and sent from the server to the device.

[0911] input:

[0912] Emotion analysis results

[0913] output:

[0914] Response text data

[0915] Step 8:

[0916] The device provides the generated response to the user, which can be displayed as text or played aloud using speech synthesis software (e.g., Amazon Polly).

[0917] input:

[0918] Response text data

[0919] output:

[0920] Text display or audio playback

[0921] Step 9:

[0922] When the user selects the virtual event mode, the server analyzes the user's current emotional state using an emotion engine and customizes the event content to include many elements that express positive emotions.

[0923] input:

[0924] The user's emotional state

[0925] output:

[0926] Customized Virtual Event Content

[0927] (Application example 2)

[0928] 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."

[0929] Traditional online shopping sites lack the ability to suggest products based on the user's emotions and psychological state, making it difficult for users to find the products that best suit their emotions. Furthermore, the lack of personalized interactions and suggestions based on user input can lead to a decrease in user satisfaction.

[0930] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having the user input basic information about the target person, means for collecting information about the target person from the Internet and a database, means for analyzing the collected information and training a generative AI model, means for generating a dialogue based on the user's input using the trained generative AI model, means for providing the generated dialogue to the user, means for analyzing the user's emotions, and means for suggesting products based on the user's emotions. This enables personalized product suggestions based on the user's emotions.

[0931] "Users" refers to people who use the system.

[0932] A "person of interest" refers to an individual about whom a user is interested and about whom the user enters basic information.

[0933] "Basic information" refers to initial data about the person, such as name, date of birth, and background.

[0934] "Internet" refers to a global network for collecting information.

[0935] A "database" refers to a structured collection of data that allows information about a person to be stored and retrieved.

[0936] A "generative AI model" is a model trained using machine learning or artificial intelligence techniques and used to generate dialogue and suggestions.

[0937] "Training" refers to the process of teaching a generative AI model using collected data.

[0938] "Emotion analysis" refers to the technology of analyzing a person's emotions from their input (text or voice).

[0939] "Dialogue" refers to communication between a user and a system.

[0940] "Product suggestion" refers to the act of recommending appropriate products and services based on the user's emotions and needs.

[0941] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a specific description of embodiments of the present invention.

[0942] System program and hardware / software configuration

[0943] The system for realizing this invention is an application for a shopping site where users can receive product suggestions based on their emotions using a smartphone. This system is composed of the following main hardware and software:

[0944] Smartphone: Provides the user interface and collects initial data.

[0945] Server: Stores data, analyzes it, and hosts generative AI models.

[0946] Sentiment analysis engine (e.g., sentiment analysis API): Analyzes user sentiment.

[0947] Natural language processing module (e.g., natural language parsing API): performs string analysis of user input.

[0948] Generative AI models (e.g., Generative AI Model APIs): Generate dialogue and suggestions.

[0949] Program processing flow and data calculation

[0950] 1. Providing the user interface:

[0951] An interface is displayed on the smartphone where the user can log in and input their emotional state (text / voice).

[0952] Example: A user types, "I'm a little tired today."

[0953] 2. Data conversion and transmission:

[0954] The smartphone converts the voice input into text (if it was entered by voice) and sends the data to the server.

[0955] 3. Emotion analysis:

[0956] The server uses an emotion analysis engine to analyze the user's input emotional state.

[0957] Example: Detecting the emotion "tired" from the input text "I'm a little tired today."

[0958] 4. Response generation using generative AI models:

[0959] The emotional information detected by the emotion analysis engine is passed to a generative AI model, which generates optimal product suggestions for the user.

[0960] Example: "Are you looking for relaxation products?"

[0961] 5. Search product database:

[0962] Based on the category and keywords of the suggested product, the server searches the database of the online shopping site for related products and retrieves a list.

[0963] 6. User Visibility:

[0964] The proposed products and their explanations are displayed on the smartphone and provided to the user.

[0965] Example: "How about this aromatherapy set? It's especially relaxing."

[0966] Examples of prompt statements

[0967] The following is an example of a prompt to input to a generative AI model (e.g., a generative AI model API):

[0968] A user types, "I'm feeling a bit tired today." The user's emotion is "tired." Suggest products related to relaxation and stress reduction.

[0969] In this way, the system according to the present invention can provide a more personalized shopping experience through interactions and product suggestions based on the user's emotions.

[0970] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0971] Step 1:

[0972] Input and Output:

[0973] The user inputs their emotional state (by text or voice) using a smartphone.

[0974] Specific behavior:

[0975] A user enters "I'm a little tired today" into the application, and this input data is saved on the smartphone.

[0976] Step 2:

[0977] Input and Output:

[0978] The smartphone converts the voice input into text and sends the text data to the server.

[0979] Specific behavior:

[0980] When a voice input is received, the device uses its voice recognition function to convert it into text data, which is then sent to the server.

[0981] Step 3:

[0982] Input and Output:

[0983] The server sends the received text data to the emotion analysis engine, analyzes the emotional state, and receives the emotion analysis results from the emotion analysis engine.

[0984] Specific behavior:

[0985] The server transfers the input text data, "I'm a little tired today," to an emotion analysis engine and detects the emotion of "tired."

[0986] Step 4:

[0987] Input and Output:

[0988] The sentiment analysis results and user input data are passed to the generative AI model to generate optimal product suggestions. The generated product suggestions are then received.

[0989] Specific behavior:

[0990] The server inputs the emotional information "I'm tired" and the user's text data into a generative AI model and generates a suggestion such as "Are you looking for relaxation products?"

[0991] Step 5:

[0992] Input and Output:

[0993] Based on the product suggestions generated by the generative AI model, the server searches for related products from the online shopping site's database and obtains a product list.

[0994] Specific behavior:

[0995] The server uses the product category and keywords to search a database to retrieve a list of related products (e.g., aromatherapy sets).

[0996] Step 6:

[0997] Input and Output:

[0998] The acquired product list and suggestions are sent to the smartphone and displayed to the user.

[0999] Specific behavior:

[1000] The server then sends the acquired product list and the suggestions generated by the generative AI model together to the smartphone. The device then visually displays this to the user. For example, it might display, "How about this aromatherapy set? It has a particularly relaxing effect."

[1001] In this way, product suggestions based on the user's emotions are made while clarifying the input data and output data at each processing step.

[1002] 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.

[1003] 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.

[1004] 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.

[1005] [Third embodiment]

[1006] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1007] 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.

[1008] 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).

[1009] 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.

[1010] 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.

[1011] 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).

[1012] 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.

[1013] 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.

[1014] 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.

[1015] 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.

[1016] 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.

[1017] 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."

[1018] This invention relates to a generative AI system that simulates interactions with deceased or historical figures. To implement this invention, it is necessary to collect basic information about the target person from a user, obtain additional information from the internet or a database, and train a generative AI model based on that information. The following describes the system's operation step by step.

[1019] First, the user accesses the system and creates an account. After logging in, a screen appears where they can enter information about the person (such as name, date of birth, and biography). The device then sends this information to the management server. The server then uses the received information to collect additional information about the person from the Internet and databases. This collection process includes relevant literary works, photographs, audio recordings, and more.

[1020] The server then analyzes the collected information to extract the target person's characteristics and features. The results of this analysis are used as training data for a generative AI model. The server then trains the generative AI model, which can then converse with the target person based on their language, speaking style, and knowledge.

[1021] Once training is complete, the user can begin a conversation with the target person. For example, the user might type, "Tell me about the special theory of relativity." This input is sent by the device to the server. The server uses the generative AI model to generate an appropriate response based on the user's input and sends it back to the device. The device then displays the generated response to the user as text or audio.

[1022] Furthermore, to enhance the user experience, the server can generate specific virtual events based on the interaction content (e.g., an event to celebrate a target person's birthday or a reenactment of a specific historical event). When the user selects the event mode, interactions and activities based on a special scenario are provided.

[1023] In addition, when a user uses voice input, the device converts the voice data into text data and sends this text data to the server. The server generates a response based on this and sends it back to the device. The device plays it back as voice data and provides it to the user. For example, if a user asks a question by voice, such as "Tell me about the era in which he lived," the device converts this voice into text and sends the generated text data to the server. The server generates a response that explains how the target person experienced that era based on the understood context.

[1024] In this way, the system of the present invention provides users with the opportunity to gain a deeper understanding of the lives and achievements of deceased or historical figures and to build personal memorials through interactions and virtual events with them.

[1025] The processing flow will be explained below.

[1026] Step 1:

[1027] The user launches the application and the account registration screen appears. The user enters registration information such as name, email address, and password.

[1028] Step 2:

[1029] The terminal receives the input user information and transmits it to the server.

[1030] Step 3:

[1031] The server stores the received information in a database and creates a user account.

[1032] Step 4:

[1033] The user opens a screen for registering the target person and enters basic information such as the target person's name, date of birth, and career history.

[1034] Step 5:

[1035] The terminal receives the basic information of the person entered and sends it to the server.

[1036] Step 6:

[1037] Based on the target person information received by the server, information related to the target person (documents, photographs, audio data, etc.) is collected from the Internet and databases.

[1038] Step 7:

[1039] The server analyzes the collected data, tags and classifies it, and generates a training dataset for the generative AI model.

[1040] Step 8:

[1041] The server trains the generative AI model to learn the characteristics and features of the target person.

[1042] Step 9:

[1043] The user opens a chat window or a voice input screen to start a conversation with the target person.

[1044] Step 10:

[1045] The user inputs what they want to talk about using text or voice. The device receives the user's input and sends it to the server either as is if it is text or as text if it is voice.

[1046] Step 11:

[1047] The server receives the user's input and makes a request to the generative AI model to generate a dialogue.

[1048] Step 12:

[1049] The server uses a generative AI model to generate a response based on the user's input, taking into account the characteristics and background information of the target person.

[1050] Step 13:

[1051] The server sends the generated response to the terminal.

[1052] Step 14:

[1053] The device displays the response received from the server as text or plays it back as audio.

[1054] Step 15:

[1055] If the user has further questions or conversations to make, they enter the information again and repeat the same process.

[1056] Step 16:

[1057] If the user selects the virtual event mode, they enter details to set up interactions and events based on a specific scenario.

[1058] Step 17:

[1059] The server receives the details of the event and generates special scenarios and interactive activities for the target person.

[1060] Step 18:

[1061] The terminal displays the contents of the virtual event, allowing the user to experience the event.

[1062] The above are the specific processing steps of the present invention.

[1063] Example 1

[1064] 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."

[1065] This invention relates to a generative AI system that simulates interactions with deceased or historical figures, allowing users to obtain rich information about the target person and gain a deeper understanding. However, existing technologies lack efficient means for achieving such advanced interactions and information provision, and are inferior in features such as voice input and virtual event generation. As a result, the user experience is limited.

[1066] 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.

[1067] In this invention, the server includes a means for allowing a user to input basic information about the target person, a means for collecting information about the target person from the Internet and databases, and a means for analyzing the collected information and training a generative AI model. This allows the user to simulate conversations with deceased or historical figures and generate virtual events, allowing the user to gain a deeper understanding of the target person and enjoy a richer experience. Furthermore, by adding a means for converting voice input into text format and conversely outputting text as voice, the user can enjoy a more intuitive and natural conversation environment.

[1068] "User" means an individual or entity that accesses the system, inputs information about a person, and obtains information through interaction.

[1069] A "target person" is a deceased or historical figure about whom a user attempts to gather information or interact using the system.

[1070] "Basic information" refers to basic and core information about the target person, such as their name, date of birth, and career history.

[1071] The "Internet" is a global network for gathering and communicating information.

[1072] A "database" is a collection of data that is structured so that information can be collected, stored, and searched systematically.

[1073] "Collection means" refers to the function of searching for and obtaining information about a target person from the Internet or a database.

[1074] "Analysis tools" are data processing functions that make sense of the collected information and identify specific patterns or characteristics.

[1075] A "generative AI model" is an artificial intelligence that uses machine learning and deep learning techniques to generate language and information based on specific input.

[1076] "Training means" refers to the processing means used to train the generative AI model to learn information about the target person and improve its accuracy.

[1077] "Dialogue generation means" refers to a function that uses a generative AI model to create an appropriate response based on user input.

[1078] "Providing means" refers to a function for displaying or reproducing the generated dialogue or information to the user.

[1079] "Voice input means" refers to a function that allows a user to input data into the system by voice.

[1080] The "voice conversion means" refers to a function for converting voice input into text data or outputting text data as voice data.

[1081] A "virtual event" is an event designed to virtually recreate a specific event or scenario relevant to a target person through interaction and information provision.

[1082] This invention relates to a generative AI system that simulates interactions with deceased or historical figures. The following hardware and software are used to implement the invention:

[1083] Hardware:

[1084] User's PC or smartphone

[1085] Internet connection

[1086] Cloud Server

[1087] software:

[1088] Web browser or dedicated app

[1089] Form entry function

[1090] HTTPS protocol

[1091] Web crawling tools

[1092] Database APIs

[1093] Text analysis tools (e.g., Python, Pandas)

[1094] Generative AI models (e.g., GPT-3)

[1095] Chat Interface

[1096] Voice Recognition Software

[1097] Text-to-speech software (TTS)

[1098] The specific steps will be explained below.

[1099] Users access the system using a web browser or a dedicated app and create an account. After logging in, the user is taken to a screen where they can enter basic information about the person (such as name, date of birth, and career history). This information is then sent from the device to the management server.

[1100] Based on the received information, the server collects supplemental information about the target person from the internet and databases. It uses web crawling tools and database APIs to retrieve and analyze the relevant information. A text analysis tool (e.g., NLTK, Pandas) is used for the analysis, and the analysis results are used as training data for a generative AI model (e.g., GPT-3).

[1101] The server trains the generative AI model, which then enables it to converse with the target person based on their vocabulary, speaking style, and knowledge. Once training is complete, the user initiates a dialogue with the target person through a dialogue interface. The user can enter questions via text or voice. In the case of voice input, the device converts the voice data into text data and sends this text data to the server.

[1102] The server uses a generative AI model to generate an appropriate response based on the user's input and sends it back to the device, which then displays or plays the generated response to the user as text or audio.

[1103] The server can also generate specific virtual events based on the dialogue. For example, if a user types, "Tell me about the time he lived in," the server can analyze the dialogue log and generate a corresponding virtual event.

[1104] For example, consider the following prompt:

[1105] "Tell me about Albert Einstein, his research and achievements."

[1106] "Please tell me in detail how Cleopatra rose to power."

[1107] "Tell me about John Lennon's experiences with the Beatles."

[1108] In this way, the system of the invention provides users with the opportunity to gain a deeper understanding of the lives and achievements of deceased or historical figures and to build personal memorials through interactions and virtual events with them.

[1109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1110] Step 1:

[1111] A user accesses the system and creates an account.

[1112] Input: User information (name, email address, password)

[1113] Output: Created account information

[1114] Operation:

[1115] 1. The user clicks the "New Registration" button in a web browser or dedicated app.

[1116] 2. The user enters the required information into the form and presses the "Register" button.

[1117] 3. The terminal sends the entered user information to the management server.

[1118] Step 2:

[1119] The user enters basic information about the person.

[1120] Input: Basic information of the target person (name, date of birth, career history)

[1121] Output: Information about the target person sent to the management server

[1122] Operation:

[1123] 1. After logging in, the user moves to the screen where they can enter information about the person in question.

[1124] 2. The user enters the target person's information into the form and presses the "Submit" button.

[1125] 3. The terminal sends the input information to the management server.

[1126] Step 3:

[1127] The server collects supplemental information about the target person based on the received information.

[1128] Input: Basic information of the target person

[1129] Output: Additional information collected about the person of interest

[1130] Operation:

[1131] 1. The server starts web crawling using the target person's name as a key.

[1132] 2. The server calls the database API to retrieve the relevant information.

[1133] 3. The server stores the collected supplemental information.

[1134] Step 4:

[1135] The server analyzes the collected information and trains a generative AI model.

[1136] Input: Supplementary information collected

[1137] Output: A trained generative AI model

[1138] Operation:

[1139] 1. The server tokenizes the collected information through a text analysis tool.

[1140] 2. The server builds a knowledge base and trains a generative AI model based on that knowledge.

[1141] Step 5:

[1142] The user accesses the dialogue interface and begins a dialogue with the target person.

[1143] Input: User question (text or voice)

[1144] Output: Send a query to the server

[1145] Operation:

[1146] 1. The user presses the "Start conversation" button and enters a question in the input field.

[1147] 2. The user submits the question by pressing the "Submit" button.

[1148] Step 6:

[1149] The terminal transmits the user's interactive input to the server.

[1150] Input: User question (text or voice data)

[1151] Output: User question sent to the management server

[1152] Operation:

[1153] 1. For voice input, the device uses voice recognition software to convert voice data into text data.

[1154] 2. Send the converted text data or text input data to the server.

[1155] Step 7:

[1156] The server uses a generative AI model to generate an appropriate response and sends it back to the device.

[1157] Input: User question data

[1158] Output: The generated response data

[1159] Operation:

[1160] 1. The server inputs the user's question data into the generative AI model and generates an appropriate response.

[1161] 2. Send the generated response back to the terminal.

[1162] Step 8:

[1163] The terminal displays or plays the received response to the user.

[1164] Input: Response data from the server

[1165] Output: what is displayed to the user or what is played as audio

[1166] Operation:

[1167] 1. The terminal displays the received text data in the display area.

[1168] 2. For audio output, convert the text into audio and play it back.

[1169] Step 9:

[1170] The server generates specific virtual events based on the content of the dialogue.

[1171] Input: Dialogue

[1172] Output: The generated virtual event

[1173] Operation:

[1174] 1. The server analyzes the interaction log and generates appropriate events using an event generation algorithm.

[1175] 2. Send details of the generated event to the user's device.

[1176] In this way, the system provides an opportunity to gain a deeper understanding of the lives and achievements of deceased and historical figures through dialogue and virtual events.

[1177] (Application example 1)

[1178] 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."

[1179] Previous systems lacked generative AI models for interacting with historical figures and deceased individuals, limiting the virtual experience users could have for gaining a deeper understanding of the subject. Furthermore, the lack of an effective means of converting voice data to text data limited the user experience. This made interactive learning difficult in historical reenactments and educational settings.

[1180] 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.

[1181] In this invention, the server includes means for having a user input basic information about the target person, means for collecting information about the target person from the Internet and a database, means for analyzing the collected information and training a generative AI model, means for generating a dialogue based on the user's input using the trained generative AI model, means for providing the generated dialogue to the user, means for displaying the dialogue using a device that reproduces a virtual space, means for converting voice data into text data, and means for converting the generated text data into voice data. This allows the user to interact with the target person through an immersive experience in a virtual space, enabling deep understanding and interactive learning.

[1182] The "means for allowing the user to input basic information about the target person" is a function that allows the user to input basic information such as the target person's name, date of birth, and career history into the system.

[1183] "Means for collecting information about the subject from the Internet and databases" refers to a function for obtaining additional details about the entered subject from online and offline databases.

[1184] "Means for analyzing collected information and training a generative AI model" refers to a function that analyzes acquired information and trains an AI model based on the results of that analysis.

[1185] "Means for generating dialogue based on user input using a trained generative AI model" refers to a function that uses a trained AI model to generate dialogue in response to a user's text input.

[1186] "Means for providing the generated dialogue to the user" is a function for providing the dialogue generated by the generative AI model to the user by display or audio.

[1187] "Means for displaying a dialogue using a device that recreates a virtual space" refers to a function for visually displaying the content of a dialogue using a device for recreating a virtual reality space (e.g., smart glasses or a head-mounted display).

[1188] The "means for converting voice data into text data" is a function for converting information input by voice by the user into text format.

[1189] The "means for converting generated text data into voice data" is a function for providing the generated text-based dialogue to the user in voice form.

[1190] The present invention relates to a system that allows users to simulate interactions with deceased or historical figures. To realize this system, the following specific components and process steps are required:

[1191] System Components

[1192] Hardware:

[1193] 1. Smart glasses or head-mounted displays:

[1194] It is used to allow users to enjoy experiences in virtual space.

[1195] 2. Server:

[1196] It plays a central role in processing data for the entire system.

[1197] 3. Terminal:

[1198] The device (smartphone, tablet, etc.) through which the user interfaces with the system.

[1199] software:

[1200] 1. Python:

[1201] Used as the main development language for programs.

[1202] 2. Transformers library (GPT-2):

[1203] A generative AI model for natural language processing.

[1204] 3. Requests library:

[1205] To obtain additional information from the Internet.

[1206] 4. pyttsx3 library:

[1207] To convert text to speech.

[1208] Data processing and calculation

[1209] Enter basic information:

[1210] The user uses a terminal to input basic information about the target person (such as name, date of birth, and career history) into the system. This input information is sent to the management server.

[1211] Collecting additional information:

[1212] The server uses the basic information entered to gather additional details about the person from the internet and databases, retrieving relevant documents, photographs, audio recordings, and more.

[1213] Training the AI ​​model:

[1214] The server analyzes the collected information and extracts the characteristics and features of the target person. The analysis results are used as training data for a generative AI model (GPT-2). Once training is complete, the model is able to converse with the target person based on their vocabulary, speaking style, and knowledge.

[1215] Dialogue generation:

[1216] When a user types a question or request into the device, the input is sent to the server, which uses a generative AI model to generate an appropriate response and sends it back to the device, which then provides the generated response to the user as text or audio.

[1217] Virtual display:

[1218] The generated dialogue content is displayed in a virtual space using smart glasses or a head-mounted display, allowing users to have a more immersive dialogue experience.

[1219] Audio data conversion:

[1220] When a user inputs something by voice, the device converts the voice data into text data and sends this text data to the server. The server generates an appropriate response based on the text data and sends it back to the device. The device then converts the generated text data into voice data and provides it to the user.

[1221] Examples and prompts

[1222] Examples:

[1223] The user asks, "Tell me about special relativity." This speech is converted to text and sent to a server. The server uses a generative AI model to generate a response: "Special relativity is a theory that explains the constant speed of light and the relativity of time and space." The device then converts this text into speech and provides it to the user.

[1224] Example prompt sentence:

[1225] Tell me about Einstein's life.

[1226] What is special relativity?

[1227] Please explain his historical background.

[1228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1229] Step 1:

[1230] input:

[1231] The user enters the target person's basic information (name, date of birth, career history, etc.) into the terminal.

[1232] Data processing:

[1233] The terminal collects the basic information entered and sends it to the management server.

[1234] output:

[1235] The management server receives basic information about the target person.

[1236] Step 2:

[1237] input:

[1238] The server generates requests to retrieve additional information from the Internet and databases based on the person's basic information.

[1239] Data processing:

[1240] The server uses an API to gather information such as relevant documents, photos, audio recordings, etc. This information is retrieved using the requests library.

[1241] output:

[1242] The server stores the obtained additional information in a database.

[1243] Step 3:

[1244] input:

[1245] The server parses the stored additional information.

[1246] Data processing:

[1247] The server uses natural language processing techniques to tokenize the information and extract the characteristics and features of the target person, then trains a generative AI model (GPT-2) using the transformers library.

[1248] output:

[1249] A trained generative AI model is generated.

[1250] Step 4:

[1251] input:

[1252] The user inputs a question or request through the terminal.

[1253] Data processing:

[1254] The terminal transmits the user's input as text data to the server.

[1255] output:

[1256] The server receives the user's input.

[1257] Step 5:

[1258] input:

[1259] The server uses the generative AI model to generate a response based on the user's input.

[1260] Data processing:

[1261] The server prompts the trained generative AI model with user input to generate an appropriate response.

[1262] output:

[1263] The generated response is obtained as text data.

[1264] Step 6:

[1265] input:

[1266] The server sends the generated response to the terminal.

[1267] Data processing:

[1268] The device displays the received text data on smart glasses or a head-mounted display.

[1269] output:

[1270] Users experience text or voice responses in a virtual space.

[1271] Step 7:

[1272] input:

[1273] When a user asks a question by voice, the terminal receives the input as voice data.

[1274] Data processing:

[1275] The device uses a speech recognition API to convert voice data into text data.

[1276] output:

[1277] The converted text data is sent to the server.

[1278] Step 8:

[1279] input:

[1280] The server receives the voice input and generates a response using a generative AI model.

[1281] Data processing:

[1282] The server generates a response using the text data and sends an instruction to the terminal to convert the generated text data into voice data.

[1283] output:

[1284] The terminal converts the text data into voice data and provides it to the user.

[1285] This allows the user to have a natural conversation with the target person in the virtual space, providing a rich experience.

[1286] 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.

[1287] This invention relates to a generative AI system that recognizes a user's emotions and provides appropriate dialogue and interaction based on those emotions. To implement this invention, the following steps are required: collecting basic information, acquiring additional information, training a generative AI model, integrating an emotion engine, and generating and providing dialogue with the user. The operation of the system is explained below step by step.

[1288] First, a user accesses the system and creates an account. Once the user logs in, a screen appears where they can enter information about the person (such as name, date of birth, and career history). The device then sends this information to the server. The server then uses the received information to collect additional information related to the person from the Internet and databases. This collection process involves searching and retrieving documents, photographs, audio recordings, and more.

[1289] The server then analyzes the collected information, tagging and classifying it to generate a training dataset for the generative AI model, which the server uses to train the generative AI model to learn the characteristics and traits of the target person.

[1290] When a user interacts with a target person, the user inputs text or voice. The input data is sent from the terminal to the server. In particular, in the case of voice data, the terminal converts the voice into text data and sends it to the server as text data.

[1291] Here, the present invention uses an emotion engine. The server applies the emotion engine to the user's input (text or voice) to analyze the user's emotional state. The emotion analysis results are fed back to the generative AI model and reflected in the dialogue response. For example, if the user expresses sadness, the generated response will be comforting.

[1292] As a concrete example, consider a case where a user voice-inputs, "I'm not feeling too good today." The device converts this speech into text and sends it to a server. The server passes the text data to an emotion engine for analysis, which detects that the user is sad. A generative AI model then takes this emotional state into account and generates a response such as, "Are you OK? Let me know if there's anything I can help you with," which is sent to the device. The device then provides this response to the user in text or voice.

[1293] The emotion engine also comes into play when users select the virtual event mode. When setting up an event, the system can take into account the user's current emotional state to customize the event content to be more personal and emotionally relevant. For example, if a user sets up an event to celebrate a special anniversary and the analysis shows that the emotion at that time is joy, the event content will be set to be positive and enjoyable.

[1294] In this way, the system according to the present invention can recognize the user's emotions and provide more intimate and emotional interactions through dialogues and virtual events based on the emotions.

[1295] The processing flow will be explained below.

[1296] Step 1:

[1297] The user launches the application and the account registration screen appears. The user enters registration information such as name, email address, and password.

[1298] Step 2:

[1299] The terminal receives the input user information and transmits it to the server.

[1300] Step 3:

[1301] The server stores the received information in a database and creates a user account.

[1302] Step 4:

[1303] The user opens a screen for registering the target person and enters basic information such as the target person's name, date of birth, and career history.

[1304] Step 5:

[1305] The terminal receives the basic information of the person entered and sends it to the server.

[1306] Step 6:

[1307] Based on the information received by the server, additional information about the target person (such as literature, photographs, and audio data) is collected from the Internet and databases.

[1308] Step 7:

[1309] The server analyzes the collected data, tags and classifies it, and generates a training dataset for the generative AI model.

[1310] Step 8:

[1311] The server uses this dataset to train a generative AI model, which learns to interact with people based on their characteristics, speaking style, and knowledge.

[1312] Step 9:

[1313] The user opens a chat window or a voice input screen to start a conversation with the target person.

[1314] Step 10:

[1315] The user inputs the dialogue content by text or voice. The device receives the user's input and sends it to the server either as is if it is text or as text if it is voice.

[1316] Step 11:

[1317] The server receives the user's input (text or voice) and asks the emotion engine to analyze it, which detects the user's emotional state.

[1318] Step 12:

[1319] The server provides the analysis results from the emotion engine to the generative AI model, which generates an appropriate response based on the user's emotional state.

[1320] Step 13:

[1321] The server sends the generated response to the terminal.

[1322] Step 14:

[1323] The device displays the response received from the server as text or plays it back as audio.

[1324] Step 15:

[1325] If the user has further questions or conversations to make, they enter the information again and repeat the same process.

[1326] Step 16:

[1327] If the user selects the virtual event mode, they enter details to set up an event based on a specific scenario.

[1328] Step 17:

[1329] The device sends the entered event details to the server, which uses an emotion engine to customize the event content, taking into account the user's emotional state.

[1330] Step 18:

[1331] The server generates a customized scenario for the virtual event and sends it to the terminal.

[1332] Step 19:

[1333] The terminal provides the content of the virtual event to the user as text or audio.

[1334] This allows a generative AI system with an embedded emotion engine to provide dialogue and interaction that responds to the user's emotional state, creating a personalized experience.

[1335] Example 2

[1336] 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."

[1337] Conventional conversational AI systems have difficulty generating responses that take the user's emotional state into account, which can result in poor dialogue quality. Furthermore, they lack the ability to customize the content of virtual events based on the user's emotions, resulting in a uniform user experience.

[1338] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for having a user input basic information about the target person, means for collecting information about the target person from a global information network and a data repository, means for analyzing the collected information and training a generative AI model, means for integrating an emotion engine into the generative AI model and analyzing the emotional state in response to the user's input, means for adjusting the response of the generative AI model based on the analyzed emotional state, and means for providing the generated dialogue to the user. This enables natural dialogue that takes the user's emotions into consideration, and makes it possible to provide a virtual event that is more emotionally sensitive.

[1339] "User" refers to an individual who accesses the System and participates in the Interactions and Virtual Events.

[1340] "Subject Person" means the individual about whom information is entered into the System by a User.

[1341] "Basic information" refers to the initial and primary information about the person, such as name, date of birth, and background.

[1342] "Means" refers to a method or device for achieving a specific function or purpose.

[1343] "Global information network" refers to an information network that can obtain data from all over the world, including the Internet.

[1344] "Data repository" means a system or location for collecting, storing, and managing data.

[1345] "Collecting information" refers to the act of locating and obtaining data related to a person from the internet or data repositories.

[1346] A "generative AI model" refers to an artificial intelligence model that generates dialogue based on collected and analyzed data.

[1347] "Emotion engine" refers to an algorithm or software for analyzing a user's emotional state from their speech.

[1348] "Generating a response" refers to the act of creating an appropriate reply based on user input.

[1349] "Virtual Event" means a simulated event presented online.

[1350] "Analysis" refers to the process of organizing collected data and understanding its meaning and relevance.

[1351] "Text data" refers to written data converted from voice input using voice recognition technology.

[1352] This invention relates to a generative AI system that recognizes a user's emotions and provides appropriate dialogue and interaction based on those emotions. To implement this invention, the system operates through multiple steps. The operation of the system is described in detail below.

[1353] First, a user accesses the system's website or application and creates an account. After logging in, the user enters basic information about the person (e.g., name, date of birth, career history, etc.). The device (e.g., smartphone, tablet, personal computer) then sends this information to the server using a secure communication protocol (e.g., HTTPS).

[1354] Based on the received basic information, the server searches and collects additional information related to the person from global information networks (the Internet) and data repositories (various databases). This collection process obtains data such as documents, photographs, and audio recordings.

[1355] The server then analyzes the collected information, typically tagging and categorizing it to generate a training dataset for the generative AI model. This analysis uses natural language processing techniques (e.g., BERT, GPT, etc.). The server then uses the generated dataset to train the generative AI model, learning the characteristics and traits of the target person. This training is achieved using a deep learning framework (e.g., TensorFlow, PyTorch).

[1356] When a user initiates a dialogue, they input text or voice. In the case of voice, the device uses voice recognition software (e.g., Google Speech-to-Text API) to convert the speech into text and send it to the server. The server then passes the received text data to an emotion engine (e.g., IBM Watson) to analyze the user's emotional state. The results of this analysis are fed back to the generative AI model, which then creates a response that takes the user's emotional state into account.

[1357] For example, if a user types "I'm not feeling too good today," the server inputs this text into the emotion engine and analyzes that the user is feeling "sad." The generative AI model takes this emotional state into account and generates a response such as "Are you OK? Let me know if there's anything I can help you with," which is then sent from the server to the device. The device then provides this response to the user in the form of a text display or voice playback using speech synthesis software (e.g., Amazon Polly).

[1358] Furthermore, when a user selects the virtual event mode, the server uses an emotion engine to analyze the user's current emotional state. Based on the analysis results, the server customizes the event content. For example, if a user sets up an event to celebrate a special anniversary and the analyzed emotion is "happy," the event content will be set to be positive and enjoyable.

[1359] Examples of prompts:

[1360] "When a user makes a statement that expresses an emotion, recognize that emotion and generate an appropriate response. For example, if the user says, 'I'm not feeling very well today,' generate a comforting response."

[1361] In this way, this generative AI system can recognize the user's emotions and provide natural dialogue and virtual events based on them.

[1362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1363] Program flow:

[1364] Step 1:

[1365] A user accesses the system's website or application and creates an account. Input information includes a username, email address, and password. Once this basic data is entered, the device sends it to the server, which then registers the received information in a database and completes the account creation.

[1366] Step 2:

[1367] The user logs in and enters basic information about the person (such as name, date of birth, and background information). The device sends this information to the server, which uses the received information to gather additional information from the internet and data repositories. This information gathering can be done using web scraping tools, APIs, etc.

[1368] input:

[1369] Basic information of the target person entered by the user

[1370] output:

[1371] Additional information about the subject (documents, photographs, audio recordings, etc.)

[1372] Step 3:

[1373] The server analyzes the collected information and performs tagging and classification, specifically using natural language processing techniques to extract meaningful keywords and phrases from the text data, which then creates a training dataset for the generative AI model.

[1374] input:

[1375] Additional Information Collected

[1376] output:

[1377] Training dataset (tagged data)

[1378] Step 4:

[1379] The server trains the generative AI model using deep learning frameworks (TensorFlow, PyTorch, etc.). Through training, the generative AI model learns the characteristics and features of the target person.

[1380] input:

[1381] Training dataset

[1382] output:

[1383] Pre-trained generative AI models

[1384] Step 5:

[1385] To initiate a dialogue, the user inputs text or voice. In the case of voice input, the device converts the speech into text using speech recognition software (e.g., Google Speech-to-Text API) and sends the text data to the server.

[1386] input:

[1387] User text or voice input

[1388] output:

[1389] Text data (in the case of voice input)

[1390] Step 6:

[1391] The server passes the received text data to an emotion engine, which analyzes the user's emotional state using an emotion analysis tool such as IBM Watson. The analysis results are fed back to the generative AI model.

[1392] input:

[1393] User text data

[1394] output:

[1395] Emotion analysis results

[1396] Step 7:

[1397] Based on the results of emotion analysis, the generative AI model generates an appropriate response based on the user's emotional state. The response is generated in text format and sent from the server to the device.

[1398] input:

[1399] Emotion analysis results

[1400] output:

[1401] Response text data

[1402] Step 8:

[1403] The device provides the generated response to the user, which can be displayed as text or played aloud using speech synthesis software (e.g., Amazon Polly).

[1404] input:

[1405] Response text data

[1406] output:

[1407] Text display or audio playback

[1408] Step 9:

[1409] When the user selects the virtual event mode, the server analyzes the user's current emotional state using an emotion engine and customizes the event content to include many elements that express positive emotions.

[1410] input:

[1411] The user's emotional state

[1412] output:

[1413] Customized Virtual Event Content

[1414] (Application example 2)

[1415] 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."

[1416] Traditional online shopping sites lack the ability to suggest products based on the user's emotions and psychological state, making it difficult for users to find the products that best suit their emotions. Furthermore, the lack of personalized interactions and suggestions based on user input can lead to a decrease in user satisfaction.

[1417] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having the user input basic information about the target person, means for collecting information about the target person from the Internet and a database, means for analyzing the collected information and training a generative AI model, means for generating a dialogue based on the user's input using the trained generative AI model, means for providing the generated dialogue to the user, means for analyzing the user's emotions, and means for suggesting products based on the user's emotions. This enables personalized product suggestions based on the user's emotions.

[1418] "Users" refers to people who use the system.

[1419] A "person of interest" refers to an individual about whom a user is interested and about whom the user enters basic information.

[1420] "Basic information" refers to initial data about the person, such as name, date of birth, and background.

[1421] "Internet" refers to a global network for collecting information.

[1422] A "database" refers to a structured collection of data that allows information about a person to be stored and retrieved.

[1423] A "generative AI model" is a model trained using machine learning or artificial intelligence techniques and used to generate dialogue and suggestions.

[1424] "Training" refers to the process of teaching a generative AI model using collected data.

[1425] "Emotion analysis" refers to the technology of analyzing a person's emotions from their input (text or voice).

[1426] "Dialogue" refers to communication between a user and a system.

[1427] "Product suggestion" refers to the act of recommending appropriate products and services based on the user's emotions and needs.

[1428] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a specific description of embodiments of the present invention.

[1429] System program and hardware / software configuration

[1430] The system for realizing this invention is an application for a shopping site where users can receive product suggestions based on their emotions using a smartphone. This system is composed of the following main hardware and software:

[1431] Smartphone: Provides the user interface and collects initial data.

[1432] Server: Stores data, analyzes it, and hosts generative AI models.

[1433] Sentiment analysis engine (e.g., sentiment analysis API): Analyzes user sentiment.

[1434] Natural language processing module (e.g., natural language parsing API): performs string analysis of user input.

[1435] Generative AI models (e.g., Generative AI Model APIs): Generate dialogue and suggestions.

[1436] Program processing flow and data calculation

[1437] 1. Providing the user interface:

[1438] An interface is displayed on the smartphone where the user can log in and input their emotional state (text / voice).

[1439] Example: A user types, "I'm a little tired today."

[1440] 2. Data conversion and transmission:

[1441] The smartphone converts the voice input into text (if it was entered by voice) and sends the data to the server.

[1442] 3. Emotion analysis:

[1443] The server uses an emotion analysis engine to analyze the user's input emotional state.

[1444] Example: Detecting the emotion "tired" from the input text "I'm a little tired today."

[1445] 4. Response generation using generative AI models:

[1446] The emotional information detected by the emotion analysis engine is passed to a generative AI model, which generates optimal product suggestions for the user.

[1447] Example: "Are you looking for relaxation products?"

[1448] 5. Search product database:

[1449] Based on the category and keywords of the suggested product, the server searches the database of the online shopping site for related products and retrieves a list.

[1450] 6. User Visibility:

[1451] The proposed products and their explanations are displayed on the smartphone and provided to the user.

[1452] Example: "How about this aromatherapy set? It's especially relaxing."

[1453] Examples of prompt statements

[1454] The following is an example of a prompt to input to a generative AI model (e.g., a generative AI model API):

[1455] A user types, "I'm feeling a bit tired today." The user's emotion is "tired." Suggest products related to relaxation and stress reduction.

[1456] In this way, the system according to the present invention can provide a more personalized shopping experience through interactions and product suggestions based on the user's emotions.

[1457] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1458] Step 1:

[1459] Input and Output:

[1460] The user inputs their emotional state (by text or voice) using a smartphone.

[1461] Specific behavior:

[1462] A user enters "I'm a little tired today" into the application, and this input data is saved on the smartphone.

[1463] Step 2:

[1464] Input and Output:

[1465] The smartphone converts the voice input into text and sends the text data to the server.

[1466] Specific behavior:

[1467] When a voice input is received, the device uses its voice recognition function to convert it into text data, which is then sent to the server.

[1468] Step 3:

[1469] Input and Output:

[1470] The server sends the received text data to the emotion analysis engine, analyzes the emotional state, and receives the emotion analysis results from the emotion analysis engine.

[1471] Specific behavior:

[1472] The server transfers the input text data, "I'm a little tired today," to an emotion analysis engine and detects the emotion of "tired."

[1473] Step 4:

[1474] Input and Output:

[1475] The sentiment analysis results and user input data are passed to the generative AI model to generate optimal product suggestions. The generated product suggestions are then received.

[1476] Specific behavior:

[1477] The server inputs the emotional information "I'm tired" and the user's text data into a generative AI model and generates a suggestion such as "Are you looking for relaxation products?"

[1478] Step 5:

[1479] Input and Output:

[1480] Based on the product suggestions generated by the generative AI model, the server searches for related products from the online shopping site's database and obtains a product list.

[1481] Specific behavior:

[1482] The server uses the product category and keywords to search a database to retrieve a list of related products (e.g., aromatherapy sets).

[1483] Step 6:

[1484] Input and Output:

[1485] The acquired product list and suggestions are sent to the smartphone and displayed to the user.

[1486] Specific behavior:

[1487] The server then sends the acquired product list and the suggestions generated by the generative AI model together to the smartphone. The device then visually displays this to the user. For example, it might display, "How about this aromatherapy set? It has a particularly relaxing effect."

[1488] In this way, product suggestions based on the user's emotions are made while clarifying the input data and output data at each processing step.

[1489] 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.

[1490] 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.

[1491] 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.

[1492] [Fourth embodiment]

[1493] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1494] 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.

[1495] 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).

[1496] 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.

[1497] 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.

[1498] 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).

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] 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.

[1505] 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."

[1506] This invention relates to a generative AI system that simulates interactions with deceased or historical figures. To implement this invention, it is necessary to collect basic information about the target person from a user, obtain additional information from the internet or a database, and train a generative AI model based on that information. The following describes the system's operation step by step.

[1507] First, the user accesses the system and creates an account. After logging in, a screen appears where they can enter information about the person (such as name, date of birth, and biography). The device then sends this information to the management server. The server then uses the received information to collect additional information about the person from the Internet and databases. This collection process includes relevant literary works, photographs, audio recordings, and more.

[1508] The server then analyzes the collected information to extract the target person's characteristics and features. The results of this analysis are used as training data for a generative AI model. The server then trains the generative AI model, which can then converse with the target person based on their language, speaking style, and knowledge.

[1509] Once training is complete, the user can begin a conversation with the target person. For example, the user might type, "Tell me about the special theory of relativity." This input is sent by the device to the server. The server uses the generative AI model to generate an appropriate response based on the user's input and sends it back to the device. The device then displays the generated response to the user as text or audio.

[1510] Furthermore, to enhance the user experience, the server can generate specific virtual events based on the interaction content (e.g., an event to celebrate a target person's birthday or a reenactment of a specific historical event). When the user selects the event mode, interactions and activities based on a special scenario are provided.

[1511] In addition, when a user uses voice input, the device converts the voice data into text data and sends this text data to the server. The server generates a response based on this and sends it back to the device. The device plays it back as voice data and provides it to the user. For example, if a user asks a question by voice, such as "Tell me about the era in which he lived," the device converts this voice into text and sends the generated text data to the server. The server generates a response that explains how the target person experienced that era based on the understood context.

[1512] In this way, the system of the present invention provides users with the opportunity to gain a deeper understanding of the lives and achievements of deceased or historical figures and to build personal memorials through interactions and virtual events with them.

[1513] The processing flow will be explained below.

[1514] Step 1:

[1515] The user launches the application and the account registration screen appears. The user enters registration information such as name, email address, and password.

[1516] Step 2:

[1517] The terminal receives the input user information and transmits it to the server.

[1518] Step 3:

[1519] The server stores the received information in a database and creates a user account.

[1520] Step 4:

[1521] The user opens a screen for registering the target person and enters basic information such as the target person's name, date of birth, and career history.

[1522] Step 5:

[1523] The terminal receives the basic information of the person entered and sends it to the server.

[1524] Step 6:

[1525] Based on the target person information received by the server, information related to the target person (documents, photographs, audio data, etc.) is collected from the Internet and databases.

[1526] Step 7:

[1527] The server analyzes the collected data, tags and classifies it, and generates a training dataset for the generative AI model.

[1528] Step 8:

[1529] The server trains the generative AI model to learn the characteristics and features of the target person.

[1530] Step 9:

[1531] The user opens a chat window or a voice input screen to start a conversation with the target person.

[1532] Step 10:

[1533] The user inputs what they want to talk about using text or voice. The device receives the user's input and sends it to the server either as is if it is text or as text if it is voice.

[1534] Step 11:

[1535] The server receives the user's input and makes a request to the generative AI model to generate a dialogue.

[1536] Step 12:

[1537] The server uses a generative AI model to generate a response based on the user's input, taking into account the characteristics and background information of the target person.

[1538] Step 13:

[1539] The server sends the generated response to the terminal.

[1540] Step 14:

[1541] The device displays the response received from the server as text or plays it back as audio.

[1542] Step 15:

[1543] If the user has further questions or conversations to make, they enter the information again and repeat the same process.

[1544] Step 16:

[1545] If the user selects the virtual event mode, they enter details to set up interactions and events based on a specific scenario.

[1546] Step 17:

[1547] The server receives the details of the event and generates special scenarios and interactive activities for the target person.

[1548] Step 18:

[1549] The terminal displays the contents of the virtual event, allowing the user to experience the event.

[1550] The above are the specific processing steps of the present invention.

[1551] Example 1

[1552] 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."

[1553] This invention relates to a generative AI system that simulates interactions with deceased or historical figures, allowing users to obtain rich information about the target person and gain a deeper understanding. However, existing technologies lack efficient means for achieving such advanced interactions and information provision, and are inferior in features such as voice input and virtual event generation. As a result, the user experience is limited.

[1554] 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.

[1555] In this invention, the server includes a means for allowing a user to input basic information about the target person, a means for collecting information about the target person from the Internet and databases, and a means for analyzing the collected information and training a generative AI model. This allows the user to simulate conversations with deceased or historical figures and generate virtual events, allowing the user to gain a deeper understanding of the target person and enjoy a richer experience. Furthermore, by adding a means for converting voice input into text format and conversely outputting text as voice, the user can enjoy a more intuitive and natural conversation environment.

[1556] "User" means an individual or entity that accesses the system, inputs information about a person, and obtains information through interaction.

[1557] A "target person" is a deceased or historical figure about whom a user attempts to gather information or interact using the system.

[1558] "Basic information" refers to basic and core information about the target person, such as their name, date of birth, and career history.

[1559] The "Internet" is a global network for gathering and communicating information.

[1560] A "database" is a collection of data that is structured so that information can be collected, stored, and searched systematically.

[1561] "Collection means" refers to the function of searching for and obtaining information about a target person from the Internet or a database.

[1562] "Analysis tools" are data processing functions that make sense of the collected information and identify specific patterns or characteristics.

[1563] A "generative AI model" is an artificial intelligence that uses machine learning and deep learning techniques to generate language and information based on specific input.

[1564] "Training means" refers to the processing means used to train the generative AI model to learn information about the target person and improve its accuracy.

[1565] "Dialogue generation means" refers to a function that uses a generative AI model to create an appropriate response based on user input.

[1566] "Providing means" refers to a function for displaying or reproducing the generated dialogue or information to the user.

[1567] "Voice input means" refers to a function that allows a user to input data into the system by voice.

[1568] The "voice conversion means" refers to a function for converting voice input into text data or outputting text data as voice data.

[1569] A "virtual event" is an event designed to virtually recreate a specific event or scenario relevant to a target person through interaction and information provision.

[1570] This invention relates to a generative AI system that simulates interactions with deceased or historical figures. The following hardware and software are used to implement the invention:

[1571] Hardware:

[1572] User's PC or smartphone

[1573] Internet connection

[1574] Cloud Server

[1575] software:

[1576] Web browser or dedicated app

[1577] Form entry function

[1578] HTTPS protocol

[1579] Web crawling tools

[1580] Database APIs

[1581] Text analysis tools (e.g., Python, Pandas)

[1582] Generative AI models (e.g., GPT-3)

[1583] Chat Interface

[1584] Voice Recognition Software

[1585] Text-to-speech software (TTS)

[1586] The specific steps will be explained below.

[1587] Users access the system using a web browser or a dedicated app and create an account. After logging in, the user is taken to a screen where they can enter basic information about the person (such as name, date of birth, and career history). This information is then sent from the device to the management server.

[1588] Based on the received information, the server collects supplemental information about the target person from the internet and databases. It uses web crawling tools and database APIs to retrieve and analyze the relevant information. A text analysis tool (e.g., NLTK, Pandas) is used for the analysis, and the analysis results are used as training data for a generative AI model (e.g., GPT-3).

[1589] The server trains the generative AI model, which then enables it to converse with the target person based on their vocabulary, speaking style, and knowledge. Once training is complete, the user initiates a dialogue with the target person through a dialogue interface. The user can enter questions via text or voice. In the case of voice input, the device converts the voice data into text data and sends this text data to the server.

[1590] The server uses a generative AI model to generate an appropriate response based on the user's input and sends it back to the device, which then displays or plays the generated response to the user as text or audio.

[1591] The server can also generate specific virtual events based on the dialogue. For example, if a user types, "Tell me about the time he lived in," the server can analyze the dialogue log and generate a corresponding virtual event.

[1592] For example, consider the following prompt:

[1593] "Tell me about Albert Einstein, his research and achievements."

[1594] "Please tell me in detail how Cleopatra rose to power."

[1595] "Tell me about John Lennon's experiences with the Beatles."

[1596] In this way, the system of the invention provides users with the opportunity to gain a deeper understanding of the lives and achievements of deceased or historical figures and to build personal memorials through interactions and virtual events with them.

[1597] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1598] Step 1:

[1599] A user accesses the system and creates an account.

[1600] Input: User information (name, email address, password)

[1601] Output: Created account information

[1602] Operation:

[1603] 1. The user clicks the "New Registration" button in a web browser or dedicated app.

[1604] 2. The user enters the required information into the form and presses the "Register" button.

[1605] 3. The terminal sends the entered user information to the management server.

[1606] Step 2:

[1607] The user enters basic information about the person.

[1608] Input: Basic information of the target person (name, date of birth, career history)

[1609] Output: Information about the target person sent to the management server

[1610] Operation:

[1611] 1. After logging in, the user moves to the screen where they can enter information about the person in question.

[1612] 2. The user enters the target person's information into the form and presses the "Submit" button.

[1613] 3. The terminal sends the input information to the management server.

[1614] Step 3:

[1615] The server collects supplemental information about the target person based on the received information.

[1616] Input: Basic information of the target person

[1617] Output: Additional information collected about the person of interest

[1618] Operation:

[1619] 1. The server starts web crawling using the target person's name as a key.

[1620] 2. The server calls the database API to retrieve the relevant information.

[1621] 3. The server stores the collected supplemental information.

[1622] Step 4:

[1623] The server analyzes the collected information and trains a generative AI model.

[1624] Input: Supplementary information collected

[1625] Output: A trained generative AI model

[1626] Operation:

[1627] 1. The server tokenizes the collected information through a text analysis tool.

[1628] 2. The server builds a knowledge base and trains a generative AI model based on that knowledge.

[1629] Step 5:

[1630] The user accesses the dialogue interface and begins a dialogue with the target person.

[1631] Input: User question (text or voice)

[1632] Output: Send a query to the server

[1633] Operation:

[1634] 1. The user presses the "Start conversation" button and enters a question in the input field.

[1635] 2. The user submits the question by pressing the "Submit" button.

[1636] Step 6:

[1637] The terminal transmits the user's interactive input to the server.

[1638] Input: User question (text or voice data)

[1639] Output: User question sent to the management server

[1640] Operation:

[1641] 1. For voice input, the device uses voice recognition software to convert voice data into text data.

[1642] 2. Send the converted text data or text input data to the server.

[1643] Step 7:

[1644] The server uses a generative AI model to generate an appropriate response and sends it back to the device.

[1645] Input: User question data

[1646] Output: The generated response data

[1647] Operation:

[1648] 1. The server inputs the user's question data into the generative AI model and generates an appropriate response.

[1649] 2. Send the generated response back to the terminal.

[1650] Step 8:

[1651] The terminal displays or plays the received response to the user.

[1652] Input: Response data from the server

[1653] Output: what is displayed to the user or what is played as audio

[1654] Operation:

[1655] 1. The terminal displays the received text data in the display area.

[1656] 2. For audio output, convert the text into audio and play it back.

[1657] Step 9:

[1658] The server generates specific virtual events based on the content of the dialogue.

[1659] Input: Dialogue

[1660] Output: The generated virtual event

[1661] Operation:

[1662] 1. The server analyzes the interaction log and generates appropriate events using an event generation algorithm.

[1663] 2. Send details of the generated event to the user's device.

[1664] In this way, the system provides an opportunity to gain a deeper understanding of the lives and achievements of deceased and historical figures through dialogue and virtual events.

[1665] (Application example 1)

[1666] 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."

[1667] Previous systems lacked generative AI models for interacting with historical figures and deceased individuals, limiting the virtual experience users could have for gaining a deeper understanding of the subject. Furthermore, the lack of an effective means of converting voice data to text data limited the user experience. This made interactive learning difficult in historical reenactments and educational settings.

[1668] 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.

[1669] In this invention, the server includes means for having a user input basic information about the target person, means for collecting information about the target person from the Internet and a database, means for analyzing the collected information and training a generative AI model, means for generating a dialogue based on the user's input using the trained generative AI model, means for providing the generated dialogue to the user, means for displaying the dialogue using a device that reproduces a virtual space, means for converting voice data into text data, and means for converting the generated text data into voice data. This allows the user to interact with the target person through an immersive experience in a virtual space, enabling deep understanding and interactive learning.

[1670] The "means for allowing the user to input basic information about the target person" is a function that allows the user to input basic information such as the target person's name, date of birth, and career history into the system.

[1671] "Means for collecting information about the subject from the Internet and databases" refers to a function for obtaining additional details about the entered subject from online and offline databases.

[1672] "Means for analyzing collected information and training a generative AI model" refers to a function that analyzes acquired information and trains an AI model based on the results of that analysis.

[1673] "Means for generating dialogue based on user input using a trained generative AI model" refers to a function that uses a trained AI model to generate dialogue in response to a user's text input.

[1674] "Means for providing the generated dialogue to the user" is a function for providing the dialogue generated by the generative AI model to the user by display or audio.

[1675] "Means for displaying a dialogue using a device that recreates a virtual space" refers to a function for visually displaying the content of a dialogue using a device for recreating a virtual reality space (e.g., smart glasses or a head-mounted display).

[1676] The "means for converting voice data into text data" is a function for converting information input by voice by the user into text format.

[1677] The "means for converting generated text data into voice data" is a function for providing the generated text-based dialogue to the user in voice form.

[1678] The present invention relates to a system that allows users to simulate interactions with deceased or historical figures. To realize this system, the following specific components and process steps are required:

[1679] System Components

[1680] Hardware:

[1681] 1. Smart glasses or head-mounted displays:

[1682] It is used to allow users to enjoy experiences in virtual space.

[1683] 2. Server:

[1684] It plays a central role in processing data for the entire system.

[1685] 3. Terminal:

[1686] The device (smartphone, tablet, etc.) through which the user interfaces with the system.

[1687] software:

[1688] 1. Python:

[1689] Used as the main development language for programs.

[1690] 2. Transformers library (GPT-2):

[1691] A generative AI model for natural language processing.

[1692] 3. Requests library:

[1693] To obtain additional information from the Internet.

[1694] 4. pyttsx3 library:

[1695] To convert text to speech.

[1696] Data processing and calculation

[1697] Enter basic information:

[1698] The user uses a terminal to input basic information about the target person (such as name, date of birth, and career history) into the system. This input information is sent to the management server.

[1699] Collecting additional information:

[1700] The server uses the basic information entered to gather additional details about the person from the internet and databases, retrieving relevant documents, photographs, audio recordings, and more.

[1701] Training the AI ​​model:

[1702] The server analyzes the collected information and extracts the characteristics and features of the target person. The analysis results are used as training data for a generative AI model (GPT-2). Once training is complete, the model is able to converse with the target person based on their vocabulary, speaking style, and knowledge.

[1703] Dialogue generation:

[1704] When a user types a question or request into the device, the input is sent to the server, which uses a generative AI model to generate an appropriate response and sends it back to the device, which then provides the generated response to the user as text or audio.

[1705] Virtual display:

[1706] The generated dialogue content is displayed in a virtual space using smart glasses or a head-mounted display, allowing users to have a more immersive dialogue experience.

[1707] Audio data conversion:

[1708] When a user inputs something by voice, the device converts the voice data into text data and sends this text data to the server. The server generates an appropriate response based on the text data and sends it back to the device. The device then converts the generated text data into voice data and provides it to the user.

[1709] Examples and prompts

[1710] Examples:

[1711] The user asks, "Tell me about special relativity." This speech is converted to text and sent to a server. The server uses a generative AI model to generate a response: "Special relativity is a theory that explains the constant speed of light and the relativity of time and space." The device then converts this text into speech and provides it to the user.

[1712] Example prompt sentence:

[1713] Tell me about Einstein's life.

[1714] What is special relativity?

[1715] Please explain his historical background.

[1716] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1717] Step 1:

[1718] input:

[1719] The user enters the target person's basic information (name, date of birth, career history, etc.) into the terminal.

[1720] Data processing:

[1721] The terminal collects the basic information entered and sends it to the management server.

[1722] output:

[1723] The management server receives basic information about the target person.

[1724] Step 2:

[1725] input:

[1726] The server generates requests to retrieve additional information from the Internet and databases based on the person's basic information.

[1727] Data processing:

[1728] The server uses an API to gather information such as relevant documents, photos, audio recordings, etc. This information is retrieved using the requests library.

[1729] output:

[1730] The server stores the obtained additional information in a database.

[1731] Step 3:

[1732] input:

[1733] The server parses the stored additional information.

[1734] Data processing:

[1735] The server uses natural language processing techniques to tokenize the information and extract the characteristics and features of the target person, then trains a generative AI model (GPT-2) using the transformers library.

[1736] output:

[1737] A trained generative AI model is generated.

[1738] Step 4:

[1739] input:

[1740] The user inputs a question or request through the terminal.

[1741] Data processing:

[1742] The terminal transmits the user's input as text data to the server.

[1743] output:

[1744] The server receives the user's input.

[1745] Step 5:

[1746] input:

[1747] The server uses the generative AI model to generate a response based on the user's input.

[1748] Data processing:

[1749] The server prompts the trained generative AI model with user input to generate an appropriate response.

[1750] output:

[1751] The generated response is obtained as text data.

[1752] Step 6:

[1753] input:

[1754] The server sends the generated response to the terminal.

[1755] Data processing:

[1756] The device displays the received text data on smart glasses or a head-mounted display.

[1757] output:

[1758] Users experience text or voice responses in a virtual space.

[1759] Step 7:

[1760] input:

[1761] When a user asks a question by voice, the terminal receives the input as voice data.

[1762] Data processing:

[1763] The device uses a speech recognition API to convert voice data into text data.

[1764] output:

[1765] The converted text data is sent to the server.

[1766] Step 8:

[1767] input:

[1768] The server receives the voice input and generates a response using a generative AI model.

[1769] Data processing:

[1770] The server generates a response using the text data and sends an instruction to the terminal to convert the generated text data into voice data.

[1771] output:

[1772] The terminal converts the text data into voice data and provides it to the user.

[1773] This allows the user to have a natural conversation with the target person in the virtual space, providing a rich experience.

[1774] 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.

[1775] This invention relates to a generative AI system that recognizes a user's emotions and provides appropriate dialogue and interaction based on those emotions. To implement this invention, the following steps are required: collecting basic information, acquiring additional information, training a generative AI model, integrating an emotion engine, and generating and providing dialogue with the user. The operation of the system is explained below step by step.

[1776] First, a user accesses the system and creates an account. Once the user logs in, a screen appears where they can enter information about the person (such as name, date of birth, and career history). The device then sends this information to the server. The server then uses the received information to collect additional information related to the person from the Internet and databases. This collection process involves searching and retrieving documents, photographs, audio recordings, and more.

[1777] The server then analyzes the collected information, tagging and classifying it to generate a training dataset for the generative AI model, which the server uses to train the generative AI model to learn the characteristics and traits of the target person.

[1778] When a user interacts with a target person, the user inputs text or voice. The input data is sent from the terminal to the server. In particular, in the case of voice data, the terminal converts the voice into text data and sends it to the server as text data.

[1779] Here, the present invention uses an emotion engine. The server applies the emotion engine to the user's input (text or voice) to analyze the user's emotional state. The emotion analysis results are fed back to the generative AI model and reflected in the dialogue response. For example, if the user expresses sadness, the generated response will be comforting.

[1780] As a concrete example, consider a case where a user voice-inputs, "I'm not feeling too good today." The device converts this speech into text and sends it to a server. The server passes the text data to an emotion engine for analysis, which detects that the user is sad. A generative AI model then takes this emotional state into account and generates a response such as, "Are you OK? Let me know if there's anything I can help you with," which is sent to the device. The device then provides this response to the user in text or voice.

[1781] The emotion engine also comes into play when users select the virtual event mode. When setting up an event, the system can take into account the user's current emotional state to customize the event content to be more personal and emotionally relevant. For example, if a user sets up an event to celebrate a special anniversary and the analysis shows that the emotion at that time is joy, the event content will be set to be positive and enjoyable.

[1782] In this way, the system according to the present invention can recognize the user's emotions and provide more intimate and emotional interactions through dialogues and virtual events based on the emotions.

[1783] The processing flow will be explained below.

[1784] Step 1:

[1785] The user launches the application and the account registration screen appears. The user enters registration information such as name, email address, and password.

[1786] Step 2:

[1787] The terminal receives the input user information and transmits it to the server.

[1788] Step 3:

[1789] The server stores the received information in a database and creates a user account.

[1790] Step 4:

[1791] The user opens a screen for registering the target person and enters basic information such as the target person's name, date of birth, and career history.

[1792] Step 5:

[1793] The terminal receives the basic information of the person entered and sends it to the server.

[1794] Step 6:

[1795] Based on the information received by the server, additional information about the target person (such as literature, photographs, and audio data) is collected from the Internet and databases.

[1796] Step 7:

[1797] The server analyzes the collected data, tags and classifies it, and generates a training dataset for the generative AI model.

[1798] Step 8:

[1799] The server uses this dataset to train a generative AI model, which learns to interact with people based on their characteristics, speaking style, and knowledge.

[1800] Step 9:

[1801] The user opens a chat window or a voice input screen to start a conversation with the target person.

[1802] Step 10:

[1803] The user inputs the dialogue content by text or voice. The device receives the user's input and sends it to the server either as is if it is text or as text if it is voice.

[1804] Step 11:

[1805] The server receives the user's input (text or voice) and asks the emotion engine to analyze it, which detects the user's emotional state.

[1806] Step 12:

[1807] The server provides the analysis results from the emotion engine to the generative AI model, which generates an appropriate response based on the user's emotional state.

[1808] Step 13:

[1809] The server sends the generated response to the terminal.

[1810] Step 14:

[1811] The device displays the response received from the server as text or plays it back as audio.

[1812] Step 15:

[1813] If the user has further questions or conversations to make, they enter the information again and repeat the same process.

[1814] Step 16:

[1815] If the user selects the virtual event mode, they enter details to set up an event based on a specific scenario.

[1816] Step 17:

[1817] The device sends the entered event details to the server, which uses an emotion engine to customize the event content, taking into account the user's emotional state.

[1818] Step 18:

[1819] The server generates a customized scenario for the virtual event and sends it to the terminal.

[1820] Step 19:

[1821] The terminal provides the content of the virtual event to the user as text or audio.

[1822] This allows a generative AI system with an embedded emotion engine to provide dialogue and interaction that responds to the user's emotional state, creating a personalized experience.

[1823] Example 2

[1824] 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."

[1825] Conventional conversational AI systems have difficulty generating responses that take the user's emotional state into account, which can result in poor dialogue quality. Furthermore, they lack the ability to customize the content of virtual events based on the user's emotions, resulting in a uniform user experience.

[1826] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for having a user input basic information about the target person, means for collecting information about the target person from a global information network and a data repository, means for analyzing the collected information and training a generative AI model, means for integrating an emotion engine into the generative AI model and analyzing the emotional state in response to the user's input, means for adjusting the response of the generative AI model based on the analyzed emotional state, and means for providing the generated dialogue to the user. This enables natural dialogue that takes the user's emotions into consideration, and makes it possible to provide a virtual event that is more emotionally sensitive.

[1827] "User" refers to an individual who accesses the System and participates in the Interactions and Virtual Events.

[1828] "Subject Person" means the individual about whom information is entered into the System by a User.

[1829] "Basic information" refers to the initial and primary information about the person, such as name, date of birth, and background.

[1830] "Means" refers to a method or device for achieving a specific function or purpose.

[1831] "Global information network" refers to an information network that can obtain data from all over the world, including the Internet.

[1832] "Data repository" means a system or location for collecting, storing, and managing data.

[1833] "Collecting information" refers to the act of locating and obtaining data related to a person from the internet or data repositories.

[1834] A "generative AI model" refers to an artificial intelligence model that generates dialogue based on collected and analyzed data.

[1835] "Emotion engine" refers to an algorithm or software for analyzing a user's emotional state from their speech.

[1836] "Generating a response" refers to the act of creating an appropriate reply based on user input.

[1837] "Virtual Event" means a simulated event presented online.

[1838] "Analysis" refers to the process of organizing collected data and understanding its meaning and relevance.

[1839] "Text data" refers to written data converted from voice input using voice recognition technology.

[1840] This invention relates to a generative AI system that recognizes a user's emotions and provides appropriate dialogue and interaction based on those emotions. To implement this invention, the system operates through multiple steps. The operation of the system is described in detail below.

[1841] First, a user accesses the system's website or application and creates an account. After logging in, the user enters basic information about the person (e.g., name, date of birth, career history, etc.). The device (e.g., smartphone, tablet, personal computer) then sends this information to the server using a secure communication protocol (e.g., HTTPS).

[1842] Based on the received basic information, the server searches and collects additional information related to the person from global information networks (the Internet) and data repositories (various databases). This collection process obtains data such as documents, photographs, and audio recordings.

[1843] The server then analyzes the collected information, typically tagging and categorizing it to generate a training dataset for the generative AI model. This analysis uses natural language processing techniques (e.g., BERT, GPT, etc.). The server then uses the generated dataset to train the generative AI model, learning the characteristics and traits of the target person. This training is achieved using a deep learning framework (e.g., TensorFlow, PyTorch).

[1844] When a user initiates a dialogue, they input text or voice. In the case of voice, the device uses voice recognition software (e.g., Google Speech-to-Text API) to convert the speech into text and send it to the server. The server then passes the received text data to an emotion engine (e.g., IBM Watson) to analyze the user's emotional state. The results of this analysis are fed back to the generative AI model, which then creates a response that takes the user's emotional state into account.

[1845] For example, if a user types "I'm not feeling too good today," the server inputs this text into the emotion engine and analyzes that the user is feeling "sad." The generative AI model takes this emotional state into account and generates a response such as "Are you OK? Let me know if there's anything I can help you with," which is then sent from the server to the device. The device then provides this response to the user in the form of a text display or voice playback using speech synthesis software (e.g., Amazon Polly).

[1846] Furthermore, when a user selects the virtual event mode, the server uses an emotion engine to analyze the user's current emotional state. Based on the analysis results, the server customizes the event content. For example, if a user sets up an event to celebrate a special anniversary and the analyzed emotion is "happy," the event content will be set to be positive and enjoyable.

[1847] Examples of prompts:

[1848] "When a user makes a statement that expresses an emotion, recognize that emotion and generate an appropriate response. For example, if the user says, 'I'm not feeling very well today,' generate a comforting response."

[1849] In this way, this generative AI system can recognize the user's emotions and provide natural dialogue and virtual events based on them.

[1850] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1851] Program flow:

[1852] Step 1:

[1853] A user accesses the system's website or application and creates an account. Input information includes a username, email address, and password. Once this basic data is entered, the device sends it to the server, which then registers the received information in a database and completes the account creation.

[1854] Step 2:

[1855] The user logs in and enters basic information about the person (such as name, date of birth, and background information). The device sends this information to the server, which uses the received information to gather additional information from the internet and data repositories. This information gathering can be done using web scraping tools, APIs, etc.

[1856] input:

[1857] Basic information of the target person entered by the user

[1858] output:

[1859] Additional information about the subject (documents, photographs, audio recordings, etc.)

[1860] Step 3:

[1861] The server analyzes the collected information and performs tagging and classification, specifically using natural language processing techniques to extract meaningful keywords and phrases from the text data, which then creates a training dataset for the generative AI model.

[1862] input:

[1863] Additional Information Collected

[1864] output:

[1865] Training dataset (tagged data)

[1866] Step 4:

[1867] The server trains the generative AI model using deep learning frameworks (TensorFlow, PyTorch, etc.). Through training, the generative AI model learns the characteristics and features of the target person.

[1868] input:

[1869] Training dataset

[1870] output:

[1871] Pre-trained generative AI models

[1872] Step 5:

[1873] To initiate a dialogue, the user inputs text or voice. In the case of voice input, the device converts the speech into text using speech recognition software (e.g., Google Speech-to-Text API) and sends the text data to the server.

[1874] input:

[1875] User text or voice input

[1876] output:

[1877] Text data (in the case of voice input)

[1878] Step 6:

[1879] The server passes the received text data to an emotion engine, which analyzes the user's emotional state using an emotion analysis tool such as IBM Watson. The analysis results are fed back to the generative AI model.

[1880] input:

[1881] User text data

[1882] output:

[1883] Emotion analysis results

[1884] Step 7:

[1885] Based on the results of emotion analysis, the generative AI model generates an appropriate response based on the user's emotional state. The response is generated in text format and sent from the server to the device.

[1886] input:

[1887] Emotion analysis results

[1888] output:

[1889] Response text data

[1890] Step 8:

[1891] The device provides the generated response to the user, which can be displayed as text or played aloud using speech synthesis software (e.g., Amazon Polly).

[1892] input:

[1893] Response text data

[1894] output:

[1895] Text display or audio playback

[1896] Step 9:

[1897] When the user selects the virtual event mode, the server analyzes the user's current emotional state using an emotion engine and customizes the event content to include many elements that express positive emotions.

[1898] input:

[1899] The user's emotional state

[1900] output:

[1901] Customized Virtual Event Content

[1902] (Application example 2)

[1903] 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."

[1904] Traditional online shopping sites lack the ability to suggest products based on the user's emotions and psychological state, making it difficult for users to find the products that best suit their emotions. Furthermore, the lack of personalized interactions and suggestions based on user input can lead to a decrease in user satisfaction.

[1905] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having the user input basic information about the target person, means for collecting information about the target person from the Internet and a database, means for analyzing the collected information and training a generative AI model, means for generating a dialogue based on the user's input using the trained generative AI model, means for providing the generated dialogue to the user, means for analyzing the user's emotions, and means for suggesting products based on the user's emotions. This enables personalized product suggestions based on the user's emotions.

[1906] "Users" refers to people who use the system.

[1907] A "person of interest" refers to an individual about whom a user is interested and about whom the user enters basic information.

[1908] "Basic information" refers to initial data about the person, such as name, date of birth, and background.

[1909] "Internet" refers to a global network for collecting information.

[1910] A "database" refers to a structured collection of data that allows information about a person to be stored and retrieved.

[1911] A "generative AI model" is a model trained using machine learning or artificial intelligence techniques and used to generate dialogue and suggestions.

[1912] "Training" refers to the process of teaching a generative AI model using collected data.

[1913] "Emotion analysis" refers to the technology of analyzing a person's emotions from their input (text or voice).

[1914] "Dialogue" refers to communication between a user and a system.

[1915] "Product suggestion" refers to the act of recommending appropriate products and services based on the user's emotions and needs.

[1916] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following provides a specific description of embodiments of the present invention.

[1917] System program and hardware / software configuration

[1918] The system for realizing this invention is an application for a shopping site where users can receive product suggestions based on their emotions using a smartphone. This system is composed of the following main hardware and software:

[1919] Smartphone: Provides the user interface and collects initial data.

[1920] Server: Stores data, analyzes it, and hosts generative AI models.

[1921] Sentiment analysis engine (e.g., sentiment analysis API): Analyzes user sentiment.

[1922] Natural language processing module (e.g., natural language parsing API): performs string analysis of user input.

[1923] Generative AI models (e.g., Generative AI Model APIs): Generate dialogue and suggestions.

[1924] Program processing flow and data calculation

[1925] 1. Providing the user interface:

[1926] An interface is displayed on the smartphone where the user can log in and input their emotional state (text / voice).

[1927] Example: A user types, "I'm a little tired today."

[1928] 2. Data conversion and transmission:

[1929] The smartphone converts the voice input into text (if it was entered by voice) and sends the data to the server.

[1930] 3. Emotion analysis:

[1931] The server uses an emotion analysis engine to analyze the user's input emotional state.

[1932] Example: Detecting the emotion "tired" from the input text "I'm a little tired today."

[1933] 4. Response generation using generative AI models:

[1934] The emotional information detected by the emotion analysis engine is passed to a generative AI model, which generates optimal product suggestions for the user.

[1935] Example: "Are you looking for relaxation products?"

[1936] 5. Search product database:

[1937] Based on the category and keywords of the suggested product, the server searches the database of the online shopping site for related products and retrieves a list.

[1938] 6. User Visibility:

[1939] The proposed products and their explanations are displayed on the smartphone and provided to the user.

[1940] Example: "How about this aromatherapy set? It's especially relaxing."

[1941] Examples of prompt statements

[1942] The following is an example of a prompt to input to a generative AI model (e.g., a generative AI model API):

[1943] A user types, "I'm feeling a bit tired today." The user's emotion is "tired." Suggest products related to relaxation and stress reduction.

[1944] In this way, the system according to the present invention can provide a more personalized shopping experience through interactions and product suggestions based on the user's emotions.

[1945] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1946] Step 1:

[1947] Input and Output:

[1948] The user inputs their emotional state (by text or voice) using a smartphone.

[1949] Specific behavior:

[1950] A user enters "I'm a little tired today" into the application, and this input data is saved on the smartphone.

[1951] Step 2:

[1952] Input and Output:

[1953] The smartphone converts the voice input into text and sends the text data to the server.

[1954] Specific behavior:

[1955] When a voice input is received, the device uses its voice recognition function to convert it into text data, which is then sent to the server.

[1956] Step 3:

[1957] Input and Output:

[1958] The server sends the received text data to the emotion analysis engine, analyzes the emotional state, and receives the emotion analysis results from the emotion analysis engine.

[1959] Specific behavior:

[1960] The server transfers the input text data, "I'm a little tired today," to an emotion analysis engine and detects the emotion of "tired."

[1961] Step 4:

[1962] Input and Output:

[1963] The sentiment analysis results and user input data are passed to the generative AI model to generate optimal product suggestions. The generated product suggestions are then received.

[1964] Specific behavior:

[1965] The server inputs the emotional information "I'm tired" and the user's text data into a generative AI model and generates a suggestion such as "Are you looking for relaxation products?"

[1966] Step 5:

[1967] Input and Output:

[1968] Based on the product suggestions generated by the generative AI model, the server searches for related products from the online shopping site's database and obtains a product list.

[1969] Specific behavior:

[1970] The server uses the product category and keywords to search a database to retrieve a list of related products (e.g., aromatherapy sets).

[1971] Step 6:

[1972] Input and Output:

[1973] The acquired product list and suggestions are sent to the smartphone and displayed to the user.

[1974] Specific behavior:

[1975] The server then sends the acquired product list and the suggestions generated by the generative AI model together to the smartphone. The device then visually displays this to the user. For example, it might display, "How about this aromatherapy set? It has a particularly relaxing effect."

[1976] In this way, product suggestions based on the user's emotions are made while clarifying the input data and output data at each processing step.

[1977] 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.

[1978] 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.

[1979] 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.

[1980] 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.

[1981] 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.

[1982] 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.

[1983] 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).

[1984] 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.

[1985] 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."

[1986] 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.

[1987] 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).

[1988] 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.

[1989] 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.

[1990] 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.

[1991] 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.

[1992] 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.

[1993] 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.

[1994] 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.

[1995] 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.

[1996] 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.

[1997] 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.

[1998] The following is further disclosed regarding the above embodiment.

[1999] (Claim 1)

[2000] A means for allowing a user to input basic information about a target person;

[2001] a means of collecting information about the person from the internet and databases;

[2002] A means to analyze the collected information and train a generative AI model; and

[2003] means for generating dialogue based on user input using the trained generative AI model;

[2004] means for providing the generated dialogue to a user;

[2005] A system including:

[2006] (Claim 2)

[2007] 10. The system of claim 1, further comprising means for generating a virtual event based on an interaction with the person of interest.

[2008] (Claim 3)

[2009] 10. The system of claim 1, further comprising means for receiving user input as voice data and converting the voice data into text data.

[2010] "Example 1"

[2011] (Claim 1)

[2012] A means for allowing a user to input basic information about a target person;

[2013] a means of collecting information about the person from the internet and databases;

[2014] A means to analyze the collected information and train a generative AI model; and

[2015] means for generating dialogue based on user input using the trained generative AI model;

[2016] means for providing the generated dialogue to a user;

[2017] a means for converting voice input into text;

[2018] means for converting the generated text data into a speech output;

[2019] A system including:

[2020] (Claim 2)

[2021] 10. The system of claim 1, wherein the system generates a specific virtual event based on the interaction.

[2022] (Claim 3)

[2023] 10. The system of claim 1, further comprising means for receiving user input as voice data and converting the voice data into text data.

[2024] "Application Example 1"

[2025] (Claim 1)

[2026] A means for allowing a user to input basic information about a target person;

[2027] a means of collecting information about the person from the internet and databases;

[2028] A means to analyze the collected information and train a generative AI model; and

[2029] means for generating dialogue based on user input using the trained generative AI model;

[2030] means for providing the generated dialogue to a user;

[2031] a means for displaying the dialogue using a device that reproduces a virtual space;

[2032] means for converting voice data into text data;

[2033] means for converting the generated text data into audio data;

[2034] A system including:

[2035] (Claim 2)

[2036] 10. The system of claim 1, further comprising means for generating a virtual event based on an interaction with the person of interest.

[2037] (Claim 3)

[2038] 10. The system of claim 1, further comprising means for receiving user input as voice data and converting the voice data into text data.

[2039] "Example 2: Combining Emotion Engines"

[2040] (Claim 1)

[2041] A means for allowing a user to input basic information about a target person;

[2042] a means for collecting information about the person of interest from global information networks and data repositories;

[2043] A means to analyze the collected information and train a generative AI model; and

[2044] A means for generating a dialogue based on a user's input using the generated AI model;

[2045] A means of integrating an emotion engine into the generative AI model to analyze the user's emotional state in response to input;

[2046] means for adjusting the response of the generative AI model based on the analyzed emotional state; and

[2047] means for providing the generated dialogue to a user;

[2048] A system including:

[2049] (Claim 2)

[2050] 10. The system of claim 1, further comprising means for generating a virtual event based on a dialogue with the target person and customizing the content of the event using an emotion engine.

[2051] (Claim 3)

[2052] 10. The system of claim 1, further comprising means for receiving user input as voice data and converting the voice data into text data.

[2053] "Application example 2 when combining emotion engines"

[2054] (Claim 1)

[2055] A means for allowing a user to input basic information about a target person;

[2056] a means of collecting information about the person from the internet and databases;

[2057] A means to analyze the collected information and train a generative AI model; and

[2058] means for generating dialogue based on user input using the trained generative AI model;

[2059] means for providing the generated dialogue to a user;

[2060] means for analyzing user emotions;

[2061] A means for suggesting products based on user emotions;

[2062] A system including:

[2063] (Claim 2)

[2064] 10. The system of claim 1, further comprising means for generating a virtual event based on an interaction with the person of interest.

[2065] (Claim 3)

[2066] 10. The system of claim 1, further comprising means for receiving user input as voice data and converting the voice data into text data. [Explanation of symbols]

[2067] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for allowing a user to input basic information about a target person; a means of collecting information about the person from the internet and databases; A means to analyze the collected information and train a generative AI model; and means for generating dialogue based on user input using the trained generative AI model; means for providing the generated dialogue to a user; A system including:

2. The system of claim 1 , further comprising means for generating a virtual event based on an interaction with the target person.

3. 10. The system of claim 1, further comprising means for receiving user input as voice data and converting the voice data into text data.

Images