System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving data from a user; means for training a generative artificial intelligence model based on the received data; means for interacting with the user through the trained generative artificial intelligence model; and means for reducing user loneliness through the interaction with 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] The feelings of loneliness and loss experienced by elderly people and those who have lost a long-term partner can lead to mental health problems and deterioration, and in the worst cases, to lonely death. To solve these serious problems, a system that recreates conversations with a living partner is needed. Existing dialogue systems only provide general responses and lack the specificity to address individual feelings of loss and loneliness. The present invention aims to solve these problems by providing a system that recreates a partner's catchphrases, way of thinking, and other characteristics in order to empathize with the user and alleviate their feelings of loneliness. [Means for solving the problem]

[0005] The present invention solves the above problems by using the following means. First, it provides a means for receiving data such as a partner's behavioral patterns, speaking style, catchphrases, and hobbies from a user. Next, it provides a means for training a generative AI model based on the received data. Finally, it provides a means for engaging in a dialogue with the user through the trained generative AI model. Furthermore, it provides a system including a means for reducing the user's sense of loneliness through this dialogue, thereby providing the user with an experience that makes them feel as if they are conversing with their partner again. This promotes the user's mental stability and reduces the risk of dying alone.

[0006] "User" refers to the person who operates and interacts with the system.

[0007] "Data" refers to information provided by users about their partner's behavioral patterns, speaking style, catchphrases, hobbies, etc.

[0008] A "generative artificial intelligence model" refers to a machine learning model that is trained based on the data it receives and behaves like a partner.

[0009] "Training" refers to the process by which the generative artificial intelligence model learns from the data it receives and generates a characteristic response for the partner.

[0010] "Means for receiving" refers to a device or program that has the function of receiving data as input from a user.

[0011] "Structuring means" refers to a device or program that has the function of organizing received data into a format that is easy for the generating artificial intelligence model to understand.

[0012] "Means for dialogue" refers to a device or program that has the ability to converse with a user using a trained generative artificial intelligence model.

[0013] "Means to reduce loneliness" refers to devices and programs that promote the user's mental stability through dialogue and alleviate feelings of loneliness. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention relates to a system that interacts with users using a generative artificial intelligence model that reproduces the behavioral patterns and speech patterns of a partner based on data provided by the user. The system aims to provide psychological support, particularly to elderly people who feel lonely and people who have lost their partners.

[0036] 1. System Configuration

[0037] The system consists of three main components:

[0038] 1. Data Collection Equipment

[0039] 2. Training equipment for generative AI models

[0040] 3. Interactive Interface Device

[0041] 2. Operation of the data acquisition device

[0042] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[0043] Terminal: Receives data entered by the user and sends it to the server.

[0044] 3. Training the generative AI model

[0045] Server: Trains a generative AI model based on the received data, using deep learning and natural language processing techniques to generate a model that replicates the partner's speech and behavior patterns.

[0046] Server: The trained model will be able to generate natural responses as if the partner were real.

[0047] 4. Operation of the interactive interface device

[0048] Terminal: The user accesses the system through a dedicated application and starts a dialogue. The dialogue interface is designed to be user-friendly and allows voice and text input.

[0049] User: Talk about memories with their partner or everyday events. For example, type "I'm very tired today."

[0050] Terminal: Sends input from the user to the server.

[0051] Server: Analyzes the input and uses a generative AI model to generate an appropriate response, such as "That must have been tough. It's important to rest, don't push yourself too hard. What happened?"

[0052] Terminal: Receives the response from the server and displays it on the user interface. The user can confirm the response and continue the conversation by speaking again.

[0053] Specific examples

[0054] Specific use cases include the following scenarios:

[0055] Scenario 1: A user complains about everyday life

[0056] User: "My boss has been scolding me a lot today, and I'm really tired."

[0057] Terminal: Receives input and sends it to the server.

[0058] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[0059] Terminal: Displays the response and shows it to the user.

[0060] Scenario 2: User discussing travel plans

[0061] User: "I'm thinking about going to the country for the weekend."

[0062] Terminal: Receives input and sends it to the server.

[0063] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[0064] Terminal: Displays the response and shows it to the user.

[0065] This system allows users to enjoy the experience of interacting with their deceased partner again, which can alleviate feelings of loneliness and loss and provide a sense of psychological security. This invention is a powerful tool for preventing lonely deaths and contributing to the mental stability of elderly people in particular.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] User: Launches the dedicated application and inputs information about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc. For example, they input details about their partner's favorite foods, common phrases, and hobbies.

[0069] Step 2:

[0070] Terminal: Receives input data and sends it to the server, where it is structured and converted into the appropriate format.

[0071] Step 3:

[0072] Server: Checks the received data and stores it in a database, which is later used to train the generative artificial intelligence model.

[0073] Step 4:

[0074] Server: Preprocesses the data, filters out unnecessary data, and extracts necessary features (such as catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative AI model.

[0075] Step 5:

[0076] Server: Deploys the trained generative AI model as an API and makes it externally accessible. The model has the ability to generate characteristic responses for partners.

[0077] Step 6:

[0078] User: Logs in to a dedicated application and starts a conversation. The user simulates a conversation with a partner through voice input or text input.

[0079] Step 7:

[0080] Terminal: Receives user input (voice or text) and sends it to the server, where it is processed in real time.

[0081] Step 8:

[0082] Server: Analyzes user input and passes it to a trained generative AI model to generate an appropriate response. For example, if a user says, "I'm tired today," the server generates a response like, "That must have been tough. It's important to rest, so don't push yourself too hard. What happened?"

[0083] Step 9:

[0084] Server: Sends the generated response to the user's terminal. At this time, if necessary, it also generates and sends an audio file of the response.

[0085] Step 10:

[0086] Terminal: The response received from the server is displayed on the user interface, and the user is informed of the dialogue. If the response is voice, it is played on the speaker.

[0087] Step 11:

[0088] User: Check the displayed response and continue the conversation by speaking again. For example, if the user inputs, "I'm thinking about going to the countryside this weekend," the system will respond with, "Great! I really liked that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[0089] This process allows users to experience the sensation of having a conversation with their deceased partner, which alleviates feelings of loneliness and loss and provides a sense of psychological security.

[0090] Example 1

[0091] 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."

[0092] There is a need for a means to alleviate the loneliness felt by the elderly and those who have lost their partners and to provide psychological support. However, current technology has difficulty replicating an individual's specific behavioral patterns and speaking style, and is therefore unable to recreate a partner. As a result, there is a problem that loneliness is not being alleviated or psychological support is not being provided sufficiently.

[0093] 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.

[0094] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model, means for alleviating the user's sense of loneliness through the interaction with the user, means for collecting detailed information about the partner using a dedicated application, means for transmitting the collected information to the server via a secure communication protocol, means for analyzing the user's input using the trained generative AI model and generating an appropriate response, and means for displaying the generated response on a user interface. This allows the user to enjoy the experience of interacting with their deceased partner again, thereby easing their sense of loneliness and loss and providing a sense of psychological security.

[0095] "User" refers to a person who uses the system to input the partner's behavioral patterns and speaking style and engage in a dialogue.

[0096] "Data" refers to detailed information collected from users about their partners' behavioral patterns, speaking style, catchphrases, hobbies, actions, etc.

[0097] A "generative artificial intelligence model" refers to a model that uses deep learning and natural language processing techniques to reproduce a partner's speaking style and behavior patterns based on received data.

[0098] "Training" refers to the process of training a generative artificial intelligence model based on collected data.

[0099] "Specialized Application" refers to specific software that allows users to enter their partner's details.

[0100] A "secure communication protocol" refers to a communication method for encrypting data and sending it securely to a server.

[0101] "User interface" refers to an interface that allows a user to interact with a system and that supports voice input and text input.

[0102] "Response" refers to a reply generated by a generative artificial intelligence model in response to input from a user.

[0103] "Reducing loneliness" refers to the alleviation of feelings of loneliness and loss that users feel through their interactions with the system.

[0104] This invention relates to a system that interacts with users using a generative artificial intelligence model that reproduces the behavioral patterns and speech patterns of a partner based on data provided by the user. The purpose is to provide psychological support, particularly to elderly people who feel lonely and people who have lost their partners.

[0105] System Configuration

[0106] The system consists of three components:

[0107] 1. Data Collection Equipment

[0108] 2. Training equipment for generative AI models

[0109] 3. Interactive Interface Device

[0110] Data collection equipment

[0111] User: Using a dedicated application, the user enters detailed information about their partner's behavioral patterns, speaking style, catchphrases, hobbies, etc. Specifically, the user enters information about the words their partner frequently used, their daily actions, and their hobbies. For example, the user enters information such as, "My partner liked to drink coffee every morning."

[0112] Terminal: Receives data entered by the user and temporarily stores it in local storage. It then transmits the data to the server using a secure communication protocol (e.g., HTTPS). The data is encrypted before transmission.

[0113] Training device for generative artificial intelligence models

[0114] Server: Analyzes the received data and converts it into an appropriate data structure. Then, using a deep learning framework (such as TENSORFLOW® or PyTorch), it trains a generative artificial intelligence model based on the collected data. This process generates a model that can reproduce the partner's speech and behavior patterns.

[0115] Server: Validates the trained model, adjusts and retrains it as needed, and the trained model is capable of generating natural responses that make the partner appear as if they were real.

[0116] Interactive Interface Device

[0117] Terminal: The user accesses the system through a dedicated application and begins a dialogue. The user interface supports both voice and text input. When the user talks about everyday events or memories with their partner, the input is sent to the server.

[0118] Server: Analyzes the input and generates an appropriate response using a generative artificial intelligence model. For example, if a user inputs "I'm very tired today," the server generates a response such as "That must have been tough. It's important to rest, so don't push yourself too hard. What happened?" and sends it back to the device.

[0119] Terminal: By displaying the generated response in the user interface, the user can have an experience as if they were interacting with their deceased partner again.

[0120] Specific examples

[0121] Specific use cases include the following scenarios:

[0122] Scenario 1: A user complains about everyday life

[0123] User: "My boss has been scolding me a lot today, and I'm really tired."

[0124] Terminal: Receives this message and sends it to the server.

[0125] Server: The generative AI model generates a response saying, "That was tough, but you can do it. Is there anything I can help you with?"

[0126] Terminal: Displays the generated response and shows it to the user.

[0127] Scenario 2: User discussing travel plans

[0128] User: "I'm thinking about going to the country for the weekend."

[0129] Terminal: Receives this message and sends it to the server.

[0130] Server: The generative AI model generates a response like, "Great! I really liked that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[0131] Terminal: Displays the generated response and shows it to the user.

[0132] As described above, this system can help users alleviate feelings of loneliness and loss and provide a sense of psychological security.

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

[0134] Step 1:

[0135] Data collection

[0136] User: Using a dedicated application, the user inputs detailed information about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc. For example, the input information might include, "My partner liked to drink coffee every morning."

[0137] Input: Partner details entered by the user.

[0138] Output: Raw data temporarily stored on the device.

[0139] Specific actions

[0140] The user enters "Partner's favorite drink: Coffee" into the application and the device stores this information.

[0141] Step 2:

[0142] Data transmission

[0143] Terminal: The terminal sends the received data to the server using a secure communication protocol (e.g., HTTPS). At this time, the data is encrypted before being sent.

[0144] Input: Temporarily saved data.

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

[0146] Specific actions

[0147] The device encrypts the data stored on it, such as "Partner's favorite drink: Coffee," and sends it to the server.

[0148] Step 3:

[0149] Preparing for data analysis

[0150] Server: Receives the transmitted data and converts it into a data structure for analysis, which is then fed into the machine learning model.

[0151] Input: Encrypted data.

[0152] Output: The parsed data is converted into the appropriate format.

[0153] Specific actions

[0154] The server receives the data "Partner's favorite drink: coffee" and converts the data structure for analysis.

[0155] Step 4:

[0156] Training an AI model

[0157] Server: The transformed data is used to train the generative AI model, using a deep learning framework (e.g., TensorFlow or PyTorch).

[0158] Input: Parsed data.

[0159] Output: A trained generative artificial intelligence model.

[0160] Specific actions

[0161] The server uses multiple data points, such as "partner's favorite drink: coffee," to train the AI ​​model and learn the partner's behavioral patterns.

[0162] Step 5:

[0163] Starting a conversation

[0164] User: Launches a dedicated application and accesses the system. The user can enter messages or questions to start a dialogue in the dialogue interface of the application.

[0165] Input: A message or question typed by the user.

[0166] Output: The entered message or question is sent to the server.

[0167] Specific actions

[0168] The user types "How was your day?" into the conversational interface and the message is sent to the server.

[0169] Step 6:

[0170] Response Generation

[0171] Server: Analyzes user input and generates appropriate responses using a trained generative artificial intelligence model.

[0172] Input: A message or question typed by the user.

[0173] Output: The generated response.

[0174] Specific actions

[0175] The server parses the user's question "How was your day?" and generates a response such as "It was a peaceful day today. I was thinking of you."

[0176] Step 7:

[0177] Viewing the response

[0178] Terminal: The generated response is displayed in a user interface and provided to the user.

[0179] Input: The response sent by the server.

[0180] Output: The response displayed in the user interface.

[0181] Specific actions

[0182] The device displays the response "It was a peaceful day today. I was thinking of you" in the user interface.

[0183] These steps allow users to recreate conversations with their deceased partner, helping to alleviate feelings of loneliness and loss and providing a sense of psychological security.

[0184] (Application example 1)

[0185] 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."

[0186] Currently, there are interactive systems that can help elderly people who feel lonely or people who have lost their partners to reduce their feelings of loneliness. However, these systems cannot provide adequate support to users who often feel lonely in their daily lives, especially when shopping. In particular, there are no systems that allow users to enjoy shopping while interacting with their partners during the shopping experience in a virtual store. Therefore, there is a need for an interactive system that can provide advice on purchasing behavior in a virtual store.

[0187] 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.

[0188] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model in a virtual environment, means for reducing the user's sense of loneliness through the interaction with the user, and means for allowing the user to consult with a partner about purchasing behavior in a virtual store. This makes it possible for users to interact with a partner while consulting with them about purchasing behavior in the virtual store, thereby reducing feelings of loneliness in their daily lives.

[0189] A "user" is an individual who wishes to use the system to reduce feelings of loneliness and enjoy a shopping experience in a virtual store.

[0190] "Data" refers to characteristic information entered by the user about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc.

[0191] A "generative artificial intelligence model" is an artificial intelligence system that is trained based on data provided by the user to reproduce the behavior and speaking style of a partner.

[0192] "Virtual environment" refers to a digital space where users can have a shopping experience in a virtual space.

[0193] "Purchasing behavior" refers to the series of actions a user takes to select and purchase a product in a virtual store.

[0194] "Dialogue" refers to the act of communication between a user and a system via a generative artificial intelligence model.

[0195] This invention is an interactive system for reducing users' feelings of loneliness and supporting their shopping experience in a virtual store. The system of the present invention mainly consists of the following three elements:

[0196] 1. Data Collection Equipment

[0197] 2. Training equipment for generative AI models

[0198] 3. Interactive Interface Device

[0199] 1. Data Collection Equipment

[0200] Using a dedicated application, users can input information about their partner's behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, they can enter details about their partner's favorite foods, common phrases, and hobbies. This data is then sent to the server via the device.

[0201] 2. Training a generative AI model

[0202] The server trains a generative AI model based on the received data. This training process uses deep learning and natural language processing techniques. The result is a model that reproduces the partner's speech and behavior patterns. This model can generate natural responses, as if the partner were actually alive.

[0203] 3. Interactive Interface Device

[0204] Users access the system through a dedicated application and begin interacting with it. The user-friendly interactive interface allows for voice and text input. Users can consult with an AI partner while selecting products in the virtual store.

[0205] For example, consider the following scenario:

[0206] Scenario 1: A user is asking for advice about a dress.

[0207] User: "What do you think of this dress?"

[0208] Terminal: Receives input and sends it to the server.

[0209] Server: The generative AI model generates a response: "That's great, let's buy it!"

[0210] This system allows users to enjoy an experience that feels like they are conversing with a partner while discussing purchasing behavior in a virtual store, which can alleviate feelings of loneliness and loss and provide a sense of psychological security.

[0211] To implement this invention, the following hardware and software are used:

[0212] Hardware: smartphone, head-mounted display, server

[0213] Software: OpenAI (registered trademark) API (generative AI model), virtual store application

[0214] Example prompt sentence:

[0215] User input: "What do you think of this dress?"

[0216] Prompt statement:

[0217] A phrase or catchphrase your partner often uses:

[0218] "That's great, let's buy it!"

[0219] "I think it suits you."

[0220] Foods and hobbies your partner liked:

[0221] "I like yakiniku"

[0222] "I love traveling"

[0223] User Typed: What do you think of this dress?

[0224] As an AI playing the role of your partner, respond using your partner's quirks and preferences, such as:

[0225] This allows users to enjoy a shopping experience in a virtual store, while also reducing feelings of loneliness and receiving psychological support.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] Using a dedicated application, users input characteristic information about their partner, such as their behavioral patterns, speaking style, catchphrases, and hobbies. The input data is structured by the device and sent to the server. The input is text data about the partner's preferences and behavioral patterns, and the output is the data sent to the server.

[0229] Step 2:

[0230] The server trains a generative AI model based on the received data. The input is structured data sent by the user, and the output is a trained generative AI model. The server analyzes the data using deep learning algorithms to generate a model that replicates the partner's speech and behavior patterns.

[0231] Step 3:

[0232] The user starts shopping in a virtual environment using a virtual store application. At this time, the user consults the generative AI model about product selection and purchasing behavior. The input is the user's question or inquiry, and the output is text data sent from the terminal to the server.

[0233] Step 4:

[0234] The server uses the text data received from the user as a prompt and generates an appropriate response using a generative artificial intelligence model. The input is the question or inquiry sent by the user, and the output is the response text generated by the AI. The server generates a prompt sentence and applies it to the AI ​​model to obtain a response.

[0235] Step 5:

[0236] The terminal receives the response from the server and displays it on the user interface. The input is the response text sent from the server, and the output is the response displayed on the user interface. The user can confirm this response and continue the dialogue again.

[0237] Step 6:

[0238] If the user has another question or inquiry, the device again sends the input to the server. This continues the dialogue, allowing the user to enjoy a series of purchasing actions while receiving advice from their AI partner. The input and output process proceeds by repeating steps 3 to 5.

[0239] This process flow allows users to enrich their shopping experience in the virtual store while reducing their sense of isolation.

[0240] 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.

[0241] This invention relates to a system that combines a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely and those who have lost their partners, to reduce loneliness and promote mental stability.

[0242] 1. System Configuration

[0243] The system consists of four main components:

[0244] 1. Data Collection Equipment

[0245] 2. Training equipment for generative AI models

[0246] 3. Interactive Interface Device

[0247] 4. Emotional Engine Device

[0248] 2. Operation of the data acquisition device

[0249] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[0250] Terminal: Receives data entered by the user and sends it to the server.

[0251] 3. Training the generative AI model

[0252] Server: Checks the received data and stores it in a database, which is later used to train the generative artificial intelligence model.

[0253] Server: Preprocesses the data, filters out unnecessary data, and extracts necessary features (such as catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative AI model.

[0254] Server: Deploys the trained generative AI model as an API and makes it externally accessible. The model has the ability to generate characteristic responses for partners.

[0255] 4. Operation of the dialogue interface device and emotion engine

[0256] Terminal: The user logs in using a dedicated application and begins a dialogue. The dialogue interface is user-friendly and allows voice and text input.

[0257] User: Talk about memories with their partner or everyday events. For example, type "I'm very tired today."

[0258] Terminal: Receives user input (voice or text) and sends it to the server. The terminal also sends the user's voice tone and text expression to the emotion engine.

[0259] Server: Analyzes user input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if a user says, "I'm tired today," the emotion engine will recognize the user's sense of fatigue and generate an appropriate response.

[0260] Server: Uses feedback from the emotion engine to tailor responses and provide support based on the user's emotional state.

[0261] Terminal: The terminal displays the response received from the server on the user interface, and shows the user the content of the dialogue. The appropriate response is played over the speaker.

[0262] Specific examples

[0263] Specific use cases include the following scenarios:

[0264] Scenario 1: A user complains about everyday life

[0265] User: "My boss has been scolding me a lot today, and I'm really tired."

[0266] Terminal: Receives input and sends it to the server and emotion engine.

[0267] Emotion Engine: Recognizes tiredness from the user's tone of voice.

[0268] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[0269] Terminal: Displays the response and shows it to the user.

[0270] Scenario 2: User discussing travel plans

[0271] User: "I'm thinking about going to the country for the weekend."

[0272] Terminal: Receives input and sends it to the server and emotion engine.

[0273] Emotion engine: Recognizes the user's emotions of excitement and enjoyment.

[0274] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[0275] Terminal: Displays the response and shows it to the user.

[0276] This system allows users to experience the sensation of reconnecting with their deceased partner, easing feelings of loneliness and loss and providing a sense of psychological security. The addition of an emotion engine allows for more personalized and optimized responses, enabling flexible responses tailored to the user's emotional state. This invention is a powerful tool for preventing lonely deaths and contributing to the mental well-being of the elderly, in particular.

[0277] The processing flow will be explained below.

[0278] Step 1:

[0279] User: Launches the dedicated application and enters detailed information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, they enter information about their partner's favorite foods, common phrases, and hobbies.

[0280] Step 2:

[0281] Terminal: Receives the data entered by the user, structures it, and sends it to the server. At this stage, the data is converted into the appropriate format.

[0282] Step 3:

[0283] Server: Checks the received data and stores it in a database, which is used to train generative artificial intelligence models.

[0284] Step 4:

[0285] Server: Preprocesses the data and filters out unnecessary data. The filtered data extracts necessary features (such as catchphrases and behavioral patterns) to form a training dataset.

[0286] Step 5:

[0287] Server: Trains a generative artificial intelligence model using the training dataset. The model learns to generate responses characteristic of the partner.

[0288] Step 6:

[0289] Server: Deploys the trained generative AI model as an API and makes it publicly accessible. The model is used online to generate responses.

[0290] Step 7:

[0291] User: Logs in to the dedicated application and talks about memories with their partner or everyday events. For example, they can type, "I'm very tired today."

[0292] Step 8:

[0293] Terminal: Receives user input (voice or text) and sends it to the server, while also sending the user's voice tone and text expression to the emotion engine.

[0294] Step 9:

[0295] Emotion engine: Analyzes emotions from the user's voice tone and text and provides that information to the server. For example, it detects the user's fatigue and stress level.

[0296] Step 10:

[0297] Server: Analyzes user input and feedback from the emotion engine. Using a generative AI model, it generates an appropriate response based on the user's emotional state. For example, it generates a response such as, "That must have been tough. It's important to take a rest, but don't push yourself too hard."

[0298] Step 11:

[0299] Server: Sends the generated response to the user's terminal. At this time, if necessary, it also generates and sends an audio file of the response.

[0300] Step 12:

[0301] Terminal: The response received from the server is displayed on the user interface, and the content of the dialogue is shown to the user. In the case of a voice response, it is played on the speaker.

[0302] Step 13:

[0303] User: The conversation continues by checking the displayed response and speaking again. For example, you might say, "I'm thinking about going to the countryside this weekend."

[0304] Step 14:

[0305] Emotion engine: Reanalyzes emotions from new user comments and provides that information to the server.

[0306] Step 15:

[0307] Server: Regenerate a response based on the new input and the results of sentiment analysis. For example, generate a response like, "Great! I loved that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[0308] Step 16:

[0309] Terminal: Displays the new response received from the server and shows it to the user.

[0310] This process allows users to have an experience that feels as if they are having a conversation with their deceased partner. The incorporation of an emotion engine optimizes responses based on the user's current emotional state, enabling more natural and empathetic interactions. This alleviates the user's sense of loneliness and loss, providing psychological comfort.

[0311] Example 2

[0312] 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."

[0313] Until now, psychological support methods for elderly people who feel lonely or people who have lost their partners have been limited, and there is a lack of effective means to alleviate feelings of loss and loneliness. In addition, systems that generate responses based on the user's emotional state often have insufficient emotion recognition, making it difficult to provide personalized and optimized responses to users.

[0314] The identification processing 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 receiving data from a user, means for filtering unnecessary data based on the received data and extracting necessary features to train a generative AI model, means for deploying the trained generative AI model as an API and making it accessible from the outside, means for a user to start a dialogue through a dedicated application and receive voice or text input, means for analyzing the user's input and generating a response from the generative AI model based on feedback from the emotion engine, means for sending the generated response to the user's terminal and displaying it as voice or text, and means for alleviating the user's sense of loneliness through dialogue with the user. This makes it possible to generate an individualized and optimized response according to the user's emotional state, effectively alleviating the user's sense of loneliness and loss.

[0315] "User" refers to any individual or end user who uses the System.

[0316] "Means for receiving data" refers to a hardware or software method for accurately and efficiently obtaining information provided by a user.

[0317] "Generative artificial intelligence model" refers to an artificial intelligence system that uses machine learning algorithms to learn specific data patterns and generate responses.

[0318] "Training means" refers to the process steps for utilizing the received data to train an artificial intelligence model and improve the accuracy of the model.

[0319] "Deployment as an API" refers to a method of publishing a trained artificial intelligence model in an externally available format so that other systems and applications can use it.

[0320] "Specialized Application" refers to the specific software application used by a user for interaction and data entry.

[0321] "Means for receiving voice or text input" refers to a method for capturing a user's voice or text data and transmitting it to the system for analysis.

[0322] "Emotion engine" refers to an algorithm or program that analyzes a user's emotional state from their tone of voice or text expression and provides it to an artificial intelligence model.

[0323] "Means for generating a response" refers to the process by which the artificial intelligence model generates an appropriate response based on the user's input data.

[0324] "Terminal" means a computer or mobile device that runs dedicated applications and allows users to input and interact.

[0325] "Measures to reduce loneliness" refers to methods that alleviate users' loneliness and provide psychological support through dialogue and responses via the system.

[0326] MODE FOR CARRYING OUT THE INVENTION

[0327] The present invention is a system that combines a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user, with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely and those who have lost their partner. The main components of the system are as follows:

[0328] 1. Data Collection Equipment

[0329] 2. Training equipment for generative AI models

[0330] 3. Interactive Interface Device

[0331] 4. Emotional Engine Device

[0332] Data acquisition device operation

[0333] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[0334] Terminal: Receives input data in real time and sends it to the server. Examples of dedicated applications are custom applications designed for smartphones and tablets.

[0335] Training generative artificial intelligence models

[0336] Server: Reviewing the data received from the devices and storing it in the appropriate database. The stored data is later used to train the generative artificial intelligence model. An example of a server is a cloud-based database system.

[0337] Server: Preprocesses the data, filters unnecessary data, and extracts necessary features (e.g., catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative artificial intelligence model. This process uses advanced data analysis tools and frameworks (e.g., TensorFlow and PyTorch).

[0338] Server: Deploys trained generative AI models as APIs and makes them publicly accessible, allowing other systems and applications to use the generated models.

[0339] Operation of the dialogue interface device and emotion engine

[0340] Terminal: The user logs in using a dedicated application and begins a dialogue. The dialogue interface is user-friendly and allows voice and text input.

[0341] User: Talk about memories and daily events with their partner. For example, the user enters "I'm very tired today" through the application.

[0342] Terminal: Receives user input (voice or text) and sends it to the server. The terminal also sends the user's voice tone and text expression to the emotion engine.

[0343] Server: Analyzes the user's input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if the user says, "I'm tired today," the emotion engine will recognize the user's sense of fatigue and generate an appropriate response.

[0344] Server: Uses feedback from the emotion engine to tailor responses and provide support based on the user's emotional state.

[0345] Terminal: The response received from the server is displayed on the user interface and the appropriate response is played on the speaker.

[0346] Specific examples

[0347] Scenario 1: A user complains about everyday life

[0348] User: "My boss has been scolding me a lot today, and I'm really tired."

[0349] Terminal: Receives input and sends it to the server and emotion engine.

[0350] Emotion Engine: Recognizes tiredness from the user's tone of voice.

[0351] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[0352] Terminal: Displays the response and shows it to the user.

[0353] Scenario 2: User discussing travel plans

[0354] User: "I'm thinking about going to the country for the weekend."

[0355] Terminal: Receives input and sends it to the server and emotion engine.

[0356] Emotion engine: Recognizes the user's emotions of excitement and enjoyment.

[0357] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[0358] Terminal: Displays the response and shows it to the user.

[0359] In this way, users can enjoy an experience that feels as if they are interacting with their partner again, and gain psychological stability. The addition of an emotion engine allows for more personalized and optimized responses, enabling flexible responses according to the user's emotional state. This system also provides great social value by reducing feelings of loneliness and loss, especially among the elderly, and contributing to psychological stability.

[0360] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0361] Step 1:

[0362] User: Uses a dedicated application to enter information about their partner, such as their behavioral patterns, speaking style, catchphrases, and hobbies.

[0363] Input: Information about your partner's behavioral patterns, speech patterns, catchphrases, and hobbies.

[0364] Output: Structured partner information data.

[0365] Data Processing: Collect information in a consistent format using text fields and multiple choice inputs.

[0366] Specific actions: For example, enter "jogging every morning" in the "activity pattern" field of the application.

[0367] Step 2:

[0368] Terminal: Receives user-entered data in real time and sends it to the server.

[0369] Input: Partner information data entered by the user.

[0370] Output: The data sent to the server.

[0371] Data processing: The data is converted into the appropriate format and into data packets for transmission.

[0372] Specific operation: Converts input data into JSON format and sends a POST request to the specified endpoint on the server.

[0373] Step 3:

[0374] Server: Checks the data received from the device and stores it in the appropriate database.

[0375] Input: Partner information data sent from the device.

[0376] Output: Data stored in the database.

[0377] Data processing: Checking data integrity and generating queries for insertion into the database.

[0378] Specific operation: Parse the received JSON data, generate an SQL query, and insert it into the database.

[0379] Step 4:

[0380] Server: Preprocesses the data, filters out unnecessary data, extracts necessary features, and trains the generative artificial intelligence model.

[0381] Input: Partner information data stored in the database.

[0382] Output: A preprocessed dataset and a trained AI model.

[0383] Data processing: Data cleansing and feature extraction are performed to convert the data into a format suitable for the model.

[0384] Specific operations: Noise removal from text data and data conversion using feature extraction algorithms.

[0385] Step 5:

[0386] Server: Deploys trained live AI models as APIs and makes them accessible externally.

[0387] Input: A trained AI model.

[0388] Output: The deployed API endpoint.

[0389] Data processing: Create a container for deployment and place it on the API server.

[0390] Specific operation: The model is containerized using Docker and deployed to a cloud server such as AWS (registered trademark) or GCP.

[0391] Step 6:

[0392] Terminal: A user logs in with a dedicated application and begins interaction.

[0393] Input: User login information.

[0394] Output: Notification that interaction is ready.

[0395] Data processing: Authenticate login information and start a session.

[0396] Specific operation: Authenticate the user ID and password and issue a session ID.

[0397] Step 7:

[0398] User: Talk about memories and everyday events with your partner.

[0399] Input: User voice or text input.

[0400] Output: Audio or text data.

[0401] Data processing: In the case of voice input, it is converted into text and text data is generated.

[0402] Specific action: Say "I'm very tired today."

[0403] Step 8:

[0404] Terminal: Receives user input (voice or text) and sends it to the server. It also sends the user's voice tone and text expression to the emotion engine.

[0405] Input: User voice or text data.

[0406] Output: Data and sentiment analysis results sent to the server.

[0407] Data processing: In the case of audio data, it is converted into text and sentiment analysis is performed.

[0408] Specific operation: Converts speech into text, adds additional emotional information, and sends it to the server.

[0409] Step 9:

[0410] Server: Analyzes user input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model.

[0411] Input: Text data and emotion information sent from the device.

[0412] Output: The generated response data.

[0413] Data processing: Analyze text data and ask the AI ​​model to generate a response, taking into account emotional information.

[0414] Specific behavior: Based on the input "I'm tired today," generate a "response to being tired."

[0415] Step 10:

[0416] Server: Adjusts the generated responses based on the emotion engine feedback and constructs the final response.

[0417] Input: Response data from the AI ​​model and emotion engine feedback.

[0418] Output: The adjusted final response.

[0419] Data processing: Adjust response data based on emotional information.

[0420] Specific behavior: Create a tailored response: "That's been tough, but I believe you can get through it."

[0421] Step 11:

[0422] Terminal: Displays the response received from the server in a user interface and plays it back as voice or text.

[0423] Input: The final response data from the server.

[0424] Output: The content displayed in the user interface.

[0425] Data processing: Convert text data into speech and display it in the user interface in an appropriate format.

[0426] Specific operation: The response "That must have been tough, but I believe you can get through it" is displayed on the screen and played back using a speech synthesis engine.

[0427] Through this series of steps, users can receive psychological support through dialogue with their partners. The system generates responses based on the user's emotional state, providing personalized and optimized dialogue.

[0428] (Application example 2)

[0429] 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."

[0430] Conventional systems have difficulty responding flexibly to a user's emotional state, and have been unable to provide sufficient psychological or security support, particularly to elderly users and users who feel anxious when alone. While dialogue systems existed to promote mental stability, they lacked the accuracy of emotion recognition and were unable to generate appropriate responses based on the user's emotions. This invention solves these problems by accurately recognizing a user's emotions, adjusting responses based on those emotions, and providing appropriate security support.

[0431] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0432] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model, means for recognizing the user's emotions and adjusting responses based on the emotions, and means for providing security in accordance with the user's emotional state, thereby enabling flexible responses and appropriate security support in accordance with the user's emotional state.

[0433] "User data" refers to personal information provided by users, such as patterns of behavior, speaking style, catchphrases, hobbies, and actions.

[0434] A "generative artificial intelligence model" is a machine learning model that is trained based on received data to generate responses tailored to a specific purpose.

[0435] "Emotion recognition means" refers to technology that analyzes and identifies a user's emotional state from their tone of voice or textual expression.

[0436] "Training" is the process of using machine learning algorithms to train a generative artificial intelligence model based on received data.

[0437] "Means for engaging in dialogue with users" refers to technology that generates appropriate responses to information input by users through a generative artificial intelligence model and engages in dialogue.

[0438] "Reducing feelings of loneliness" means that the goal is to reduce the loneliness and anxiety that users feel when they are alone and to promote psychological stability.

[0439] "Means for adjusting responses" refers to technology that optimizes and appropriates responses generated according to the user's emotional state.

[0440] "Means for providing security" refers to technology that checks the security status of the home according to the user's emotional state and takes measures as necessary.

[0441] This system uses a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user, and combines it with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely or who feel anxious when alone, thereby reducing feelings of loneliness and promoting mental stability.

[0442] The main components of the system are:

[0443] 1. Data Collection Equipment

[0444] Using a dedicated application, users input information about their partner's behavioral patterns, speaking style, catchphrases, hobbies, etc. Specific examples include their partner's favorite foods, common phrases, and information about their hobbies. The device receives this data and sends it to the server.

[0445] 2. Training equipment for generative AI models

[0446] The server trains a generative AI model based on the received data. It filters unnecessary data as preprocessing and extracts necessary features. The trained generative AI model is deployed as an API and made accessible externally. This model has the ability to generate characteristic responses from partners.

[0447] 3. Interactive Interface Device

[0448] The user logs in to a dedicated application and begins a conversation. The conversation interface is user-friendly and allows voice and text input. For example, if the user types "I'm very tired today," the device sends the input to the server.

[0449] 4. Emotional Engine Device

[0450] The server analyzes the user's input and recognizes their emotional state. The emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if the user says, "I'm tired today," the emotion engine recognizes the user's sense of fatigue and generates an appropriate response. The generative AI model then provides a response such as, "You've had a hard day today. Is there anything I can help you with?"

[0451] This system provides users who feel lonely or anxious with an experience that makes them feel as if they are talking to their partner again, providing a sense of psychological security. In certain situations, it can also ensure users' safety by suggesting and implementing security measures.

[0452] Illustrative scenario

[0453] 1. If you hear a noise in the middle of the night

[0454] User: "I hear noises in the middle of the night and it scares me."

[0455] Assistant: "That's scary. Did you notice anything else unusual?"

[0456] The emotion engine recognizes the user's fear and adjusts the response.

[0457] 2. Anxiety when alone

[0458] User: "I feel anxious when I'm alone."

[0459] Assistant: "It's fine. I'm checking the security system. I've checked that the doors and windows are locked. You're safe."

[0460] The emotion engine recognizes the user's anxiety and the security system checks it.

[0461] Prompt Sentence Examples

[0462] "Analyze user input and identify emotional state using an emotion recognition engine. Generate responses using generative AI models. Implement security measures as needed to alleviate user anxiety or fear."

[0463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0464] Step 1:

[0465] User Data Entry

[0466] Through a dedicated application, users input data such as their partner's behavioral patterns, speaking style, catchphrases, and hobbies.

[0467] Input: What the user types into the application (e.g., "My partner's favorite food is pasta. His favorite phrase is 'It's okay.'")

[0468] Output: The terminal sends these input data to the server.

[0469] Step 2:

[0470] Data collection and analysis

[0471] The server receives the data sent from the device and stores it in a database, after which it preprocesses the stored data, filtering out unnecessary information and extracting necessary features.

[0472] Input: Data sent from the terminal

[0473] Output: Preprocessed dataset

[0474] Step 3:

[0475] Training generative artificial intelligence models

[0476] The server uses the preprocessed dataset to train a generative artificial intelligence model, which is then deployed as an API and made publicly accessible.

[0477] Input: Preprocessed dataset

[0478] Output: A trained generative artificial intelligence model

[0479] Step 4:

[0480] Initiating a conversation and receiving user input

[0481] The user logs in to a dedicated application and begins a dialogue. The user provides input by voice or text, which is then sent by the terminal to the server.

[0482] Input: User voice or text input (e.g., "I'm very tired today")

[0483] Output: Sending input data from the terminal to the server

[0484] Step 5:

[0485] Emotion Recognition and Response Generation

[0486] The server receives the user's input and analyzes their emotional state using an emotion engine, and a generative artificial intelligence model generates an appropriate response based on the analysis results.

[0487] Input: User input data, emotion analysis results by emotion engine

[0488] Output: An appropriately tailored response (e.g., "You've had a rough day. Is there anything I can help you with?")

[0489] Step 6:

[0490] Response display and security checks

[0491] The device displays the response received from the server on a user interface and also checks the security system if the user's emotional state exceeds a certain threshold. For example, if the user inputs "I heard a noise in the middle of the night and it scares me," the security system will check the locks on the doors and windows of the house.

[0492] Input: Response data from the server

[0493] Output: The response displayed in the user interface, including the result of any security checks (e.g., "I've checked the locks on my doors and windows. I'm safe.").

[0494] This will enable the realization of a system that can respond flexibly to the user's emotional state and also provide security support as needed.

[0495] 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.

[0496] 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.

[0497] 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.

[0498] [Second embodiment]

[0499] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0500] 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.

[0501] 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).

[0502] 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.

[0503] 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.

[0504] 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).

[0505] 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.

[0506] 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.

[0507] 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.

[0508] 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.

[0509] In the smart glasses 214, 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.

[0510] 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."

[0511] This invention relates to a system that interacts with users using a generative artificial intelligence model that reproduces the behavioral patterns and speech patterns of a partner based on data provided by the user. The system aims to provide psychological support, particularly to elderly people who feel lonely and people who have lost their partners.

[0512] 1. System Configuration

[0513] The system consists of three main components:

[0514] 1. Data Collection Equipment

[0515] 2. Training equipment for generative AI models

[0516] 3. Interactive Interface Device

[0517] 2. Operation of the data acquisition device

[0518] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[0519] Terminal: Receives data entered by the user and sends it to the server.

[0520] 3. Training the generative AI model

[0521] Server: Trains a generative AI model based on the received data, using deep learning and natural language processing techniques to generate a model that replicates the partner's speech and behavior patterns.

[0522] Server: The trained model will be able to generate natural responses as if the partner were real.

[0523] 4. Operation of the interactive interface device

[0524] Terminal: The user accesses the system through a dedicated application and starts a dialogue. The dialogue interface is designed to be user-friendly and allows voice and text input.

[0525] User: Talk about memories with their partner or everyday events. For example, type "I'm very tired today."

[0526] Terminal: Sends input from the user to the server.

[0527] Server: Analyzes the input and uses a generative AI model to generate an appropriate response, such as "That must have been tough. It's important to rest, don't push yourself too hard. What happened?"

[0528] Terminal: Receives the response from the server and displays it on the user interface. The user can confirm the response and continue the conversation by speaking again.

[0529] Specific examples

[0530] Specific use cases include the following scenarios:

[0531] Scenario 1: A user complains about everyday life

[0532] User: "My boss has been scolding me a lot today, and I'm really tired."

[0533] Terminal: Receives input and sends it to the server.

[0534] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[0535] Terminal: Displays the response and shows it to the user.

[0536] Scenario 2: User discussing travel plans

[0537] User: "I'm thinking about going to the country for the weekend."

[0538] Terminal: Receives input and sends it to the server.

[0539] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[0540] Terminal: Displays the response and shows it to the user.

[0541] This system allows users to enjoy the experience of interacting with their deceased partner again, which can alleviate feelings of loneliness and loss and provide a sense of psychological security. This invention is a powerful tool for preventing lonely deaths and contributing to the mental stability of elderly people in particular.

[0542] The processing flow will be explained below.

[0543] Step 1:

[0544] User: Launches the dedicated application and inputs information about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc. For example, they input details about their partner's favorite foods, common phrases, and hobbies.

[0545] Step 2:

[0546] Terminal: Receives input data and sends it to the server, where it is structured and converted into the appropriate format.

[0547] Step 3:

[0548] Server: Checks the received data and stores it in a database, which is later used to train the generative artificial intelligence model.

[0549] Step 4:

[0550] Server: Preprocesses the data, filters out unnecessary data, and extracts necessary features (such as catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative AI model.

[0551] Step 5:

[0552] Server: Deploys the trained generative AI model as an API and makes it externally accessible. The model has the ability to generate characteristic responses for partners.

[0553] Step 6:

[0554] User: Logs in to a dedicated application and starts a conversation. The user simulates a conversation with a partner through voice input or text input.

[0555] Step 7:

[0556] Terminal: Receives user input (voice or text) and sends it to the server, where it is processed in real time.

[0557] Step 8:

[0558] Server: Analyzes user input and passes it to a trained generative AI model to generate an appropriate response. For example, if a user says, "I'm tired today," the server generates a response like, "That must have been tough. It's important to rest, so don't push yourself too hard. What happened?"

[0559] Step 9:

[0560] Server: Sends the generated response to the user's terminal. At this time, if necessary, it also generates and sends an audio file of the response.

[0561] Step 10:

[0562] Terminal: The response received from the server is displayed on the user interface, and the user is informed of the dialogue. If the response is voice, it is played on the speaker.

[0563] Step 11:

[0564] User: Check the displayed response and continue the conversation by speaking again. For example, if the user inputs, "I'm thinking about going to the countryside this weekend," the system will respond with, "Great! I really liked that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[0565] This process allows users to experience the sensation of having a conversation with their deceased partner, which alleviates feelings of loneliness and loss and provides a sense of psychological security.

[0566] Example 1

[0567] 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."

[0568] There is a need for a means to alleviate the loneliness felt by the elderly and those who have lost their partners and to provide psychological support. However, current technology has difficulty replicating an individual's specific behavioral patterns and speaking style, and is therefore unable to recreate a partner. As a result, there is a problem that loneliness is not being alleviated or psychological support is not being provided sufficiently.

[0569] 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.

[0570] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model, means for alleviating the user's sense of loneliness through the interaction with the user, means for collecting detailed information about the partner using a dedicated application, means for transmitting the collected information to the server via a secure communication protocol, means for analyzing the user's input using the trained generative AI model and generating an appropriate response, and means for displaying the generated response on a user interface. This allows the user to enjoy the experience of interacting with their deceased partner again, thereby easing their sense of loneliness and loss and providing a sense of psychological security.

[0571] "User" refers to a person who uses the system to input the partner's behavioral patterns and speaking style and engage in a dialogue.

[0572] "Data" refers to detailed information collected from users about their partners' behavioral patterns, speaking style, catchphrases, hobbies, actions, etc.

[0573] A "generative artificial intelligence model" refers to a model that uses deep learning and natural language processing techniques to reproduce a partner's speaking style and behavior patterns based on received data.

[0574] "Training" refers to the process of training a generative artificial intelligence model based on collected data.

[0575] "Specialized Application" refers to specific software that allows users to enter their partner's details.

[0576] A "secure communication protocol" refers to a communication method for encrypting data and sending it securely to a server.

[0577] "User interface" refers to an interface that allows a user to interact with a system and that supports voice input and text input.

[0578] "Response" refers to a reply generated by a generative artificial intelligence model in response to input from a user.

[0579] "Reducing loneliness" refers to the alleviation of feelings of loneliness and loss that users feel through their interactions with the system.

[0580] This invention relates to a system that interacts with users using a generative artificial intelligence model that reproduces the behavioral patterns and speech patterns of a partner based on data provided by the user. The purpose is to provide psychological support, particularly to elderly people who feel lonely and people who have lost their partners.

[0581] System Configuration

[0582] The system consists of three components:

[0583] 1. Data Collection Equipment

[0584] 2. Training equipment for generative AI models

[0585] 3. Interactive Interface Device

[0586] Data collection equipment

[0587] User: Using a dedicated application, the user enters detailed information about their partner's behavioral patterns, speaking style, catchphrases, hobbies, etc. Specifically, the user enters information about the words their partner frequently used, their daily actions, and their hobbies. For example, the user enters information such as, "My partner liked to drink coffee every morning."

[0588] Terminal: Receives data entered by the user and temporarily stores it in local storage. It then transmits the data to the server using a secure communication protocol (e.g., HTTPS). The data is encrypted before transmission.

[0589] Training device for generative artificial intelligence models

[0590] Server: Analyzes the received data and converts it into an appropriate data structure. Then, using a deep learning framework (e.g., TensorFlow or PyTorch), it trains a generative artificial intelligence model based on the collected data. This process generates a model that can reproduce the partner's speech and behavior patterns.

[0591] Server: Validates the trained model, adjusts and retrains it as needed, and the trained model is capable of generating natural responses that make the partner appear as if they were real.

[0592] Interactive Interface Device

[0593] Terminal: The user accesses the system through a dedicated application and begins a dialogue. The user interface supports both voice and text input. When the user talks about everyday events or memories with their partner, the input is sent to the server.

[0594] Server: Analyzes the input and generates an appropriate response using a generative artificial intelligence model. For example, if a user inputs "I'm very tired today," the server generates a response such as "That must have been tough. It's important to rest, so don't push yourself too hard. What happened?" and sends it back to the device.

[0595] Terminal: By displaying the generated response in the user interface, the user can have an experience as if they were interacting with their deceased partner again.

[0596] Specific examples

[0597] Specific use cases include the following scenarios:

[0598] Scenario 1: A user complains about everyday life

[0599] User: "My boss has been scolding me a lot today, and I'm really tired."

[0600] Terminal: Receives this message and sends it to the server.

[0601] Server: The generative AI model generates a response saying, "That was tough, but you can do it. Is there anything I can help you with?"

[0602] Terminal: Displays the generated response and shows it to the user.

[0603] Scenario 2: User discussing travel plans

[0604] User: "I'm thinking about going to the country for the weekend."

[0605] Terminal: Receives this message and sends it to the server.

[0606] Server: The generative AI model generates a response like, "Great! I really liked that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[0607] Terminal: Displays the generated response and shows it to the user.

[0608] As described above, this system can help users alleviate feelings of loneliness and loss and provide a sense of psychological security.

[0609] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0610] Step 1:

[0611] Data collection

[0612] User: Using a dedicated application, the user inputs detailed information about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc. For example, the input information might include, "My partner liked to drink coffee every morning."

[0613] Input: Partner details entered by the user.

[0614] Output: Raw data temporarily stored on the device.

[0615] Specific actions

[0616] The user enters "Partner's favorite drink: Coffee" into the application and the device stores this information.

[0617] Step 2:

[0618] Data transmission

[0619] Terminal: The terminal sends the received data to the server using a secure communication protocol (e.g., HTTPS). At this time, the data is encrypted before being sent.

[0620] Input: Temporarily saved data.

[0621] Output: The encrypted data is sent to the server.

[0622] Specific actions

[0623] The device encrypts the data stored on it, such as "Partner's favorite drink: Coffee," and sends it to the server.

[0624] Step 3:

[0625] Preparing for data analysis

[0626] Server: Receives the transmitted data and converts it into a data structure for analysis, which is then fed into the machine learning model.

[0627] Input: Encrypted data.

[0628] Output: The parsed data is converted into the appropriate format.

[0629] Specific actions

[0630] The server receives the data "Partner's favorite drink: coffee" and converts the data structure for analysis.

[0631] Step 4:

[0632] Training an AI model

[0633] Server: The transformed data is used to train the generative AI model, using a deep learning framework (e.g., TensorFlow or PyTorch).

[0634] Input: Parsed data.

[0635] Output: A trained generative artificial intelligence model.

[0636] Specific actions

[0637] The server uses multiple data points, such as "partner's favorite drink: coffee," to train the AI ​​model and learn the partner's behavioral patterns.

[0638] Step 5:

[0639] Starting a conversation

[0640] User: Launches a dedicated application and accesses the system. The user can enter messages or questions to start a dialogue in the dialogue interface of the application.

[0641] Input: A message or question typed by the user.

[0642] Output: The entered message or question is sent to the server.

[0643] Specific actions

[0644] The user types "How was your day?" into the conversational interface and the message is sent to the server.

[0645] Step 6:

[0646] Response Generation

[0647] Server: Analyzes user input and generates appropriate responses using a trained generative artificial intelligence model.

[0648] Input: A message or question typed by the user.

[0649] Output: The generated response.

[0650] Specific actions

[0651] The server parses the user's question "How was your day?" and generates a response such as "It was a peaceful day today. I was thinking of you."

[0652] Step 7:

[0653] Viewing the response

[0654] Terminal: The generated response is displayed in a user interface and provided to the user.

[0655] Input: The response sent by the server.

[0656] Output: The response displayed in the user interface.

[0657] Specific actions

[0658] The device displays the response "It was a peaceful day today. I was thinking of you" in the user interface.

[0659] These steps allow users to recreate conversations with their deceased partner, helping to alleviate feelings of loneliness and loss and providing a sense of psychological security.

[0660] (Application example 1)

[0661] 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."

[0662] Currently, there are interactive systems that can help elderly people who feel lonely or people who have lost their partners to reduce their feelings of loneliness. However, these systems cannot provide adequate support to users who often feel lonely in their daily lives, especially when shopping. In particular, there are no systems that allow users to enjoy shopping while interacting with their partners during the shopping experience in a virtual store. Therefore, there is a need for an interactive system that can provide advice on purchasing behavior in a virtual store.

[0663] 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.

[0664] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model in a virtual environment, means for reducing the user's sense of loneliness through the interaction with the user, and means for allowing the user to consult with a partner about purchasing behavior in a virtual store. This makes it possible for users to interact with a partner while consulting with them about purchasing behavior in the virtual store, thereby reducing feelings of loneliness in their daily lives.

[0665] A "user" is an individual who wishes to use the system to reduce feelings of loneliness and enjoy a shopping experience in a virtual store.

[0666] "Data" refers to characteristic information entered by the user about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc.

[0667] A "generative artificial intelligence model" is an artificial intelligence system that is trained based on data provided by the user to reproduce the behavior and speaking style of a partner.

[0668] "Virtual environment" refers to a digital space where users can have a shopping experience in a virtual space.

[0669] "Purchasing behavior" refers to the series of actions a user takes to select and purchase a product in a virtual store.

[0670] "Dialogue" refers to the act of communication between a user and a system via a generative artificial intelligence model.

[0671] This invention is an interactive system for reducing users' feelings of loneliness and supporting their shopping experience in a virtual store. The system of the present invention mainly consists of the following three elements:

[0672] 1. Data Collection Equipment

[0673] 2. Training equipment for generative AI models

[0674] 3. Interactive Interface Device

[0675] 1. Data Collection Equipment

[0676] Using a dedicated application, users can input information about their partner's behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, they can enter details about their partner's favorite foods, common phrases, and hobbies. This data is then sent to the server via the device.

[0677] 2. Training a generative AI model

[0678] The server trains a generative AI model based on the received data. This training process uses deep learning and natural language processing techniques. The result is a model that reproduces the partner's speech and behavior patterns. This model can generate natural responses, as if the partner were actually alive.

[0679] 3. Interactive Interface Device

[0680] Users access the system through a dedicated application and begin interacting with it. The user-friendly interactive interface allows for voice and text input. Users can consult with an AI partner while selecting products in the virtual store.

[0681] For example, consider the following scenario:

[0682] Scenario 1: A user is asking for advice about a dress.

[0683] User: "What do you think of this dress?"

[0684] Terminal: Receives input and sends it to the server.

[0685] Server: The generative AI model generates a response: "That's great, let's buy it!"

[0686] This system allows users to enjoy an experience that feels like they are conversing with a partner while discussing purchasing behavior in a virtual store, which can alleviate feelings of loneliness and loss and provide a sense of psychological security.

[0687] To implement this invention, the following hardware and software are used:

[0688] Hardware: smartphone, head-mounted display, server

[0689] Software: OpenAI API (generative AI model), virtual store application

[0690] Example prompt sentence:

[0691] User input: "What do you think of this dress?"

[0692] Prompt statement:

[0693] A phrase or catchphrase your partner often uses:

[0694] "That's great, let's buy it!"

[0695] "I think it suits you."

[0696] Foods and hobbies your partner liked:

[0697] "I like yakiniku"

[0698] "I love traveling"

[0699] User Typed: What do you think of this dress?

[0700] As an AI playing the role of your partner, respond using your partner's quirks and preferences, such as:

[0701] This allows users to enjoy a shopping experience in a virtual store, while also reducing feelings of loneliness and receiving psychological support.

[0702] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0703] Step 1:

[0704] Using a dedicated application, users input characteristic information about their partner, such as their behavioral patterns, speaking style, catchphrases, and hobbies. The input data is structured by the device and sent to the server. The input is text data about the partner's preferences and behavioral patterns, and the output is the data sent to the server.

[0705] Step 2:

[0706] The server trains a generative AI model based on the received data. The input is structured data sent by the user, and the output is a trained generative AI model. The server analyzes the data using deep learning algorithms to generate a model that replicates the partner's speech and behavior patterns.

[0707] Step 3:

[0708] The user starts shopping in a virtual environment using a virtual store application. At this time, the user consults the generative AI model about product selection and purchasing behavior. The input is the user's question or inquiry, and the output is text data sent from the terminal to the server.

[0709] Step 4:

[0710] The server uses the text data received from the user as a prompt and generates an appropriate response using a generative artificial intelligence model. The input is the question or inquiry sent by the user, and the output is the response text generated by the AI. The server generates a prompt sentence and applies it to the AI ​​model to obtain a response.

[0711] Step 5:

[0712] The terminal receives the response from the server and displays it on the user interface. The input is the response text sent from the server, and the output is the response displayed on the user interface. The user can confirm this response and continue the dialogue again.

[0713] Step 6:

[0714] If the user has another question or inquiry, the device again sends the input to the server. This continues the dialogue, allowing the user to enjoy a series of purchasing actions while receiving advice from their AI partner. The input and output process proceeds by repeating steps 3 to 5.

[0715] This process flow allows users to enrich their shopping experience in the virtual store while reducing their sense of isolation.

[0716] 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.

[0717] This invention relates to a system that combines a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely and those who have lost their partners, to reduce loneliness and promote mental stability.

[0718] 1. System Configuration

[0719] The system consists of four main components:

[0720] 1. Data Collection Equipment

[0721] 2. Training equipment for generative AI models

[0722] 3. Interactive Interface Device

[0723] 4. Emotional Engine Device

[0724] 2. Operation of the data acquisition device

[0725] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[0726] Terminal: Receives data entered by the user and sends it to the server.

[0727] 3. Training the generative AI model

[0728] Server: Checks the received data and stores it in a database, which is later used to train the generative artificial intelligence model.

[0729] Server: Preprocesses the data, filters out unnecessary data, and extracts necessary features (such as catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative AI model.

[0730] Server: Deploys the trained generative AI model as an API and makes it externally accessible. The model has the ability to generate characteristic responses for partners.

[0731] 4. Operation of the dialogue interface device and emotion engine

[0732] Terminal: The user logs in using a dedicated application and begins a dialogue. The dialogue interface is user-friendly and allows voice and text input.

[0733] User: Talk about memories with their partner or everyday events. For example, type "I'm very tired today."

[0734] Terminal: Receives user input (voice or text) and sends it to the server. The terminal also sends the user's voice tone and text expression to the emotion engine.

[0735] Server: Analyzes user input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if a user says, "I'm tired today," the emotion engine will recognize the user's sense of fatigue and generate an appropriate response.

[0736] Server: Uses feedback from the emotion engine to tailor responses and provide support based on the user's emotional state.

[0737] Terminal: The terminal displays the response received from the server on the user interface, and shows the user the content of the dialogue. The appropriate response is played over the speaker.

[0738] Specific examples

[0739] Specific use cases include the following scenarios:

[0740] Scenario 1: A user complains about everyday life

[0741] User: "My boss has been scolding me a lot today, and I'm really tired."

[0742] Terminal: Receives input and sends it to the server and emotion engine.

[0743] Emotion Engine: Recognizes tiredness from the user's tone of voice.

[0744] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[0745] Terminal: Displays the response and shows it to the user.

[0746] Scenario 2: User discussing travel plans

[0747] User: "I'm thinking about going to the country for the weekend."

[0748] Terminal: Receives input and sends it to the server and emotion engine.

[0749] Emotion engine: Recognizes the user's emotions of excitement and enjoyment.

[0750] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[0751] Terminal: Displays the response and shows it to the user.

[0752] This system allows users to experience the sensation of reconnecting with their deceased partner, easing feelings of loneliness and loss and providing a sense of psychological security. The addition of an emotion engine allows for more personalized and optimized responses, enabling flexible responses tailored to the user's emotional state. This invention is a powerful tool for preventing lonely deaths and contributing to the mental well-being of the elderly, in particular.

[0753] The processing flow will be explained below.

[0754] Step 1:

[0755] User: Launches the dedicated application and enters detailed information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, they enter information about their partner's favorite foods, common phrases, and hobbies.

[0756] Step 2:

[0757] Terminal: Receives the data entered by the user, structures it, and sends it to the server. At this stage, the data is converted into the appropriate format.

[0758] Step 3:

[0759] Server: Checks the received data and stores it in a database, which is used to train generative artificial intelligence models.

[0760] Step 4:

[0761] Server: Preprocesses the data and filters out unnecessary data. The filtered data extracts necessary features (such as catchphrases and behavioral patterns) to form a training dataset.

[0762] Step 5:

[0763] Server: Trains a generative artificial intelligence model using the training dataset. The model learns to generate responses characteristic of the partner.

[0764] Step 6:

[0765] Server: Deploys the trained generative AI model as an API and makes it publicly accessible. The model is used online to generate responses.

[0766] Step 7:

[0767] User: Logs in to the dedicated application and talks about memories with their partner or everyday events. For example, they can type, "I'm very tired today."

[0768] Step 8:

[0769] Terminal: Receives user input (voice or text) and sends it to the server, while also sending the user's voice tone and text expression to the emotion engine.

[0770] Step 9:

[0771] Emotion engine: Analyzes emotions from the user's voice tone and text and provides that information to the server. For example, it detects the user's fatigue and stress level.

[0772] Step 10:

[0773] Server: Analyzes user input and feedback from the emotion engine. Using a generative AI model, it generates an appropriate response based on the user's emotional state. For example, it generates a response such as, "That must have been tough. It's important to take a rest, but don't push yourself too hard."

[0774] Step 11:

[0775] Server: Sends the generated response to the user's terminal. At this time, if necessary, it also generates and sends an audio file of the response.

[0776] Step 12:

[0777] Terminal: The response received from the server is displayed on the user interface, and the content of the dialogue is shown to the user. In the case of a voice response, it is played on the speaker.

[0778] Step 13:

[0779] User: The conversation continues by checking the displayed response and speaking again. For example, you might say, "I'm thinking about going to the countryside this weekend."

[0780] Step 14:

[0781] Emotion engine: Reanalyzes emotions from new user comments and provides that information to the server.

[0782] Step 15:

[0783] Server: Regenerate a response based on the new input and the results of sentiment analysis. For example, generate a response like, "Great! I loved that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[0784] Step 16:

[0785] Terminal: Displays the new response received from the server and shows it to the user.

[0786] This process allows users to have an experience that feels as if they are having a conversation with their deceased partner. The incorporation of an emotion engine optimizes responses based on the user's current emotional state, enabling more natural and empathetic interactions. This alleviates the user's sense of loneliness and loss, providing psychological comfort.

[0787] Example 2

[0788] 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."

[0789] Until now, psychological support methods for elderly people who feel lonely or people who have lost their partners have been limited, and there is a lack of effective means to alleviate feelings of loss and loneliness. In addition, systems that generate responses based on the user's emotional state often have insufficient emotion recognition, making it difficult to provide personalized and optimized responses to users.

[0790] The identification processing 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 receiving data from a user, means for filtering unnecessary data based on the received data and extracting necessary features to train a generative AI model, means for deploying the trained generative AI model as an API and making it accessible from the outside, means for a user to start a dialogue through a dedicated application and receive voice or text input, means for analyzing the user's input and generating a response from the generative AI model based on feedback from the emotion engine, means for sending the generated response to the user's terminal and displaying it as voice or text, and means for alleviating the user's sense of loneliness through dialogue with the user. This makes it possible to generate an individualized and optimized response according to the user's emotional state, effectively alleviating the user's sense of loneliness and loss.

[0791] "User" refers to any individual or end user who uses the System.

[0792] "Means for receiving data" refers to a hardware or software method for accurately and efficiently obtaining information provided by a user.

[0793] "Generative artificial intelligence model" refers to an artificial intelligence system that uses machine learning algorithms to learn specific data patterns and generate responses.

[0794] "Training means" refers to the process steps for utilizing the received data to train an artificial intelligence model and improve the accuracy of the model.

[0795] "Deployment as an API" refers to a method of publishing a trained artificial intelligence model in an externally available format so that other systems and applications can use it.

[0796] "Specialized Application" refers to the specific software application used by a user for interaction and data entry.

[0797] "Means for receiving voice or text input" refers to a method for capturing a user's voice or text data and transmitting it to the system for analysis.

[0798] "Emotion engine" refers to an algorithm or program that analyzes a user's emotional state from their tone of voice or text expression and provides it to an artificial intelligence model.

[0799] "Means for generating a response" refers to the process by which the artificial intelligence model generates an appropriate response based on the user's input data.

[0800] "Terminal" means a computer or mobile device that runs dedicated applications and allows users to input and interact.

[0801] "Measures to reduce loneliness" refers to methods that alleviate users' loneliness and provide psychological support through dialogue and responses via the system.

[0802] MODE FOR CARRYING OUT THE INVENTION

[0803] The present invention is a system that combines a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user, with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely and those who have lost their partner. The main components of the system are as follows:

[0804] 1. Data Collection Equipment

[0805] 2. Training equipment for generative AI models

[0806] 3. Interactive Interface Device

[0807] 4. Emotional Engine Device

[0808] Data acquisition device operation

[0809] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[0810] Terminal: Receives input data in real time and sends it to the server. Examples of dedicated applications are custom applications designed for smartphones and tablets.

[0811] Training generative artificial intelligence models

[0812] Server: Reviewing the data received from the devices and storing it in the appropriate database. The stored data is later used to train the generative artificial intelligence model. An example of a server is a cloud-based database system.

[0813] Server: Preprocesses the data, filters unnecessary data, and extracts necessary features (e.g., catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative artificial intelligence model. This process uses advanced data analysis tools and frameworks (e.g., TensorFlow and PyTorch).

[0814] Server: Deploys trained generative AI models as APIs and makes them publicly accessible, allowing other systems and applications to use the generated models.

[0815] Operation of the dialogue interface device and emotion engine

[0816] Terminal: The user logs in using a dedicated application and begins a dialogue. The dialogue interface is user-friendly and allows voice and text input.

[0817] User: Talk about memories and daily events with their partner. For example, the user enters "I'm very tired today" through the application.

[0818] Terminal: Receives user input (voice or text) and sends it to the server. The terminal also sends the user's voice tone and text expression to the emotion engine.

[0819] Server: Analyzes the user's input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if the user says, "I'm tired today," the emotion engine will recognize the user's sense of fatigue and generate an appropriate response.

[0820] Server: Uses feedback from the emotion engine to tailor responses and provide support based on the user's emotional state.

[0821] Terminal: The response received from the server is displayed on the user interface and the appropriate response is played on the speaker.

[0822] Specific examples

[0823] Scenario 1: A user complains about everyday life

[0824] User: "My boss has been scolding me a lot today, and I'm really tired."

[0825] Terminal: Receives input and sends it to the server and emotion engine.

[0826] Emotion Engine: Recognizes tiredness from the user's tone of voice.

[0827] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[0828] Terminal: Displays the response and shows it to the user.

[0829] Scenario 2: User discussing travel plans

[0830] User: "I'm thinking about going to the country for the weekend."

[0831] Terminal: Receives input and sends it to the server and emotion engine.

[0832] Emotion engine: Recognizes the user's emotions of excitement and enjoyment.

[0833] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[0834] Terminal: Displays the response and shows it to the user.

[0835] In this way, users can enjoy an experience that feels as if they are interacting with their partner again, and gain psychological stability. The addition of an emotion engine allows for more personalized and optimized responses, enabling flexible responses according to the user's emotional state. This system also provides great social value by reducing feelings of loneliness and loss, especially among the elderly, and contributing to psychological stability.

[0836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0837] Step 1:

[0838] User: Uses a dedicated application to enter information about their partner, such as their behavioral patterns, speaking style, catchphrases, and hobbies.

[0839] Input: Information about your partner's behavioral patterns, speech patterns, catchphrases, and hobbies.

[0840] Output: Structured partner information data.

[0841] Data Processing: Collect information in a consistent format using text fields and multiple choice inputs.

[0842] Specific actions: For example, enter "jogging every morning" in the "activity pattern" field of the application.

[0843] Step 2:

[0844] Terminal: Receives user-entered data in real time and sends it to the server.

[0845] Input: Partner information data entered by the user.

[0846] Output: The data sent to the server.

[0847] Data processing: The data is converted into the appropriate format and into data packets for transmission.

[0848] Specific operation: Converts input data into JSON format and sends a POST request to the specified endpoint on the server.

[0849] Step 3:

[0850] Server: Checks the data received from the device and stores it in the appropriate database.

[0851] Input: Partner information data sent from the device.

[0852] Output: Data stored in the database.

[0853] Data processing: Checking data integrity and generating queries for insertion into the database.

[0854] Specific operation: Parse the received JSON data, generate an SQL query, and insert it into the database.

[0855] Step 4:

[0856] Server: Preprocesses the data, filters out unnecessary data, extracts necessary features, and trains the generative artificial intelligence model.

[0857] Input: Partner information data stored in the database.

[0858] Output: A preprocessed dataset and a trained AI model.

[0859] Data processing: Data cleansing and feature extraction are performed to convert the data into a format suitable for the model.

[0860] Specific operations: Noise removal from text data and data conversion using feature extraction algorithms.

[0861] Step 5:

[0862] Server: Deploys trained live AI models as APIs and makes them accessible externally.

[0863] Input: A trained AI model.

[0864] Output: The deployed API endpoint.

[0865] Data processing: Create a container for deployment and place it on the API server.

[0866] Specific operation: The model is containerized using Docker and deployed to a cloud server such as AWS or GCP.

[0867] Step 6:

[0868] Terminal: A user logs in with a dedicated application and begins interaction.

[0869] Input: User login information.

[0870] Output: Notification that interaction is ready.

[0871] Data processing: Authenticate login information and start a session.

[0872] Specific operation: Authenticate the user ID and password and issue a session ID.

[0873] Step 7:

[0874] User: Talk about memories and everyday events with your partner.

[0875] Input: User voice or text input.

[0876] Output: Audio or text data.

[0877] Data processing: In the case of voice input, it is converted into text and text data is generated.

[0878] Specific action: Say "I'm very tired today."

[0879] Step 8:

[0880] Terminal: Receives user input (voice or text) and sends it to the server. It also sends the user's voice tone and text expression to the emotion engine.

[0881] Input: User voice or text data.

[0882] Output: Data and sentiment analysis results sent to the server.

[0883] Data processing: In the case of audio data, it is converted into text and sentiment analysis is performed.

[0884] Specific operation: Converts speech into text, adds additional emotional information, and sends it to the server.

[0885] Step 9:

[0886] Server: Analyzes user input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model.

[0887] Input: Text data and emotion information sent from the device.

[0888] Output: The generated response data.

[0889] Data processing: Analyze text data and ask the AI ​​model to generate a response, taking into account emotional information.

[0890] Specific behavior: Based on the input "I'm tired today," generate a "response to being tired."

[0891] Step 10:

[0892] Server: Adjusts the generated responses based on the emotion engine feedback and constructs the final response.

[0893] Input: Response data from the AI ​​model and emotion engine feedback.

[0894] Output: The adjusted final response.

[0895] Data processing: Adjust response data based on emotional information.

[0896] Specific behavior: Create a tailored response: "That's been tough, but I believe you can get through it."

[0897] Step 11:

[0898] Terminal: Displays the response received from the server in a user interface and plays it back as voice or text.

[0899] Input: The final response data from the server.

[0900] Output: The content displayed in the user interface.

[0901] Data processing: Convert text data into speech and display it in the user interface in an appropriate format.

[0902] Specific operation: The response "That must have been tough, but I believe you can get through it" is displayed on the screen and played back using a speech synthesis engine.

[0903] Through this series of steps, users can receive psychological support through dialogue with their partners. The system generates responses based on the user's emotional state, providing personalized and optimized dialogue.

[0904] (Application example 2)

[0905] 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."

[0906] Conventional systems have difficulty responding flexibly to a user's emotional state, and have been unable to provide sufficient psychological or security support, particularly to elderly users and users who feel anxious when alone. While dialogue systems existed to promote mental stability, they lacked the accuracy of emotion recognition and were unable to generate appropriate responses based on the user's emotions. This invention solves these problems by accurately recognizing a user's emotions, adjusting responses based on those emotions, and providing appropriate security support.

[0907] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0908] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model, means for recognizing the user's emotions and adjusting responses based on the emotions, and means for providing security in accordance with the user's emotional state, thereby enabling flexible responses and appropriate security support in accordance with the user's emotional state.

[0909] "User data" refers to personal information provided by users, such as patterns of behavior, speaking style, catchphrases, hobbies, and actions.

[0910] A "generative artificial intelligence model" is a machine learning model that is trained based on received data to generate responses tailored to a specific purpose.

[0911] "Emotion recognition means" refers to technology that analyzes and identifies a user's emotional state from their tone of voice or textual expression.

[0912] "Training" is the process of using machine learning algorithms to train a generative artificial intelligence model based on received data.

[0913] "Means for engaging in dialogue with users" refers to technology that generates appropriate responses to information input by users through a generative artificial intelligence model and engages in dialogue.

[0914] "Reducing feelings of loneliness" means that the goal is to reduce the loneliness and anxiety that users feel when they are alone and to promote psychological stability.

[0915] "Means for adjusting responses" refers to technology that optimizes and appropriates responses generated according to the user's emotional state.

[0916] "Means for providing security" refers to technology that checks the security status of the home according to the user's emotional state and takes measures as necessary.

[0917] This system uses a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user, and combines it with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely or who feel anxious when alone, thereby reducing feelings of loneliness and promoting mental stability.

[0918] The main components of the system are:

[0919] 1. Data Collection Equipment

[0920] Using a dedicated application, users input information about their partner's behavioral patterns, speaking style, catchphrases, hobbies, etc. Specific examples include their partner's favorite foods, common phrases, and information about their hobbies. The device receives this data and sends it to the server.

[0921] 2. Training equipment for generative AI models

[0922] The server trains a generative AI model based on the received data. It filters unnecessary data as preprocessing and extracts necessary features. The trained generative AI model is deployed as an API and made accessible externally. This model has the ability to generate characteristic responses from partners.

[0923] 3. Interactive Interface Device

[0924] The user logs in to a dedicated application and begins a conversation. The conversation interface is user-friendly and allows voice and text input. For example, if the user types "I'm very tired today," the device sends the input to the server.

[0925] 4. Emotional Engine Device

[0926] The server analyzes the user's input and recognizes their emotional state. The emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if the user says, "I'm tired today," the emotion engine recognizes the user's sense of fatigue and generates an appropriate response. The generative AI model then provides a response such as, "You've had a hard day today. Is there anything I can help you with?"

[0927] This system provides users who feel lonely or anxious with an experience that makes them feel as if they are talking to their partner again, providing a sense of psychological security. In certain situations, it can also ensure users' safety by suggesting and implementing security measures.

[0928] Illustrative scenario

[0929] 1. If you hear a noise in the middle of the night

[0930] User: "I hear noises in the middle of the night and it scares me."

[0931] Assistant: "That's scary. Did you notice anything else unusual?"

[0932] The emotion engine recognizes the user's fear and adjusts the response.

[0933] 2. Anxiety when alone

[0934] User: "I feel anxious when I'm alone."

[0935] Assistant: "It's fine. I'm checking the security system. I've checked that the doors and windows are locked. You're safe."

[0936] The emotion engine recognizes the user's anxiety and the security system checks it.

[0937] Prompt Sentence Examples

[0938] "Analyze user input and identify emotional state using an emotion recognition engine. Generate responses using generative AI models. Implement security measures as needed to alleviate user anxiety or fear."

[0939] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0940] Step 1:

[0941] User Data Entry

[0942] Through a dedicated application, users input data such as their partner's behavioral patterns, speaking style, catchphrases, and hobbies.

[0943] Input: What the user types into the application (e.g., "My partner's favorite food is pasta. His favorite phrase is 'It's okay.'")

[0944] Output: The terminal sends these input data to the server.

[0945] Step 2:

[0946] Data collection and analysis

[0947] The server receives the data sent from the device and stores it in a database, after which it preprocesses the stored data, filtering out unnecessary information and extracting necessary features.

[0948] Input: Data sent from the terminal

[0949] Output: Preprocessed dataset

[0950] Step 3:

[0951] Training generative artificial intelligence models

[0952] The server uses the preprocessed dataset to train a generative artificial intelligence model, which is then deployed as an API and made publicly accessible.

[0953] Input: Preprocessed dataset

[0954] Output: A trained generative artificial intelligence model

[0955] Step 4:

[0956] Initiating a conversation and receiving user input

[0957] The user logs in to a dedicated application and begins a dialogue. The user provides input by voice or text, which is then sent by the terminal to the server.

[0958] Input: User voice or text input (e.g., "I'm very tired today")

[0959] Output: Sending input data from the terminal to the server

[0960] Step 5:

[0961] Emotion Recognition and Response Generation

[0962] The server receives the user's input and analyzes their emotional state using an emotion engine, and a generative artificial intelligence model generates an appropriate response based on the analysis results.

[0963] Input: User input data, emotion analysis results by emotion engine

[0964] Output: An appropriately tailored response (e.g., "You've had a rough day. Is there anything I can help you with?")

[0965] Step 6:

[0966] Response display and security checks

[0967] The device displays the response received from the server on a user interface and also checks the security system if the user's emotional state exceeds a certain threshold. For example, if the user inputs "I heard a noise in the middle of the night and it scares me," the security system will check the locks on the doors and windows of the house.

[0968] Input: Response data from the server

[0969] Output: The response displayed in the user interface, and the result of any security checks (e.g., "I've checked the locks on my doors and windows. They're safe.").

[0970] This will enable the realization of a system that can respond flexibly to the user's emotional state and also provide security support as needed.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] [Third embodiment]

[0975] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0976] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0977] 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).

[0978] 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.

[0979] 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.

[0980] 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).

[0981] 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.

[0982] 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.

[0983] 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.

[0984] 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.

[0985] 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.

[0986] 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."

[0987] This invention relates to a system that interacts with users using a generative artificial intelligence model that reproduces the behavioral patterns and speech patterns of a partner based on data provided by the user. The system aims to provide psychological support, particularly to elderly people who feel lonely and people who have lost their partners.

[0988] 1. System Configuration

[0989] The system consists of three main components:

[0990] 1. Data Collection Equipment

[0991] 2. Training equipment for generative AI models

[0992] 3. Interactive Interface Device

[0993] 2. Operation of the data acquisition device

[0994] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[0995] Terminal: Receives data entered by the user and sends it to the server.

[0996] 3. Training the generative AI model

[0997] Server: Trains a generative AI model based on the received data, using deep learning and natural language processing techniques to generate a model that replicates the partner's speech and behavior patterns.

[0998] Server: The trained model will be able to generate natural responses as if the partner were real.

[0999] 4. Operation of the interactive interface device

[1000] Terminal: The user accesses the system through a dedicated application and starts a dialogue. The dialogue interface is designed to be user-friendly and allows voice and text input.

[1001] User: Talk about memories with their partner or everyday events. For example, type "I'm very tired today."

[1002] Terminal: Sends input from the user to the server.

[1003] Server: Analyzes the input and uses a generative AI model to generate an appropriate response, such as "That must have been tough. It's important to rest, don't push yourself too hard. What happened?"

[1004] Terminal: Receives the response from the server and displays it on the user interface. The user can confirm the response and continue the conversation by speaking again.

[1005] Specific examples

[1006] Specific use cases include the following scenarios:

[1007] Scenario 1: A user complains about everyday life

[1008] User: "My boss has been scolding me a lot today, and I'm really tired."

[1009] Terminal: Receives input and sends it to the server.

[1010] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[1011] Terminal: Displays the response and shows it to the user.

[1012] Scenario 2: User discussing travel plans

[1013] User: "I'm thinking about going to the country for the weekend."

[1014] Terminal: Receives input and sends it to the server.

[1015] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[1016] Terminal: Displays the response and shows it to the user.

[1017] This system allows users to enjoy the experience of interacting with their deceased partner again, which can alleviate feelings of loneliness and loss and provide a sense of psychological security. This invention is a powerful tool for preventing lonely deaths and contributing to the mental stability of elderly people in particular.

[1018] The processing flow will be explained below.

[1019] Step 1:

[1020] User: Launches the dedicated application and inputs information about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc. For example, they input details about their partner's favorite foods, common phrases, and hobbies.

[1021] Step 2:

[1022] Terminal: Receives input data and sends it to the server, where it is structured and converted into the appropriate format.

[1023] Step 3:

[1024] Server: Checks the received data and stores it in a database, which is later used to train the generative artificial intelligence model.

[1025] Step 4:

[1026] Server: Preprocesses the data, filters out unnecessary data, and extracts necessary features (such as catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative AI model.

[1027] Step 5:

[1028] Server: Deploys the trained generative AI model as an API and makes it externally accessible. The model has the ability to generate characteristic responses for partners.

[1029] Step 6:

[1030] User: Logs in to a dedicated application and starts a conversation. The user simulates a conversation with a partner through voice input or text input.

[1031] Step 7:

[1032] Terminal: Receives user input (voice or text) and sends it to the server, where it is processed in real time.

[1033] Step 8:

[1034] Server: Analyzes user input and passes it to a trained generative AI model to generate an appropriate response. For example, if a user says, "I'm tired today," the server generates a response like, "That must have been tough. It's important to rest, so don't push yourself too hard. What happened?"

[1035] Step 9:

[1036] Server: Sends the generated response to the user's terminal. At this time, if necessary, it also generates and sends an audio file of the response.

[1037] Step 10:

[1038] Terminal: The response received from the server is displayed on the user interface, and the user is informed of the dialogue. If the response is voice, it is played on the speaker.

[1039] Step 11:

[1040] User: Check the displayed response and continue the conversation by speaking again. For example, if the user inputs, "I'm thinking about going to the countryside this weekend," the system will respond with, "Great! I really liked that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[1041] This process allows users to experience the sensation of having a conversation with their deceased partner, which alleviates feelings of loneliness and loss and provides a sense of psychological security.

[1042] Example 1

[1043] 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."

[1044] There is a need for a means to alleviate the loneliness felt by the elderly and those who have lost their partners and to provide psychological support. However, current technology has difficulty replicating an individual's specific behavioral patterns and speaking style, and is therefore unable to recreate a partner. As a result, there is a problem that loneliness is not being alleviated or psychological support is not being provided sufficiently.

[1045] 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.

[1046] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model, means for alleviating the user's sense of loneliness through the interaction with the user, means for collecting detailed information about the partner using a dedicated application, means for transmitting the collected information to the server via a secure communication protocol, means for analyzing the user's input using the trained generative AI model and generating an appropriate response, and means for displaying the generated response on a user interface. This allows the user to enjoy the experience of interacting with their deceased partner again, thereby easing their sense of loneliness and loss and providing a sense of psychological security.

[1047] "User" refers to a person who uses the system to input the partner's behavioral patterns and speaking style and engage in a dialogue.

[1048] "Data" refers to detailed information collected from users about their partners' behavioral patterns, speaking style, catchphrases, hobbies, actions, etc.

[1049] A "generative artificial intelligence model" refers to a model that uses deep learning and natural language processing techniques to reproduce a partner's speaking style and behavior patterns based on received data.

[1050] "Training" refers to the process of training a generative artificial intelligence model based on collected data.

[1051] "Specialized Application" refers to specific software that allows users to enter their partner's details.

[1052] A "secure communication protocol" refers to a communication method for encrypting data and sending it securely to a server.

[1053] "User interface" refers to an interface that allows a user to interact with a system and that supports voice input and text input.

[1054] "Response" refers to a reply generated by a generative artificial intelligence model in response to input from a user.

[1055] "Reducing loneliness" refers to the alleviation of feelings of loneliness and loss that users feel through their interactions with the system.

[1056] This invention relates to a system that interacts with users using a generative artificial intelligence model that reproduces the behavioral patterns and speech patterns of a partner based on data provided by the user. The purpose is to provide psychological support, particularly to elderly people who feel lonely and people who have lost their partners.

[1057] System Configuration

[1058] The system consists of three components:

[1059] 1. Data Collection Equipment

[1060] 2. Training equipment for generative AI models

[1061] 3. Interactive Interface Device

[1062] Data collection equipment

[1063] User: Using a dedicated application, the user enters detailed information about their partner's behavioral patterns, speaking style, catchphrases, hobbies, etc. Specifically, the user enters information about the words their partner frequently used, their daily actions, and their hobbies. For example, the user enters information such as, "My partner liked to drink coffee every morning."

[1064] Terminal: Receives data entered by the user and temporarily stores it in local storage. It then transmits the data to the server using a secure communication protocol (e.g., HTTPS). The data is encrypted before transmission.

[1065] Training device for generative artificial intelligence models

[1066] Server: Analyzes the received data and converts it into an appropriate data structure. Then, using a deep learning framework (e.g., TensorFlow or PyTorch), it trains a generative artificial intelligence model based on the collected data. This process generates a model that can reproduce the partner's speech and behavior patterns.

[1067] Server: Validates the trained model, adjusts and retrains it as needed, and the trained model is capable of generating natural responses that make the partner appear as if they were real.

[1068] Interactive Interface Device

[1069] Terminal: The user accesses the system through a dedicated application and begins a dialogue. The user interface supports both voice and text input. When the user talks about everyday events or memories with their partner, the input is sent to the server.

[1070] Server: Analyzes the input and generates an appropriate response using a generative artificial intelligence model. For example, if a user inputs "I'm very tired today," the server generates a response such as "That must have been tough. It's important to rest, so don't push yourself too hard. What happened?" and sends it back to the device.

[1071] Terminal: By displaying the generated response in the user interface, the user can have an experience as if they were interacting with their deceased partner again.

[1072] Specific examples

[1073] Specific use cases include the following scenarios:

[1074] Scenario 1: A user complains about everyday life

[1075] User: "My boss has been scolding me a lot today, and I'm really tired."

[1076] Terminal: Receives this message and sends it to the server.

[1077] Server: The generative AI model generates a response saying, "That was tough, but you can do it. Is there anything I can help you with?"

[1078] Terminal: Displays the generated response and shows it to the user.

[1079] Scenario 2: User discussing travel plans

[1080] User: "I'm thinking about going to the country for the weekend."

[1081] Terminal: Receives this message and sends it to the server.

[1082] Server: The generative AI model generates a response like, "Great! I really liked that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[1083] Terminal: Displays the generated response and shows it to the user.

[1084] As described above, this system can help users alleviate feelings of loneliness and loss and provide a sense of psychological security.

[1085] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1086] Step 1:

[1087] Data collection

[1088] User: Using a dedicated application, the user inputs detailed information about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc. For example, the input information might include, "My partner liked to drink coffee every morning."

[1089] Input: Partner details entered by the user.

[1090] Output: Raw data temporarily stored on the device.

[1091] Specific actions

[1092] The user enters "Partner's favorite drink: Coffee" into the application and the device stores this information.

[1093] Step 2:

[1094] Data transmission

[1095] Terminal: The terminal sends the received data to the server using a secure communication protocol (e.g., HTTPS). At this time, the data is encrypted before being sent.

[1096] Input: Temporarily saved data.

[1097] Output: The encrypted data is sent to the server.

[1098] Specific actions

[1099] The device encrypts the data stored on it, such as "Partner's favorite drink: Coffee," and sends it to the server.

[1100] Step 3:

[1101] Preparing for data analysis

[1102] Server: Receives the transmitted data and converts it into a data structure for analysis, which is then fed into the machine learning model.

[1103] Input: Encrypted data.

[1104] Output: The parsed data is converted into the appropriate format.

[1105] Specific actions

[1106] The server receives the data "Partner's favorite drink: coffee" and converts the data structure for analysis.

[1107] Step 4:

[1108] Training an AI model

[1109] Server: The transformed data is used to train the generative AI model, using a deep learning framework (e.g., TensorFlow or PyTorch).

[1110] Input: Parsed data.

[1111] Output: A trained generative artificial intelligence model.

[1112] Specific actions

[1113] The server uses multiple data points, such as "partner's favorite drink: coffee," to train the AI ​​model and learn the partner's behavioral patterns.

[1114] Step 5:

[1115] Starting a conversation

[1116] User: Launches a dedicated application and accesses the system. The user can enter messages or questions to start a dialogue in the dialogue interface of the application.

[1117] Input: A message or question typed by the user.

[1118] Output: The entered message or question is sent to the server.

[1119] Specific actions

[1120] The user types "How was your day?" into the conversational interface and the message is sent to the server.

[1121] Step 6:

[1122] Response Generation

[1123] Server: Analyzes user input and generates appropriate responses using a trained generative artificial intelligence model.

[1124] Input: A message or question typed by the user.

[1125] Output: The generated response.

[1126] Specific actions

[1127] The server parses the user's question "How was your day?" and generates a response such as "It was a peaceful day today. I was thinking of you."

[1128] Step 7:

[1129] Viewing the response

[1130] Terminal: The generated response is displayed in a user interface and provided to the user.

[1131] Input: The response sent by the server.

[1132] Output: The response displayed in the user interface.

[1133] Specific actions

[1134] The device displays the response "It was a peaceful day today. I was thinking of you" in the user interface.

[1135] These steps allow users to recreate conversations with their deceased partner, helping to alleviate feelings of loneliness and loss and providing a sense of psychological security.

[1136] (Application example 1)

[1137] 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."

[1138] Currently, there are interactive systems that can help elderly people who feel lonely or people who have lost their partners to reduce their feelings of loneliness. However, these systems cannot provide adequate support to users who often feel lonely in their daily lives, especially when shopping. In particular, there are no systems that allow users to enjoy shopping while interacting with their partners during the shopping experience in a virtual store. Therefore, there is a need for an interactive system that can provide advice on purchasing behavior in a virtual store.

[1139] 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.

[1140] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model in a virtual environment, means for reducing the user's sense of loneliness through the interaction with the user, and means for allowing the user to consult with a partner about purchasing behavior in a virtual store. This makes it possible for users to interact with a partner while consulting with them about purchasing behavior in the virtual store, thereby reducing feelings of loneliness in their daily lives.

[1141] A "user" is an individual who wishes to use the system to reduce feelings of loneliness and enjoy a shopping experience in a virtual store.

[1142] "Data" refers to characteristic information entered by the user about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc.

[1143] A "generative artificial intelligence model" is an artificial intelligence system that is trained based on data provided by the user to reproduce the behavior and speaking style of a partner.

[1144] "Virtual environment" refers to a digital space where users can have a shopping experience in a virtual space.

[1145] "Purchasing behavior" refers to the series of actions a user takes to select and purchase a product in a virtual store.

[1146] "Dialogue" refers to the act of communication between a user and a system via a generative artificial intelligence model.

[1147] This invention is an interactive system for reducing users' feelings of loneliness and supporting their shopping experience in a virtual store. The system of the present invention is mainly composed of the following three elements:

[1148] 1. Data Collection Equipment

[1149] 2. Training equipment for generative AI models

[1150] 3. Interactive Interface Device

[1151] 1. Data Collection Equipment

[1152] Using a dedicated application, users can input information about their partner's behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, they can enter details about their partner's favorite foods, common phrases, and hobbies. This data is then sent to the server via the device.

[1153] 2. Training a generative AI model

[1154] The server trains a generative AI model based on the received data. This training process uses deep learning and natural language processing techniques. The result is a model that reproduces the partner's speech and behavior patterns. This model can generate natural responses, as if the partner were actually alive.

[1155] 3. Interactive Interface Device

[1156] Users access the system through a dedicated application and begin interacting with it. The user-friendly interactive interface allows for voice and text input. Users can consult with an AI partner while selecting products in the virtual store.

[1157] For example, consider the following scenario:

[1158] Scenario 1: A user is asking for advice about a dress.

[1159] User: "What do you think of this dress?"

[1160] Terminal: Receives input and sends it to the server.

[1161] Server: The generative AI model generates a response: "That's great, let's buy it!"

[1162] This system allows users to enjoy an experience that feels like they are conversing with a partner while discussing purchasing behavior in a virtual store, which can alleviate feelings of loneliness and loss and provide a sense of psychological security.

[1163] To implement this invention, the following hardware and software are used:

[1164] Hardware: smartphone, head-mounted display, server

[1165] Software: OpenAI API (generative AI model), virtual store application

[1166] Example prompt sentence:

[1167] User input: "What do you think of this dress?"

[1168] Prompt statement:

[1169] A phrase or catchphrase your partner often uses:

[1170] "That's great, let's buy it!"

[1171] "I think it suits you."

[1172] Foods and hobbies your partner liked:

[1173] "I like yakiniku"

[1174] "I love traveling"

[1175] User Typed: What do you think of this dress?

[1176] As an AI playing the role of your partner, respond using your partner's quirks and preferences, such as:

[1177] This allows users to enjoy a shopping experience in a virtual store, while also reducing feelings of loneliness and receiving psychological support.

[1178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1179] Step 1:

[1180] Using a dedicated application, users input characteristic information about their partner, such as their behavioral patterns, speaking style, catchphrases, and hobbies. The input data is structured by the device and sent to the server. The input is text data about the partner's preferences and behavioral patterns, and the output is the data sent to the server.

[1181] Step 2:

[1182] The server trains a generative AI model based on the received data. The input is structured data sent by the user, and the output is a trained generative AI model. The server analyzes the data using deep learning algorithms to generate a model that replicates the partner's speech and behavior patterns.

[1183] Step 3:

[1184] The user starts shopping in a virtual environment using a virtual store application. At this time, the user consults the generative AI model about product selection and purchasing behavior. The input is the user's question or inquiry, and the output is text data sent from the terminal to the server.

[1185] Step 4:

[1186] The server uses the text data received from the user as a prompt and generates an appropriate response using a generative artificial intelligence model. The input is the question or inquiry sent by the user, and the output is the response text generated by the AI. The server generates a prompt sentence and applies it to the AI ​​model to obtain a response.

[1187] Step 5:

[1188] The terminal receives the response from the server and displays it on the user interface. The input is the response text sent from the server, and the output is the response displayed on the user interface. The user can confirm this response and continue the dialogue again.

[1189] Step 6:

[1190] If the user has another question or inquiry, the device again sends the input to the server. This continues the dialogue, allowing the user to enjoy a series of purchasing actions while receiving advice from their AI partner. The input and output process proceeds by repeating steps 3 to 5.

[1191] This process flow allows users to enrich their shopping experience in the virtual store while reducing their sense of isolation.

[1192] 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.

[1193] This invention relates to a system that combines a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely and those who have lost their partners, to reduce loneliness and promote mental stability.

[1194] 1. System Configuration

[1195] The system consists of four main components:

[1196] 1. Data Collection Equipment

[1197] 2. Training equipment for generative AI models

[1198] 3. Interactive Interface Device

[1199] 4. Emotional Engine Device

[1200] 2. Operation of the data acquisition device

[1201] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[1202] Terminal: Receives data entered by the user and sends it to the server.

[1203] 3. Training the generative AI model

[1204] Server: Checks the received data and stores it in a database, which is later used to train the generative artificial intelligence model.

[1205] Server: Preprocesses the data, filters out unnecessary data, and extracts necessary features (such as catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative AI model.

[1206] Server: Deploys the trained generative AI model as an API and makes it externally accessible. The model has the ability to generate characteristic responses for partners.

[1207] 4. Operation of the dialogue interface device and emotion engine

[1208] Terminal: The user logs in using a dedicated application and begins a dialogue. The dialogue interface is user-friendly and allows voice and text input.

[1209] User: Talk about memories with their partner or everyday events. For example, type "I'm very tired today."

[1210] Terminal: Receives user input (voice or text) and sends it to the server. The terminal also sends the user's voice tone and text expression to the emotion engine.

[1211] Server: Analyzes user input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if a user says, "I'm tired today," the emotion engine will recognize the user's sense of fatigue and generate an appropriate response.

[1212] Server: Uses feedback from the emotion engine to tailor responses and provide support based on the user's emotional state.

[1213] Terminal: The terminal displays the response received from the server on the user interface, and shows the user the content of the dialogue. The appropriate response is played over the speaker.

[1214] Specific examples

[1215] Specific use cases include the following scenarios:

[1216] Scenario 1: A user complains about everyday life

[1217] User: "My boss has been scolding me a lot today, and I'm really tired."

[1218] Terminal: Receives input and sends it to the server and emotion engine.

[1219] Emotion Engine: Recognizes tiredness from the user's tone of voice.

[1220] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[1221] Terminal: Displays the response and shows it to the user.

[1222] Scenario 2: User discussing travel plans

[1223] User: "I'm thinking about going to the country for the weekend."

[1224] Terminal: Receives input and sends it to the server and emotion engine.

[1225] Emotion engine: Recognizes the user's emotions of excitement and enjoyment.

[1226] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[1227] Terminal: Displays the response and shows it to the user.

[1228] This system allows users to experience the sensation of reconnecting with their deceased partner, easing feelings of loneliness and loss and providing a sense of psychological security. The addition of an emotion engine allows for more personalized and optimized responses, enabling flexible responses tailored to the user's emotional state. This invention is a powerful tool for preventing lonely deaths and contributing to the mental well-being of the elderly, in particular.

[1229] The processing flow will be explained below.

[1230] Step 1:

[1231] User: Launches the dedicated application and enters detailed information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, they enter information about their partner's favorite foods, common phrases, and hobbies.

[1232] Step 2:

[1233] Terminal: Receives the data entered by the user, structures it, and sends it to the server. At this stage, the data is converted into the appropriate format.

[1234] Step 3:

[1235] Server: Checks the received data and stores it in a database, which is used to train generative artificial intelligence models.

[1236] Step 4:

[1237] Server: Preprocesses the data and filters out unnecessary data. The filtered data extracts necessary features (such as catchphrases and behavioral patterns) to form a training dataset.

[1238] Step 5:

[1239] Server: Trains a generative artificial intelligence model using the training dataset. The model learns to generate responses characteristic of the partner.

[1240] Step 6:

[1241] Server: Deploys the trained generative AI model as an API and makes it accessible to the public. The model is used online to generate responses.

[1242] Step 7:

[1243] User: Logs in to the dedicated application and talks about memories with their partner or everyday events. For example, they can type, "I'm very tired today."

[1244] Step 8:

[1245] Terminal: Receives user input (voice or text) and sends it to the server, while also sending the user's voice tone and text expression to the emotion engine.

[1246] Step 9:

[1247] Emotion engine: Analyzes emotions from the user's voice tone and text and provides that information to the server. For example, it detects the user's fatigue and stress level.

[1248] Step 10:

[1249] Server: Analyzes user input and feedback from the emotion engine. Using a generative AI model, it generates an appropriate response based on the user's emotional state. For example, it generates a response such as, "That must have been tough. It's important to take a rest, but don't push yourself too hard."

[1250] Step 11:

[1251] Server: Sends the generated response to the user's terminal. At this time, if necessary, it also generates and sends an audio file of the response.

[1252] Step 12:

[1253] Terminal: The response received from the server is displayed on the user interface, and the content of the dialogue is shown to the user. In the case of a voice response, it is played on the speaker.

[1254] Step 13:

[1255] User: The conversation continues by checking the displayed response and speaking again. For example, you might say, "I'm thinking about going to the countryside this weekend."

[1256] Step 14:

[1257] Emotion engine: Reanalyzes emotions from new user comments and provides that information to the server.

[1258] Step 15:

[1259] Server: Regenerate a response based on the new input and the results of sentiment analysis. For example, generate a response like, "Great! I loved that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[1260] Step 16:

[1261] Terminal: Displays the new response received from the server and shows it to the user.

[1262] This process allows users to have an experience that feels as if they are having a conversation with their deceased partner. The incorporation of an emotion engine optimizes responses based on the user's current emotional state, enabling more natural and empathetic interactions. This alleviates the user's sense of loneliness and loss, providing psychological comfort.

[1263] Example 2

[1264] 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."

[1265] Until now, psychological support methods for elderly people who feel lonely or people who have lost their partners have been limited, and there is a lack of effective means to alleviate feelings of loss and loneliness. In addition, systems that generate responses based on the user's emotional state often have insufficient emotion recognition, making it difficult to provide personalized and optimized responses to users.

[1266] The identification processing 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 receiving data from a user, means for filtering unnecessary data based on the received data and extracting necessary features to train a generative AI model, means for deploying the trained generative AI model as an API and making it accessible from the outside, means for a user to start a dialogue through a dedicated application and receive voice or text input, means for analyzing the user's input and generating a response from the generative AI model based on feedback from the emotion engine, means for sending the generated response to the user's terminal and displaying it as voice or text, and means for alleviating the user's sense of loneliness through dialogue with the user. This makes it possible to generate an individualized and optimized response according to the user's emotional state, effectively alleviating the user's sense of loneliness and loss.

[1267] "User" refers to any individual or end user who uses the System.

[1268] "Means for receiving data" refers to a hardware or software method for accurately and efficiently obtaining information provided by a user.

[1269] "Generative artificial intelligence model" refers to an artificial intelligence system that uses machine learning algorithms to learn specific data patterns and generate responses.

[1270] "Training means" refers to the process steps for utilizing the received data to train an artificial intelligence model and improve the accuracy of the model.

[1271] "Deployment as an API" refers to a method of publishing a trained artificial intelligence model in an externally available format so that other systems and applications can use it.

[1272] "Specialized Application" refers to the specific software application used by a user for interaction and data entry.

[1273] "Means for receiving voice or text input" refers to a method for capturing a user's voice or text data and transmitting it to the system for analysis.

[1274] "Emotion engine" refers to an algorithm or program that analyzes a user's emotional state from their tone of voice or text expression and provides it to an artificial intelligence model.

[1275] "Means for generating a response" refers to the process by which the artificial intelligence model generates an appropriate response based on the user's input data.

[1276] "Terminal" means a computer or mobile device that runs dedicated applications and allows users to input and interact.

[1277] "Measures to reduce loneliness" refers to methods that alleviate users' loneliness and provide psychological support through dialogue and responses via the system.

[1278] MODE FOR CARRYING OUT THE INVENTION

[1279] The present invention is a system that combines a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user, with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely and those who have lost their partner. The main components of the system are as follows:

[1280] 1. Data Collection Equipment

[1281] 2. Training equipment for generative AI models

[1282] 3. Interactive Interface Device

[1283] 4. Emotional Engine Device

[1284] Data acquisition device operation

[1285] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[1286] Terminal: Receives input data in real time and sends it to the server. Examples of dedicated applications are custom applications designed for smartphones and tablets.

[1287] Training generative artificial intelligence models

[1288] Server: Reviewing the data received from the devices and storing it in the appropriate database. The stored data is later used to train the generative artificial intelligence model. An example of a server is a cloud-based database system.

[1289] Server: Preprocesses the data, filters unnecessary data, and extracts necessary features (e.g., catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative artificial intelligence model. This process uses advanced data analysis tools and frameworks (e.g., TensorFlow and PyTorch).

[1290] Server: Deploys trained generative AI models as APIs and makes them publicly accessible, allowing other systems and applications to use the generated models.

[1291] Operation of the dialogue interface device and emotion engine

[1292] Terminal: The user logs in using a dedicated application and begins a dialogue. The dialogue interface is user-friendly and allows voice and text input.

[1293] User: Talk about memories and daily events with their partner. For example, the user enters "I'm very tired today" through the application.

[1294] Terminal: Receives user input (voice or text) and sends it to the server. The terminal also sends the user's voice tone and text expression to the emotion engine.

[1295] Server: Analyzes the user's input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if the user says, "I'm tired today," the emotion engine will recognize the user's sense of fatigue and generate an appropriate response.

[1296] Server: Uses feedback from the emotion engine to tailor responses and provide support based on the user's emotional state.

[1297] Terminal: The response received from the server is displayed on the user interface and the appropriate response is played on the speaker.

[1298] Specific examples

[1299] Scenario 1: A user complains about everyday life

[1300] User: "My boss has been scolding me a lot today, and I'm really tired."

[1301] Terminal: Receives input and sends it to the server and emotion engine.

[1302] Emotion Engine: Recognizes tiredness from the user's tone of voice.

[1303] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[1304] Terminal: Displays the response and shows it to the user.

[1305] Scenario 2: User discussing travel plans

[1306] User: "I'm thinking about going to the country for the weekend."

[1307] Terminal: Receives input and sends it to the server and emotion engine.

[1308] Emotion engine: Recognizes the user's emotions of excitement and enjoyment.

[1309] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[1310] Terminal: Displays the response and shows it to the user.

[1311] In this way, users can enjoy an experience that feels as if they are interacting with their partner again, and gain psychological stability. The addition of an emotion engine allows for more personalized and optimized responses, enabling flexible responses according to the user's emotional state. This system also provides great social value by reducing feelings of loneliness and loss, especially among the elderly, and contributing to psychological stability.

[1312] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1313] Step 1:

[1314] User: Uses a dedicated application to enter information about their partner, such as their behavioral patterns, speaking style, catchphrases, and hobbies.

[1315] Input: Information about your partner's behavioral patterns, speech patterns, catchphrases, and hobbies.

[1316] Output: Structured partner information data.

[1317] Data Processing: Collect information in a consistent format using text fields and multiple choice inputs.

[1318] Specific actions: For example, enter "jogging every morning" in the "activity pattern" field of the application.

[1319] Step 2:

[1320] Terminal: Receives user-entered data in real time and sends it to the server.

[1321] Input: Partner information data entered by the user.

[1322] Output: The data sent to the server.

[1323] Data processing: The data is converted into the appropriate format and into data packets for transmission.

[1324] Specific operation: Converts input data into JSON format and sends a POST request to the specified endpoint on the server.

[1325] Step 3:

[1326] Server: Checks the data received from the device and stores it in the appropriate database.

[1327] Input: Partner information data sent from the device.

[1328] Output: Data stored in the database.

[1329] Data processing: Checking data integrity and generating queries for insertion into the database.

[1330] Specific operation: Parse the received JSON data, generate an SQL query, and insert it into the database.

[1331] Step 4:

[1332] Server: Preprocesses the data, filters out unnecessary data, extracts necessary features, and trains the generative artificial intelligence model.

[1333] Input: Partner information data stored in the database.

[1334] Output: A preprocessed dataset and a trained AI model.

[1335] Data processing: Data cleansing and feature extraction are performed to convert the data into a format suitable for the model.

[1336] Specific operations: Noise removal from text data and data conversion using feature extraction algorithms.

[1337] Step 5:

[1338] Server: Deploys trained live AI models as APIs and makes them accessible externally.

[1339] Input: A trained AI model.

[1340] Output: The deployed API endpoint.

[1341] Data processing: Create a container for deployment and place it on the API server.

[1342] Specific operation: The model is containerized using Docker and deployed to a cloud server such as AWS or GCP.

[1343] Step 6:

[1344] Terminal: A user logs in with a dedicated application and begins interaction.

[1345] Input: User login information.

[1346] Output: Notification that interaction is ready.

[1347] Data processing: Authenticate login information and start a session.

[1348] Specific operation: Authenticate the user ID and password and issue a session ID.

[1349] Step 7:

[1350] User: Talk about memories and everyday events with your partner.

[1351] Input: User voice or text input.

[1352] Output: Audio or text data.

[1353] Data processing: In the case of voice input, it is converted into text and text data is generated.

[1354] Specific action: Say "I'm very tired today."

[1355] Step 8:

[1356] Terminal: Receives user input (voice or text) and sends it to the server. It also sends the user's voice tone and text expression to the emotion engine.

[1357] Input: User voice or text data.

[1358] Output: Data and sentiment analysis results sent to the server.

[1359] Data processing: In the case of audio data, it is converted into text and sentiment analysis is performed.

[1360] Specific operation: Converts speech into text, adds additional emotional information, and sends it to the server.

[1361] Step 9:

[1362] Server: Analyzes user input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model.

[1363] Input: Text data and emotion information sent from the device.

[1364] Output: The generated response data.

[1365] Data processing: Analyze text data and ask the AI ​​model to generate a response, taking into account emotional information.

[1366] Specific behavior: Based on the input "I'm tired today," generate a "response to being tired."

[1367] Step 10:

[1368] Server: Adjusts the generated responses based on the emotion engine feedback and constructs the final response.

[1369] Input: Response data from the AI ​​model and emotion engine feedback.

[1370] Output: The adjusted final response.

[1371] Data processing: Adjust response data based on emotional information.

[1372] Specific behavior: Create a tailored response: "That's been tough, but I believe you can get through it."

[1373] Step 11:

[1374] Terminal: Displays the response received from the server in a user interface and plays it back as voice or text.

[1375] Input: The final response data from the server.

[1376] Output: The content displayed in the user interface.

[1377] Data processing: Convert text data into speech and display it in the user interface in an appropriate format.

[1378] Specific operation: The response "That must have been tough, but I believe you can get through it" is displayed on the screen and played back using a speech synthesis engine.

[1379] Through this series of steps, users can receive psychological support through dialogue with their partners. The system generates responses based on the user's emotional state, providing personalized and optimized dialogue.

[1380] (Application example 2)

[1381] 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."

[1382] Conventional systems have difficulty responding flexibly to a user's emotional state, and have been unable to provide sufficient psychological or security support, particularly to elderly users and users who feel anxious when alone. While dialogue systems existed to promote mental stability, they lacked the accuracy of emotion recognition and were unable to generate appropriate responses based on the user's emotions. This invention solves these problems by accurately recognizing a user's emotions, adjusting responses based on those emotions, and providing appropriate security support.

[1383] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1384] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model, means for recognizing the user's emotions and adjusting responses based on the emotions, and means for providing security in accordance with the user's emotional state, thereby enabling flexible responses and appropriate security support in accordance with the user's emotional state.

[1385] "User data" refers to personal information provided by users, such as patterns of behavior, speaking style, catchphrases, hobbies, and actions.

[1386] A "generative artificial intelligence model" is a machine learning model that is trained based on received data to generate responses tailored to a specific purpose.

[1387] "Emotion recognition means" refers to technology that analyzes and identifies a user's emotional state from their tone of voice or textual expression.

[1388] "Training" is the process of using machine learning algorithms to train a generative artificial intelligence model based on received data.

[1389] "Means for engaging in dialogue with users" refers to technology that generates appropriate responses to information input by users through a generative artificial intelligence model and engages in dialogue.

[1390] "Reducing feelings of loneliness" means that the goal is to reduce the loneliness and anxiety that users feel when they are alone and to promote psychological stability.

[1391] "Means for adjusting responses" refers to technology that optimizes and appropriates responses generated according to the user's emotional state.

[1392] "Means for providing security" refers to technology that checks the security status of the home according to the user's emotional state and takes measures as necessary.

[1393] This system uses a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user, and combines it with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely or who feel anxious when alone, thereby reducing feelings of loneliness and promoting mental stability.

[1394] The main components of the system are:

[1395] 1. Data Collection Equipment

[1396] Using a dedicated application, users input information about their partner's behavioral patterns, speaking style, catchphrases, hobbies, etc. Specific examples include their partner's favorite foods, common phrases, and information about their hobbies. The device receives this data and sends it to the server.

[1397] 2. Training equipment for generative AI models

[1398] The server trains a generative AI model based on the received data. It filters unnecessary data as preprocessing and extracts necessary features. The trained generative AI model is deployed as an API and made accessible externally. This model has the ability to generate characteristic responses from partners.

[1399] 3. Interactive Interface Device

[1400] The user logs in to a dedicated application and begins a conversation. The conversation interface is user-friendly and allows voice and text input. For example, if the user types "I'm very tired today," the device sends the input to the server.

[1401] 4. Emotional Engine Device

[1402] The server analyzes the user's input and recognizes their emotional state. The emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if the user says, "I'm tired today," the emotion engine recognizes the user's sense of fatigue and generates an appropriate response. The generative AI model then provides a response such as, "You've had a hard day today. Is there anything I can help you with?"

[1403] This system provides users who feel lonely or anxious with an experience that makes them feel as if they are talking to their partner again, providing a sense of psychological security. In certain situations, it can also ensure users' safety by suggesting and implementing security measures.

[1404] Illustrative scenario

[1405] 1. If you hear a noise in the middle of the night

[1406] User: "I hear noises in the middle of the night and it scares me."

[1407] Assistant: "That's scary. Did you notice anything else unusual?"

[1408] The emotion engine recognizes the user's fear and adjusts the response.

[1409] 2. Anxiety when alone

[1410] User: "I feel anxious when I'm alone."

[1411] Assistant: "It's fine. I'm checking the security system. I've checked that the doors and windows are locked. You're safe."

[1412] The emotion engine recognizes the user's anxiety and the security system checks it.

[1413] Prompt Sentence Examples

[1414] "Analyze user input and identify emotional state using an emotion recognition engine. Generate responses using generative AI models. Implement security measures as needed to alleviate user anxiety or fear."

[1415] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1416] Step 1:

[1417] User Data Entry

[1418] Through a dedicated application, users input data such as their partner's behavioral patterns, speaking style, catchphrases, and hobbies.

[1419] Input: What the user types into the application (e.g., "My partner's favorite food is pasta. His favorite phrase is 'It's okay.'")

[1420] Output: The terminal sends these input data to the server.

[1421] Step 2:

[1422] Data collection and analysis

[1423] The server receives the data sent from the device and stores it in a database, after which it preprocesses the stored data, filtering out unnecessary information and extracting necessary features.

[1424] Input: Data sent from the terminal

[1425] Output: Preprocessed dataset

[1426] Step 3:

[1427] Training generative artificial intelligence models

[1428] The server uses the preprocessed dataset to train a generative artificial intelligence model, which is then deployed as an API and made publicly accessible.

[1429] Input: Preprocessed dataset

[1430] Output: A trained generative artificial intelligence model

[1431] Step 4:

[1432] Initiating a conversation and receiving user input

[1433] The user logs in to a dedicated application and begins a dialogue. The user provides input by voice or text, which is then sent by the terminal to the server.

[1434] Input: User voice or text input (e.g., "I'm very tired today")

[1435] Output: Sending input data from the terminal to the server

[1436] Step 5:

[1437] Emotion Recognition and Response Generation

[1438] The server receives the user's input and analyzes their emotional state using an emotion engine, and a generative artificial intelligence model generates an appropriate response based on the analysis results.

[1439] Input: User input data, emotion analysis results by emotion engine

[1440] Output: An appropriately tailored response (e.g., "You've had a rough day. Is there anything I can help you with?")

[1441] Step 6:

[1442] Response display and security checks

[1443] The device displays the response received from the server on a user interface and also checks the security system if the user's emotional state exceeds a certain threshold. For example, if the user inputs "I heard a noise in the middle of the night and it scares me," the security system will check the locks on the doors and windows of the house.

[1444] Input: Response data from the server

[1445] Output: The response displayed in the user interface, and the result of any security checks (e.g., "I've checked the locks on my doors and windows. They're safe.").

[1446] This will enable the realization of a system that can respond flexibly to the user's emotional state and also provide security support as needed.

[1447] 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.

[1448] 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.

[1449] 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.

[1450] [Fourth embodiment]

[1451] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1452] 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.

[1453] 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).

[1454] 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.

[1455] 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.

[1456] 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).

[1457] 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.

[1458] 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.

[1459] 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.

[1460] 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.

[1461] 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.

[1462] 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.

[1463] 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."

[1464] This invention relates to a system that interacts with users using a generative artificial intelligence model that reproduces the behavioral patterns and speech patterns of a partner based on data provided by the user. The system aims to provide psychological support, particularly to elderly people who feel lonely and people who have lost their partners.

[1465] 1. System Configuration

[1466] The system consists of three main components:

[1467] 1. Data Collection Equipment

[1468] 2. Training equipment for generative AI models

[1469] 3. Interactive Interface Device

[1470] 2. Operation of the data acquisition device

[1471] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[1472] Terminal: Receives data entered by the user and sends it to the server.

[1473] 3. Training the generative AI model

[1474] Server: Trains a generative AI model based on the received data, using deep learning and natural language processing techniques to generate a model that replicates the partner's speech and behavior patterns.

[1475] Server: The trained model will be able to generate natural responses as if the partner were real.

[1476] 4. Operation of the interactive interface device

[1477] Terminal: The user accesses the system through a dedicated application and starts a dialogue. The dialogue interface is designed to be user-friendly and allows voice and text input.

[1478] User: Talk about memories with their partner or everyday events. For example, type "I'm very tired today."

[1479] Terminal: Sends input from the user to the server.

[1480] Server: Analyzes the input and uses a generative AI model to generate an appropriate response, such as "That must have been tough. It's important to rest, don't push yourself too hard. What happened?"

[1481] Terminal: Receives the response from the server and displays it on the user interface. The user can confirm the response and continue the conversation by speaking again.

[1482] Specific examples

[1483] Specific use cases include the following scenarios:

[1484] Scenario 1: A user complains about everyday life

[1485] User: "My boss has been scolding me a lot today, and I'm really tired."

[1486] Terminal: Receives input and sends it to the server.

[1487] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[1488] Terminal: Displays the response and shows it to the user.

[1489] Scenario 2: User discussing travel plans

[1490] User: "I'm thinking about going to the country for the weekend."

[1491] Terminal: Receives input and sends it to the server.

[1492] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[1493] Terminal: Displays the response and shows it to the user.

[1494] This system allows users to enjoy the experience of interacting with their deceased partner again, which can alleviate feelings of loneliness and loss and provide a sense of psychological security. This invention is a powerful tool for preventing lonely deaths and contributing to the mental stability of elderly people in particular.

[1495] The processing flow will be explained below.

[1496] Step 1:

[1497] User: Launches the dedicated application and inputs information about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc. For example, they input details about their partner's favorite foods, common phrases, and hobbies.

[1498] Step 2:

[1499] Terminal: Receives input data and sends it to the server, where it is structured and converted into the appropriate format.

[1500] Step 3:

[1501] Server: Checks the received data and stores it in a database, which is later used to train the generative artificial intelligence model.

[1502] Step 4:

[1503] Server: Preprocesses the data, filters out unnecessary data, and extracts necessary features (such as catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative AI model.

[1504] Step 5:

[1505] Server: Deploys the trained generative AI model as an API and makes it externally accessible. The model has the ability to generate characteristic responses for partners.

[1506] Step 6:

[1507] User: Logs in to a dedicated application and starts a conversation. The user simulates a conversation with a partner through voice input or text input.

[1508] Step 7:

[1509] Terminal: Receives user input (voice or text) and sends it to the server, where it is processed in real time.

[1510] Step 8:

[1511] Server: Analyzes user input and passes it to a trained generative AI model to generate an appropriate response. For example, if a user says, "I'm tired today," the server generates a response like, "That must have been tough. It's important to rest, so don't push yourself too hard. What happened?"

[1512] Step 9:

[1513] Server: Sends the generated response to the user's terminal. At this time, if necessary, it also generates and sends an audio file of the response.

[1514] Step 10:

[1515] Terminal: The response received from the server is displayed on the user interface, and the user is informed of the dialogue. If the response is voice, it is played on the speaker.

[1516] Step 11:

[1517] User: Check the displayed response and continue the conversation by speaking again. For example, if the user inputs, "I'm thinking about going to the countryside this weekend," the system will respond with, "Great! I really liked that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[1518] This process allows users to experience the sensation of having a conversation with their deceased partner, which alleviates feelings of loneliness and loss and provides a sense of psychological security.

[1519] Example 1

[1520] 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."

[1521] There is a need for a means to alleviate the loneliness felt by the elderly and those who have lost their partners and to provide psychological support. However, current technology has difficulty replicating an individual's specific behavioral patterns and speaking style, and is therefore unable to recreate a partner. As a result, there is a problem that loneliness is not being alleviated or psychological support is not being provided sufficiently.

[1522] 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.

[1523] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model, means for alleviating the user's sense of loneliness through the interaction with the user, means for collecting detailed information about the partner using a dedicated application, means for transmitting the collected information to the server via a secure communication protocol, means for analyzing the user's input using the trained generative AI model and generating an appropriate response, and means for displaying the generated response on a user interface. This allows the user to enjoy the experience of interacting with their deceased partner again, thereby easing their sense of loneliness and loss and providing a sense of psychological security.

[1524] "User" refers to a person who uses the system to input the partner's behavioral patterns and speaking style and engage in a dialogue.

[1525] "Data" refers to detailed information collected from users about their partners' behavioral patterns, speaking style, catchphrases, hobbies, actions, etc.

[1526] A "generative artificial intelligence model" refers to a model that uses deep learning and natural language processing techniques to reproduce a partner's speaking style and behavior patterns based on received data.

[1527] "Training" refers to the process of training a generative artificial intelligence model based on collected data.

[1528] "Specialized Application" refers to specific software that allows users to enter their partner's details.

[1529] A "secure communication protocol" refers to a communication method for encrypting data and sending it securely to a server.

[1530] "User interface" refers to an interface that allows a user to interact with a system and that supports voice input and text input.

[1531] "Response" refers to a reply generated by a generative artificial intelligence model in response to input from a user.

[1532] "Reducing loneliness" refers to the alleviation of feelings of loneliness and loss that users feel through their interactions with the system.

[1533] This invention relates to a system that interacts with users using a generative artificial intelligence model that reproduces the behavioral patterns and speech patterns of a partner based on data provided by the user. The purpose is to provide psychological support, particularly to elderly people who feel lonely and people who have lost their partners.

[1534] System Configuration

[1535] The system consists of three components:

[1536] 1. Data Collection Equipment

[1537] 2. Training equipment for generative AI models

[1538] 3. Interactive Interface Device

[1539] Data collection equipment

[1540] User: Using a dedicated application, the user enters detailed information about their partner's behavioral patterns, speaking style, catchphrases, hobbies, etc. Specifically, the user enters information about the words their partner frequently used, their daily actions, and their hobbies. For example, the user enters information such as, "My partner liked to drink coffee every morning."

[1541] Terminal: Receives data entered by the user and temporarily stores it in local storage. It then transmits the data to the server using a secure communication protocol (e.g., HTTPS). The data is encrypted before transmission.

[1542] Training device for generative artificial intelligence models

[1543] Server: Analyzes the received data and converts it into an appropriate data structure. Then, using a deep learning framework (e.g., TensorFlow or PyTorch), it trains a generative artificial intelligence model based on the collected data. This process generates a model that can reproduce the partner's speech and behavior patterns.

[1544] Server: Validates the trained model, adjusts and retrains it as needed, and the trained model is capable of generating natural responses that make the partner appear as if they were real.

[1545] Interactive Interface Device

[1546] Terminal: The user accesses the system through a dedicated application and begins a dialogue. The user interface supports both voice and text input. When the user talks about everyday events or memories with their partner, the input is sent to the server.

[1547] Server: Analyzes the input and generates an appropriate response using a generative artificial intelligence model. For example, if a user inputs "I'm very tired today," the server generates a response such as "That must have been tough. It's important to rest, so don't push yourself too hard. What happened?" and sends it back to the device.

[1548] Terminal: By displaying the generated response in the user interface, the user can have an experience as if they were interacting with their deceased partner again.

[1549] Specific examples

[1550] Specific use cases include the following scenarios:

[1551] Scenario 1: A user complains about everyday life

[1552] User: "My boss has been scolding me a lot today, and I'm really tired."

[1553] Terminal: Receives this message and sends it to the server.

[1554] Server: The generative AI model generates a response saying, "That was tough, but you can do it. Is there anything I can help you with?"

[1555] Terminal: Displays the generated response and shows it to the user.

[1556] Scenario 2: User discussing travel plans

[1557] User: "I'm thinking about going to the country for the weekend."

[1558] Terminal: Receives this message and sends it to the server.

[1559] Server: The generative AI model generates a response like, "Great! I really liked that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[1560] Terminal: Displays the generated response and shows it to the user.

[1561] As described above, this system can help users alleviate feelings of loneliness and loss and provide a sense of psychological security.

[1562] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1563] Step 1:

[1564] Data collection

[1565] User: Using a dedicated application, the user inputs detailed information about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc. For example, the input information might include, "My partner liked to drink coffee every morning."

[1566] Input: Partner details entered by the user.

[1567] Output: Raw data temporarily stored on the device.

[1568] Specific actions

[1569] The user enters "Partner's favorite drink: Coffee" into the application and the device stores this information.

[1570] Step 2:

[1571] Data transmission

[1572] Terminal: The terminal sends the received data to the server using a secure communication protocol (e.g., HTTPS). At this time, the data is encrypted before being sent.

[1573] Input: Temporarily saved data.

[1574] Output: The encrypted data is sent to the server.

[1575] Specific actions

[1576] The device encrypts the data stored on it, such as "Partner's favorite drink: Coffee," and sends it to the server.

[1577] Step 3:

[1578] Preparing for data analysis

[1579] Server: Receives the transmitted data and converts it into a data structure for analysis, which is then fed into the machine learning model.

[1580] Input: Encrypted data.

[1581] Output: The parsed data is converted into the appropriate format.

[1582] Specific actions

[1583] The server receives the data "Partner's favorite drink: coffee" and converts the data structure for analysis.

[1584] Step 4:

[1585] Training an AI model

[1586] Server: The transformed data is used to train the generative AI model, using a deep learning framework (e.g., TensorFlow or PyTorch).

[1587] Input: Parsed data.

[1588] Output: A trained generative artificial intelligence model.

[1589] Specific actions

[1590] The server uses multiple data points, such as "partner's favorite drink: coffee," to train the AI ​​model and learn the partner's behavioral patterns.

[1591] Step 5:

[1592] Starting a conversation

[1593] User: Launches a dedicated application and accesses the system. The user can enter messages or questions to start a dialogue in the dialogue interface of the application.

[1594] Input: A message or question typed by the user.

[1595] Output: The entered message or question is sent to the server.

[1596] Specific actions

[1597] The user types "How was your day?" into the conversational interface and the message is sent to the server.

[1598] Step 6:

[1599] Response Generation

[1600] Server: Analyzes user input and generates appropriate responses using a trained generative artificial intelligence model.

[1601] Input: A message or question typed by the user.

[1602] Output: The generated response.

[1603] Specific actions

[1604] The server parses the user's question "How was your day?" and generates a response such as "It was a peaceful day today. I was thinking of you."

[1605] Step 7:

[1606] Viewing the response

[1607] Terminal: The generated response is displayed in a user interface and provided to the user.

[1608] Input: The response sent by the server.

[1609] Output: The response displayed in the user interface.

[1610] Specific actions

[1611] The device displays the response "It was a peaceful day today. I was thinking of you" in the user interface.

[1612] These steps allow users to recreate conversations with their deceased partner, helping to alleviate feelings of loneliness and loss and providing a sense of psychological security.

[1613] (Application example 1)

[1614] 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."

[1615] Currently, there are interactive systems that can help elderly people who feel lonely or people who have lost their partners to reduce their feelings of loneliness. However, these systems cannot provide adequate support to users who often feel lonely in their daily lives, especially when shopping. In particular, there are no systems that allow users to enjoy shopping while interacting with their partners during the shopping experience in a virtual store. Therefore, there is a need for an interactive system that can provide advice on purchasing behavior in a virtual store.

[1616] 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.

[1617] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model in a virtual environment, means for reducing the user's sense of loneliness through the interaction with the user, and means for allowing the user to consult with a partner about purchasing behavior in a virtual store. This makes it possible for users to interact with a partner while consulting with them about purchasing behavior in the virtual store, thereby reducing feelings of loneliness in their daily lives.

[1618] A "user" is an individual who wishes to use the system to reduce feelings of loneliness and enjoy a shopping experience in a virtual store.

[1619] "Data" refers to characteristic information entered by the user about their partner, such as their behavioral patterns, speaking style, catchphrases, hobbies, etc.

[1620] A "generative artificial intelligence model" is an artificial intelligence system that is trained based on data provided by the user to reproduce the behavior and speaking style of a partner.

[1621] "Virtual environment" refers to a digital space where users can have a shopping experience in a virtual space.

[1622] "Purchasing behavior" refers to the series of actions a user takes to select and purchase a product in a virtual store.

[1623] "Dialogue" refers to the act of communication between a user and a system via a generative artificial intelligence model.

[1624] This invention is an interactive system for reducing users' feelings of loneliness and supporting their shopping experience in a virtual store. The system of the present invention is mainly composed of the following three elements:

[1625] 1. Data Collection Equipment

[1626] 2. Training equipment for generative AI models

[1627] 3. Interactive Interface Device

[1628] 1. Data Collection Equipment

[1629] Using a dedicated application, users can input information about their partner's behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, they can enter details about their partner's favorite foods, common phrases, and hobbies. This data is then sent to the server via the device.

[1630] 2. Training a generative AI model

[1631] The server trains a generative AI model based on the received data. This training process uses deep learning and natural language processing techniques. The result is a model that reproduces the partner's speech and behavior patterns. This model can generate natural responses, as if the partner were actually alive.

[1632] 3. Interactive Interface Device

[1633] Users access the system through a dedicated application and begin interacting with it. The user-friendly interactive interface allows for voice and text input. Users can consult with an AI partner while selecting products in the virtual store.

[1634] For example, consider the following scenario:

[1635] Scenario 1: A user is asking for advice about a dress.

[1636] User: "What do you think of this dress?"

[1637] Terminal: Receives input and sends it to the server.

[1638] Server: The generative AI model generates a response: "That's great, let's buy it!"

[1639] This system allows users to enjoy an experience that feels like they are conversing with a partner while discussing purchasing behavior in a virtual store, which can alleviate feelings of loneliness and loss and provide a sense of psychological security.

[1640] To implement this invention, the following hardware and software are used:

[1641] Hardware: smartphone, head-mounted display, server

[1642] Software: OpenAI API (generative AI model), virtual store application

[1643] Example prompt sentence:

[1644] User input: "What do you think of this dress?"

[1645] Prompt statement:

[1646] A phrase or catchphrase your partner often uses:

[1647] "That's great, let's buy it!"

[1648] "I think it suits you."

[1649] Foods and hobbies your partner liked:

[1650] "I like yakiniku"

[1651] "I love traveling"

[1652] User Typed: What do you think of this dress?

[1653] As an AI playing the role of your partner, respond using your partner's quirks and preferences, such as:

[1654] This allows users to enjoy a shopping experience in a virtual store, while also reducing feelings of loneliness and receiving psychological support.

[1655] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1656] Step 1:

[1657] Using a dedicated application, users input characteristic information about their partner, such as their behavioral patterns, speaking style, catchphrases, and hobbies. The input data is structured by the device and sent to the server. The input is text data about the partner's preferences and behavioral patterns, and the output is the data sent to the server.

[1658] Step 2:

[1659] The server trains a generative AI model based on the received data. The input is structured data sent by the user, and the output is a trained generative AI model. The server analyzes the data using deep learning algorithms to generate a model that replicates the partner's speech and behavior patterns.

[1660] Step 3:

[1661] The user starts shopping in a virtual environment using a virtual store application. At this time, the user consults the generative AI model about product selection and purchasing behavior. The input is the user's question or inquiry, and the output is text data sent from the terminal to the server.

[1662] Step 4:

[1663] The server uses the text data received from the user as a prompt and generates an appropriate response using a generative artificial intelligence model. The input is the question or inquiry sent by the user, and the output is the response text generated by the AI. The server generates a prompt sentence and applies it to the AI ​​model to obtain a response.

[1664] Step 5:

[1665] The terminal receives the response from the server and displays it on the user interface. The input is the response text sent from the server, and the output is the response displayed on the user interface. The user can confirm this response and continue the dialogue again.

[1666] Step 6:

[1667] If the user has another question or inquiry, the device again sends the input to the server. This continues the dialogue, allowing the user to enjoy a series of purchasing actions while receiving advice from their AI partner. The input and output process proceeds by repeating steps 3 to 5.

[1668] This process flow allows users to enrich their shopping experience in the virtual store while reducing their sense of isolation.

[1669] 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.

[1670] This invention relates to a system that combines a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely and those who have lost their partners, to reduce loneliness and promote mental stability.

[1671] 1. System Configuration

[1672] The system consists of four main components:

[1673] 1. Data Collection Equipment

[1674] 2. Training equipment for generative AI models

[1675] 3. Interactive Interface Device

[1676] 4. Emotional Engine Device

[1677] 2. Operation of the data acquisition device

[1678] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[1679] Terminal: Receives data entered by the user and sends it to the server.

[1680] 3. Training the generative AI model

[1681] Server: Checks the received data and stores it in a database, which is later used to train the generative artificial intelligence model.

[1682] Server: Preprocesses the data, filters out unnecessary data, and extracts necessary features (such as catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative AI model.

[1683] Server: Deploys the trained generative AI model as an API and makes it externally accessible. The model has the ability to generate characteristic responses for partners.

[1684] 4. Operation of the dialogue interface device and emotion engine

[1685] Terminal: The user logs in using a dedicated application and begins a dialogue. The dialogue interface is user-friendly and allows voice and text input.

[1686] User: Talk about memories with their partner or everyday events. For example, type "I'm very tired today."

[1687] Terminal: Receives user input (voice or text) and sends it to the server. The terminal also sends the user's voice tone and text expression to the emotion engine.

[1688] Server: Analyzes user input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if a user says, "I'm tired today," the emotion engine will recognize the user's sense of fatigue and generate an appropriate response.

[1689] Server: Uses feedback from the emotion engine to tailor responses and provide support based on the user's emotional state.

[1690] Terminal: The terminal displays the response received from the server on the user interface, and shows the user the content of the dialogue. The appropriate response is played over the speaker.

[1691] Specific examples

[1692] Specific use cases include the following scenarios:

[1693] Scenario 1: A user complains about everyday life

[1694] User: "My boss has been scolding me a lot today, and I'm really tired."

[1695] Terminal: Receives input and sends it to the server and emotion engine.

[1696] Emotion Engine: Recognizes tiredness from the user's tone of voice.

[1697] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[1698] Terminal: Displays the response and shows it to the user.

[1699] Scenario 2: User discussing travel plans

[1700] User: "I'm thinking about going to the country for the weekend."

[1701] Terminal: Receives input and sends it to the server and emotion engine.

[1702] Emotion engine: Recognizes the user's emotions of excitement and enjoyment.

[1703] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[1704] Terminal: Displays the response and shows it to the user.

[1705] This system allows users to experience the sensation of reconnecting with their deceased partner, easing feelings of loneliness and loss and providing a sense of psychological security. The addition of an emotion engine allows for more personalized and optimized responses, enabling flexible responses tailored to the user's emotional state. This invention is a powerful tool for preventing lonely deaths and contributing to the mental well-being of the elderly, in particular.

[1706] The processing flow will be explained below.

[1707] Step 1:

[1708] User: Launches the dedicated application and enters detailed information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, they enter information about their partner's favorite foods, common phrases, and hobbies.

[1709] Step 2:

[1710] Terminal: Receives the data entered by the user, structures it, and sends it to the server. At this stage, the data is converted into the appropriate format.

[1711] Step 3:

[1712] Server: Checks the received data and stores it in a database, which is used to train generative artificial intelligence models.

[1713] Step 4:

[1714] Server: Preprocesses the data and filters out unnecessary data. The filtered data extracts necessary features (such as catchphrases and behavioral patterns) to form a training dataset.

[1715] Step 5:

[1716] Server: Trains a generative artificial intelligence model using the training dataset. The model learns to generate responses characteristic of the partner.

[1717] Step 6:

[1718] Server: Deploys the trained generative AI model as an API and makes it accessible to the public. The model is used online to generate responses.

[1719] Step 7:

[1720] User: Logs in to the dedicated application and talks about memories with their partner or everyday events. For example, they can type, "I'm very tired today."

[1721] Step 8:

[1722] Terminal: Receives user input (voice or text) and sends it to the server, while also sending the user's voice tone and text expression to the emotion engine.

[1723] Step 9:

[1724] Emotion engine: Analyzes emotions from the user's voice tone and text and provides that information to the server. For example, it detects the user's fatigue and stress level.

[1725] Step 10:

[1726] Server: Analyzes user input and feedback from the emotion engine. Using a generative AI model, it generates an appropriate response based on the user's emotional state. For example, it generates a response such as, "That must have been tough. It's important to take a rest, but don't push yourself too hard."

[1727] Step 11:

[1728] Server: Sends the generated response to the user's terminal. At this time, if necessary, it also generates and sends an audio file of the response.

[1729] Step 12:

[1730] Terminal: The response received from the server is displayed on the user interface, and the content of the dialogue is shown to the user. In the case of a voice response, it is played on the speaker.

[1731] Step 13:

[1732] User: The conversation continues by checking the displayed response and speaking again. For example, you might say, "I'm thinking about going to the countryside this weekend."

[1733] Step 14:

[1734] Emotion engine: Reanalyzes emotions from new user comments and provides that information to the server.

[1735] Step 15:

[1736] Server: Regenerate a response based on the new input and the results of sentiment analysis. For example, generate a response like, "Great! I loved that quiet riverside. I'd love to join you in the fun, and I'm sure you'll enjoy it too."

[1737] Step 16:

[1738] Terminal: Displays the new response received from the server and shows it to the user.

[1739] This process allows users to have an experience that feels as if they are having a conversation with their deceased partner. The incorporation of an emotion engine optimizes responses based on the user's current emotional state, enabling more natural and empathetic interactions. This alleviates the user's sense of loneliness and loss, providing psychological comfort.

[1740] Example 2

[1741] 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."

[1742] Until now, psychological support methods for elderly people who feel lonely or people who have lost their partners have been limited, and there is a lack of effective means to alleviate feelings of loss and loneliness. In addition, systems that generate responses based on the user's emotional state often have insufficient emotion recognition, making it difficult to provide personalized and optimized responses to users.

[1743] The identification processing 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 receiving data from a user, means for filtering unnecessary data based on the received data and extracting necessary features to train a generative AI model, means for deploying the trained generative AI model as an API and making it accessible from the outside, means for a user to start a dialogue through a dedicated application and receive voice or text input, means for analyzing the user's input and generating a response from the generative AI model based on feedback from the emotion engine, means for sending the generated response to the user's terminal and displaying it as voice or text, and means for alleviating the user's sense of loneliness through dialogue with the user. This makes it possible to generate an individualized and optimized response according to the user's emotional state, effectively alleviating the user's sense of loneliness and loss.

[1744] "User" refers to any individual or end user who uses the System.

[1745] "Means for receiving data" refers to a hardware or software method for accurately and efficiently obtaining information provided by a user.

[1746] "Generative artificial intelligence model" refers to an artificial intelligence system that uses machine learning algorithms to learn specific data patterns and generate responses.

[1747] "Training means" refers to the process steps for utilizing the received data to train an artificial intelligence model and improve the accuracy of the model.

[1748] "Deployment as an API" refers to a method of publishing a trained artificial intelligence model in an externally available format so that other systems and applications can use it.

[1749] "Specialized Application" refers to the specific software application used by a user for interaction and data entry.

[1750] "Means for receiving voice or text input" refers to a method for capturing a user's voice or text data and transmitting it to the system for analysis.

[1751] "Emotion engine" refers to an algorithm or program that analyzes a user's emotional state from their tone of voice or text expression and provides it to an artificial intelligence model.

[1752] "Means for generating a response" refers to the process by which the artificial intelligence model generates an appropriate response based on the user's input data.

[1753] "Terminal" means a computer or mobile device that runs dedicated applications and allows users to input and interact.

[1754] "Measures to reduce loneliness" refers to methods that alleviate users' loneliness and provide psychological support through dialogue and responses via the system.

[1755] MODE FOR CARRYING OUT THE INVENTION

[1756] The present invention is a system that combines a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user, with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely and those who have lost their partner. The main components of the system are as follows:

[1757] 1. Data Collection Equipment

[1758] 2. Training equipment for generative AI models

[1759] 3. Interactive Interface Device

[1760] 4. Emotional Engine Device

[1761] Data acquisition device operation

[1762] User: Using a dedicated application, the user inputs information about their partner, such as their behavioral patterns, speech patterns, catchphrases, hobbies, etc. For example, the user inputs details about their partner's favorite foods, common phrases, and hobbies.

[1763] Terminal: Receives input data in real time and sends it to the server. Examples of dedicated applications are custom applications designed for smartphones and tablets.

[1764] Training generative artificial intelligence models

[1765] Server: Reviewing the data received from the devices and storing it in the appropriate database. The stored data is later used to train the generative artificial intelligence model. An example of a server is a cloud-based database system.

[1766] Server: Preprocesses the data, filters unnecessary data, and extracts necessary features (e.g., catchphrases and behavioral patterns). This preprocessed dataset is used to train a generative artificial intelligence model. This process uses advanced data analysis tools and frameworks (e.g., TensorFlow and PyTorch).

[1767] Server: Deploys trained generative AI models as APIs and makes them publicly accessible, allowing other systems and applications to use the generated models.

[1768] Operation of the dialogue interface device and emotion engine

[1769] Terminal: The user logs in using a dedicated application and begins a dialogue. The dialogue interface is user-friendly and allows voice and text input.

[1770] User: Talk about memories and daily events with their partner. For example, the user enters "I'm very tired today" through the application.

[1771] Terminal: Receives user input (voice or text) and sends it to the server. The terminal also sends the user's voice tone and text expression to the emotion engine.

[1772] Server: Analyzes the user's input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if the user says, "I'm tired today," the emotion engine will recognize the user's sense of fatigue and generate an appropriate response.

[1773] Server: Uses feedback from the emotion engine to tailor responses and provide support based on the user's emotional state.

[1774] Terminal: The response received from the server is displayed on the user interface and the appropriate response is played on the speaker.

[1775] Specific examples

[1776] Scenario 1: A user complains about everyday life

[1777] User: "My boss has been scolding me a lot today, and I'm really tired."

[1778] Terminal: Receives input and sends it to the server and emotion engine.

[1779] Emotion Engine: Recognizes tiredness from the user's tone of voice.

[1780] Server: The generative AI model generates a response: "That was tough, but you can handle it. Is there anything I can help you with?"

[1781] Terminal: Displays the response and shows it to the user.

[1782] Scenario 2: User discussing travel plans

[1783] User: "I'm thinking about going to the country for the weekend."

[1784] Terminal: Receives input and sends it to the server and emotion engine.

[1785] Emotion engine: Recognizes the user's emotions of excitement and enjoyment.

[1786] Server: The generative AI model generates a response: "Great! I really enjoyed that quiet riverside. I'd love to join you in the fun, but I'm sure you'll enjoy it too."

[1787] Terminal: Displays the response and shows it to the user.

[1788] In this way, users can enjoy an experience that feels as if they are interacting with their partner again, and gain psychological stability. The addition of an emotion engine allows for more personalized and optimized responses, enabling flexible responses according to the user's emotional state. This system also provides great social value by reducing feelings of loneliness and loss, especially among the elderly, and contributing to psychological stability.

[1789] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1790] Step 1:

[1791] User: Uses a dedicated application to enter information about their partner, such as their behavioral patterns, speaking style, catchphrases, and hobbies.

[1792] Input: Information about your partner's behavioral patterns, speech patterns, catchphrases, and hobbies.

[1793] Output: Structured partner information data.

[1794] Data Processing: Collect information in a consistent format using text fields and multiple choice inputs.

[1795] Specific actions: For example, enter "jogging every morning" in the "activity pattern" field of the application.

[1796] Step 2:

[1797] Terminal: Receives user-entered data in real time and sends it to the server.

[1798] Input: Partner information data entered by the user.

[1799] Output: The data sent to the server.

[1800] Data processing: The data is converted into the appropriate format and into data packets for transmission.

[1801] Specific operation: Converts input data into JSON format and sends a POST request to the specified endpoint on the server.

[1802] Step 3:

[1803] Server: Checks the data received from the device and stores it in the appropriate database.

[1804] Input: Partner information data sent from the device.

[1805] Output: Data stored in the database.

[1806] Data processing: Checking data integrity and generating queries for insertion into the database.

[1807] Specific operation: Parse the received JSON data, generate an SQL query, and insert it into the database.

[1808] Step 4:

[1809] Server: Preprocesses the data, filters out unnecessary data, extracts necessary features, and trains the generative artificial intelligence model.

[1810] Input: Partner information data stored in the database.

[1811] Output: A preprocessed dataset and a trained AI model.

[1812] Data processing: Data cleansing and feature extraction are performed to convert the data into a format suitable for the model.

[1813] Specific operations: Noise removal from text data and data conversion using feature extraction algorithms.

[1814] Step 5:

[1815] Server: Deploys trained live AI models as APIs and makes them accessible externally.

[1816] Input: A trained AI model.

[1817] Output: The deployed API endpoint.

[1818] Data processing: Create a container for deployment and place it on the API server.

[1819] Specific operation: The model is containerized using Docker and deployed to a cloud server such as AWS or GCP.

[1820] Step 6:

[1821] Terminal: A user logs in with a dedicated application and begins interaction.

[1822] Input: User login information.

[1823] Output: Notification that interaction is ready.

[1824] Data processing: Authenticate login information and start a session.

[1825] Specific operation: Authenticate the user ID and password and issue a session ID.

[1826] Step 7:

[1827] User: Talk about memories and everyday events with your partner.

[1828] Input: User voice or text input.

[1829] Output: Audio or text data.

[1830] Data processing: In the case of voice input, it is converted into text and text data is generated.

[1831] Specific action: Say "I'm very tired today."

[1832] Step 8:

[1833] Terminal: Receives user input (voice or text) and sends it to the server. It also sends the user's voice tone and text expression to the emotion engine.

[1834] Input: User voice or text data.

[1835] Output: Data and sentiment analysis results sent to the server.

[1836] Data processing: In the case of audio data, it is converted into text and sentiment analysis is performed.

[1837] Specific operation: Converts speech into text, adds additional emotional information, and sends it to the server.

[1838] Step 9:

[1839] Server: Analyzes user input and passes it to the generative AI model to generate an appropriate response. At the same time, the emotion engine analyzes the user's emotional state and provides that information to the generative AI model.

[1840] Input: Text data and emotion information sent from the device.

[1841] Output: The generated response data.

[1842] Data processing: Analyze text data and ask the AI ​​model to generate a response, taking into account emotional information.

[1843] Specific behavior: Based on the input "I'm tired today," generate a "response to being tired."

[1844] Step 10:

[1845] Server: Adjusts the generated responses based on the emotion engine feedback and constructs the final response.

[1846] Input: Response data from the AI ​​model and emotion engine feedback.

[1847] Output: The adjusted final response.

[1848] Data processing: Adjust response data based on emotional information.

[1849] Specific behavior: Create a tailored response: "That's been tough, but I believe you can get through it."

[1850] Step 11:

[1851] Terminal: Displays the response received from the server in a user interface and plays it back as voice or text.

[1852] Input: The final response data from the server.

[1853] Output: The content displayed in the user interface.

[1854] Data processing: Convert text data into speech and display it in the user interface in an appropriate format.

[1855] Specific operation: The response "That must have been tough, but I believe you can get through it" is displayed on the screen and played back using a speech synthesis engine.

[1856] Through this series of steps, users can receive psychological support through dialogue with their partners. The system generates responses based on the user's emotional state, providing personalized and optimized dialogue.

[1857] (Application example 2)

[1858] 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."

[1859] Conventional systems have difficulty responding flexibly to a user's emotional state, and have been unable to provide sufficient psychological or security support, particularly to elderly users and users who feel anxious when alone. While dialogue systems existed to promote mental stability, they lacked the accuracy of emotion recognition and were unable to generate appropriate responses based on the user's emotions. This invention solves these problems by accurately recognizing a user's emotions, adjusting responses based on those emotions, and providing appropriate security support.

[1860] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1861] In this invention, the server includes means for receiving data from a user, means for training a generative AI model based on the received data, means for interacting with the user through the trained generative AI model, means for recognizing the user's emotions and adjusting responses based on the emotions, and means for providing security in accordance with the user's emotional state, thereby enabling flexible responses and appropriate security support in accordance with the user's emotional state.

[1862] "User data" refers to personal information provided by users, such as patterns of behavior, speaking style, catchphrases, hobbies, and actions.

[1863] A "generative artificial intelligence model" is a machine learning model that is trained based on received data to generate responses tailored to a specific purpose.

[1864] "Emotion recognition means" refers to technology that analyzes and identifies a user's emotional state from their tone of voice or textual expression.

[1865] "Training" is the process of using machine learning algorithms to train a generative artificial intelligence model based on received data.

[1866] "Means for engaging in dialogue with users" refers to technology that generates appropriate responses to information input by users through a generative artificial intelligence model and engages in dialogue.

[1867] "Reducing feelings of loneliness" means that the goal is to reduce the loneliness and anxiety that users feel when they are alone and to promote psychological stability.

[1868] "Means for adjusting responses" refers to technology that optimizes and appropriates responses generated according to the user's emotional state.

[1869] "Means for providing security" refers to technology that checks the security status of the home according to the user's emotional state and takes measures as necessary.

[1870] This system uses a generative artificial intelligence model that reproduces a partner's behavioral patterns and speech patterns based on data provided by the user, and combines it with an emotion engine that recognizes the user's emotions. The system aims to provide psychological support, particularly to elderly people who feel lonely or who feel anxious when alone, thereby reducing feelings of loneliness and promoting mental stability.

[1871] The main components of the system are:

[1872] 1. Data Collection Equipment

[1873] Using a dedicated application, users input information about their partner's behavioral patterns, speaking style, catchphrases, hobbies, etc. Specific examples include their partner's favorite foods, common phrases, and information about their hobbies. The device receives this data and sends it to the server.

[1874] 2. Training equipment for generative AI models

[1875] The server trains a generative AI model based on the received data. It filters unnecessary data as preprocessing and extracts necessary features. The trained generative AI model is deployed as an API and made accessible externally. This model has the ability to generate characteristic responses from partners.

[1876] 3. Interactive Interface Device

[1877] The user logs in to a dedicated application and begins a conversation. The conversation interface is user-friendly and allows voice and text input. For example, if the user types "I'm very tired today," the device sends the input to the server.

[1878] 4. Emotional Engine Device

[1879] The server analyzes the user's input and recognizes their emotional state. The emotion engine analyzes the user's emotional state and provides that information to the generative AI model. For example, if the user says, "I'm tired today," the emotion engine recognizes the user's sense of fatigue and generates an appropriate response. The generative AI model then provides a response such as, "You've had a hard day today. Is there anything I can help you with?"

[1880] This system provides users who feel lonely or anxious with an experience that makes them feel as if they are talking to their partner again, providing a sense of psychological security. In certain situations, it can also ensure users' safety by suggesting and implementing security measures.

[1881] Illustrative scenario

[1882] 1. If you hear a noise in the middle of the night

[1883] User: "I hear noises in the middle of the night and it scares me."

[1884] Assistant: "That's scary. Did you notice anything else unusual?"

[1885] The emotion engine recognizes the user's fear and adjusts the response.

[1886] 2. Anxiety when alone

[1887] User: "I feel anxious when I'm alone."

[1888] Assistant: "It's fine. I'm checking the security system. I've checked that the doors and windows are locked. You're safe."

[1889] The emotion engine recognizes the user's anxiety and the security system checks it.

[1890] Prompt Sentence Examples

[1891] "Analyze user input and identify emotional state using an emotion recognition engine. Generate responses using generative AI models. Implement security measures as needed to alleviate user anxiety or fear."

[1892] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1893] Step 1:

[1894] User Data Entry

[1895] Through a dedicated application, users input data such as their partner's behavioral patterns, speaking style, catchphrases, and hobbies.

[1896] Input: What the user types into the application (e.g., "My partner's favorite food is pasta. His favorite phrase is 'It's okay.'")

[1897] Output: The terminal sends these input data to the server.

[1898] Step 2:

[1899] Data collection and analysis

[1900] The server receives the data sent from the device and stores it in a database, after which it preprocesses the stored data, filtering out unnecessary information and extracting necessary features.

[1901] Input: Data sent from the terminal

[1902] Output: Preprocessed dataset

[1903] Step 3:

[1904] Training generative artificial intelligence models

[1905] The server uses the preprocessed dataset to train a generative artificial intelligence model, which is then deployed as an API and made publicly accessible.

[1906] Input: Preprocessed dataset

[1907] Output: A trained generative artificial intelligence model

[1908] Step 4:

[1909] Initiating a conversation and receiving user input

[1910] The user logs in to a dedicated application and begins a dialogue. The user provides input by voice or text, which is then sent by the terminal to the server.

[1911] Input: User voice or text input (e.g., "I'm very tired today")

[1912] Output: Sending input data from the terminal to the server

[1913] Step 5:

[1914] Emotion Recognition and Response Generation

[1915] The server receives the user's input and analyzes their emotional state using an emotion engine, and a generative artificial intelligence model generates an appropriate response based on the analysis results.

[1916] Input: User input data, emotion analysis results by emotion engine

[1917] Output: An appropriately tailored response (e.g., "You've had a rough day. Is there anything I can help you with?")

[1918] Step 6:

[1919] Response display and security checks

[1920] The device displays the response received from the server on a user interface and also checks the security system if the user's emotional state exceeds a certain threshold. For example, if the user inputs "I heard a noise in the middle of the night and it scares me," the security system will check the locks on the doors and windows of the house.

[1921] Input: Response data from the server

[1922] Output: The response displayed in the user interface, and the result of any security checks (e.g., "I've checked the locks on my doors and windows. They're safe.").

[1923] This will enable the realization of a system that can respond flexibly to the user's emotional state and also provide security support as needed.

[1924] 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.

[1925] 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.

[1926] 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 robot 414.

[1927] 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.

[1928] 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.

[1929] 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.

[1930] 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).

[1931] 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.

[1932] 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."

[1933] 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.

[1934] 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).

[1935] 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.

[1936] 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.

[1937] 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.

[1938] 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.

[1939] 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.

[1940] 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.

[1941] 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.

[1942] 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.

[1943] 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.

[1944] 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.

[1945] The following is further disclosed regarding the above embodiment.

[1946] (Claim 1)

[1947] means for receiving data from a user;

[1948] means for training a generative artificial intelligence model based on the received data;

[1949] a means for interacting with a user through a trained generative artificial intelligence model;

[1950] a means for reducing the user's sense of loneliness through dialogue with the user;

[1951] A system including:

[1952] (Claim 2)

[1953] 2. The system according to claim 1, further comprising means for structuring characteristic information such as a user's daily behavioral patterns, speaking style, catchphrases, hobbies, and actions as data.

[1954] (Claim 3)

[1955] 10. The system of claim 1, further comprising: means for receiving and analyzing information input from a user to generate a response based on a generative artificial intelligence model, and transmitting the response to the user's terminal.

[1956] "Example 1"

[1957] (Claim 1)

[1958] means for receiving data from a user;

[1959] means for training a generative artificial intelligence model based on the received data;

[1960] a means for interacting with a user through a trained generative artificial intelligence model;

[1961] a means for reducing the user's sense of loneliness through dialogue with the user;

[1962] A means of collecting detailed information about partners using a dedicated application;

[1963] means for transmitting the collected information to a server using a secure communication protocol;

[1964] means for analyzing a user's input and generating an appropriate response using a trained generative artificial intelligence model;

[1965] means for displaying the generated response in a user interface;

[1966] A system including:

[1967] (Claim 2)

[1968] 2. The system according to claim 1, further comprising means for structuring characteristic information such as a user's daily behavioral patterns, speaking style, catchphrases, hobbies, and actions as data.

[1969] (Claim 3)

[1970] 10. The system of claim 1, further comprising: means for receiving and analyzing information input from a user to generate a response based on a generative artificial intelligence model, and transmitting the response to the user's terminal.

[1971] "Application Example 1"

[1972] (Claim 1)

[1973] means for receiving data from a user;

[1974] means for training a generative artificial intelligence model based on the received data;

[1975] a means for interacting with a user through a trained generative artificial intelligence model in a virtual environment;

[1976] a means for reducing the user's sense of loneliness through dialogue with the user;

[1977] A means to allow users to consult on their purchasing behavior in virtual stores;

[1978] A system including:

[1979] (Claim 2)

[1980] 2. The system according to claim 1, further comprising means for structuring characteristic information such as a user's daily behavioral patterns, speaking style, catchphrases, hobbies, and actions as data.

[1981] (Claim 3)

[1982] 10. The system of claim 1, further comprising: means for receiving and analyzing information input from a user to generate a response based on a generative artificial intelligence model, and transmitting the response to the user's terminal.

[1983] "Example 2: Combining Emotion Engines"

[1984] (Claim 1)

[1985] means for receiving data from a user;

[1986] A means for filtering unnecessary data based on the received data and extracting necessary features to train a generative artificial intelligence model;

[1987] A means to deploy trained generative AI models as APIs and make them publicly accessible;

[1988] a means for a user to initiate a dialogue through a dedicated application and receive voice or text input;

[1989] means for analyzing a user's input and generating a response from the generative artificial intelligence model based on feedback from the emotion engine;

[1990] means for transmitting the generated response to the user's terminal for display as speech or text;

[1991] A means to reduce users' feelings of loneliness through dialogue with them;

[1992] A system including:

[1993] (Claim 2)

[1994] 2. The system according to claim 1, further comprising means for structuring characteristic information such as a user's daily behavioral patterns, speaking style, catchphrases, hobbies, and actions as data.

[1995] (Claim 3)

[1996] 10. The system of claim 1, further comprising: means for receiving and analyzing information input from a user to generate a response based on a generative artificial intelligence model, and transmitting the response to the user's terminal.

[1997] "Application example 2 when combining emotion engines"

[1998] (Claim 1)

[1999] means for receiving data from a user;

[2000] means for training a generative artificial intelligence model based on the received data;

[2001] a means for interacting with a user through a trained generative artificial intelligence model;

[2002] a means for reducing the user's sense of loneliness through dialogue with the user;

[2003] means for recognizing a user's emotion and tailoring a response based on the emotion;

[2004] a means for providing security according to the emotional state of a user;

[2005] A system including:

[2006] (Claim 2)

[2007] 2. The system according to claim 1, further comprising means for structuring characteristic information such as a user's daily behavioral patterns, speaking style, catchphrases, hobbies, and actions as data.

[2008] (Claim 3)

[2009] 10. The system of claim 1, further comprising: means for receiving and analyzing information input from a user to generate a response based on a generative artificial intelligence model, and transmitting the response to the user's terminal. [Explanation of symbols]

[2010] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving data from a user; means for training a generative artificial intelligence model based on the received data; a means for interacting with a user through a trained generative artificial intelligence model; a means for reducing the user's sense of loneliness through dialogue with the user; A system including:

2. 2. The system according to claim 1, further comprising means for structuring characteristic information such as the user's daily behavioral patterns, speaking style, catchphrases, hobbies, and actions as data.

3. The system of claim 1 , further comprising means for receiving and analyzing information input from a user to generate a response based on a generative artificial intelligence model, and transmitting the response to the user's terminal.

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