System

Abstract

A system is provided.SOLUTION: The system includes a means for setting a role by a user and having a conversation based on the setting, a means for performing natural language analysis based on the role setting and generating an appropriate response, and a means for performing adaptive learning for having continuous conversation 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] In modern society, the number of single elderly, middle-aged, and young people is increasing, creating a need for ways to reduce loneliness and stress. Existing care robots and services are specialized for the elderly and are not easily accessible to people of all ages. Furthermore, personalized dialogue based on role assignments is rarely realized, making it difficult to provide a satisfying user experience. Therefore, there is a need for a system that allows users to assign roles, such as friends or grandchildren, and enjoy conversations based on those roles. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including a means for a user to set a role and converse based on that setting, a means for performing natural language analysis based on the role setting and generating appropriate responses, and a means for adaptive learning to maintain a continuous conversation with the user. Furthermore, by including a means for allowing the user to select characters such as friends, family, and colleagues as role settings, a personalized experience tailored to the user is provided. Furthermore, by including a means for utilizing a knowledge base that supports multiple topics such as weather, cooking, entertainment, and hobbies in the natural language analysis, a wide range of topics can be addressed. This makes the system easy to use for single users of a wide range of ages and improves their satisfaction.

[0006] "Role setting" is the process in which a user selects a specific character, such as a friend, grandchild, or colleague, and has the AI ​​play that character's role.

[0007] "Natural language analysis" is a technology that uses machine learning algorithms and rule-based methods to analyze and understand the meaning of text and voice data provided by users.

[0008] An "appropriate response" is a conversational response that generates the most appropriate reply or information in response to the user's intent or question obtained through natural language analysis.

[0009] "Adaptive learning" is a technology that allows AI models to continuously learn based on past conversation data and user behavior patterns, providing more personalized responses.

[0010] A "knowledge base" is a database that contains information and rules on a wide range of topics, such as weather, cooking, entertainment, and hobbies, and is used for natural language analysis and response generation.

[0011] A "system" is a collection of hardware and software in which multiple elements, such as role setting, natural language analysis, appropriate response generation, adaptive learning, and knowledge bases, work together to achieve a goal. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] The present invention is a system for providing a chat service that can be easily used by single users. Specific embodiments for carrying out the present invention will be described below.

[0034] This system allows users to interact with AI via devices such as smartphones. Users begin by downloading and installing the app. After installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is sent to a server via their device. The server receives this information and securely stores it in a database.

[0035] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, such as friend, grandchild, or colleague, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[0036] When a conversation begins, the device displays an initial message from the AI ​​character, greeting the user with something like, "Hello, Mr. / Ms. X! How was your day?" If the user replies via text or voice, the message is sent from the device to the server. The server uses a natural language processing engine to analyze the message and understand the user's intent. For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response.

[0037] To generate a response, the server uses a knowledge base covering topics such as weather, cooking, entertainment, and hobbies. For example, it generates specific questions such as "Did you enjoy your shopping trip? What did you buy?" and sends them to the device. The device displays the response in voice or text and continues the conversation with the user.

[0038] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[0039] As a concrete example, if user Tanaka uses the app to set the friend role, the following dialogue will take place:

[0040] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[0041] 2. User: "I went shopping this morning."

[0042] 3. The device sends the user input to the server, where natural language analysis is performed.

[0043] 4. Server: "Did you enjoy your shopping trip? What did you buy?"

[0044] 5. The device receives the server's response and displays it to the user.

[0045] 6. User: "I bought a new cookbook. I'm going to try some new recipes tonight."

[0046] 7. The device again sends a message to the server, and the same process of natural language analysis and response generation is repeated.

[0047] This allows even single users to casually enjoy chatting on a variety of topics, and by providing a personalized experience tailored to each user, it is possible to achieve a service that provides high levels of satisfaction.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] The device will guide the user through the download and installation of the app, and the user will follow the instructions to download and install the app.

[0051] Step 2:

[0052] When the app is launched for the first time, the device displays an account creation screen. The user enters basic information such as name, age, and gender, and the device sends that information to the server.

[0053] Step 3:

[0054] The server receives the entered account information and stores it in a database. The device notifies the user that account creation is complete.

[0055] Step 4:

[0056] The device displays a role setting screen, and the user selects a role from among friends, grandchildren, colleagues, etc. The device then sends the selected role information to the server.

[0057] Step 5:

[0058] The server receives the role information and loads the AI ​​character file corresponding to that role. The device displays the initial message from the AI ​​character.

[0059] Step 6:

[0060] The user enters a message using text or voice, and the device sends the message to the server.

[0061] Step 7:

[0062] The server passes the received message to a natural language analysis engine, which analyzes the user's intent and generates an appropriate response based on the analysis results.

[0063] Step 8:

[0064] The server generates a response and sends it to the device, which displays the response to the user in voice or text.

[0065] Step 9:

[0066] The user continues the conversation by entering another message. The message is sent from the device to the server, and natural language analysis and response generation are repeated.

[0067] Step 10:

[0068] The device sends past conversation data to the server, which then adaptively learns from it, providing more personalized responses in subsequent interactions.

[0069] The above are the specific processing steps of the program.

[0070] Example 1

[0071] 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."

[0072] In modern society, single people are increasingly feeling lonely. To solve this problem, a system that is easy to use and provides personalized dialogue for each user is needed. However, existing dialogue systems are unable to provide responses that are personalized enough, making it difficult for users to have a satisfying dialogue.

[0073] 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.

[0074] In this invention, the server includes a means for allowing a user to set a role and converse based on that setting, a means for performing natural language analysis based on the role setting and generating an appropriate response, a means for receiving and safely storing basic information about the user, a means for loading an AI character file and adopting a corresponding conversation style, and a means for adaptive learning to maintain a continuous conversation with the user. This enables a personalized conversation tailored to the user, allowing even single users to enjoy casual, satisfying conversations.

[0075] "User" means any individual or organization that uses the System.

[0076] "Role" refers to the position and relationship of the character that the user selects within the dialogue system.

[0077] "Dialogue" refers to communication between a user and a system, whether through text or voice.

[0078] "Natural language analysis" refers to techniques used to understand messages sent by users, including grammatical analysis, semantic analysis, and sentiment analysis.

[0079] "Response" refers to a reply message generated by the system in response to user input.

[0080] "Basic information" refers to personal information such as the user's name, age, and gender.

[0081] "AI character file" refers to a file with a conversation style and specific dialogue patterns that correspond to the selected role.

[0082] "Adaptive learning" refers to a technology that allows the system to continuously learn from past interaction data and provide more accurate responses.

[0083] A "knowledge base" is a database that stores information and knowledge about a specific topic and is used to generate responses.

[0084] "Personalization" refers to providing tailored responses tailored to a user's individual preferences and interests.

[0085] A "server" is a central computer in a system that receives, stores, processes, and generates responses to data.

[0086] This invention is a system that provides a casual chat service for single users. This system allows users to converse with AI via devices such as smartphones.

[0087] First, users download and install the app. Once the installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is then sent to the server via their device. The server receives this information and securely stores it in a database.

[0088] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, including friends, family, and colleagues, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[0089] When a conversation begins, the device displays an initial message from the AI ​​character and greets the user, such as "Hello, Mr. / Ms. X! How's your day?" When the user replies by text or voice, the message is sent from the device to the server. The server uses a natural language analysis engine to analyze the message and understand the user's intent. Examples of natural language analysis engines used include Google® Cloud Natural Language API and Microsoft® Azure® Text Analytics.

[0090] For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response. In generating the response, the server utilizes a knowledge base that covers topics such as weather, cooking, entertainment, and hobbies. For example, the server generates a specific question such as, "Did you enjoy shopping? What did you buy?" and sends it to the device. The device displays this response in voice or text and continues the conversation with the user.

[0091] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[0092] As a specific example, when a user named Tanaka uses the app to set the role of friend, the following dialogue takes place:

[0093] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[0094] 2. User: "I went shopping this morning."

[0095] 3. Terminal: User input is sent to the server where natural language analysis is performed.

[0096] 4. Server: "Did you enjoy your shopping trip? What did you buy?"

[0097] 5. Terminal: Receives the server's response and displays it to the user.

[0098] 6. User: "I bought a new cookbook. I'm going to try out some new recipes tonight."

[0099] 7. Terminal: The message is sent to the server again, and the same process of natural language analysis and response generation is repeated.

[0100] The following is a specific example of a prompt sentence to be input to the generative AI model:

[0101] Prompt statement:

[0102] The user is greeted by the AI ​​with "Hello, Tanaka-san! What were you doing today?" If the user replies "I went shopping this morning," generate the appropriate natural language analysis and response.

[0103] This allows even single users to casually enjoy chatting on a variety of topics, and by providing a personalized experience tailored to each user, it is possible to achieve a service that provides high levels of satisfaction.

[0104] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0105] Step 1:

[0106] The user downloads and installs the app.

[0107] Input: The user accesses the app store on their smartphone, searches for a chat service app, and selects it.

[0108] Output: The app is downloaded and installed on the user's smartphone.

[0109] How it works: A user visits the app store and downloads an app. Once installed, the app icon appears on the home screen.

[0110] Step 2:

[0111] A user creates an account.

[0112] Input: A user launches the app and enters basic information such as name, age, and gender.

[0113] Output: The basic information entered is sent from the terminal to the server and stored in a database.

[0114] How it works: When a user opens the app for the first time, they are presented with an account creation screen. They enter their basic information and press the Create Account button. The device then sends this information to our server, which stores it securely.

[0115] Step 3:

[0116] The user sets the role of the AI ​​character.

[0117] Input: The user selects a role from among several characters displayed.

[0118] Output: The selected role information is sent from the device to the server, and the AI ​​character file is loaded.

[0119] How it works: The user selects a role from friends, family, colleagues, etc. on the in-app settings screen and completes the role setup. The device sends the selection information to the server, which then loads the corresponding AI character file.

[0120] Step 4:

[0121] The server loads the AI ​​character file.

[0122] Input: User role configuration information.

[0123] Output: The loaded AI character file is sent to the device.

[0124] How it works: The server loads the corresponding AI character file based on the selected role and sends it to the device.

[0125] Step 5:

[0126] The user initiates the interaction.

[0127] Input: AI character file and initial message sent from the server.

[0128] Output: The terminal displays an initial message to the user.

[0129] Action: The device displays an initial message from the AI ​​character: "Hello, Mr. / Ms. X! How's your day?" The user begins the conversation.

[0130] Step 6:

[0131] A user sends a message.

[0132] Input: The user types a message by text or voice.

[0133] Output: The user's message is sent from the terminal to the server.

[0134] How it works: The user types a message by text or voice, such as "I went shopping this morning," and the device sends it to the server.

[0135] Step 7:

[0136] The server parses the user's message.

[0137] Input: The user's message sent from the terminal.

[0138] Output: The parsed result is an understanding of the user's intent.

[0139] How it works: The server uses the natural language analysis engines of Google Cloud Natural Language API and Microsoft Azure Text Analytics to syntactically and semantically analyze the user's message.

[0140] Step 8:

[0141] The server generates an appropriate response.

[0142] Input: Parsed user messages and knowledge base.

[0143] Output: The generated response is sent to the device.

[0144] How it works: Based on the analysis results, the server uses a knowledge base covering topics such as weather, cooking, entertainment, and hobbies to generate specific responses such as "Did you enjoy your shopping? What did you buy?" and sends them to the device.

[0145] Step 9:

[0146] The terminal displays the server's response.

[0147] Input: The response sent by the server.

[0148] Output: The text or audio response that is displayed to the user.

[0149] What it does: The device receives a response from the server and displays it to the user as audio or text.

[0150] Step 10:

[0151] The server uses the conversation data for adaptive learning.

[0152] Input: Past conversation data with the user.

[0153] Output: The learning results in more personalized responses in future interactions.

[0154] How it works: The server analyzes past conversation data, learns trends and patterns, and applies them to future conversations. This allows the AI ​​to provide more personalized responses to each user.

[0155] (Application example 1)

[0156] 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."

[0157] In today's brick-and-mortar stores, there is a lack of systems that allow customers to instantly obtain detailed product information or easily ask for purchasing advice. This results in a decline in customer satisfaction and a loss of motivation to purchase. Furthermore, there are limited opportunities for single customers to casually enjoy conversation, which results in a shorter time spent in the store.

[0158] 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.

[0159] In this invention, the server includes a means for conducting a dialogue based on a role set by a user, a means for generating an appropriate response through natural language analysis corresponding to the role setting, an adaptive learning means for conducting continuous dialogue with the user, and a means for supporting product-related questions and consultations in a physical store. This allows customers to instantly obtain detailed information about products and easily consult about purchasing. Furthermore, since single customers can easily enjoy conversations in a physical store, it is expected that customer satisfaction will improve and the time spent in the store will increase.

[0160] "User" refers to a person who uses the system.

[0161] "Set role" refers to the character or relationship (e.g., friend, family member, colleague) that the user selects when interacting.

[0162] "Dialogue" refers to two-way communication between a user and a system.

[0163] "Natural language analysis" refers to the technology of analyzing text or speech entered by a user to understand its meaning and intent.

[0164] "Appropriate response" refers to a response to a user that is generated based on the results obtained by natural language analysis.

[0165] "Adaptive learning" refers to a technology in which the system learns from past interaction data and provides more personalized responses in subsequent interactions.

[0166] "Brick and mortar store" refers to a physical commercial establishment where customers can visit and purchase products in person.

[0167] "Means to support questions and inquiries" refers to support functions that allow customers to seek product information or purchasing advice.

[0168] To realize this application example, a program must first be installed on the smartphone. This program is a web application using Flask that manages data communication between the server and the smartphone. The hardware and software required are as follows:

[0169] Hardware: Smartphones, servers

[0170] Software: Flask (web framework), requests (HTTP request library), NLP API (natural language analysis engine)

[0171] Program processing and data processing

[0172] 1. Customer Registration:

[0173] The user enters basic information such as name, age, and gender using a smartphone. This information is sent from the smartphone to the server and stored in a dummy database. This generates a unique customer ID for each user.

[0174] 2. Start a conversation:

[0175] The user uses a smartphone to set a role (e.g., friend, family member, colleague), which is then sent to the server, which then generates a corresponding initial message ("Hello, [username]! What product are you looking for today?") and sends it to the user.

[0176] 3. Continuing the dialogue:

[0177] A message (text) from the user is sent to the server via the smartphone. The server uses a natural language processing engine to analyze the message and understand the user's intent. It then generates an appropriate response and sends it to the smartphone. During this process, the server uses adaptive learning to provide more personalized responses in subsequent interactions based on past interaction data.

[0178] Specific examples

[0179] Below is a scenario in which a new customer, Nakamura-san, uses his smartphone to ask a question about his new smartphone.

[0180] 1. Customer Registration:

[0181] Input: "Nakamura, 35 years old, male"

[0182] Output: "Customer ID: 1"

[0183] 2. Start a conversation:

[0184] Enter: "Friends"

[0185] Output: "Hello, Nakamura-san! What product are you looking for today?"

[0186] 3. Continuing the dialogue:

[0187] Type: "I came in today to look at new smartphones."

[0188] Output: "That sounds fun. What features do you need?"

[0189] Prompt Sentence Examples

[0190] Customer Registration:

[0191] curl -X POST -H "Content-Type: application / json" -d '{"name": "Nakamura", "age": 35, "gender": "Male"}' http: / / localhost:5000 / register

[0192] Start the conversation:

[0193] curl -X POST -H "Content-Type: application / json" -d '{"customer_id": 1, "role": "friend"}' http: / / localhost:5000 / start_conversation

[0194] Continuing the dialogue:

[0195] curl -X POST -H "Content-Type: application / json" -d '{"customer_id": 1, "message": "I came to see my new smartphone today."}' http: / / localhost:5000 / continue_conversation

[0196] The above system will enable customers to easily ask questions and get advice about products in real stores, which is expected to improve customer satisfaction and extend the time they spend in the store.

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] Customer Registration:

[0200] Input: The user uses a smartphone to input basic information such as name, age, and gender.

[0201] Operation: The terminal sends the input information to the server.

[0202] Data processing: The server stores the received information in a dummy database.

[0203] Output: The server generates a unique customer ID and returns it to the terminal.

[0204] Step 2:

[0205] Start the conversation:

[0206] Input: The user uses a smartphone to set the role (e.g., friend, family member, colleague) with which to start a conversation.

[0207] Operation: The device sends the role information that has been set to the server.

[0208] Data processing: The server generates an initial message based on the role information.

[0209] Output: The server returns an initial message (e.g., "Hello, [username]! What products are you looking for today?") to the terminal. The terminal displays this message.

[0210] Step 3:

[0211] Continuation of conversation (user sending message):

[0212] Input: The user uses the smartphone to input a text message (e.g., "I came to look at the new smartphone today.").

[0213] Operation: The terminal sends the entered message to the server.

[0214] Data processing: The server sends the received message to the natural language analysis engine and receives the analysis results.

[0215] Output: The server generates an appropriate response based on the analysis and returns it to the device, which displays this message (e.g., "That sounds fun. What features do you need?").

[0216] Step 4:

[0217] Adaptive Learning:

[0218] Input: Past interaction data between the user and the system

[0219] Operation: The server uses an adaptive learning algorithm to analyze past interaction data and learn the user's interaction patterns.

[0220] Data processing: Based on the learning results, the system prepares to generate more personalized responses in subsequent interactions.

[0221] Output: A more appropriate response can be provided to the user in subsequent interactions.

[0222] The above processing steps realize a system that allows customers in physical stores to easily ask questions or ask for advice about products. This system operates by combining a server, terminals, a natural language analysis engine, and an adaptive learning algorithm.

[0223] 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.

[0224] The present invention is a system that provides a casual chat service to single users, and in particular, recognizes the user's emotions and provides personalized responses based on those emotions. Specific embodiments for carrying out the present invention will be described below.

[0225] This system allows users to interact with AI via devices such as smartphones. Users begin by downloading and installing the app. After installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is sent to a server via their device. The server receives this information and securely stores it in a database.

[0226] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, such as friend, grandchild, or colleague, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[0227] When a conversation begins, the device displays an initial message from the AI ​​character, greeting the user with something like, "Hello, Mr. / Ms. X! How was your day?" If the user replies via text or voice, the message is sent from the device to the server. The server uses a natural language processing engine to analyze the message and understand the user's intent. For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response.

[0228] A feature of the present invention is the inclusion of an emotion engine for recognizing emotions. The emotion engine analyzes emotions from the user's text, voice tone, expressions, and vocabulary. For example, if a user inputs "I'm very tired today," the emotion engine recognizes this as "fatigue" and generates a response that is in line with that emotion. Specifically, it could be a message such as "That must have been tough. Let's take it easy and get some rest today."

[0229] When generating a response, the server adjusts the appropriate response based on the analysis results of the emotion engine. For example, it generates specific questions such as "Did you enjoy shopping? What did you buy?" and sends them to the device. The device then displays this response in voice or text, continuing the conversation with the user.

[0230] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. Emotion analysis data from the emotion engine is also incorporated into adaptive learning, enabling more emotionally sensitive conversations. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[0231] As a concrete example, when user Tanaka uses the app to set the role of friend and the emotion engine is activated, the following dialogue takes place:

[0232] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[0233] 2. User: "I went shopping this morning."

[0234] 3. The device sends the user input to the server, where natural language analysis and sentiment analysis are performed.

[0235] 4. The server analyzes the text information, and the emotion engine recognizes the user's comment "It was fun" as "positive." The server then generates a response based on the positive emotion.

[0236] 5. Server: Generates the question "Did you enjoy your shopping? What did you buy?" and sends it to the device.

[0237] 6. The device receives the server's response and displays it to the user.

[0238] 7. User: "I bought a new cookbook. I'm going to try some new recipes tonight."

[0239] 8. The device again sends a message to the server, and the same process of natural language analysis, sentiment analysis, and response generation is repeated.

[0240] This allows even single users to casually enjoy chatting on a variety of topics. In addition, by providing a personalized experience that is sensitive to users' emotions, it is possible to realize a service that provides high levels of satisfaction.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] The device will guide the user through the download and installation of the app, and the user will follow the instructions to download and install the app.

[0244] Step 2:

[0245] When the app is launched for the first time, the device displays an account creation screen. The user enters basic information such as name, age, and gender, and the device sends that information to the server.

[0246] Step 3:

[0247] The server receives the entered account information and stores it in a database. The device notifies the user that account creation is complete.

[0248] Step 4:

[0249] The device displays a role setting screen, and the user selects a role from among friends, grandchildren, colleagues, etc. The device then sends the selected role information to the server.

[0250] Step 5:

[0251] The server receives the role information and loads the AI ​​character file corresponding to that role. The device displays the initial message from the AI ​​character.

[0252] Step 6:

[0253] The user enters a message using text or voice, and the device sends the message to the server.

[0254] Step 7:

[0255] The server passes the received message to a natural language analysis engine to analyze the user's intent. An appropriate response is generated based on the analysis results. In parallel, the server uses an emotion engine to analyze the user's emotions.

[0256] Step 8:

[0257] The server reflects the analysis results of the emotion engine in the response and adjusts it to reflect the emotion. For example, if a user enters "I'm very tired today," the emotion engine will recognize this as "fatigue" and generate a response such as "That must have been tough. Let's take it easy and get some rest today."

[0258] Step 9:

[0259] The server generates a response and sends it to the device, which displays the response to the user in voice or text.

[0260] Step 10:

[0261] The user continues the conversation by entering another message. The message is then sent from the device to the server, where natural language analysis and sentiment analysis are repeated.

[0262] Step 11:

[0263] The device sends past conversation data to the server, which then adaptively learns from it, providing more personalized and emotionally attuned responses in subsequent interactions.

[0264] The above are the specific processing steps of the program.

[0265] Example 2

[0266] 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."

[0267] Conventional chat systems have difficulty responding appropriately to users' emotions, resulting in insufficient personalized dialogue. Furthermore, they are limited to simple natural language analysis and standardized responses, resulting in a poor user experience. This makes it difficult for users to use them for extended periods of time, and they lack the adaptive learning required for continuous conversation.

[0268] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0269] In this invention, the server includes means for allowing a user to set a role and for interacting based on that setting, means for transmitting text or voice input by the user to the server and for the server to perform natural language processing, means for performing natural language analysis and generating an appropriate response, means for performing emotion analysis and generating a personalized response that is in line with the user's emotions, and means for adaptive learning to enable continuous conversation with the user. This makes it possible to understand the user's emotions and provide appropriate responses in response to those emotions, thereby realizing a continuous and satisfying conversation experience.

[0270] "User" means an individual who uses the System.

[0271] "Role" refers to the character or role that a user sets within the system, including friends, family, colleagues, etc.

[0272] "Dialogue" refers to text or voice communication between a system and a user.

[0273] "Natural language processing" refers to the technology that allows artificial intelligence and computers to understand and analyze human language.

[0274] "Sentiment analysis" refers to technology that identifies a user's emotions from input text or voice.

[0275] "Personalized responses" refer to customized replies generated based on the user's individual emotions and conversational context.

[0276] "Adaptive learning" refers to a technology that allows a system to learn from past conversational data with a user and provide more appropriate responses in future interactions.

[0277] A "knowledge base" refers to the collection of information or data that a system has about a particular topic.

[0278] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and generates natural language.

[0279] "Prompt sentence" refers to the input sentence required by a generative AI model, based on which the model generates a response.

[0280] This invention is a system that provides a casual chat service for single users, and in particular, recognizes the user's emotions and provides personalized responses based on those emotions. This system is based on the user interacting with AI via a device such as a smartphone.

[0281] Hardware and software used

[0282] Hardware

[0283] 1. Smartphones (e.g., iPhone (registered trademark), Android (registered trademark) devices)

[0284] software

[0285] 1. Chat service application (installed by the user)

[0286] 2. Server-side natural language processing engines (e.g., SpaCy, Google Natural Language API)

[0287] 3. Sentiment analysis engine (e.g., IBM Watson® Tone Analyzer)

[0288] 4. Database system (e.g., MySQL (registered trademark), PostgreSQL)

[0289] Specific operation of the system

[0290] User

[0291] Users first download and install a chat service app from the app store. After launching the app, they create an account by entering basic information such as their name, age, and gender. They then select a character from a list of characters such as a friend, grandchild, or colleague and complete the settings.

[0292] Terminal

[0293] The device sends the basic information entered by the user to the server and generates an initial message based on the received character setting information. This message is displayed as "Hello, Mr. / Ms. X! How is your day?"

[0294] server

[0295] The server stores the received information in a database and analyzes the user's message using a natural language processing engine to extract the message's subject and keywords, and a sentiment analysis engine to identify the sentiment of the input text or voice.

[0296] Specific examples

[0297] 1. The user types, "I went shopping this morning."

[0298] 2. The server performs natural language analysis and sentiment analysis to identify the message's intent and sentiment, which in this case is identified as "positive."

[0299] 3. The server generates a response such as "Did you enjoy your shopping? What did you buy?" and sends it to the device.

[0300] 4. The terminal displays the received response to the user.

[0301] Continuous conversation and adaptive learning

[0302] The server accumulates continuous conversation data with the user and performs adaptive learning, which makes future conversations more personalized. Emotion analysis results are also incorporated into the adaptive learning, enabling conversations that are more in tune with the user's emotions.

[0303] Prompt Sentence Examples

[0304] Prompt: "I went shopping this morning."

[0305] Expected response: The emotion engine will recognize this as "positive" and respond with "Did you enjoy your shopping trip? What did you buy?"

[0306] The present invention makes it possible to realize a system that allows even single users to casually enjoy casual conversation on a variety of topics, and provides a personalized experience that is sensitive to their emotions.

[0307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0308] Step 1:

[0309] Download and install the app

[0310] User: Download and install the chat service app from the app store. Tap the app icon to launch it.

[0311] Input: Download link from the app store

[0312] Output: The app is installed on the user's device and the icon is displayed.

[0313] Step 2:

[0314] Create an account

[0315] User: Enter basic information such as name, age, and gender, then tap "Next."

[0316] Terminal: Converts the entered information into JSON format and sends it to the server as an HTTPS request.

[0317] Server: Stores the received information in a database and creates an account.

[0318] Input: Basic information such as name, age, and gender

[0319] Output: User basic information and new account stored on the server

[0320] Step 3:

[0321] AI character settings

[0322] User: On the settings screen, select a character such as a friend, grandchild, or colleague, and tap "Decide."

[0323] Device: Sends the selected character information to the server.

[0324] Server: Loads the appropriate AI character file based on the character information received.

[0325] Input: Role setting (friend, grandchild, colleague, etc.)

[0326] Output: AI character file loaded on the server side

[0327] Step 4:

[0328] Starting the initial conversation

[0329] Terminal: The loaded AI character generates an initial text message that displays to the user: "Hello, Mr. / Ms. X! How's your day?"

[0330] Input: The initial message sent by the server

[0331] Output: Initial message displayed on the terminal

[0332] Step 5:

[0333] Sending a user message

[0334] User: Enter a message to the AI ​​via text or voice and tap the "Send" button.

[0335] Terminal: Sends the entered message to the server.

[0336] Input: Message from the user (e.g. "I went shopping this morning")

[0337] Output: User's message sent to the server

[0338] Step 6:

[0339] Message analysis (natural language analysis and sentiment analysis)

[0340] Server: Analyzes received messages using a natural language processing engine to extract topics and keywords, and a sentiment analysis engine to identify emotions.

[0341] Input: Message from the user (e.g. "I went shopping this morning")

[0342] Output: Extracted keywords, themes, and sentiment analysis results

[0343] Step 7:

[0344] Response Generation

[0345] Server: Generates an appropriate response based on the results of natural language analysis and sentiment analysis. Example: "Did you enjoy your shopping? What did you buy?"

[0346] Input: Results of natural language analysis and sentiment analysis

[0347] Output: The generated response (e.g., "Did you enjoy your shopping trip? What did you buy?")

[0348] Step 8:

[0349] Viewing the response

[0350] Terminal: Displays the response received from the server to the user as text.

[0351] Input: Response from the server

[0352] Output: Response displayed on the terminal

[0353] Step 9:

[0354] Adaptive learning for improved performance

[0355] Server: Accumulates past conversation data with users and retrains the machine learning model. Sentiment analysis results are also incorporated into adaptive learning.

[0356] Input: Past conversation data and sentiment analysis results

[0357] Output: Improved accuracy of personalized responses

[0358] By executing each processing step successively in this manner, the user can enjoy a continuous and personalized chat service.

[0359] (Application example 2)

[0360] 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."

[0361] Conventional electronic payment services lacked an interactive experience for users. This resulted in low user satisfaction and a sense of loneliness. For single users in particular, the lack of human interaction during the payment process was problematic. Furthermore, there was no system that could analyze users' emotions and provide personalized responses based on them.

[0362] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for allowing a user to set a role and for interacting based on the role setting, means for performing natural language analysis based on the role setting and generating an appropriate response, means for performing adaptive learning to maintain a continuous conversation with the user, means for analyzing the user's emotions, means for generating a personalized response based on the emotion analysis results, and means for interacting when the user makes a payment within the electronic payment service. This allows the user to enjoy a human-like interaction during the payment process, thereby improving user satisfaction.

[0363] "Means for users to set roles" refers to a setting function that allows users to select characters such as friends, family, and colleagues within the application, and the AI ​​will interact with them based on that character.

[0364] "Means for natural language analysis" refers to technology that enables AI to analyze text and voice input from users, understand their intent, and generate appropriate responses.

[0365] "Means for adaptive learning" refers to a function that enables the AI ​​to learn from past interaction data with the user and provide more personalized responses in subsequent interactions.

[0366] "Means for analyzing user emotions" refers to technology that analyzes the emotional state of a user based on the user's input text, voice tone, expressions, and vocabulary, and reflects the results in the dialogue.

[0367] The "means for generating personalized responses" is a technology for generating responses for individually optimized dialogue based on the analyzed emotions of a user.

[0368] "Means for engaging in dialogue when a user makes a payment within an electronic payment service" refers to a function that enables AI to engage in dialogue based on the results of emotion analysis while the user is making a payment, thereby improving the user experience.

[0369] A "means for utilizing a knowledge base" is a platform for storing information on specific topics such as weather, cooking, entertainment, and hobbies, and using that information to generate dialogue.

[0370] The "dialogue generation process using a generative AI model" is a process that utilizes a pre-trained AI model to generate natural and appropriate dialogue in response to user input.

[0371] A "prompt sentence" is an input sentence to obtain an appropriate response from a generative AI model, and is text that provides instructions for generating a dialogue.

[0372] In order to implement this invention, it is necessary to build a system that allows advanced interactive dialogue through data communication between a server, a terminal, and a user. The embodiments of the present invention are as follows.

[0373] 1. Hardware and Software Requirements

[0374] Hardware

[0375] Device: Smartphone

[0376] Server: Cloud service (e.g., AWS (registered trademark) EC2)

[0377] software

[0378] Natural language analysis tool: Google Cloud Natural Language

[0379] Sentiment analysis engine: IBM Watson Tone Analyzer

[0380] Database: AWS RDS

[0381] Generative AI models: OpenAI (registered trademark), GPT-3 (registered trademark), etc.

[0382] 2. Program processing explanation

[0383] User Interaction

[0384] Users start the application via their smartphone and first set their role. In this role setting, they can select characters such as friends, family, colleagues, etc. Once this selection is complete, the setting information is sent from the device to the server.

[0385] Server Processing

[0386] The server loads the appropriate character file based on the received configuration information. At the same time, it uses a natural language analysis tool to analyze the user's input and understand their intent. It also uses an emotion analysis engine to analyze the user's emotions from their text and voice, and generates a response based on the results.

[0387] Generating a response

[0388] Based on the analyzed information, the generative AI model generates an appropriate response. The model uses prompts to generate the appropriate response. For example, if a user types, "I bought new headphones," the model generates a prompt: "My new headphones are great! How do they sound?"

[0389] Ongoing dialogue

[0390] The server uses adaptive learning to learn from past conversation data to make future conversations more personalized. All conversation data and sentiment analysis data is securely stored in the server's database.

[0391] Specific examples

[0392] Specifically, if a user types "I tried a new cooking recipe today," the following dialogue will occur:

[0393] 1. User: "I tried a new cooking recipe today."

[0394] 2. Server: Performs natural language analysis and sentiment analysis

[0395] 3. Server: Sends the following prompt to the generative AI model:

[0396] "You tried a new cooking recipe! How was it?"

[0397] This allows users to enjoy a conversation with AI while also receiving an emotionally supportive experience.

[0398] This embodiment allows a user to have a dialogue when making a payment within the electronic payment service, thereby improving user satisfaction.

[0399] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0400] Step 1:

[0401] Users launch the application via their smartphone. They select characters such as friends, family, or colleagues and set their roles. Input includes information about the characters selected by the user.

[0402] Step 2:

[0403] The terminal sends the role information set by the user to the server. The input is the character information selected by the user, and the output is the role setting data sent to the server.

[0404] Step 3:

[0405] The server loads the appropriate character file based on the role configuration data it receives. This character file contains the interaction style for the selected role. The input is the role configuration data, and the output is the loaded character file.

[0406] Step 4:

[0407] The user inputs a message using text or voice. The user's input is sent to the terminal, and the input includes text or voice data.

[0408] Step 5:

[0409] The terminal sends the received input to the server. The input is the user's message, and the output is the text or voice data sent to the server.

[0410] Step 6:

[0411] The server uses a natural language analysis tool (Google Cloud Natural Language) to analyze the user's input and understand their intent. The input is the user's message data, and the output is the analysis result.

[0412] Step 7:

[0413] The server uses an emotion analysis engine (IBM Watson Tone Analyzer) to analyze the user's emotions. The input is the user's message data, and the output is the analyzed emotion data.

[0414] Step 8:

[0415] Based on the analysis results, the server generates an appropriate response using a generative AI model (such as OpenAI GPT-3). A prompt sentence is used, and the generated response is based on the input prompt. The input is a prompt sentence based on the analyzed intent and emotion data, and the output is the generated response.

[0416] Step 9:

[0417] The server sends the generated response to the terminal. The input is the generated response data, and the output is the response message sent to the terminal.

[0418] Step 10:

[0419] The terminal displays the received response message to the user in text or voice. The input is the response message, and the output is the displayed text or voice data.

[0420] Step 11:

[0421] The server uses adaptive learning to learn from past conversation data with the user and apply it to future conversations. The input is past conversation data and sentiment analysis data, and the output is the learned model.

[0422] This series of processes enables the system to maintain a continuous dialogue with the user and provide personalized responses.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] [Second embodiment]

[0427] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0438] 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."

[0439] The present invention is a system for providing a chat service that can be easily used by single users. Specific embodiments for carrying out the present invention will be described below.

[0440] This system allows users to interact with AI via devices such as smartphones. Users begin by downloading and installing the app. After installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is sent to a server via their device. The server receives this information and securely stores it in a database.

[0441] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, such as friend, grandchild, or colleague, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[0442] When a conversation begins, the device displays an initial message from the AI ​​character, greeting the user with something like, "Hello, Mr. / Ms. X! How was your day?" If the user replies via text or voice, the message is sent from the device to the server. The server uses a natural language processing engine to analyze the message and understand the user's intent. For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response.

[0443] To generate a response, the server uses a knowledge base covering topics such as weather, cooking, entertainment, and hobbies. For example, it generates specific questions such as "Did you enjoy your shopping trip? What did you buy?" and sends them to the device. The device displays the response in voice or text and continues the conversation with the user.

[0444] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[0445] As a concrete example, if user Tanaka uses the app to set the friend role, the following dialogue will take place:

[0446] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[0447] 2. User: "I went shopping this morning."

[0448] 3. The device sends the user input to the server, where natural language analysis is performed.

[0449] 4. Server: "Did you enjoy your shopping trip? What did you buy?"

[0450] 5. The device receives the server's response and displays it to the user.

[0451] 6. User: "I bought a new cookbook. I'm going to try some new recipes tonight."

[0452] 7. The device again sends a message to the server, and the same process of natural language analysis and response generation is repeated.

[0453] This allows even single users to casually enjoy chatting on a variety of topics, and by providing a personalized experience tailored to each user, it is possible to achieve a service that provides high levels of satisfaction.

[0454] The processing flow will be explained below.

[0455] Step 1:

[0456] The device will guide the user through the download and installation of the app, and the user will follow the instructions to download and install the app.

[0457] Step 2:

[0458] When the app is launched for the first time, the device displays an account creation screen. The user enters basic information such as name, age, and gender, and the device sends that information to the server.

[0459] Step 3:

[0460] The server receives the entered account information and stores it in a database. The device notifies the user that account creation is complete.

[0461] Step 4:

[0462] The device displays a role setting screen, and the user selects a role from among friends, grandchildren, colleagues, etc. The device then sends the selected role information to the server.

[0463] Step 5:

[0464] The server receives the role information and loads the AI ​​character file corresponding to that role. The device displays the initial message from the AI ​​character.

[0465] Step 6:

[0466] The user enters a message using text or voice, and the device sends the message to the server.

[0467] Step 7:

[0468] The server passes the received message to a natural language analysis engine, which analyzes the user's intent and generates an appropriate response based on the analysis results.

[0469] Step 8:

[0470] The server generates a response and sends it to the device, which displays the response to the user in voice or text.

[0471] Step 9:

[0472] The user continues the conversation by entering another message. The message is sent from the device to the server, and natural language analysis and response generation are repeated.

[0473] Step 10:

[0474] The device sends past conversation data to the server, which then adaptively learns from it, providing more personalized responses in subsequent interactions.

[0475] The above are the specific processing steps of the program.

[0476] Example 1

[0477] 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."

[0478] In modern society, single people are increasingly feeling lonely. To solve this problem, a system that is easy to use and provides personalized dialogue for each user is needed. However, existing dialogue systems are unable to provide responses that are personalized enough, making it difficult for users to have a satisfying dialogue.

[0479] 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.

[0480] In this invention, the server includes a means for allowing a user to set a role and converse based on that setting, a means for performing natural language analysis based on the role setting and generating an appropriate response, a means for receiving and safely storing basic information about the user, a means for loading an AI character file and adopting a corresponding conversation style, and a means for adaptive learning to maintain a continuous conversation with the user. This enables a personalized conversation tailored to the user, allowing even single users to enjoy casual, satisfying conversations.

[0481] "User" means any individual or organization that uses the System.

[0482] "Role" refers to the position and relationship of the character that the user selects within the dialogue system.

[0483] "Dialogue" refers to communication between a user and a system, whether through text or voice.

[0484] "Natural language analysis" refers to techniques used to understand messages sent by users, including grammatical analysis, semantic analysis, and sentiment analysis.

[0485] "Response" refers to a reply message generated by the system in response to user input.

[0486] "Basic information" refers to personal information such as the user's name, age, and gender.

[0487] "AI character file" refers to a file with a conversation style and specific dialogue patterns that correspond to the selected role.

[0488] "Adaptive learning" refers to a technology that allows the system to continuously learn from past interaction data and provide more accurate responses.

[0489] A "knowledge base" is a database that stores information and knowledge about a specific topic and is used to generate responses.

[0490] "Personalization" refers to providing tailored responses tailored to a user's individual preferences and interests.

[0491] A "server" is a central computer in a system that receives, stores, processes, and generates responses to data.

[0492] This invention is a system that provides a casual chat service for single users. This system allows users to converse with AI via devices such as smartphones.

[0493] First, users download and install the app. Once the installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is then sent to the server via their device. The server receives this information and securely stores it in a database.

[0494] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, including friends, family, and colleagues, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[0495] When a conversation begins, the device displays an initial message from the AI ​​character and greets the user, such as "Hello, Mr. / Ms. X! How's your day?" When the user replies by text or voice, the message is sent from the device to the server. The server uses a natural language analysis engine to analyze the message and understand the user's intent. Examples of natural language analysis engines used include Google Cloud Natural Language API and Microsoft Azure Text Analytics.

[0496] For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response. In generating the response, the server utilizes a knowledge base that covers topics such as weather, cooking, entertainment, and hobbies. For example, the server generates a specific question such as, "Did you enjoy shopping? What did you buy?" and sends it to the device. The device displays this response in voice or text and continues the conversation with the user.

[0497] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[0498] As a specific example, when a user named Tanaka uses the app to set the role of friend, the following dialogue takes place:

[0499] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[0500] 2. User: "I went shopping this morning."

[0501] 3. Terminal: User input is sent to the server where natural language analysis is performed.

[0502] 4. Server: "Did you enjoy your shopping trip? What did you buy?"

[0503] 5. Terminal: Receives the server's response and displays it to the user.

[0504] 6. User: "I bought a new cookbook. I'm going to try out some new recipes tonight."

[0505] 7. Terminal: The message is sent to the server again, and the same process of natural language analysis and response generation is repeated.

[0506] The following is a specific example of a prompt sentence to be input to the generative AI model:

[0507] Prompt statement:

[0508] The user is greeted by the AI ​​with "Hello, Tanaka-san! What were you doing today?" If the user replies "I went shopping this morning," generate the appropriate natural language analysis and response.

[0509] This allows even single users to casually enjoy chatting on a variety of topics, and by providing a personalized experience tailored to each user, it is possible to achieve a service that provides high levels of satisfaction.

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

[0511] Step 1:

[0512] The user downloads and installs the app.

[0513] Input: The user accesses the app store on their smartphone, searches for a chat service app, and selects it.

[0514] Output: The app is downloaded and installed on the user's smartphone.

[0515] How it works: A user visits the app store and downloads an app. Once installed, the app icon appears on the home screen.

[0516] Step 2:

[0517] A user creates an account.

[0518] Input: A user launches the app and enters basic information such as name, age, and gender.

[0519] Output: The basic information entered is sent from the terminal to the server and stored in a database.

[0520] How it works: When a user opens the app for the first time, they are presented with an account creation screen. They enter their basic information and press the Create Account button. The device then sends this information to our server, which stores it securely.

[0521] Step 3:

[0522] The user sets the role of the AI ​​character.

[0523] Input: The user selects a role from among several characters displayed.

[0524] Output: The selected role information is sent from the device to the server, and the AI ​​character file is loaded.

[0525] How it works: The user selects a role from friends, family, colleagues, etc. on the in-app settings screen and completes the role setup. The device sends the selection information to the server, which then loads the corresponding AI character file.

[0526] Step 4:

[0527] The server loads the AI ​​character file.

[0528] Input: User role configuration information.

[0529] Output: The loaded AI character file is sent to the device.

[0530] How it works: The server loads the corresponding AI character file based on the selected role and sends it to the device.

[0531] Step 5:

[0532] The user initiates the interaction.

[0533] Input: AI character file and initial message sent from the server.

[0534] Output: The terminal displays an initial message to the user.

[0535] Action: The device displays an initial message from the AI ​​character: "Hello, Mr. / Ms. X! How's your day?" The user begins the conversation.

[0536] Step 6:

[0537] A user sends a message.

[0538] Input: The user types a message by text or voice.

[0539] Output: The user's message is sent from the terminal to the server.

[0540] How it works: The user types a message by text or voice, such as "I went shopping this morning," and the device sends it to the server.

[0541] Step 7:

[0542] The server parses the user's message.

[0543] Input: The user's message sent from the terminal.

[0544] Output: The parsed result is an understanding of the user's intent.

[0545] How it works: The server uses the natural language analysis engines of Google Cloud Natural Language API and Microsoft Azure Text Analytics to syntactically and semantically analyze the user's message.

[0546] Step 8:

[0547] The server generates an appropriate response.

[0548] Input: Parsed user messages and knowledge base.

[0549] Output: The generated response is sent to the device.

[0550] How it works: Based on the analysis results, the server uses a knowledge base covering topics such as weather, cooking, entertainment, and hobbies to generate specific responses such as "Did you enjoy your shopping? What did you buy?" and sends them to the device.

[0551] Step 9:

[0552] The terminal displays the server's response.

[0553] Input: The response sent by the server.

[0554] Output: The text or audio response that is displayed to the user.

[0555] What it does: The device receives a response from the server and displays it to the user as audio or text.

[0556] Step 10:

[0557] The server uses the conversation data for adaptive learning.

[0558] Input: Past conversation data with the user.

[0559] Output: The learning results in more personalized responses in future interactions.

[0560] How it works: The server analyzes past conversation data, learns trends and patterns, and applies them to future conversations. This allows the AI ​​to provide more personalized responses to each user.

[0561] (Application example 1)

[0562] 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."

[0563] In today's brick-and-mortar stores, there is a lack of systems that allow customers to instantly obtain detailed product information or easily ask for purchasing advice. This results in a decline in customer satisfaction and a loss of motivation to purchase. Furthermore, there are limited opportunities for single customers to casually enjoy conversation, which results in a shorter time spent in the store.

[0564] 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.

[0565] In this invention, the server includes a means for conducting a dialogue based on a role set by a user, a means for generating an appropriate response through natural language analysis corresponding to the role setting, an adaptive learning means for conducting continuous dialogue with the user, and a means for supporting product-related questions and consultations in a physical store. This allows customers to instantly obtain detailed information about products and easily consult about purchasing. Furthermore, since single customers can easily enjoy conversations in a physical store, it is expected that customer satisfaction will improve and the time spent in the store will increase.

[0566] "User" refers to a person who uses the system.

[0567] "Set role" refers to the character or relationship (e.g., friend, family member, colleague) that the user selects when interacting.

[0568] "Dialogue" refers to two-way communication between a user and a system.

[0569] "Natural language analysis" refers to the technology of analyzing text or speech entered by a user to understand its meaning and intent.

[0570] "Appropriate response" refers to a response to a user that is generated based on the results obtained by natural language analysis.

[0571] "Adaptive learning" refers to a technology in which the system learns from past interaction data and provides more personalized responses in subsequent interactions.

[0572] "Brick and mortar store" refers to a physical commercial establishment where customers can visit and purchase products in person.

[0573] "Means to support questions and inquiries" refers to support functions that allow customers to seek product information or purchasing advice.

[0574] To realize this application example, a program must first be installed on the smartphone. This program is a web application using Flask that manages data communication between the server and the smartphone. The hardware and software required are as follows:

[0575] Hardware: Smartphones, servers

[0576] Software: Flask (web framework), requests (HTTP request library), NLP API (natural language analysis engine)

[0577] Program processing and data processing

[0578] 1. Customer Registration:

[0579] The user enters basic information such as name, age, and gender using a smartphone. This information is sent from the smartphone to the server and stored in a dummy database. This generates a unique customer ID for each user.

[0580] 2. Start a conversation:

[0581] The user uses a smartphone to set a role (e.g., friend, family member, colleague), which is then sent to the server, which then generates a corresponding initial message ("Hello, [username]! What product are you looking for today?") and sends it to the user.

[0582] 3. Continuing the dialogue:

[0583] A message (text) from the user is sent to the server via the smartphone. The server uses a natural language processing engine to analyze the message and understand the user's intent. It then generates an appropriate response and sends it to the smartphone. During this process, the server uses adaptive learning to provide more personalized responses in subsequent interactions based on past interaction data.

[0584] Specific examples

[0585] Below is a scenario in which a new customer, Nakamura-san, uses his smartphone to ask a question about his new smartphone.

[0586] 1. Customer Registration:

[0587] Input: "Nakamura, 35 years old, male"

[0588] Output: "Customer ID: 1"

[0589] 2. Start a conversation:

[0590] Enter: "Friends"

[0591] Output: "Hello, Nakamura-san! What product are you looking for today?"

[0592] 3. Continuing the dialogue:

[0593] Type: "I came in today to look at new smartphones."

[0594] Output: "That sounds fun. What features do you need?"

[0595] Prompt Sentence Examples

[0596] Customer Registration:

[0597] curl -X POST -H "Content-Type: application / json" -d '{"name": "Nakamura", "age": 35, "gender": "Male"}' http: / / localhost:5000 / register

[0598] Start the conversation:

[0599] curl -X POST -H "Content-Type: application / json" -d '{"customer_id": 1, "role": "friend"}' http: / / localhost:5000 / start_conversation

[0600] Continuing the dialogue:

[0601] curl -X POST -H "Content-Type: application / json" -d '{"customer_id": 1, "message": "I came to see my new smartphone today."}' http: / / localhost:5000 / continue_conversation

[0602] The above system will enable customers to easily ask questions and get advice about products in real stores, which is expected to improve customer satisfaction and extend the time they spend in the store.

[0603] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0604] Step 1:

[0605] Customer Registration:

[0606] Input: The user uses a smartphone to input basic information such as name, age, and gender.

[0607] Operation: The terminal sends the input information to the server.

[0608] Data processing: The server stores the received information in a dummy database.

[0609] Output: The server generates a unique customer ID and returns it to the terminal.

[0610] Step 2:

[0611] Start the conversation:

[0612] Input: The user uses a smartphone to set the role (e.g., friend, family member, colleague) with which to start a conversation.

[0613] Operation: The device sends the role information that has been set to the server.

[0614] Data processing: The server generates an initial message based on the role information.

[0615] Output: The server returns an initial message (e.g., "Hello, [username]! What products are you looking for today?") to the terminal. The terminal displays this message.

[0616] Step 3:

[0617] Continuation of conversation (user sending message):

[0618] Input: The user uses the smartphone to input a text message (e.g., "I came to look at the new smartphone today.").

[0619] Operation: The terminal sends the entered message to the server.

[0620] Data processing: The server sends the received message to the natural language analysis engine and receives the analysis results.

[0621] Output: The server generates an appropriate response based on the analysis and returns it to the device, which displays this message (e.g., "That sounds fun. What features do you need?").

[0622] Step 4:

[0623] Adaptive Learning:

[0624] Input: Past interaction data between the user and the system

[0625] Operation: The server uses an adaptive learning algorithm to analyze past interaction data and learn the user's interaction patterns.

[0626] Data processing: Based on the learning results, the system prepares to generate more personalized responses in subsequent interactions.

[0627] Output: A more appropriate response can be provided to the user in subsequent interactions.

[0628] The above processing steps realize a system that allows customers in physical stores to easily ask questions or ask for advice about products. This system operates by combining a server, terminals, a natural language analysis engine, and an adaptive learning algorithm.

[0629] 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.

[0630] The present invention is a system that provides a casual chat service to single users, and in particular, recognizes the user's emotions and provides personalized responses based on those emotions. Specific embodiments for carrying out the present invention will be described below.

[0631] This system allows users to interact with AI via devices such as smartphones. Users begin by downloading and installing the app. After installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is sent to a server via their device. The server receives this information and securely stores it in a database.

[0632] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, such as friend, grandchild, or colleague, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[0633] When a conversation begins, the device displays an initial message from the AI ​​character, greeting the user with something like, "Hello, Mr. / Ms. X! How was your day?" If the user replies via text or voice, the message is sent from the device to the server. The server uses a natural language processing engine to analyze the message and understand the user's intent. For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response.

[0634] A feature of the present invention is the inclusion of an emotion engine for recognizing emotions. The emotion engine analyzes emotions from the user's text, voice tone, expressions, and vocabulary. For example, if a user inputs "I'm very tired today," the emotion engine recognizes this as "fatigue" and generates a response that is in line with that emotion. Specifically, it could be a message such as "That must have been tough. Let's take it easy and get some rest today."

[0635] When generating a response, the server adjusts the appropriate response based on the analysis results of the emotion engine. For example, it generates specific questions such as "Did you enjoy shopping? What did you buy?" and sends them to the device. The device then displays this response in voice or text, continuing the conversation with the user.

[0636] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. Emotion analysis data from the emotion engine is also incorporated into adaptive learning, enabling more emotionally sensitive conversations. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[0637] As a concrete example, when user Tanaka uses the app to set the role of friend and the emotion engine is activated, the following dialogue takes place:

[0638] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[0639] 2. User: "I went shopping this morning."

[0640] 3. The device sends the user input to the server, where natural language analysis and sentiment analysis are performed.

[0641] 4. The server analyzes the text information, and the emotion engine recognizes the user's comment "It was fun" as "positive." The server then generates a response based on the positive emotion.

[0642] 5. Server: Generates the question "Did you enjoy your shopping? What did you buy?" and sends it to the device.

[0643] 6. The device receives the server's response and displays it to the user.

[0644] 7. User: "I bought a new cookbook. I'm going to try some new recipes tonight."

[0645] 8. The device again sends a message to the server, and the same process of natural language analysis, sentiment analysis, and response generation is repeated.

[0646] This allows even single users to casually enjoy chatting on a variety of topics. In addition, by providing a personalized experience that is sensitive to users' emotions, it is possible to realize a service that provides high levels of satisfaction.

[0647] The processing flow will be explained below.

[0648] Step 1:

[0649] The device will guide the user through the download and installation of the app, and the user will follow the instructions to download and install the app.

[0650] Step 2:

[0651] When the app is launched for the first time, the device displays an account creation screen. The user enters basic information such as name, age, and gender, and the device sends that information to the server.

[0652] Step 3:

[0653] The server receives the entered account information and stores it in a database. The device notifies the user that account creation is complete.

[0654] Step 4:

[0655] The device displays a role setting screen, and the user selects a role from among friends, grandchildren, colleagues, etc. The device then sends the selected role information to the server.

[0656] Step 5:

[0657] The server receives the role information and loads the AI ​​character file corresponding to that role. The device displays the initial message from the AI ​​character.

[0658] Step 6:

[0659] The user enters a message using text or voice, and the device sends the message to the server.

[0660] Step 7:

[0661] The server passes the received message to a natural language analysis engine to analyze the user's intent. An appropriate response is generated based on the analysis results. In parallel, the server uses an emotion engine to analyze the user's emotions.

[0662] Step 8:

[0663] The server reflects the analysis results of the emotion engine in the response and adjusts it to reflect the emotion. For example, if a user enters "I'm very tired today," the emotion engine will recognize this as "fatigue" and generate a response such as "That must have been tough. Let's take it easy and get some rest today."

[0664] Step 9:

[0665] The server generates a response and sends it to the device, which displays the response to the user in voice or text.

[0666] Step 10:

[0667] The user continues the conversation by entering another message. The message is then sent from the device to the server, where natural language analysis and sentiment analysis are repeated.

[0668] Step 11:

[0669] The device sends past conversation data to the server, which then adaptively learns from it, providing more personalized and emotionally attuned responses in subsequent interactions.

[0670] The above are the specific processing steps of the program.

[0671] Example 2

[0672] 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."

[0673] Conventional chat systems have difficulty responding appropriately to users' emotions, resulting in insufficient personalized dialogue. Furthermore, they are limited to simple natural language analysis and standardized responses, resulting in a poor user experience. This makes it difficult for users to use them for extended periods of time, and they lack the adaptive learning required for continuous conversation.

[0674] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0675] In this invention, the server includes means for allowing a user to set a role and for interacting based on that setting, means for transmitting text or voice input by the user to the server and for the server to perform natural language processing, means for performing natural language analysis and generating an appropriate response, means for performing emotion analysis and generating a personalized response that is in line with the user's emotions, and means for adaptive learning to enable continuous conversation with the user. This makes it possible to understand the user's emotions and provide appropriate responses in response to those emotions, thereby realizing a continuous and satisfying conversation experience.

[0676] "User" means an individual who uses the System.

[0677] "Role" refers to the character or role that a user sets within the system, including friends, family, colleagues, etc.

[0678] "Dialogue" refers to text or voice communication between a system and a user.

[0679] "Natural language processing" refers to the technology that allows artificial intelligence and computers to understand and analyze human language.

[0680] "Sentiment analysis" refers to technology that identifies a user's emotions from input text or voice.

[0681] "Personalized responses" refer to customized replies generated based on the user's individual emotions and conversational context.

[0682] "Adaptive learning" refers to a technology that allows a system to learn from past conversational data with a user and provide more appropriate responses in future interactions.

[0683] A "knowledge base" refers to the collection of information or data that a system has about a particular topic.

[0684] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and generates natural language.

[0685] "Prompt sentence" refers to the input sentence required by a generative AI model, based on which the model generates a response.

[0686] This invention is a system that provides a casual chat service for single users, and in particular, recognizes the user's emotions and provides personalized responses based on those emotions. This system is based on the user interacting with AI via a device such as a smartphone.

[0687] Hardware and software used

[0688] Hardware

[0689] 1. Smartphone (e.g. iPhone, Android device)

[0690] software

[0691] 1. Chat service application (installed by the user)

[0692] 2. Server-side natural language processing engines (e.g., SpaCy, Google Natural Language API)

[0693] 3. Sentiment analysis engine (e.g., IBM Watson Tone Analyzer)

[0694] 4. Database system (e.g., MySQL, PostgreSQL)

[0695] Specific operation of the system

[0696] User

[0697] Users first download and install a chat service app from the app store. After launching the app, they create an account by entering basic information such as their name, age, and gender. They then select a character from a list of characters such as a friend, grandchild, or colleague and complete the settings.

[0698] Terminal

[0699] The device sends the basic information entered by the user to the server and generates an initial message based on the received character setting information. This message is displayed as "Hello, Mr. / Ms. X! How is your day?"

[0700] server

[0701] The server stores the received information in a database and analyzes the user's message using a natural language processing engine to extract the message's subject and keywords, and a sentiment analysis engine to identify the sentiment of the input text or voice.

[0702] Specific examples

[0703] 1. The user types, "I went shopping this morning."

[0704] 2. The server performs natural language analysis and sentiment analysis to identify the message's intent and sentiment, which in this case is identified as "positive."

[0705] 3. The server generates a response such as "Did you enjoy your shopping? What did you buy?" and sends it to the device.

[0706] 4. The terminal displays the received response to the user.

[0707] Continuous conversation and adaptive learning

[0708] The server accumulates continuous conversation data with the user and performs adaptive learning, which makes future conversations more personalized. Emotion analysis results are also incorporated into the adaptive learning, enabling conversations that are more in tune with the user's emotions.

[0709] Prompt Sentence Examples

[0710] Prompt: "I went shopping this morning."

[0711] Expected response: The emotion engine will recognize this as "positive" and respond with "Did you enjoy your shopping trip? What did you buy?"

[0712] The present invention makes it possible to realize a system that allows even single users to casually enjoy casual conversation on a variety of topics, and provides a personalized experience that is sensitive to their emotions.

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

[0714] Step 1:

[0715] Download and install the app

[0716] User: Download and install the chat service app from the app store. Tap the app icon to launch it.

[0717] Input: Download link from the app store

[0718] Output: The app is installed on the user's device and the icon is displayed.

[0719] Step 2:

[0720] Create an account

[0721] User: Enter basic information such as name, age, and gender, then tap "Next."

[0722] Terminal: Converts the entered information into JSON format and sends it to the server as an HTTPS request.

[0723] Server: Stores the received information in a database and creates an account.

[0724] Input: Basic information such as name, age, and gender

[0725] Output: User basic information and new account stored on the server

[0726] Step 3:

[0727] AI character settings

[0728] User: On the settings screen, select a character such as a friend, grandchild, or colleague, and tap "Decide."

[0729] Device: Sends the selected character information to the server.

[0730] Server: Loads the appropriate AI character file based on the character information received.

[0731] Input: Role setting (friend, grandchild, colleague, etc.)

[0732] Output: AI character file loaded on the server side

[0733] Step 4:

[0734] Starting the initial conversation

[0735] Terminal: The loaded AI character generates an initial text message that displays to the user: "Hello, Mr. / Ms. X! How's your day?"

[0736] Input: The initial message sent by the server

[0737] Output: Initial message displayed on the terminal

[0738] Step 5:

[0739] Sending a user message

[0740] User: Enter a message to the AI ​​via text or voice and tap the "Send" button.

[0741] Terminal: Sends the entered message to the server.

[0742] Input: Message from the user (e.g. "I went shopping this morning")

[0743] Output: User's message sent to the server

[0744] Step 6:

[0745] Message analysis (natural language analysis and sentiment analysis)

[0746] Server: Analyzes received messages using a natural language processing engine to extract topics and keywords, and a sentiment analysis engine to identify emotions.

[0747] Input: Message from the user (e.g. "I went shopping this morning")

[0748] Output: Extracted keywords, themes, and sentiment analysis results

[0749] Step 7:

[0750] Response Generation

[0751] Server: Generates an appropriate response based on the results of natural language analysis and sentiment analysis. Example: "Did you enjoy your shopping? What did you buy?"

[0752] Input: Results of natural language analysis and sentiment analysis

[0753] Output: The generated response (e.g., "Did you enjoy your shopping trip? What did you buy?")

[0754] Step 8:

[0755] Viewing the response

[0756] Terminal: Displays the response received from the server to the user as text.

[0757] Input: Response from the server

[0758] Output: Response displayed on the terminal

[0759] Step 9:

[0760] Adaptive learning for improved performance

[0761] Server: Accumulates past conversation data with users and retrains the machine learning model. Sentiment analysis results are also incorporated into adaptive learning.

[0762] Input: Past conversation data and sentiment analysis results

[0763] Output: Improved accuracy of personalized responses

[0764] By executing each processing step successively in this manner, the user can enjoy a continuous and personalized chat service.

[0765] (Application example 2)

[0766] 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."

[0767] Conventional electronic payment services lacked an interactive experience for users. This resulted in low user satisfaction and a sense of loneliness. For single users in particular, the lack of human interaction during the payment process was problematic. Furthermore, there was no system that could analyze users' emotions and provide personalized responses based on them.

[0768] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for allowing a user to set a role and for interacting based on the role setting, means for performing natural language analysis based on the role setting and generating an appropriate response, means for performing adaptive learning to maintain a continuous conversation with the user, means for analyzing the user's emotions, means for generating a personalized response based on the emotion analysis results, and means for interacting when the user makes a payment within the electronic payment service. This allows the user to enjoy a human-like interaction during the payment process, thereby improving user satisfaction.

[0769] "Means for users to set roles" refers to a setting function that allows users to select characters such as friends, family, and colleagues within the application, and the AI ​​will interact with them based on that character.

[0770] "Means for natural language analysis" refers to technology that enables AI to analyze text and voice input from users, understand their intent, and generate appropriate responses.

[0771] "Means for adaptive learning" refers to a function that enables the AI ​​to learn from past interaction data with the user and provide more personalized responses in subsequent interactions.

[0772] "Means for analyzing user emotions" refers to technology that analyzes the emotional state of a user based on the user's input text, voice tone, expressions, and vocabulary, and reflects the results in the dialogue.

[0773] The "means for generating personalized responses" is a technology for generating responses for individually optimized dialogue based on the analyzed emotions of a user.

[0774] "Means for engaging in dialogue when a user makes a payment within an electronic payment service" refers to a function that enables AI to engage in dialogue based on the results of emotion analysis while the user is making a payment, thereby improving the user experience.

[0775] A "means for utilizing a knowledge base" is a platform for storing information on specific topics such as weather, cooking, entertainment, and hobbies, and using that information to generate dialogue.

[0776] The "dialogue generation process using a generative AI model" is a process that utilizes a pre-trained AI model to generate natural and appropriate dialogue in response to user input.

[0777] A "prompt sentence" is an input sentence to obtain an appropriate response from a generative AI model, and is text that provides instructions for generating a dialogue.

[0778] In order to implement this invention, it is necessary to build a system that allows advanced interactive dialogue through data communication between a server, a terminal, and a user. The embodiments of the present invention are as follows.

[0779] 1. Hardware and Software Requirements

[0780] Hardware

[0781] Device: Smartphone

[0782] Server: Cloud service (e.g., AWS EC2)

[0783] software

[0784] Natural language analysis tool: Google Cloud Natural Language

[0785] Sentiment analysis engine: IBM Watson Tone Analyzer

[0786] Database: AWS RDS

[0787] Generative AI models: OpenAI GPT-3, etc.

[0788] 2. Program processing explanation

[0789] User Interaction

[0790] Users start the application via their smartphone and first set their role. In this role setting, they can select characters such as friends, family, colleagues, etc. Once this selection is complete, the setting information is sent from the device to the server.

[0791] Server Processing

[0792] The server loads the appropriate character file based on the received configuration information. At the same time, it uses a natural language analysis tool to analyze the user's input and understand their intent. It also uses an emotion analysis engine to analyze the user's emotions from their text and voice, and generates a response based on the results.

[0793] Generating a response

[0794] Based on the analyzed information, the generative AI model generates an appropriate response. The model uses prompts to generate the appropriate response. For example, if a user types, "I bought new headphones," the model generates a prompt: "My new headphones are great! How do they sound?"

[0795] Ongoing dialogue

[0796] The server uses adaptive learning to learn from past conversation data to make future conversations more personalized. All conversation data and sentiment analysis data is securely stored in the server's database.

[0797] Specific examples

[0798] Specifically, if a user types "I tried a new cooking recipe today," the following dialogue will occur:

[0799] 1. User: "I tried a new cooking recipe today."

[0800] 2. Server: Performs natural language analysis and sentiment analysis

[0801] 3. Server: Sends the following prompt to the generative AI model:

[0802] "You tried a new cooking recipe! How was it?"

[0803] This allows users to enjoy a conversation with AI while also receiving an emotionally supportive experience.

[0804] This embodiment allows a user to have a dialogue when making a payment within the electronic payment service, thereby improving user satisfaction.

[0805] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0806] Step 1:

[0807] Users launch the application via their smartphone. They select characters such as friends, family, or colleagues and set their roles. Input includes information about the characters selected by the user.

[0808] Step 2:

[0809] The terminal sends the role information set by the user to the server. The input is the character information selected by the user, and the output is the role setting data sent to the server.

[0810] Step 3:

[0811] The server loads the appropriate character file based on the role configuration data it receives. This character file contains the interaction style for the selected role. The input is the role configuration data, and the output is the loaded character file.

[0812] Step 4:

[0813] The user inputs a message using text or voice. The user's input is sent to the terminal, and the input includes text or voice data.

[0814] Step 5:

[0815] The terminal sends the received input to the server. The input is the user's message, and the output is the text or voice data sent to the server.

[0816] Step 6:

[0817] The server uses a natural language analysis tool (Google Cloud Natural Language) to analyze the user's input and understand their intent. The input is the user's message data, and the output is the analysis result.

[0818] Step 7:

[0819] The server uses an emotion analysis engine (IBM Watson Tone Analyzer) to analyze the user's emotions. The input is the user's message data, and the output is the analyzed emotion data.

[0820] Step 8:

[0821] Based on the analysis results, the server generates an appropriate response using a generative AI model (such as OpenAI GPT-3). A prompt sentence is used, and the generated response is based on the input prompt. The input is a prompt sentence based on the analyzed intent and emotion data, and the output is the generated response.

[0822] Step 9:

[0823] The server sends the generated response to the terminal. The input is the generated response data, and the output is the response message sent to the terminal.

[0824] Step 10:

[0825] The terminal displays the received response message to the user in text or voice. The input is the response message, and the output is the displayed text or voice data.

[0826] Step 11:

[0827] The server uses adaptive learning to learn from past conversation data with the user and apply it to future conversations. The input is past conversation data and sentiment analysis data, and the output is the learned model.

[0828] This series of processes enables the system to maintain a continuous dialogue with the user and provide personalized responses.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] [Third embodiment]

[0833] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0834] 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.

[0835] 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).

[0836] 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.

[0837] 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.

[0838] 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).

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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."

[0845] The present invention is a system for providing a chat service that can be easily used by single users. Specific embodiments for carrying out the present invention will be described below.

[0846] This system allows users to interact with AI via devices such as smartphones. Users begin by downloading and installing the app. After installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is sent to a server via their device. The server receives this information and securely stores it in a database.

[0847] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, such as friend, grandchild, or colleague, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[0848] When a conversation begins, the device displays an initial message from the AI ​​character, greeting the user with something like, "Hello, Mr. / Ms. X! How was your day?" If the user replies via text or voice, the message is sent from the device to the server. The server uses a natural language processing engine to analyze the message and understand the user's intent. For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response.

[0849] To generate a response, the server uses a knowledge base covering topics such as weather, cooking, entertainment, and hobbies. For example, it generates specific questions such as "Did you enjoy your shopping trip? What did you buy?" and sends them to the device. The device displays the response in voice or text and continues the conversation with the user.

[0850] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[0851] As a concrete example, if user Tanaka uses the app to set the friend role, the following dialogue will take place:

[0852] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[0853] 2. User: "I went shopping this morning."

[0854] 3. The device sends the user input to the server, where natural language analysis is performed.

[0855] 4. Server: "Did you enjoy your shopping trip? What did you buy?"

[0856] 5. The device receives the server's response and displays it to the user.

[0857] 6. User: "I bought a new cookbook. I'm going to try some new recipes tonight."

[0858] 7. The device again sends a message to the server, and the same process of natural language analysis and response generation is repeated.

[0859] This allows even single users to casually enjoy chatting on a variety of topics, and by providing a personalized experience tailored to each user, it is possible to achieve a service that provides high levels of satisfaction.

[0860] The processing flow will be explained below.

[0861] Step 1:

[0862] The device will guide the user through the download and installation of the app, and the user will follow the instructions to download and install the app.

[0863] Step 2:

[0864] When the app is launched for the first time, the device displays an account creation screen. The user enters basic information such as name, age, and gender, and the device sends that information to the server.

[0865] Step 3:

[0866] The server receives the entered account information and stores it in a database. The device notifies the user that account creation is complete.

[0867] Step 4:

[0868] The device displays a role setting screen, and the user selects a role from among friends, grandchildren, colleagues, etc. The device then sends the selected role information to the server.

[0869] Step 5:

[0870] The server receives the role information and loads the AI ​​character file corresponding to that role. The device displays the initial message from the AI ​​character.

[0871] Step 6:

[0872] The user enters a message using text or voice, and the device sends the message to the server.

[0873] Step 7:

[0874] The server passes the received message to a natural language analysis engine, which analyzes the user's intent and generates an appropriate response based on the analysis results.

[0875] Step 8:

[0876] The server generates a response and sends it to the device, which displays the response to the user in voice or text.

[0877] Step 9:

[0878] The user continues the conversation by entering another message. The message is sent from the device to the server, and natural language analysis and response generation are repeated.

[0879] Step 10:

[0880] The device sends past conversation data to the server, which then adaptively learns from it, providing more personalized responses in subsequent interactions.

[0881] The above are the specific processing steps of the program.

[0882] Example 1

[0883] 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."

[0884] In modern society, single people are increasingly feeling lonely. To solve this problem, a system that is easy to use and provides personalized dialogue for each user is needed. However, existing dialogue systems are unable to provide responses that are personalized enough, making it difficult for users to have a satisfying dialogue.

[0885] 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.

[0886] In this invention, the server includes a means for allowing a user to set a role and converse based on that setting, a means for performing natural language analysis based on the role setting and generating an appropriate response, a means for receiving and safely storing basic information about the user, a means for loading an AI character file and adopting a corresponding conversation style, and a means for adaptive learning to maintain a continuous conversation with the user. This enables a personalized conversation tailored to the user, allowing even single users to enjoy casual, satisfying conversations.

[0887] "User" means any individual or organization that uses the System.

[0888] "Role" refers to the position and relationship of the character that the user selects within the dialogue system.

[0889] "Dialogue" refers to communication between a user and a system, whether through text or voice.

[0890] "Natural language analysis" refers to techniques used to understand messages sent by users, including grammatical analysis, semantic analysis, and sentiment analysis.

[0891] "Response" refers to a reply message generated by the system in response to user input.

[0892] "Basic information" refers to personal information such as the user's name, age, and gender.

[0893] "AI character file" refers to a file with a conversation style and specific dialogue patterns that correspond to the selected role.

[0894] "Adaptive learning" refers to a technology that allows the system to continuously learn from past interaction data and provide more accurate responses.

[0895] A "knowledge base" is a database that stores information and knowledge about a specific topic and is used to generate responses.

[0896] "Personalization" refers to providing tailored responses tailored to a user's individual preferences and interests.

[0897] A "server" is a central computer in a system that receives, stores, processes, and generates responses to data.

[0898] This invention is a system that provides a casual chat service for single users. This system allows users to converse with AI via devices such as smartphones.

[0899] First, users download and install the app. Once the installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is then sent to the server via their device. The server receives this information and securely stores it in a database.

[0900] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, including friends, family, and colleagues, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[0901] When a conversation begins, the device displays an initial message from the AI ​​character and greets the user, such as "Hello, Mr. / Ms. X! How's your day?" When the user replies by text or voice, the message is sent from the device to the server. The server uses a natural language analysis engine to analyze the message and understand the user's intent. Examples of natural language analysis engines used include Google Cloud Natural Language API and Microsoft Azure Text Analytics.

[0902] For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response. In generating the response, the server utilizes a knowledge base that covers topics such as weather, cooking, entertainment, and hobbies. For example, the server generates a specific question such as, "Did you enjoy shopping? What did you buy?" and sends it to the device. The device displays this response in voice or text and continues the conversation with the user.

[0903] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[0904] As a specific example, when a user named Tanaka uses the app to set the role of friend, the following dialogue takes place:

[0905] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[0906] 2. User: "I went shopping this morning."

[0907] 3. Terminal: User input is sent to the server where natural language analysis is performed.

[0908] 4. Server: "Did you enjoy your shopping trip? What did you buy?"

[0909] 5. Terminal: Receives the server's response and displays it to the user.

[0910] 6. User: "I bought a new cookbook. I'm going to try out some new recipes tonight."

[0911] 7. Terminal: The message is sent to the server again, and the same process of natural language analysis and response generation is repeated.

[0912] The following is a specific example of a prompt sentence to be input to the generative AI model:

[0913] Prompt statement:

[0914] The user is greeted by the AI ​​with "Hello, Tanaka-san! What were you doing today?" If the user replies "I went shopping this morning," generate the appropriate natural language analysis and response.

[0915] This allows even single users to casually enjoy chatting on a variety of topics, and by providing a personalized experience tailored to each user, it is possible to achieve a service that provides high levels of satisfaction.

[0916] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0917] Step 1:

[0918] The user downloads and installs the app.

[0919] Input: The user accesses the app store on their smartphone, searches for a chat service app, and selects it.

[0920] Output: The app is downloaded and installed on the user's smartphone.

[0921] How it works: A user visits the app store and downloads an app. Once installed, the app icon appears on the home screen.

[0922] Step 2:

[0923] A user creates an account.

[0924] Input: A user launches the app and enters basic information such as name, age, and gender.

[0925] Output: The basic information entered is sent from the terminal to the server and stored in a database.

[0926] How it works: When a user opens the app for the first time, they are presented with an account creation screen. They enter their basic information and press the Create Account button. The device then sends this information to our server, which stores it securely.

[0927] Step 3:

[0928] The user sets the role of the AI ​​character.

[0929] Input: The user selects a role from among several characters displayed.

[0930] Output: The selected role information is sent from the device to the server, and the AI ​​character file is loaded.

[0931] How it works: The user selects a role from friends, family, colleagues, etc. on the in-app settings screen and completes the role setup. The device sends the selection information to the server, which then loads the corresponding AI character file.

[0932] Step 4:

[0933] The server loads the AI ​​character file.

[0934] Input: User role configuration information.

[0935] Output: The loaded AI character file is sent to the device.

[0936] How it works: The server loads the corresponding AI character file based on the selected role and sends it to the device.

[0937] Step 5:

[0938] The user initiates the interaction.

[0939] Input: AI character file and initial message sent from the server.

[0940] Output: The terminal displays an initial message to the user.

[0941] Action: The device displays an initial message from the AI ​​character: "Hello, Mr. / Ms. X! How's your day?" The user begins the conversation.

[0942] Step 6:

[0943] A user sends a message.

[0944] Input: The user types a message by text or voice.

[0945] Output: The user's message is sent from the terminal to the server.

[0946] How it works: The user types a message by text or voice, such as "I went shopping this morning," and the device sends it to the server.

[0947] Step 7:

[0948] The server parses the user's message.

[0949] Input: The user's message sent from the terminal.

[0950] Output: The parsed result is an understanding of the user's intent.

[0951] How it works: The server uses the natural language analysis engines of Google Cloud Natural Language API and Microsoft Azure Text Analytics to syntactically and semantically analyze the user's message.

[0952] Step 8:

[0953] The server generates an appropriate response.

[0954] Input: Parsed user messages and knowledge base.

[0955] Output: The generated response is sent to the device.

[0956] How it works: Based on the analysis results, the server uses a knowledge base covering topics such as weather, cooking, entertainment, and hobbies to generate specific responses such as "Did you enjoy your shopping? What did you buy?" and sends them to the device.

[0957] Step 9:

[0958] The terminal displays the server's response.

[0959] Input: The response sent by the server.

[0960] Output: The text or audio response that is displayed to the user.

[0961] What it does: The device receives a response from the server and displays it to the user as audio or text.

[0962] Step 10:

[0963] The server uses the conversation data for adaptive learning.

[0964] Input: Past conversation data with the user.

[0965] Output: The learning results in more personalized responses in future interactions.

[0966] How it works: The server analyzes past conversation data, learns trends and patterns, and applies them to future conversations. This allows the AI ​​to provide more personalized responses to each user.

[0967] (Application example 1)

[0968] 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."

[0969] In today's brick-and-mortar stores, there is a lack of systems that allow customers to instantly obtain detailed product information or easily ask for purchasing advice. This results in a decline in customer satisfaction and a loss of motivation to purchase. Furthermore, there are limited opportunities for single customers to casually enjoy conversation, which results in a shorter time spent in the store.

[0970] 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.

[0971] In this invention, the server includes a means for conducting a dialogue based on a role set by a user, a means for generating an appropriate response through natural language analysis corresponding to the role setting, an adaptive learning means for conducting continuous dialogue with the user, and a means for supporting product-related questions and consultations in a physical store. This allows customers to instantly obtain detailed information about products and easily consult about purchasing. Furthermore, since single customers can easily enjoy conversations in a physical store, it is expected that customer satisfaction will improve and the time spent in the store will increase.

[0972] "User" refers to a person who uses the system.

[0973] "Set role" refers to the character or relationship (e.g., friend, family member, colleague) that the user selects when interacting.

[0974] "Dialogue" refers to two-way communication between a user and a system.

[0975] "Natural language analysis" refers to the technology of analyzing text or speech entered by a user to understand its meaning and intent.

[0976] "Appropriate response" refers to a response to a user that is generated based on the results obtained by natural language analysis.

[0977] "Adaptive learning" refers to a technology in which the system learns from past interaction data and provides more personalized responses in subsequent interactions.

[0978] "Brick and mortar store" refers to a physical commercial establishment where customers can visit and purchase products in person.

[0979] "Means to support questions and inquiries" refers to support functions that allow customers to seek product information or purchasing advice.

[0980] To realize this application example, a program must first be installed on the smartphone. This program is a web application using Flask that manages data communication between the server and the smartphone. The hardware and software required are as follows:

[0981] Hardware: Smartphones, servers

[0982] Software: Flask (web framework), requests (HTTP request library), NLP API (natural language analysis engine)

[0983] Program processing and data processing

[0984] 1. Customer Registration:

[0985] The user enters basic information such as name, age, and gender using a smartphone. This information is sent from the smartphone to the server and stored in a dummy database. This generates a unique customer ID for each user.

[0986] 2. Start a conversation:

[0987] The user uses a smartphone to set a role (e.g., friend, family member, colleague), which is then sent to the server, which then generates a corresponding initial message ("Hello, [username]! What product are you looking for today?") and sends it to the user.

[0988] 3. Continuing the dialogue:

[0989] A message (text) from the user is sent to the server via the smartphone. The server uses a natural language processing engine to analyze the message and understand the user's intent. It then generates an appropriate response and sends it to the smartphone. During this process, the server uses adaptive learning to provide more personalized responses in subsequent interactions based on past interaction data.

[0990] Specific examples

[0991] Below is a scenario in which a new customer, Nakamura-san, uses his smartphone to ask a question about his new smartphone.

[0992] 1. Customer Registration:

[0993] Input: "Nakamura, 35 years old, male"

[0994] Output: "Customer ID: 1"

[0995] 2. Start a conversation:

[0996] Enter: "Friends"

[0997] Output: "Hello, Nakamura-san! What product are you looking for today?"

[0998] 3. Continuing the dialogue:

[0999] Type: "I came in today to look at new smartphones."

[1000] Output: "That sounds fun. What features do you need?"

[1001] Prompt Sentence Examples

[1002] Customer Registration:

[1003] curl -X POST -H "Content-Type: application / json" -d '{"name": "Nakamura", "age": 35, "gender": "Male"}' http: / / localhost:5000 / register

[1004] Start the conversation:

[1005] curl -X POST -H "Content-Type: application / json" -d '{"customer_id": 1, "role": "friend"}' http: / / localhost:5000 / start_conversation

[1006] Continuing the dialogue:

[1007] curl -X POST -H "Content-Type: application / json" -d '{"customer_id": 1, "message": "I came to see my new smartphone today."}' http: / / localhost:5000 / continue_conversation

[1008] The above system will enable customers to easily ask questions and get advice about products in real stores, which is expected to improve customer satisfaction and extend the time they spend in the store.

[1009] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1010] Step 1:

[1011] Customer Registration:

[1012] Input: The user uses a smartphone to input basic information such as name, age, and gender.

[1013] Operation: The terminal sends the input information to the server.

[1014] Data processing: The server stores the received information in a dummy database.

[1015] Output: The server generates a unique customer ID and returns it to the terminal.

[1016] Step 2:

[1017] Start the conversation:

[1018] Input: The user uses a smartphone to set the role (e.g., friend, family member, colleague) with which to start a conversation.

[1019] Operation: The device sends the role information that has been set to the server.

[1020] Data processing: The server generates an initial message based on the role information.

[1021] Output: The server returns an initial message (e.g., "Hello, [username]! What products are you looking for today?") to the terminal. The terminal displays this message.

[1022] Step 3:

[1023] Continuation of conversation (user sending message):

[1024] Input: The user uses the smartphone to input a text message (e.g., "I came to look at the new smartphone today.").

[1025] Operation: The terminal sends the entered message to the server.

[1026] Data processing: The server sends the received message to the natural language analysis engine and receives the analysis results.

[1027] Output: The server generates an appropriate response based on the analysis and returns it to the device, which displays this message (e.g., "That sounds fun. What features do you need?").

[1028] Step 4:

[1029] Adaptive Learning:

[1030] Input: Past interaction data between the user and the system

[1031] Operation: The server uses an adaptive learning algorithm to analyze past interaction data and learn the user's interaction patterns.

[1032] Data processing: Based on the learning results, the system prepares to generate more personalized responses in subsequent interactions.

[1033] Output: A more appropriate response can be provided to the user in subsequent interactions.

[1034] The above processing steps realize a system that allows customers in physical stores to easily ask questions or ask for advice about products. This system operates by combining a server, terminals, a natural language analysis engine, and an adaptive learning algorithm.

[1035] 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.

[1036] The present invention is a system that provides a casual chat service to single users, and in particular, recognizes the user's emotions and provides personalized responses based on those emotions. Specific embodiments for carrying out the present invention will be described below.

[1037] This system allows users to interact with AI via devices such as smartphones. Users begin by downloading and installing the app. After installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is sent to a server via their device. The server receives this information and securely stores it in a database.

[1038] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, such as friend, grandchild, or colleague, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[1039] When a conversation begins, the device displays an initial message from the AI ​​character, greeting the user with something like, "Hello, Mr. / Ms. X! How was your day?" If the user replies via text or voice, the message is sent from the device to the server. The server uses a natural language processing engine to analyze the message and understand the user's intent. For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response.

[1040] A feature of the present invention is the inclusion of an emotion engine for recognizing emotions. The emotion engine analyzes emotions from the user's text, voice tone, expressions, and vocabulary. For example, if a user inputs "I'm very tired today," the emotion engine recognizes this as "fatigue" and generates a response that is in line with that emotion. Specifically, it could be a message such as "That must have been tough. Let's take it easy and get some rest today."

[1041] When generating a response, the server adjusts the appropriate response based on the analysis results of the emotion engine. For example, it generates specific questions such as "Did you enjoy shopping? What did you buy?" and sends them to the device. The device then displays this response in voice or text, continuing the conversation with the user.

[1042] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. Emotion analysis data from the emotion engine is also incorporated into adaptive learning, enabling more emotionally sensitive conversations. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[1043] As a concrete example, when user Tanaka uses the app to set the role of friend and the emotion engine is activated, the following dialogue takes place:

[1044] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[1045] 2. User: "I went shopping this morning."

[1046] 3. The device sends the user input to the server, where natural language analysis and sentiment analysis are performed.

[1047] 4. The server analyzes the text information, and the emotion engine recognizes the user's comment "It was fun" as "positive." The server then generates a response based on the positive emotion.

[1048] 5. Server: Generates the question "Did you enjoy your shopping? What did you buy?" and sends it to the device.

[1049] 6. The device receives the server's response and displays it to the user.

[1050] 7. User: "I bought a new cookbook. I'm going to try some new recipes tonight."

[1051] 8. The device again sends a message to the server, and the same process of natural language analysis, sentiment analysis, and response generation is repeated.

[1052] This allows even single users to casually enjoy chatting on a variety of topics. In addition, by providing a personalized experience that is sensitive to users' emotions, it is possible to realize a service that provides high levels of satisfaction.

[1053] The processing flow will be explained below.

[1054] Step 1:

[1055] The device will guide the user through the download and installation of the app, and the user will follow the instructions to download and install the app.

[1056] Step 2:

[1057] When the app is launched for the first time, the device displays an account creation screen. The user enters basic information such as name, age, and gender, and the device sends that information to the server.

[1058] Step 3:

[1059] The server receives the entered account information and stores it in a database. The device notifies the user that account creation is complete.

[1060] Step 4:

[1061] The device displays a role setting screen, and the user selects a role from among friends, grandchildren, colleagues, etc. The device then sends the selected role information to the server.

[1062] Step 5:

[1063] The server receives the role information and loads the AI ​​character file corresponding to that role. The device displays the initial message from the AI ​​character.

[1064] Step 6:

[1065] The user enters a message using text or voice, and the device sends the message to the server.

[1066] Step 7:

[1067] The server passes the received message to a natural language analysis engine to analyze the user's intent. An appropriate response is generated based on the analysis results. In parallel, the server uses an emotion engine to analyze the user's emotions.

[1068] Step 8:

[1069] The server reflects the analysis results of the emotion engine in the response and adjusts it to reflect the emotion. For example, if a user enters "I'm very tired today," the emotion engine will recognize this as "fatigue" and generate a response such as "That must have been tough. Let's take it easy and get some rest today."

[1070] Step 9:

[1071] The server generates a response and sends it to the device, which displays the response to the user in voice or text.

[1072] Step 10:

[1073] The user continues the conversation by entering another message. The message is then sent from the device to the server, where natural language analysis and sentiment analysis are repeated.

[1074] Step 11:

[1075] The device sends past conversation data to the server, which then adaptively learns from it, providing more personalized and emotionally attuned responses in subsequent interactions.

[1076] The above are the specific processing steps of the program.

[1077] Example 2

[1078] 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."

[1079] Conventional chat systems have difficulty responding appropriately to users' emotions, resulting in insufficient personalized dialogue. Furthermore, they are limited to simple natural language analysis and standardized responses, resulting in a poor user experience. This makes it difficult for users to use them for extended periods of time, and they lack the adaptive learning required for continuous conversation.

[1080] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1081] In this invention, the server includes means for allowing a user to set a role and for interacting based on that setting, means for transmitting text or voice input by the user to the server and for the server to perform natural language processing, means for performing natural language analysis and generating an appropriate response, means for performing emotion analysis and generating a personalized response that is in line with the user's emotions, and means for adaptive learning to enable continuous conversation with the user. This makes it possible to understand the user's emotions and provide appropriate responses in response to those emotions, thereby realizing a continuous and satisfying conversation experience.

[1082] "User" means an individual who uses the System.

[1083] "Role" refers to the character or role that a user sets within the system, including friends, family, colleagues, etc.

[1084] "Dialogue" refers to text or voice communication between a system and a user.

[1085] "Natural language processing" refers to the technology that allows artificial intelligence and computers to understand and analyze human language.

[1086] "Sentiment analysis" refers to technology that identifies a user's emotions from input text or voice.

[1087] "Personalized responses" refer to customized replies generated based on the user's individual emotions and conversational context.

[1088] "Adaptive learning" refers to a technology that allows a system to learn from past conversational data with a user and provide more appropriate responses in future interactions.

[1089] A "knowledge base" refers to the collection of information or data that a system has about a particular topic.

[1090] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and generates natural language.

[1091] "Prompt sentence" refers to the input sentence required by a generative AI model, based on which the model generates a response.

[1092] This invention is a system that provides a casual chat service for single users, and in particular, recognizes the user's emotions and provides personalized responses based on those emotions. This system is based on the user interacting with AI via a device such as a smartphone.

[1093] Hardware and software used

[1094] Hardware

[1095] 1. Smartphone (e.g. iPhone, Android device)

[1096] software

[1097] 1. Chat service application (installed by the user)

[1098] 2. Server-side natural language processing engines (e.g., SpaCy, Google Natural Language API)

[1099] 3. Sentiment analysis engine (e.g., IBM Watson Tone Analyzer)

[1100] 4. Database system (e.g., MySQL, PostgreSQL)

[1101] Specific operation of the system

[1102] User

[1103] Users first download and install a chat service app from the app store. After launching the app, they create an account by entering basic information such as their name, age, and gender. They then select a character from a list of characters such as a friend, grandchild, or colleague and complete the settings.

[1104] Terminal

[1105] The device sends the basic information entered by the user to the server and generates an initial message based on the received character setting information. This message is displayed as "Hello, Mr. / Ms. X! How is your day?"

[1106] server

[1107] The server stores the received information in a database and analyzes the user's message using a natural language processing engine to extract the message's subject and keywords, and a sentiment analysis engine to identify the sentiment of the input text or voice.

[1108] Specific examples

[1109] 1. The user types, "I went shopping this morning."

[1110] 2. The server performs natural language analysis and sentiment analysis to identify the message's intent and sentiment, which in this case is identified as "positive."

[1111] 3. The server generates a response such as "Did you enjoy your shopping? What did you buy?" and sends it to the device.

[1112] 4. The terminal displays the received response to the user.

[1113] Continuous conversation and adaptive learning

[1114] The server accumulates continuous conversation data with the user and performs adaptive learning, which makes future conversations more personalized. Emotion analysis results are also incorporated into the adaptive learning, enabling conversations that are more in tune with the user's emotions.

[1115] Prompt Sentence Examples

[1116] Prompt: "I went shopping this morning."

[1117] Expected response: The emotion engine will recognize this as "positive" and respond with "Did you enjoy your shopping trip? What did you buy?"

[1118] The present invention makes it possible to realize a system that allows even single users to casually enjoy casual conversation on a variety of topics, and provides a personalized experience that is sensitive to their emotions.

[1119] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1120] Step 1:

[1121] Download and install the app

[1122] User: Download and install the chat service app from the app store. Tap the app icon to launch it.

[1123] Input: Download link from the app store

[1124] Output: The app is installed on the user's device and the icon is displayed.

[1125] Step 2:

[1126] Create an account

[1127] User: Enter basic information such as name, age, and gender, then tap "Next."

[1128] Terminal: Converts the entered information into JSON format and sends it to the server as an HTTPS request.

[1129] Server: Stores the received information in a database and creates an account.

[1130] Input: Basic information such as name, age, and gender

[1131] Output: User basic information and new account stored on the server

[1132] Step 3:

[1133] AI character settings

[1134] User: On the settings screen, select a character such as a friend, grandchild, or colleague, and tap "Decide."

[1135] Device: Sends the selected character information to the server.

[1136] Server: Loads the appropriate AI character file based on the character information received.

[1137] Input: Role setting (friend, grandchild, colleague, etc.)

[1138] Output: AI character file loaded on the server side

[1139] Step 4:

[1140] Starting the initial conversation

[1141] Terminal: The loaded AI character generates an initial text message that displays to the user: "Hello, Mr. / Ms. X! How's your day?"

[1142] Input: The initial message sent by the server

[1143] Output: Initial message displayed on the terminal

[1144] Step 5:

[1145] Sending a user message

[1146] User: Enter a message to the AI ​​via text or voice and tap the "Send" button.

[1147] Terminal: Sends the entered message to the server.

[1148] Input: Message from the user (e.g. "I went shopping this morning")

[1149] Output: User's message sent to the server

[1150] Step 6:

[1151] Message analysis (natural language analysis and sentiment analysis)

[1152] Server: Analyzes received messages using a natural language processing engine to extract topics and keywords, and a sentiment analysis engine to identify emotions.

[1153] Input: Message from the user (e.g. "I went shopping this morning")

[1154] Output: Extracted keywords, themes, and sentiment analysis results

[1155] Step 7:

[1156] Response Generation

[1157] Server: Generates an appropriate response based on the results of natural language analysis and sentiment analysis. Example: "Did you enjoy your shopping? What did you buy?"

[1158] Input: Results of natural language analysis and sentiment analysis

[1159] Output: The generated response (e.g., "Did you enjoy your shopping trip? What did you buy?")

[1160] Step 8:

[1161] Viewing the response

[1162] Terminal: Displays the response received from the server to the user as text.

[1163] Input: Response from the server

[1164] Output: Response displayed on the terminal

[1165] Step 9:

[1166] Adaptive learning for improved performance

[1167] Server: Accumulates past conversation data with users and retrains the machine learning model. Sentiment analysis results are also incorporated into adaptive learning.

[1168] Input: Past conversation data and sentiment analysis results

[1169] Output: Improved accuracy of personalized responses

[1170] By executing each processing step successively in this manner, the user can enjoy a continuous and personalized chat service.

[1171] (Application example 2)

[1172] 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."

[1173] Conventional electronic payment services lacked an interactive experience for users. This resulted in low user satisfaction and a sense of loneliness. For single users in particular, the lack of human interaction during the payment process was problematic. Furthermore, there was no system that could analyze users' emotions and provide personalized responses based on them.

[1174] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for allowing a user to set a role and for interacting based on the role setting, means for performing natural language analysis based on the role setting and generating an appropriate response, means for performing adaptive learning to maintain a continuous conversation with the user, means for analyzing the user's emotions, means for generating a personalized response based on the emotion analysis results, and means for interacting when the user makes a payment within the electronic payment service. This allows the user to enjoy a human-like interaction during the payment process, thereby improving user satisfaction.

[1175] "Means for users to set roles" refers to a setting function that allows users to select characters such as friends, family, and colleagues within the application, and the AI ​​will interact with them based on that character.

[1176] "Means for natural language analysis" refers to technology that enables AI to analyze text and voice input from users, understand their intent, and generate appropriate responses.

[1177] "Means for adaptive learning" refers to a function that enables the AI ​​to learn from past interaction data with the user and provide more personalized responses in subsequent interactions.

[1178] "Means for analyzing user emotions" refers to technology that analyzes the emotional state of a user based on the user's input text, voice tone, expressions, and vocabulary, and reflects the results in the dialogue.

[1179] The "means for generating personalized responses" is a technology for generating responses for individually optimized dialogue based on the analyzed emotions of a user.

[1180] "Means for engaging in dialogue when a user makes a payment within an electronic payment service" refers to a function that enables AI to engage in dialogue based on the results of emotion analysis while the user is making a payment, thereby improving the user experience.

[1181] A "means for utilizing a knowledge base" is a platform for storing information on specific topics such as weather, cooking, entertainment, and hobbies, and using that information to generate dialogue.

[1182] The "dialogue generation process using a generative AI model" is a process that utilizes a pre-trained AI model to generate natural and appropriate dialogue in response to user input.

[1183] A "prompt sentence" is an input sentence to obtain an appropriate response from a generative AI model, and is text that provides instructions for generating a dialogue.

[1184] In order to implement this invention, it is necessary to build a system that allows advanced interactive dialogue through data communication between a server, a terminal, and a user. The embodiments of the present invention are as follows.

[1185] 1. Hardware and Software Requirements

[1186] Hardware

[1187] Device: Smartphone

[1188] Server: Cloud service (e.g., AWS EC2)

[1189] software

[1190] Natural language analysis tool: Google Cloud Natural Language

[1191] Sentiment analysis engine: IBM Watson Tone Analyzer

[1192] Database: AWS RDS

[1193] Generative AI models: OpenAI GPT-3, etc.

[1194] 2. Program processing explanation

[1195] User Interaction

[1196] Users start the application via their smartphone and first set their role. In this role setting, they can select characters such as friends, family, colleagues, etc. Once this selection is complete, the setting information is sent from the device to the server.

[1197] Server Processing

[1198] The server loads the appropriate character file based on the received configuration information. At the same time, it uses a natural language analysis tool to analyze the user's input and understand their intent. It also uses an emotion analysis engine to analyze the user's emotions from their text and voice, and generates a response based on the results.

[1199] Generating a response

[1200] Based on the analyzed information, the generative AI model generates an appropriate response. The model uses prompts to generate the appropriate response. For example, if a user types, "I bought new headphones," the model generates a prompt: "My new headphones are great! How do they sound?"

[1201] Ongoing dialogue

[1202] The server uses adaptive learning to learn from past conversation data to make future conversations more personalized. All conversation data and sentiment analysis data is securely stored in the server's database.

[1203] Specific examples

[1204] Specifically, if a user types "I tried a new cooking recipe today," the following dialogue will occur:

[1205] 1. User: "I tried a new cooking recipe today."

[1206] 2. Server: Performs natural language analysis and sentiment analysis

[1207] 3. Server: Sends the following prompt to the generative AI model:

[1208] "You tried a new cooking recipe! How was it?"

[1209] This allows users to enjoy a conversation with AI while also receiving an emotionally supportive experience.

[1210] This embodiment allows a user to have a dialogue when making a payment within the electronic payment service, thereby improving user satisfaction.

[1211] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1212] Step 1:

[1213] Users launch the application via their smartphone. They select characters such as friends, family, or colleagues and set their roles. Input includes information about the characters selected by the user.

[1214] Step 2:

[1215] The terminal sends the role information set by the user to the server. The input is the character information selected by the user, and the output is the role setting data sent to the server.

[1216] Step 3:

[1217] The server loads the appropriate character file based on the role configuration data it receives. This character file contains the interaction style for the selected role. The input is the role configuration data, and the output is the loaded character file.

[1218] Step 4:

[1219] The user inputs a message using text or voice. The user's input is sent to the terminal, and the input includes text or voice data.

[1220] Step 5:

[1221] The terminal sends the received input to the server. The input is the user's message, and the output is the text or voice data sent to the server.

[1222] Step 6:

[1223] The server uses a natural language analysis tool (Google Cloud Natural Language) to analyze the user's input and understand their intent. The input is the user's message data, and the output is the analysis result.

[1224] Step 7:

[1225] The server uses an emotion analysis engine (IBM Watson Tone Analyzer) to analyze the user's emotions. The input is the user's message data, and the output is the analyzed emotion data.

[1226] Step 8:

[1227] Based on the analysis results, the server generates an appropriate response using a generative AI model (such as OpenAI GPT-3). A prompt sentence is used, and the generated response is based on the input prompt. The input is a prompt sentence based on the analyzed intent and emotion data, and the output is the generated response.

[1228] Step 9:

[1229] The server sends the generated response to the terminal. The input is the generated response data, and the output is the response message sent to the terminal.

[1230] Step 10:

[1231] The terminal displays the received response message to the user in text or voice. The input is the response message, and the output is the displayed text or voice data.

[1232] Step 11:

[1233] The server uses adaptive learning to learn from past conversation data with the user and apply it to future conversations. The input is past conversation data and sentiment analysis data, and the output is the learned model.

[1234] This series of processes enables the system to maintain a continuous dialogue with the user and provide personalized responses.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] [Fourth embodiment]

[1239] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1240] 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.

[1241] 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).

[1242] 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.

[1243] 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.

[1244] 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).

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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."

[1252] The present invention is a system for providing a chat service that can be easily used by single users. Specific embodiments for carrying out the present invention will be described below.

[1253] This system allows users to interact with AI via devices such as smartphones. Users begin by downloading and installing the app. After installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is sent to a server via their device. The server receives this information and securely stores it in a database.

[1254] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, such as friend, grandchild, or colleague, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[1255] When a conversation begins, the device displays an initial message from the AI ​​character, greeting the user with something like, "Hello, Mr. / Ms. X! How was your day?" If the user replies via text or voice, the message is sent from the device to the server. The server uses a natural language processing engine to analyze the message and understand the user's intent. For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response.

[1256] To generate a response, the server uses a knowledge base covering topics such as weather, cooking, entertainment, and hobbies. For example, it generates specific questions such as "Did you enjoy your shopping trip? What did you buy?" and sends them to the device. The device displays the response in voice or text and continues the conversation with the user.

[1257] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[1258] As a concrete example, if user Tanaka uses the app to set the friend role, the following dialogue will take place:

[1259] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[1260] 2. User: "I went shopping this morning."

[1261] 3. The device sends the user input to the server, where natural language analysis is performed.

[1262] 4. Server: "Did you enjoy your shopping trip? What did you buy?"

[1263] 5. The device receives the server's response and displays it to the user.

[1264] 6. User: "I bought a new cookbook. I'm going to try some new recipes tonight."

[1265] 7. The device again sends a message to the server, and the same process of natural language analysis and response generation is repeated.

[1266] This allows even single users to casually enjoy chatting on a variety of topics, and by providing a personalized experience tailored to each user, it is possible to achieve a service that provides high levels of satisfaction.

[1267] The processing flow will be explained below.

[1268] Step 1:

[1269] The device will guide the user through the download and installation of the app, and the user will follow the instructions to download and install the app.

[1270] Step 2:

[1271] When the app is launched for the first time, the device displays an account creation screen. The user enters basic information such as name, age, and gender, and the device sends that information to the server.

[1272] Step 3:

[1273] The server receives the entered account information and stores it in a database. The device notifies the user that account creation is complete.

[1274] Step 4:

[1275] The device displays a role setting screen, and the user selects a role from among friends, grandchildren, colleagues, etc. The device then sends the selected role information to the server.

[1276] Step 5:

[1277] The server receives the role information and loads the AI ​​character file corresponding to that role. The device displays the initial message from the AI ​​character.

[1278] Step 6:

[1279] The user enters a message using text or voice, and the device sends the message to the server.

[1280] Step 7:

[1281] The server passes the received message to a natural language analysis engine, which analyzes the user's intent and generates an appropriate response based on the analysis results.

[1282] Step 8:

[1283] The server generates a response and sends it to the device, which displays the response to the user in voice or text.

[1284] Step 9:

[1285] The user continues the conversation by entering another message. The message is sent from the device to the server, and natural language analysis and response generation are repeated.

[1286] Step 10:

[1287] The device sends past conversation data to the server, which then adaptively learns from it, providing more personalized responses in subsequent interactions.

[1288] The above are the specific processing steps of the program.

[1289] Example 1

[1290] 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."

[1291] In modern society, single people are increasingly feeling lonely. To solve this problem, a system that is easy to use and provides personalized dialogue for each user is needed. However, existing dialogue systems are unable to provide responses that are personalized enough, making it difficult for users to have a satisfying dialogue.

[1292] 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.

[1293] In this invention, the server includes a means for allowing a user to set a role and converse based on that setting, a means for performing natural language analysis based on the role setting and generating an appropriate response, a means for receiving and safely storing basic information about the user, a means for loading an AI character file and adopting a corresponding conversation style, and a means for adaptive learning to maintain a continuous conversation with the user. This enables a personalized conversation tailored to the user, allowing even single users to enjoy casual, satisfying conversations.

[1294] "User" means any individual or organization that uses the System.

[1295] "Role" refers to the position and relationship of the character that the user selects within the dialogue system.

[1296] "Dialogue" refers to communication between a user and a system, whether through text or voice.

[1297] "Natural language analysis" refers to techniques used to understand messages sent by users, including grammatical analysis, semantic analysis, and sentiment analysis.

[1298] "Response" refers to a reply message generated by the system in response to user input.

[1299] "Basic information" refers to personal information such as the user's name, age, and gender.

[1300] "AI character file" refers to a file with a conversation style and specific dialogue patterns that correspond to the selected role.

[1301] "Adaptive learning" refers to a technology that allows the system to continuously learn from past interaction data and provide more accurate responses.

[1302] A "knowledge base" is a database that stores information and knowledge about a specific topic and is used to generate responses.

[1303] "Personalization" refers to providing tailored responses tailored to a user's individual preferences and interests.

[1304] A "server" is a central computer in a system that receives, stores, processes, and generates responses to data.

[1305] This invention is a system that provides a casual chat service for single users. This system allows users to converse with AI via devices such as smartphones.

[1306] First, users download and install the app. Once the installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is then sent to the server via their device. The server receives this information and securely stores it in a database.

[1307] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, including friends, family, and colleagues, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[1308] When a conversation begins, the device displays an initial message from the AI ​​character and greets the user, such as "Hello, Mr. / Ms. X! How's your day?" When the user replies by text or voice, the message is sent from the device to the server. The server uses a natural language analysis engine to analyze the message and understand the user's intent. Examples of natural language analysis engines used include Google Cloud Natural Language API and Microsoft Azure Text Analytics.

[1309] For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response. In generating the response, the server utilizes a knowledge base that covers topics such as weather, cooking, entertainment, and hobbies. For example, the server generates a specific question such as, "Did you enjoy shopping? What did you buy?" and sends it to the device. The device displays this response in voice or text and continues the conversation with the user.

[1310] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[1311] As a specific example, when a user named Tanaka uses the app to set the role of friend, the following dialogue takes place:

[1312] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[1313] 2. User: "I went shopping this morning."

[1314] 3. Terminal: User input is sent to the server where natural language analysis is performed.

[1315] 4. Server: "Did you enjoy your shopping trip? What did you buy?"

[1316] 5. Terminal: Receives the server's response and displays it to the user.

[1317] 6. User: "I bought a new cookbook. I'm going to try out some new recipes tonight."

[1318] 7. Terminal: The message is sent to the server again, and the same process of natural language analysis and response generation is repeated.

[1319] The following is a specific example of a prompt sentence to be input to the generative AI model:

[1320] Prompt statement:

[1321] The user is greeted by the AI ​​with "Hello, Tanaka-san! What were you doing today?" If the user replies "I went shopping this morning," generate the appropriate natural language analysis and response.

[1322] This allows even single users to casually enjoy chatting on a variety of topics, and by providing a personalized experience tailored to each user, it is possible to achieve a service that provides high levels of satisfaction.

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

[1324] Step 1:

[1325] The user downloads and installs the app.

[1326] Input: The user accesses the app store on their smartphone, searches for a chat service app, and selects it.

[1327] Output: The app is downloaded and installed on the user's smartphone.

[1328] How it works: A user visits the app store and downloads an app. Once installed, the app icon appears on the home screen.

[1329] Step 2:

[1330] A user creates an account.

[1331] Input: A user launches the app and enters basic information such as name, age, and gender.

[1332] Output: The basic information entered is sent from the terminal to the server and stored in a database.

[1333] How it works: When a user opens the app for the first time, they are presented with an account creation screen. They enter their basic information and press the Create Account button. The device then sends this information to our server, which stores it securely.

[1334] Step 3:

[1335] The user sets the role of the AI ​​character.

[1336] Input: The user selects a role from among several characters displayed.

[1337] Output: The selected role information is sent from the device to the server, and the AI ​​character file is loaded.

[1338] How it works: The user selects a role from friends, family, colleagues, etc. on the in-app settings screen and completes the role setup. The device sends the selection information to the server, which then loads the corresponding AI character file.

[1339] Step 4:

[1340] The server loads the AI ​​character file.

[1341] Input: User role configuration information.

[1342] Output: The loaded AI character file is sent to the device.

[1343] How it works: The server loads the corresponding AI character file based on the selected role and sends it to the device.

[1344] Step 5:

[1345] The user initiates the interaction.

[1346] Input: AI character file and initial message sent from the server.

[1347] Output: The terminal displays an initial message to the user.

[1348] Action: The device displays an initial message from the AI ​​character: "Hello, Mr. / Ms. X! How's your day?" The user begins the conversation.

[1349] Step 6:

[1350] A user sends a message.

[1351] Input: The user types a message by text or voice.

[1352] Output: The user's message is sent from the terminal to the server.

[1353] How it works: The user types a message by text or voice, such as "I went shopping this morning," and the device sends it to the server.

[1354] Step 7:

[1355] The server parses the user's message.

[1356] Input: The user's message sent from the terminal.

[1357] Output: The parsed result is an understanding of the user's intent.

[1358] How it works: The server uses the natural language analysis engines of Google Cloud Natural Language API and Microsoft Azure Text Analytics to syntactically and semantically analyze the user's message.

[1359] Step 8:

[1360] The server generates an appropriate response.

[1361] Input: Parsed user messages and knowledge base.

[1362] Output: The generated response is sent to the device.

[1363] How it works: Based on the analysis results, the server uses a knowledge base covering topics such as weather, cooking, entertainment, and hobbies to generate specific responses such as "Did you enjoy your shopping? What did you buy?" and sends them to the device.

[1364] Step 9:

[1365] The terminal displays the server's response.

[1366] Input: The response sent by the server.

[1367] Output: The text or audio response that is displayed to the user.

[1368] What it does: The device receives a response from the server and displays it to the user as audio or text.

[1369] Step 10:

[1370] The server uses the conversation data for adaptive learning.

[1371] Input: Past conversation data with the user.

[1372] Output: The learning results in more personalized responses in future interactions.

[1373] How it works: The server analyzes past conversation data, learns trends and patterns, and applies them to future conversations. This allows the AI ​​to provide more personalized responses to each user.

[1374] (Application example 1)

[1375] 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."

[1376] In today's brick-and-mortar stores, there is a lack of systems that allow customers to instantly obtain detailed product information or easily ask for purchasing advice. This results in a decline in customer satisfaction and a loss of motivation to purchase. Furthermore, there are limited opportunities for single customers to casually enjoy conversation, which results in a shorter time spent in the store.

[1377] 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.

[1378] In this invention, the server includes a means for conducting a dialogue based on a role set by a user, a means for generating an appropriate response through natural language analysis corresponding to the role setting, an adaptive learning means for conducting continuous dialogue with the user, and a means for supporting product-related questions and consultations in a physical store. This allows customers to instantly obtain detailed information about products and easily consult about purchasing. Furthermore, since single customers can easily enjoy conversations in a physical store, it is expected that customer satisfaction will improve and the time spent in the store will increase.

[1379] "User" refers to a person who uses the system.

[1380] "Set role" refers to the character or relationship (e.g., friend, family member, colleague) that the user selects when interacting.

[1381] "Dialogue" refers to two-way communication between a user and a system.

[1382] "Natural language analysis" refers to the technology of analyzing text or speech entered by a user to understand its meaning and intent.

[1383] "Appropriate response" refers to a response to a user that is generated based on the results obtained by natural language analysis.

[1384] "Adaptive learning" refers to a technology in which the system learns from past interaction data and provides more personalized responses in subsequent interactions.

[1385] "Brick and mortar store" refers to a physical commercial establishment where customers can visit and purchase products in person.

[1386] "Means to support questions and inquiries" refers to support functions that allow customers to seek product information or purchasing advice.

[1387] To realize this application example, a program must first be installed on the smartphone. This program is a web application using Flask that manages data communication between the server and the smartphone. The hardware and software required are as follows:

[1388] Hardware: Smartphones, servers

[1389] Software: Flask (web framework), requests (HTTP request library), NLP API (natural language analysis engine)

[1390] Program processing and data processing

[1391] 1. Customer Registration:

[1392] The user enters basic information such as name, age, and gender using a smartphone. This information is sent from the smartphone to the server and stored in a dummy database. This generates a unique customer ID for each user.

[1393] 2. Start a conversation:

[1394] The user uses a smartphone to set a role (e.g., friend, family member, colleague), which is then sent to the server, which then generates a corresponding initial message ("Hello, [username]! What product are you looking for today?") and sends it to the user.

[1395] 3. Continuing the dialogue:

[1396] A message (text) from the user is sent to the server via the smartphone. The server uses a natural language processing engine to analyze the message and understand the user's intent. It then generates an appropriate response and sends it to the smartphone. During this process, the server uses adaptive learning to provide more personalized responses in subsequent interactions based on past interaction data.

[1397] Specific examples

[1398] Below is a scenario in which a new customer, Nakamura-san, uses his smartphone to ask a question about his new smartphone.

[1399] 1. Customer Registration:

[1400] Input: "Nakamura, 35 years old, male"

[1401] Output: "Customer ID: 1"

[1402] 2. Start a conversation:

[1403] Enter: "Friends"

[1404] Output: "Hello, Nakamura-san! What product are you looking for today?"

[1405] 3. Continuing the dialogue:

[1406] Type: "I came in today to look at new smartphones."

[1407] Output: "That sounds fun. What features do you need?"

[1408] Prompt Sentence Examples

[1409] Customer Registration:

[1410] curl -X POST -H "Content-Type: application / json" -d '{"name": "Nakamura", "age": 35, "gender": "Male"}' http: / / localhost:5000 / register

[1411] Start the conversation:

[1412] curl -X POST -H "Content-Type: application / json" -d '{"customer_id": 1, "role": "friend"}' http: / / localhost:5000 / start_conversation

[1413] Continuing the dialogue:

[1414] curl -X POST -H "Content-Type: application / json" -d '{"customer_id": 1, "message": "I came to see my new smartphone today."}' http: / / localhost:5000 / continue_conversation

[1415] The above system will enable customers to easily ask questions and get advice about products in real stores, which is expected to improve customer satisfaction and extend the time they spend in the store.

[1416] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1417] Step 1:

[1418] Customer Registration:

[1419] Input: The user uses a smartphone to input basic information such as name, age, and gender.

[1420] Operation: The terminal sends the input information to the server.

[1421] Data processing: The server stores the received information in a dummy database.

[1422] Output: The server generates a unique customer ID and returns it to the terminal.

[1423] Step 2:

[1424] Start the conversation:

[1425] Input: The user uses a smartphone to set the role (e.g., friend, family member, colleague) with which to start a conversation.

[1426] Operation: The device sends the role information that has been set to the server.

[1427] Data processing: The server generates an initial message based on the role information.

[1428] Output: The server returns an initial message (e.g., "Hello, [username]! What products are you looking for today?") to the terminal. The terminal displays this message.

[1429] Step 3:

[1430] Continuation of conversation (user sending message):

[1431] Input: The user uses the smartphone to input a text message (e.g., "I came to look at the new smartphone today.").

[1432] Operation: The terminal sends the entered message to the server.

[1433] Data processing: The server sends the received message to the natural language analysis engine and receives the analysis results.

[1434] Output: The server generates an appropriate response based on the analysis and returns it to the device, which displays this message (e.g., "That sounds fun. What features do you need?").

[1435] Step 4:

[1436] Adaptive Learning:

[1437] Input: Past interaction data between the user and the system

[1438] Operation: The server uses an adaptive learning algorithm to analyze past interaction data and learn the user's interaction patterns.

[1439] Data processing: Based on the learning results, the system prepares to generate more personalized responses in subsequent interactions.

[1440] Output: A more appropriate response can be provided to the user in subsequent interactions.

[1441] The above processing steps realize a system that allows customers in physical stores to easily ask questions or ask for advice about products. This system operates by combining a server, terminals, a natural language analysis engine, and an adaptive learning algorithm.

[1442] 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.

[1443] The present invention is a system that provides a casual chat service to single users, and in particular, recognizes the user's emotions and provides personalized responses based on those emotions. Specific embodiments for carrying out the present invention will be described below.

[1444] This system allows users to interact with AI via devices such as smartphones. Users begin by downloading and installing the app. After installation is complete, they launch the app and begin the process of creating an account. During this process, users enter basic information such as their name, age, and gender, which is sent to a server via their device. The server receives this information and securely stores it in a database.

[1445] Next, the user proceeds to a screen within the app where they can set the role they want the AI ​​to play. Several characters are displayed, such as friend, grandchild, or colleague, and the user can choose one from the options. The selected role information is then sent from the device to the server, which then loads the AI ​​character file based on this information. This allows the AI ​​to adopt a conversation style that matches the selected role.

[1446] When a conversation begins, the device displays an initial message from the AI ​​character, greeting the user with something like, "Hello, Mr. / Ms. X! How was your day?" If the user replies via text or voice, the message is sent from the device to the server. The server uses a natural language processing engine to analyze the message and understand the user's intent. For example, if the user replies, "I went shopping this morning," the server analyzes this and generates a relevant response.

[1447] A feature of the present invention is the inclusion of an emotion engine for recognizing emotions. The emotion engine analyzes emotions from the user's text, voice tone, expressions, and vocabulary. For example, if a user inputs "I'm very tired today," the emotion engine recognizes this as "fatigue" and generates a response that is in line with that emotion. Specifically, it could be a message such as "That must have been tough. Let's take it easy and get some rest today."

[1448] When generating a response, the server adjusts the appropriate response based on the analysis results of the emotion engine. For example, it generates specific questions such as "Did you enjoy shopping? What did you buy?" and sends them to the device. The device then displays this response in voice or text, continuing the conversation with the user.

[1449] Furthermore, by incorporating adaptive learning, the AI ​​learns from past conversation data with the user and provides more personalized responses in future interactions. Emotion analysis data from the emotion engine is also incorporated into adaptive learning, enabling more emotionally sensitive conversations. This data is sent from the device to a server and securely stored. As a result, users can enjoy conversations with the AI ​​that are more tailored to them.

[1450] As a concrete example, when user Tanaka uses the app to set the role of friend and the emotion engine is activated, the following dialogue takes place:

[1451] 1. Terminal: "Hello, Tanaka-san! What have you been up to today?"

[1452] 2. User: "I went shopping this morning."

[1453] 3. The device sends the user input to the server, where natural language analysis and sentiment analysis are performed.

[1454] 4. The server analyzes the text information, and the emotion engine recognizes the user's comment "It was fun" as "positive." The server then generates a response based on the positive emotion.

[1455] 5. Server: Generates the question "Did you enjoy your shopping? What did you buy?" and sends it to the device.

[1456] 6. The device receives the server's response and displays it to the user.

[1457] 7. User: "I bought a new cookbook. I'm going to try some new recipes tonight."

[1458] 8. The device again sends a message to the server, and the same process of natural language analysis, sentiment analysis, and response generation is repeated.

[1459] This allows even single users to casually enjoy chatting on a variety of topics. In addition, by providing a personalized experience that is sensitive to users' emotions, it is possible to realize a service that provides high levels of satisfaction.

[1460] The processing flow will be explained below.

[1461] Step 1:

[1462] The device will guide the user through the download and installation of the app, and the user will follow the instructions to download and install the app.

[1463] Step 2:

[1464] When the app is launched for the first time, the device displays an account creation screen. The user enters basic information such as name, age, and gender, and the device sends that information to the server.

[1465] Step 3:

[1466] The server receives the entered account information and stores it in a database. The device notifies the user that account creation is complete.

[1467] Step 4:

[1468] The device displays a role setting screen, and the user selects a role from among friends, grandchildren, colleagues, etc. The device then sends the selected role information to the server.

[1469] Step 5:

[1470] The server receives the role information and loads the AI ​​character file corresponding to that role. The device displays the initial message from the AI ​​character.

[1471] Step 6:

[1472] The user enters a message using text or voice, and the device sends the message to the server.

[1473] Step 7:

[1474] The server passes the received message to a natural language analysis engine to analyze the user's intent. An appropriate response is generated based on the analysis results. In parallel, the server uses an emotion engine to analyze the user's emotions.

[1475] Step 8:

[1476] The server reflects the analysis results of the emotion engine in the response and adjusts it to reflect the emotion. For example, if a user enters "I'm very tired today," the emotion engine will recognize this as "fatigue" and generate a response such as "That must have been tough. Let's take it easy and get some rest today."

[1477] Step 9:

[1478] The server generates a response and sends it to the device, which displays the response to the user in voice or text.

[1479] Step 10:

[1480] The user continues the conversation by entering another message. The message is then sent from the device to the server, where natural language analysis and sentiment analysis are repeated.

[1481] Step 11:

[1482] The device sends past conversation data to the server, which then adaptively learns from it, providing more personalized and emotionally attuned responses in subsequent interactions.

[1483] The above are the specific processing steps of the program.

[1484] Example 2

[1485] 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."

[1486] Conventional chat systems have difficulty responding appropriately to users' emotions, resulting in insufficient personalized dialogue. Furthermore, they are limited to simple natural language analysis and standardized responses, resulting in a poor user experience. This makes it difficult for users to use them for extended periods of time, and they lack the adaptive learning required for continuous conversation.

[1487] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1488] In this invention, the server includes means for allowing a user to set a role and for interacting based on that setting, means for transmitting text or voice input by the user to the server and for the server to perform natural language processing, means for performing natural language analysis and generating an appropriate response, means for performing emotion analysis and generating a personalized response that is in line with the user's emotions, and means for adaptive learning to enable continuous conversation with the user. This makes it possible to understand the user's emotions and provide appropriate responses in response to those emotions, thereby realizing a continuous and satisfying conversation experience.

[1489] "User" means an individual who uses the System.

[1490] "Role" refers to the character or role that a user sets within the system, including friends, family, colleagues, etc.

[1491] "Dialogue" refers to text or voice communication between a system and a user.

[1492] "Natural language processing" refers to the technology that allows artificial intelligence and computers to understand and analyze human language.

[1493] "Sentiment analysis" refers to technology that identifies a user's emotions from input text or voice.

[1494] "Personalized responses" refer to customized replies generated based on the user's individual emotions and conversational context.

[1495] "Adaptive learning" refers to a technology that allows a system to learn from past conversational data with a user and provide more appropriate responses in future interactions.

[1496] A "knowledge base" refers to the collection of information or data that a system has about a particular topic.

[1497] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data and generates natural language.

[1498] "Prompt sentence" refers to the input sentence required by a generative AI model, based on which the model generates a response.

[1499] This invention is a system that provides a casual chat service for single users, and in particular, recognizes the user's emotions and provides personalized responses based on those emotions. This system is based on the user interacting with AI via a device such as a smartphone.

[1500] Hardware and software used

[1501] Hardware

[1502] 1. Smartphone (e.g. iPhone, Android device)

[1503] software

[1504] 1. Chat service application (installed by the user)

[1505] 2. Server-side natural language processing engines (e.g., SpaCy, Google Natural Language API)

[1506] 3. Sentiment analysis engine (e.g., IBM Watson Tone Analyzer)

[1507] 4. Database system (e.g., MySQL, PostgreSQL)

[1508] Specific operation of the system

[1509] User

[1510] Users first download and install a chat service app from the app store. After launching the app, they create an account by entering basic information such as their name, age, and gender. They then select a character from a list of characters such as a friend, grandchild, or colleague and complete the settings.

[1511] Terminal

[1512] The device sends the basic information entered by the user to the server and generates an initial message based on the received character setting information. This message is displayed as "Hello, Mr. / Ms. X! How is your day?"

[1513] server

[1514] The server stores the received information in a database and analyzes the user's message using a natural language processing engine to extract the message's subject and keywords, and a sentiment analysis engine to identify the sentiment of the input text or voice.

[1515] Specific examples

[1516] 1. The user types, "I went shopping this morning."

[1517] 2. The server performs natural language analysis and sentiment analysis to identify the message's intent and sentiment, which in this case is identified as "positive."

[1518] 3. The server generates a response such as "Did you enjoy your shopping? What did you buy?" and sends it to the device.

[1519] 4. The terminal displays the received response to the user.

[1520] Continuous conversation and adaptive learning

[1521] The server accumulates continuous conversation data with the user and performs adaptive learning, which makes future conversations more personalized. Emotion analysis results are also incorporated into the adaptive learning, enabling conversations that are more in tune with the user's emotions.

[1522] Prompt Sentence Examples

[1523] Prompt: "I went shopping this morning."

[1524] Expected response: The emotion engine will recognize this as "positive" and respond with "Did you enjoy your shopping trip? What did you buy?"

[1525] The present invention makes it possible to realize a system that allows even single users to casually enjoy casual conversation on a variety of topics, and provides a personalized experience that is sensitive to their emotions.

[1526] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1527] Step 1:

[1528] Download and install the app

[1529] User: Download and install the chat service app from the app store. Tap the app icon to launch it.

[1530] Input: Download link from the app store

[1531] Output: The app is installed on the user's device and the icon is displayed.

[1532] Step 2:

[1533] Create an account

[1534] User: Enter basic information such as name, age, and gender, then tap "Next."

[1535] Terminal: Converts the entered information into JSON format and sends it to the server as an HTTPS request.

[1536] Server: Stores the received information in a database and creates an account.

[1537] Input: Basic information such as name, age, and gender

[1538] Output: User basic information and new account stored on the server

[1539] Step 3:

[1540] AI character settings

[1541] User: On the settings screen, select a character such as a friend, grandchild, or colleague, and tap "Decide."

[1542] Device: Sends the selected character information to the server.

[1543] Server: Loads the appropriate AI character file based on the character information received.

[1544] Input: Role setting (friend, grandchild, colleague, etc.)

[1545] Output: AI character file loaded on the server side

[1546] Step 4:

[1547] Starting the initial conversation

[1548] Terminal: The loaded AI character generates an initial text message that displays to the user: "Hello, Mr. / Ms. X! How's your day?"

[1549] Input: The initial message sent by the server

[1550] Output: Initial message displayed on the terminal

[1551] Step 5:

[1552] Sending a user message

[1553] User: Enter a message to the AI ​​via text or voice and tap the "Send" button.

[1554] Terminal: Sends the entered message to the server.

[1555] Input: Message from the user (e.g. "I went shopping this morning")

[1556] Output: User's message sent to the server

[1557] Step 6:

[1558] Message analysis (natural language analysis and sentiment analysis)

[1559] Server: Analyzes received messages using a natural language processing engine to extract topics and keywords, and a sentiment analysis engine to identify emotions.

[1560] Input: Message from the user (e.g. "I went shopping this morning")

[1561] Output: Extracted keywords, themes, and sentiment analysis results

[1562] Step 7:

[1563] Response Generation

[1564] Server: Generates an appropriate response based on the results of natural language analysis and sentiment analysis. Example: "Did you enjoy your shopping? What did you buy?"

[1565] Input: Results of natural language analysis and sentiment analysis

[1566] Output: The generated response (e.g., "Did you enjoy your shopping trip? What did you buy?")

[1567] Step 8:

[1568] Viewing the response

[1569] Terminal: Displays the response received from the server to the user as text.

[1570] Input: Response from the server

[1571] Output: Response displayed on the terminal

[1572] Step 9:

[1573] Adaptive learning for improved performance

[1574] Server: Accumulates past conversation data with users and retrains the machine learning model. Sentiment analysis results are also incorporated into adaptive learning.

[1575] Input: Past conversation data and sentiment analysis results

[1576] Output: Improved accuracy of personalized responses

[1577] By executing each processing step successively in this manner, the user can enjoy a continuous and personalized chat service.

[1578] (Application example 2)

[1579] 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."

[1580] Conventional electronic payment services lacked an interactive experience for users. This resulted in low user satisfaction and a sense of loneliness. For single users in particular, the lack of human interaction during the payment process was problematic. Furthermore, there was no system that could analyze users' emotions and provide personalized responses based on them.

[1581] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for allowing a user to set a role and for interacting based on the role setting, means for performing natural language analysis based on the role setting and generating an appropriate response, means for performing adaptive learning to maintain a continuous conversation with the user, means for analyzing the user's emotions, means for generating a personalized response based on the emotion analysis results, and means for interacting when the user makes a payment within the electronic payment service. This allows the user to enjoy a human-like interaction during the payment process, thereby improving user satisfaction.

[1582] "Means for users to set roles" refers to a setting function that allows users to select characters such as friends, family, and colleagues within the application, and the AI ​​will interact with them based on that character.

[1583] "Means for natural language analysis" refers to technology that enables AI to analyze text and voice input from users, understand their intent, and generate appropriate responses.

[1584] "Means for adaptive learning" refers to a function that enables the AI ​​to learn from past interaction data with the user and provide more personalized responses in subsequent interactions.

[1585] "Means for analyzing user emotions" refers to technology that analyzes the emotional state of a user based on the user's input text, voice tone, expressions, and vocabulary, and reflects the results in the dialogue.

[1586] The "means for generating personalized responses" is a technology for generating responses for individually optimized dialogue based on the analyzed emotions of a user.

[1587] "Means for engaging in dialogue when a user makes a payment within an electronic payment service" refers to a function that enables AI to engage in dialogue based on the results of emotion analysis while the user is making a payment, thereby improving the user experience.

[1588] A "means for utilizing a knowledge base" is a platform for storing information on specific topics such as weather, cooking, entertainment, and hobbies, and using that information to generate dialogue.

[1589] The "dialogue generation process using a generative AI model" is a process that utilizes a pre-trained AI model to generate natural and appropriate dialogue in response to user input.

[1590] A "prompt sentence" is an input sentence to obtain an appropriate response from a generative AI model, and is text that provides instructions for generating a dialogue.

[1591] In order to implement this invention, it is necessary to build a system that allows advanced interactive dialogue through data communication between a server, a terminal, and a user. The embodiments of the present invention are as follows.

[1592] 1. Hardware and Software Requirements

[1593] Hardware

[1594] Device: Smartphone

[1595] Server: Cloud service (e.g., AWS EC2)

[1596] software

[1597] Natural language analysis tool: Google Cloud Natural Language

[1598] Sentiment analysis engine: IBM Watson Tone Analyzer

[1599] Database: AWS RDS

[1600] Generative AI models: OpenAI GPT-3, etc.

[1601] 2. Program processing explanation

[1602] User Interaction

[1603] Users start the application via their smartphone and first set their role. In this role setting, they can select characters such as friends, family, colleagues, etc. Once this selection is complete, the setting information is sent from the device to the server.

[1604] Server Processing

[1605] The server loads the appropriate character file based on the received configuration information. At the same time, it uses a natural language analysis tool to analyze the user's input and understand their intent. It also uses an emotion analysis engine to analyze the user's emotions from their text and voice, and generates a response based on the results.

[1606] Generating a response

[1607] Based on the analyzed information, the generative AI model generates an appropriate response. The model uses prompts to generate the appropriate response. For example, if a user types, "I bought new headphones," the model generates a prompt: "My new headphones are great! How do they sound?"

[1608] Ongoing dialogue

[1609] The server uses adaptive learning to learn from past conversation data to make future conversations more personalized. All conversation data and sentiment analysis data is securely stored in the server's database.

[1610] Specific examples

[1611] Specifically, if a user types "I tried a new cooking recipe today," the following dialogue will occur:

[1612] 1. User: "I tried a new cooking recipe today."

[1613] 2. Server: Performs natural language analysis and sentiment analysis

[1614] 3. Server: Sends the following prompt to the generative AI model:

[1615] "You tried a new cooking recipe! How was it?"

[1616] This allows users to enjoy a conversation with AI while also receiving an emotionally supportive experience.

[1617] This embodiment allows a user to have a dialogue when making a payment within the electronic payment service, thereby improving user satisfaction.

[1618] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1619] Step 1:

[1620] Users launch the application via their smartphone. They select characters such as friends, family, or colleagues and set their roles. Input includes information about the characters selected by the user.

[1621] Step 2:

[1622] The terminal sends the role information set by the user to the server. The input is the character information selected by the user, and the output is the role setting data sent to the server.

[1623] Step 3:

[1624] The server loads the appropriate character file based on the role configuration data it receives. This character file contains the interaction style for the selected role. The input is the role configuration data, and the output is the loaded character file.

[1625] Step 4:

[1626] The user inputs a message using text or voice. The user's input is sent to the terminal, and the input includes text or voice data.

[1627] Step 5:

[1628] The terminal sends the received input to the server. The input is the user's message, and the output is the text or voice data sent to the server.

[1629] Step 6:

[1630] The server uses a natural language analysis tool (Google Cloud Natural Language) to analyze the user's input and understand their intent. The input is the user's message data, and the output is the analysis result.

[1631] Step 7:

[1632] The server uses an emotion analysis engine (IBM Watson Tone Analyzer) to analyze the user's emotions. The input is the user's message data, and the output is the analyzed emotion data.

[1633] Step 8:

[1634] Based on the analysis results, the server generates an appropriate response using a generative AI model (such as OpenAI GPT-3). A prompt sentence is used, and the generated response is based on the input prompt. The input is a prompt sentence based on the analyzed intent and emotion data, and the output is the generated response.

[1635] Step 9:

[1636] The server sends the generated response to the terminal. The input is the generated response data, and the output is the response message sent to the terminal.

[1637] Step 10:

[1638] The terminal displays the received response message to the user in text or voice. The input is the response message, and the output is the displayed text or voice data.

[1639] Step 11:

[1640] The server uses adaptive learning to learn from past conversation data with the user and apply it to future conversations. The input is past conversation data and sentiment analysis data, and the output is the learned model.

[1641] This series of processes enables the system to maintain a continuous dialogue with the user and provide personalized responses.

[1642] 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.

[1643] 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.

[1644] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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).

[1649] 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.

[1650] 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."

[1651] 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.

[1652] 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).

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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.

[1663] The following is further disclosed regarding the above embodiment.

[1664] (Claim 1)

[1665] A means for users to set roles and interact based on those roles;

[1666] A means of performing natural language analysis based on role settings and generating appropriate responses;

[1667] means for adaptive learning to engage in a continuous conversation with the user;

[1668] A system including:

[1669] (Claim 2)

[1670] 10. The system of claim 1, further comprising means for allowing the user to select characters such as friends, family members, and colleagues as role settings.

[1671] (Claim 3)

[1672] 10. The system of claim 1, further comprising means for utilizing a knowledge base covering multiple topics, such as weather, cooking, entertainment, and hobbies, in natural language analysis.

[1673] "Example 1"

[1674] (Claim 1)

[1675] A means for users to set roles and interact based on those roles;

[1676] A means of performing natural language analysis based on role settings and generating appropriate responses;

[1677] A means of receiving and securely storing your basic information;

[1678] A means to load AI character files and assume corresponding conversation styles;

[1679] means for adaptive learning to engage in a continuous conversation with the user;

[1680] A system including:

[1681] (Claim 2)

[1682] 10. The system of claim 1, further comprising means for allowing the user to select characters such as friends, family members, and colleagues as role settings.

[1683] (Claim 3)

[1684] 10. The system of claim 1, further comprising means for utilizing a knowledge base covering multiple topics, such as weather, cooking, entertainment, and hobbies, in natural language analysis.

[1685] "Application Example 1"

[1686] (Claim 1)

[1687] a means for interacting with the user based on the role they have set;

[1688] a means for generating an appropriate response via natural language analysis corresponding to the role setting;

[1689] an adaptive learning means for conducting a continuous dialogue with a user;

[1690] A means to support product-related questions and consultations in physical stores,

[1691] A system including:

[1692] (Claim 2)

[1693] 10. The system of claim 1, further comprising means for allowing characters such as friends, family members, and coworkers to be selected as roles.

[1694] (Claim 3)

[1695] 10. The system of claim 1, further comprising means for utilizing a knowledge base for natural language analysis that addresses a variety of topics, such as weather, cooking, entertainment, and hobbies.

[1696] "Example 2: Combining Emotion Engines"

[1697] (Claim 1)

[1698] A means for users to set roles and interact based on those roles;

[1699] A means for transmitting text or voice input by a user to a server, and for the server to perform natural language processing;

[1700] A means of performing natural language analysis and generating appropriate responses;

[1701] A means for performing sentiment analysis and generating a personalized response that is in tune with the user's emotions;

[1702] means for adaptive learning to engage in a continuous conversation with the user;

[1703] …

[1704] A system including:

[1705] (Claim 2)

[1706] 10. The system of claim 1, further comprising means for allowing the user to select characters such as friends, family members, and colleagues as role settings.

[1707] (Claim 3)

[1708] 10. The system of claim 1, further comprising means for utilizing a knowledge base covering multiple topics, such as weather, cooking, entertainment, and hobbies, in natural language analysis.

[1709] (Claim 4)

[1710] 10. The system of claim 1, further comprising means for analyzing tone, expression, and vocabulary of text and / or speech to perform sentiment analysis.

[1711] (Claim 5)

[1712] 10. The system of claim 1, further comprising means for utilizing a generative AI model in natural language processing to conduct dialogue based on prompt sentences.

[1713] "Application example 2 when combining emotion engines"

[1714] (Claim 1)

[1715] A means for users to set roles and interact based on those roles;

[1716] A means of performing natural language analysis based on role settings and generating appropriate responses;

[1717] means for adaptive learning to engage in a continuous conversation with the user;

[1718] A means for analyzing user emotions;

[1719] means for generating a personalized response based on the sentiment analysis results;

[1720] A means for interacting with a user when making a payment within the electronic payment service;

[1721] A system including:

[1722] (Claim 2)

[1723] 10. The system of claim 1, further comprising means for allowing the user to select characters such as friends, family members, and colleagues as role settings.

[1724] (Claim 3)

[1725] A means of utilizing knowledge bases covering multiple topics such as weather, cooking, entertainment, and hobbies in natural language analysis;

[1726] The prompt sentences related to the dialogue generation process using the generative AI model are included;

[1727] 10. The system of claim 1, comprising: [Explanation of symbols]

[1728] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to set roles and interact based on those roles; A means of performing natural language analysis based on role settings and generating appropriate responses; means for adaptive learning to engage in a continuous conversation with the user; A system including:

2. The system according to claim 1, further comprising means for allowing the user to select characters such as friends, family members, and colleagues as role settings.

3. 10. The system of claim 1, further comprising means for utilizing a knowledge base covering multiple topics such as weather, cooking, entertainment, and hobbies in natural language analysis.

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