system

Abstract

We provide the system. [Solution] A means by which the user operates a communication device to access the reply generation screen, A means of sending a message entered by a user into a communication device to a server, A means of passing messages received by the server to the natural language processing engine, A means by which a natural language processing engine generates the optimal reply, A means of sending the generated reply message back from the server to the user's communication device, A system that includes means for the user's communication device to display a reply message on a screen.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the hospitality and food service industry, it is important for a wide range of employees from newbies to veterans to communicate with customers quickly and effectively. However, it takes time and effort to come up with appropriate reply sentences, SNS posting texts, and educational manuals by oneself. Also, when inexperienced staff perform these tasks, there is a possibility of variation in quality. This may lead to a decrease in customer satisfaction and a risk of having an adverse impact on business performance.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides means for a user to operate a communication device to access a reply generation screen and send the entered message to a server. Furthermore, the system includes means for the server to pass the received message to a natural language processing engine to generate the optimal reply, and means for the server to return the generated reply to the user's communication device and display it. The invention also provides a system for a user to operate a communication device to access a screen for creating a post or video script and send the entered theme or keywords to a server, means for the server to pass the received theme or keywords to a natural language processing engine to generate the optimal post or video script, and means for the server to return the generated post or video script to the user's communication device and display it. Furthermore, the invention provides a system for a user to operate a communication device to access a screen for creating a manual for cast training and send the entered situation keywords to a server, means for the server to process the received situation keywords for database searching, means for the server to extract relevant know-how or success stories, means for the natural language processing engine to generate the optimal manual based on the extracted information, and means for the server to return the generated manual to the user's communication device and display it.

[0006] A "user" refers to a person who operates communication devices and uses a system.

[0007] "Communication equipment" refers to devices that users operate and use to communicate with servers, and includes smartphones and personal computers.

[0008] A "server" refers to a computer system that receives data transmitted from a user's communication device and processes it in conjunction with a natural language processing engine and a database.

[0009] A "reply generation screen" refers to a screen that provides an interface for generating a reply to a message entered by the user.

[0010] A "natural language processing engine" refers to a program that analyzes text data entered by a user and generates the most suitable reply, post, video script, or educational manual.

[0011] "Post" refers to text written for posting on social media platforms such as Twitter and Instagram.

[0012] A "video script" refers to a script intended for use on video platforms such as YouTube (registered trademark) and TikTok.

[0013] A "manual" refers to a document used to train new cast members, containing instructions and know-how for handling specific situations.

[0014] "Input message" refers to the text data that a user enters into a communication device to generate a reply.

[0015] "Theme" refers to the subject or topic that the user sets when creating a post or video script.

[0016] "Keywords" refer to important words that the server uses for searching its natural language processing engine and database.

[0017] A "database" refers to a collection of information that contains the know-how and success stories of popular cast members.

[0018] "Know-how" refers to the accumulation of techniques and knowledge that are considered effective in specific tasks or situations.

[0019] A "success story" refers to a specific example of a successful case in a particular situation in the past.

[0020] "Display screen" refers to a screen used to display replies, posts, video scripts, or manuals generated on the user's communication device. [Brief explanation of the drawing]

[0021] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. <S [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0022] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0023] First, let's explain the terminology used in the following explanation.

[0024] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0025] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0026] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0027] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0029] [First Embodiment]

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

[0031] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0034] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0038] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0041] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0042] Three main systems are required to implement the present invention. The embodiments are described below.

[0043] System (1): Suggested replies to customers via LINE

[0044] This system provides a process for users to generate appropriate LINE reply messages using communication equipment.

[0045] System operation

[0046] 1. User: Open the application on a communication device such as a smartphone or PC and access the reply generation screen.

[0047] 2. User: Enter the message you want to reply to on LINE into your communication device.

[0048] 3. Terminal: Send this entered message to the server.

[0049] 4. Server: Passes the received message to the natural language processing engine and generates an appropriate reply.

[0050] 5. Server: Sends the generated reply message back to the user's communication device.

[0051] 6. Terminal: Display the reply text to the user and make it usable as an actual LINE message.

[0052] Specific example

[0053] When a user types the message "What are you doing tonight?", the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[0054] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0055] This system provides a process that allows users to automatically generate social media posts and video scripts using communication devices.

[0056] System operation

[0057] 1. User: Open the application on a communication device such as a smartphone or PC and access the screen for creating posts or scripts.

[0058] 2. User: Enter keywords or themes related to the content you want to post into your communication device.

[0059] 3. Terminal: Sends the entered keywords and themes to the server.

[0060] 4. Server: Passes the received data to the natural language processing engine to generate the most suitable post text or script.

[0061] 5. Server: Sends the generated posts and scripts back to the user's communication device.

[0062] 6. Device: Displays posts and scripts to users, allowing them to use them for actual social media and video posting.

[0063] Specific example

[0064] When a user enters the theme "How to enjoy a host club," the server automatically generates a post saying, "Thanks to everyone who had fun with us today! There might be even more surprising things waiting for you next time you visit 😉," and displays it on the user's device.

[0065] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0066] This system provides a process for users to automatically generate educational manuals tailored to specific situations using communication devices.

[0067] System operation

[0068] 1. User: Open the application on a communication device such as a smartphone or PC and access the manual creation screen.

[0069] 2. User: Enter keywords related to the educational situation into the communication device.

[0070] 3. Terminal: Sends this entered keyword to the server.

[0071] 4. Server: Searches the database for the received keywords and extracts relevant know-how and success stories.

[0072] 5. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[0073] 6. Server: Sends the generated manual back to the user's communication device.

[0074] 7. Terminal: Display the manual to the user and make it available for educational purposes.

[0075] Specific example

[0076] When a user inputs a situation such as "How to interact with a customer for the first time," the server automatically generates a manual that says, "When interacting with a customer for the first time, first introduce yourself properly and choose a topic that will interest them. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device.

[0077] ---

[0078] The above describes the embodiments for carrying out the present invention. Each system combines the user's communication equipment, a server, and a natural language processing engine to provide replies, SNS posts, video scripts, and educational manuals that can be responded to quickly and effectively.

[0079] The following describes the processing flow.

[0080] System (1): Suggested replies to customers via LINE

[0081] Program processing steps

[0082] Step 1:

[0083] User: Open the application on your smartphone or PC and access the reply generation screen.

[0084] Specific steps: Launch the app and select the "Generate Reply" option.

[0085] Step 2:

[0086] User: Enter the message you want to reply to on LINE into your communication device.

[0087] Specific action: Enter a message such as "What are you doing tonight?" into the text box and click the "Send" button.

[0088] Step 3:

[0089] Terminal: Sends the entered message to the server.

[0090] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[0091] Step 4:

[0092] Server: Passes the received message to the natural language processing engine.

[0093] Specific operation: The server receives the HTTP request and forwards the message text to the natural language processing engine.

[0094] Step 5:

[0095] Server: The natural language processing engine generates the optimal reply.

[0096] Specific operation: The engine analyzes the input data and generates an appropriate reply while referring to past data and trend information.

[0097] Step 6:

[0098] Server: Sends the generated reply message back to the terminal.

[0099] Specific operation: The generated text is sent back to the user's terminal as an HTTP response.

[0100] Step 7:

[0101] Terminal: Displays the reply message to the user.

[0102] Specific action: Display the received reply on the screen so that the user can review it.

[0103] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0104] Program processing steps

[0105] Step 1:

[0106] User: Open the application on your smartphone or PC and access the screen for creating posts or scripts.

[0107] Specific steps: Launch the app and select the "Create Post / Script" option.

[0108] Step 2:

[0109] User: Enter keywords or themes related to the content you want to post into your communication device.

[0110] Specific action: Enter a topic such as "How to enjoy a host club" into the text box and click the "Submit" button.

[0111] Step 3:

[0112] Terminal: Sends the entered keywords and themes to the server.

[0113] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[0114] Step 4:

[0115] Server: Passes the received data to the natural language processing engine.

[0116] Specific operation: The server receives an HTTP request and transfers the input text to the natural language processing engine.

[0117] Step 5:

[0118] Server: A natural language processing engine generates the most suitable posts and scripts.

[0119] Specific operation: The engine analyzes the input data and generates appropriate social media posts and video scripts.

[0120] Step 6:

[0121] Server: Sends generated posts and scripts back to the terminal.

[0122] Specific operation: The generated text is sent back to the user's terminal as an HTTP response.

[0123] Step 7:

[0124] Terminal: Displays posts and scripts to the user.

[0125] Specific action: Display the received text on the screen so that the user can review it.

[0126] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0127] Program processing steps

[0128] Step 1:

[0129] User: Open the application on your smartphone or PC and access the manual creation screen.

[0130] Specific steps: Launch the app and select the "Create Manual" option.

[0131] Step 2:

[0132] User: Enter keywords related to the educational situation into the communication device.

[0133] Specific action: Enter keywords such as "How to interact with first-time customers" into the text box and click the "Send" button.

[0134] Step 3:

[0135] Terminal: Sends the entered keyword to the server.

[0136] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[0137] Step 4:

[0138] Server: Searches the database for the received keywords and extracts relevant know-how and success stories.

[0139] Specific operation: The server searches the database and extracts relevant know-how and case studies.

[0140] Step 5:

[0141] Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[0142] Specific operation: Based on the extracted information, generate a manual outlining the optimal response methods for specific situations.

[0143] Step 6:

[0144] Server: Sends the generated manual back to the terminal.

[0145] Specific action: The generated manual is sent back to the user's terminal as an HTTP response.

[0146] Step 7:

[0147] Terminal: Displays the manual to the user.

[0148] Specific action: Display the received manual on the screen so that the user can review it.

[0149] (Example 1)

[0150] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0151] In modern society, the use of communication tools and social media is rapidly expanding. However, generating appropriate replies, posts, and educational manuals requires considerable time and effort, necessitating efficient methods. Therefore, a system is needed that enables individual users to communicate quickly and accurately, providing consistent and high-quality content.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0153] In this invention, the server includes means for the user to access a reply generation screen by operating a communication device, means for sending a message entered by the user on the communication device to the server, means for the server to pass the received message to a natural language processing engine, means for the natural language processing engine to generate an optimal reply, means for the server to return the generated reply to the user's communication device, means for the user's communication device to display the reply on a screen, means for automatically generating a reply based on prompt input using a generation AI model, and means for the user's communication device to make the generated reply usable as a LINE message. This enables the user to efficiently and quickly generate appropriate replies, posts, and educational manuals, saving effort and time.

[0154] A "user" refers to an individual or legal entity that operates communication devices to input messages, keywords, etc., and utilizes the generated replies, posts, manuals, etc.

[0155] "Communication equipment" refers to devices such as smartphones and personal computers that can connect to the internet and communicate with servers.

[0156] A "reply message generation screen" refers to an interface on a communication device that allows the user to input message content and generate a reply message.

[0157] A "message" refers to text data entered by a user into a communication device, which is sent to a server and used to generate a reply.

[0158] A "server" refers to a computer system that processes data received from communication devices via the internet, generates necessary information, and sends it back to the communication devices.

[0159] A "natural language processing engine" refers to software or algorithms that analyze input messages and keywords to generate human-readable text.

[0160] A "generative AI model" refers to an artificial intelligence model that generates text data using machine learning techniques.

[0161] A "prompt message" refers to the text input into the generative AI model, which then uses this text to generate replies, posts, and manuals.

[0162] The term "screen for creating posts and video scripts" refers to an interface on a communication device that allows users to input themes and keywords for posts and video scripts, and then displays the generated text based on those inputs.

[0163] The "Manual Creation Screen for Cast Training" refers to an interface on a communication device that allows users to input keywords describing situations necessary for training, and then displays the training manual generated based on those keywords.

[0164] A "database" refers to a collection of information managed by a server, containing specific information, know-how, success stories, and other data.

[0165] To implement this invention, three main systems are required. Each system consists of a combination of the user's communication equipment, a server, a natural language processing engine, and a generative AI model.

[0166] System (1): Suggested replies to customers via LINE

[0167] This system provides a process for users to generate appropriate LINE replies using their communication devices. Users open the application on their communication devices, such as smartphones or PCs, and access the reply generation screen. When a user enters the message they want to reply to on LINE into their communication device, the device sends this message to the server. The server passes the received message to a natural language processing engine (for example, OpenAI®'s GPT-3®) and generates an appropriate reply. The generated reply is sent back from the server to the user's communication device, and the device displays the reply to the user. For example, if a user enters the message "What are you doing tonight?", the server will automatically generate a reply such as "I might be a little busy tonight, but I'd love to meet up" and display it on the user's device. An example of a prompt is "Generate an appropriate reply to the message 'What are you doing tonight?'"

[0168] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0169] This system provides a process for users to automatically generate social media posts and video scripts using their communication devices. Users open the application on their communication devices, such as smartphones or PCs, and access the post / script creation screen. When the user enters keywords or themes related to the content they want to post into their communication device, the device sends these keywords or themes to the server. The server passes the received data to a natural language processing engine, which generates the most suitable post or script. The generated post or script is sent back from the server to the user's communication device, and the device displays the post or script to the user. For example, if the user enters the theme "How to enjoy a host club," the server will automatically generate a post such as "Thanks to everyone who had fun with me today! There might be an even more surprising twist next time you visit 😉" and display it on the user's device. An example of a prompt is "Please generate a social media post on the theme 'How to enjoy a host club'."

[0170] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0171] This system provides a process for users to automatically generate educational manuals tailored to specific situations using communication devices. Users open the application on a communication device such as a smartphone or PC and access the manual creation screen. When the user enters keywords related to the situation required for education into the communication device, the device sends these keywords to the server. The server searches the database for the received keywords and extracts relevant know-how and success stories. Based on the extracted information, a natural language processing engine generates the optimal manual, and the generated manual is sent back from the server to the user's communication device. The device displays the manual to the user, making it available for educational use. For example, if the user enters the situation "How to interact with a first-time customer," the server automatically generates a manual such as "When interacting with a first-time customer, first introduce yourself properly and choose a topic that will interest them. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device. An example of a prompt is "Please generate an educational manual for the situation 'How to interact with a first-time customer'."

[0172] Through the system described above, users can efficiently and quickly generate appropriate replies, posts, and training manuals, saving effort and time. This enables users to provide consistent, high-quality content.

[0173] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0174] System (1): Suggested replies to customers via LINE

[0175] * We will refer to smartphones as "devices," servers as "servers," and customers as "users."

[0176] Step 1:

[0177] The user operates the device to launch the application and access the reply generation screen. The input is accessing the application's launch screen, and the output is the display of the reply generation screen. Specifically, the user taps the device icon, and the application launches.

[0178] Step 2:

[0179] The user enters the message they want to reply in the input field on their device. The input is the message the user has entered (e.g., "What are you doing tonight?"), and the output is the preparation for sending this message data to the server. Specifically, the user enters the message and presses the send button.

[0180] Step 3:

[0181] The terminal sends the message content entered by the user to the server. The input is the message content entered by the user, and the output is the HTTP request received by the server. Specifically, the terminal generates an HTTP request and sends the data to a specific API endpoint on the server.

[0182] Step 4:

[0183] The server parses the received message content and passes it to a natural language processing engine (e.g., OpenAI's GPT-3). The input is the message data of the received HTTP request, and the output is the input data for the natural language processing engine. Specifically, the server parses the request, extracts the necessary data, and passes it to the processing engine.

[0184] Step 5:

[0185] The natural language processing engine generates a reply based on the message content. The input is the message data provided by the server, and the output is the generated reply. Specifically, the natural language processing engine uses a generative AI model to analyze the prompt and generates a reply based on the results. The generated reply is temporarily stored on the server.

[0186] Step 6:

[0187] The server sends the generated reply back to the user's terminal. The input is the reply generated by the natural language processing engine, and the output is the HTTP response to the user's terminal. Specifically, the server sends an HTTP response containing the generated reply to the terminal.

[0188] Step 7:

[0189] The terminal analyzes the received reply data and displays it to the user. The input is the HTTP response received from the server, and the output is the display of the reply text on the screen. Specifically, the terminal analyzes the response and outputs the reply text to the display area. The user can then send the displayed reply text as a LINE message.

[0190] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0191] Step 1:

[0192] The user operates their device to launch the application and access the screen for creating posts and video scripts. Input is accessing the application's launch screen, and output is the display of the post or script creation screen. Specifically, the user taps the icon on their device, and the application launches.

[0193] Step 2:

[0194] The user enters keywords or themes related to the content they want to post into the input field on their device. The input is the keywords or themes entered by the user, and the output is the preparation for sending this data to the server. Specifically, the user enters keywords and presses the submit button.

[0195] Step 3:

[0196] The device sends keywords and themes entered by the user to the server. The input is the keywords and themes entered by the user, and the output is the HTTP request received by the server. Specifically, the device generates an HTTP request and sends the data to a specific API endpoint on the server.

[0197] Step 4:

[0198] The server analyzes the received keywords and themes and passes them to the natural language processing engine. The input is the data from the received HTTP request, and the output is the input data for the natural language processing engine. Specifically, the server analyzes the request, extracts the necessary data, and passes it to the processing engine.

[0199] Step 5:

[0200] The natural language processing engine generates optimal posts and scripts based on keywords and themes. The input is data provided by the server, and the output is the generated posts and scripts. Specifically, the natural language processing engine uses a generative AI model to analyze prompt text and generates posts and scripts based on the results. The generated documents are temporarily stored on the server.

[0201] Step 6:

[0202] The server sends the generated post text or script back to the user's terminal. The input is a document generated by a natural language processing engine, and the output is an HTTP response to the user's terminal. Specifically, the server sends an HTTP response containing the generated document to the terminal.

[0203] Step 7:

[0204] The terminal analyzes received posts and script data and displays them to the user. The input is the HTTP response received from the server, and the output is the display of the posts and scripts on the screen. Specifically, the terminal analyzes the response and outputs the document to the display area. The user can then use the displayed document for actual social media or video posting.

[0205] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0206] Step 1:

[0207] The user operates the device to launch the application and access the manual creation screen for cast training. The input is access to the application's launch screen, and the output is the display of the manual creation screen. Specifically, the user taps the icon on the device, and the application launches.

[0208] Step 2:

[0209] The user enters keywords representing the educational situation into the input field on the terminal. The input is the keywords entered by the user, and the output is a preparation for sending this data to the server. Specifically, the user enters keywords and presses the submit button.

[0210] Step 3:

[0211] The terminal sends the keywords entered by the user to the server. The input is the keywords entered by the user, and the output is the HTTP request received by the server. Specifically, the terminal generates an HTTP request and sends the data to a specific API endpoint on the server.

[0212] Step 4:

[0213] The server analyzes the received keywords and performs a search in the database. The input is the data from the received HTTP request, and the output is the database search query. Specifically, the server analyzes the request, searches the database based on the keywords, and extracts relevant know-how and success stories.

[0214] Step 5:

[0215] The server passes the extracted information to a natural language processing engine to generate the optimal manual. The input is information extracted from the database, and the output is the generated manual. Specifically, the server passes the extracted information to the natural language processing engine, which uses a generative AI model to analyze it into prompt sentences and generate the manual.

[0216] Step 6:

[0217] The server sends the generated manual back to the user's terminal. The input is the manual generated by the natural language processing engine, and the output is the HTTP response to the user's terminal. Specifically, the server sends the HTTP response containing the generated manual to the terminal.

[0218] Step 7:

[0219] The terminal analyzes the received manual data and displays it to the user. The input is the HTTP response received from the server, and the output is the display of the manual on the screen. Specifically, the terminal analyzes the response and outputs the manual to the display area. The user can then use the displayed manual for educational purposes.

[0220] (Application Example 1)

[0221] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0222] Traditional customer support systems have presented challenges, including the significant time and effort required for support staff to respond quickly and accurately to customer inquiries. Furthermore, inconsistent response quality due to varying skill levels among support staff led to variability in customer satisfaction. Additionally, there was a lack of efficient tools for instantly generating appropriate replies. To address these issues, there is a need to streamline customer support and achieve consistently high-quality responses.

[0223] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0224] In this invention, the server includes means for the user to access a customer response message generation screen by operating a communication device, means for the user to send an inquiry message entered into the communication device to the server, means for the server to pass the received inquiry message to a natural language processing engine, means for the natural language processing engine to generate an optimal response, means for the generated response to be sent back from the server to the user's communication device, and means for the user's communication device to display the response on a screen. This enables customer support personnel to generate fast, consistent, and high-quality response messages.

[0225] "A means by which a user operates a communication device to access a customer response message generation screen" refers to an operating interface that allows a user to access a specific application screen using a communication device such as a smartphone or personal computer.

[0226] "A means of sending inquiry messages entered by a user into a communication device to a server" refers to a function that allows a user to input a message using an input device (keyboard, touch panel, etc.) on a communication device and send it to a remote server via the internet.

[0227] "A means of passing query messages received by the server to the natural language processing engine" refers to a data transfer function that allows the server to pass received data to algorithms and models for natural language processing.

[0228] "A means by which a natural language processing engine generates the optimal reply" refers to the process of generating an appropriate reply to a received message using natural language processing technology (e.g., a natural language generation model).

[0229] "Means for sending the generated reply message back from the server to the user's communication device" refers to a function for sending data back from the server to the user's communication device after the reply message has been generated.

[0230] "Means for displaying the reply on the user's communication device screen" refers to a function that visually displays the generated reply on the user's smartphone or computer screen.

[0231] To implement this invention, it is necessary to combine a user, a server, and a natural language processing engine. The embodiments thereof are described in detail below.

[0232] First, the user accesses the customer response generation screen using a communication device such as a smartphone or PC. At this point, the user enters the customer inquiry message and sends it to the server. This communication process is achieved through two-way communication over the internet.

[0233] The server receives inquiry messages sent by users. These messages are passed to a natural language processing engine (e.g., OpenAI's GPT-3 model). The natural language processing engine generates the most appropriate reply based on the received message. This process utilizes a generative AI model to generate the reply using appropriate prompts.

[0234] The generated reply is sent back to the user's communication device via the server. The user's communication device displays the received reply on its screen, and the user can review and edit it.

[0235] For example, suppose a user receives the following customer inquiry message: "My delivery is delayed. When will it arrive?" The user enters this message and sends it to the server. The server passes this message to a natural language processing engine and generates a reply message like this: "Thank you for your inquiry. We are currently investigating the delivery delay. You can check the specific estimated arrival date using tracking number XYZ1234. We apologize for the inconvenience." This reply message is sent back from the server to the user's device and displayed on the user's screen.

[0236] In embodiments of this invention, the following specific hardware and software are used:

[0237] Hardware: Servers, smartphones, personal computers

[0238] Software: OpenAI GPT-3 API, Python

[0239] An example of a prompt statement is to use the following format:

[0240] Customer inquiry: My order is delayed. Please tell me when it will arrive.

[0241] Appropriate reply:

[0242] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0243] Step 1:

[0244] Users access the customer response generation screen by operating communication devices such as smartphones or personal computers. The screen the user views is displayed as an interface. The user enters their inquiry message into this interface. The entered message is then converted into the format required for subsequent processing.

[0245] Input: Customer inquiry message

[0246] Output: Query message converted to format

[0247] Step 2:

[0248] The terminal sends the entered query message to the server. This transmission is performed using network protocols such as HTTP requests. During this process, the input data is encoded into a format that is easily processed by the server.

[0249] Input: Inquiry message converted to format

[0250] Output: Query message sent to the server

[0251] Step 3:

[0252] The server receives the query message from the terminal. The server then performs the necessary preprocessing to pass the received message to the natural language processing engine. This preprocessing includes message cleansing and tokenization.

[0253] Input: Inquiry message sent to the server

[0254] Output: Preprocessed query message

[0255] Step 4:

[0256] The server passes the pre-processed query message to a natural language processing engine (e.g., OpenAI's GPT-3). The natural language processing engine generates the best possible response based on the received message. This process involves applying prompts and running a generative AI model.

[0257] Input: Preprocessed query message

[0258] Output: Generated reply

[0259] Step 5:

[0260] The server sends the generated reply message back to the terminal. This return is done using a network protocol such as an HTTP response. In this case, the generated reply message is encoded as needed.

[0261] Input: Generated reply

[0262] Output: Reply message sent back to the terminal

[0263] Step 6:

[0264] The terminal displays the reply received from the server on its screen. At this time, it decodes the message into a format that is easy for the user to understand and displays it on the interface.

[0265] Input: Reply sent back to the device

[0266] Output: Reply text displayed on the screen

[0267] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0268] The embodiments for carrying out the present invention will be described with explanations of the operation and specific examples of three systems that combine emotion engines.

[0269] System (1): Suggested replies to customers via LINE

[0270] This system provides a process where users use communication devices to generate appropriate LINE replies using an emotion engine.

[0271] System operation

[0272] 1. User: Open the application on a communication device such as a smartphone or PC and access the reply generation screen.

[0273] 2. User: Enter the message you want to reply to on LINE into your communication device.

[0274] 3. Terminal: The emotion engine analyzes the user's emotions from the input message.

[0275] 4. Terminal: Sends emotional data and messages to the server.

[0276] 5. Server: Passes the received sentiment data and messages to the natural language processing engine to generate an appropriate reply.

[0277] 6. Server: Sends the generated reply message back to the user's communication device.

[0278] 7. Terminal: Display the reply text to the user and make it available for use as an actual LINE message.

[0279] Specific example

[0280] When a user enters the message "What are you doing tonight?", the emotion engine analyzes the user's emotions and recognizes it as "expectation." Based on that emotion and message, the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[0281] System (2): Creation of posts on Twitter / Instagram and scripts for YouTube / TikTok

[0282] This system provides a process in which a user uses a communication device to automatically generate SNS posts and video scripts by utilizing an emotion engine.

[0283] Operation of the system

[0284] 1. User: Open an application on a communication device such as a smartphone or PC and access the post or script creation screen.

[0285] 2. User: Enter keywords or themes of the content to be posted into the communication device.

[0286] 3. Terminal: The emotion engine analyzes the user's emotion from the entered keywords or themes.

[0287] 4. Terminal: Transmit the emotion data and keywords / themes to the server.

[0288] 5. Server: Pass the received emotion data and keywords / themes to the natural language processing engine to generate an optimal post or script.

[0289]

[0290] 7. Terminal: Display the post or script to the user so that it can be used for actual SNS or video posting.

[0291] Specific example

[0292] When the user enters the theme "How to enjoy a host club" and the emotion engine recognizes it as "excitement", the server automatically generates a post such as "Thank you everyone who had fun with me today! There might be an even bigger surprise when you come to the store next time 😉" based on that emotion and theme, and displays it on the user's terminal.

[0293] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0294] This system provides a process that allows users to use communication devices to automatically generate educational manuals tailored to specific situations, utilizing an emotion engine.

[0295] System operation

[0296] 1. User: Open the application on a communication device such as a smartphone or PC and access the manual creation screen.

[0297] 2. User: Enter keywords related to the educational situation into the communication device.

[0298] 3. Terminal: The emotion engine analyzes the user's emotions based on the entered keywords.

[0299] 4. Terminal: Sends sentiment data and keywords to the server.

[0300] 5. Server: Searches the database for received sentiment data and keywords, and extracts relevant know-how and success stories.

[0301] 6. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[0302] 7. Server: Sends the generated manual back to the user's communication device.

[0303] 8. Terminal: Display the manual to the user and make it available for educational purposes.

[0304] Specific example

[0305] When the user inputs the keyword of the situation "How to interact with a first-time customer" and the emotion engine recognizes it as "uneasy", based on this emotion and the keyword, the server automatically generates a manual such as "When interacting with a first-time customer, first make a proper self-introduction and choose a topic to arouse the other person's interest. Example: 'I'm [your name]. I heard you like [topic]. What's your recent recommendation?'", and displays it on the user's terminal.

[0306] ---

[0307] The above is the form for implementing the present invention. Each system can provide a reply text, SNS post text, video script, and educational manual that can respond quickly and effectively by combining the user's communication device, server, natural language processing engine, and emotion engine. <000097> The processing flow will be described below.

[0309] System (1): Proposal of reply text for LINE with customers

[0310] Processing steps of the program

[0311] Step 1:

[0312] User: Open the application on the smartphone or PC and access the reply text generation screen.

[0313] Specific operation: Start the application and select the option of "reply text generation".

[0314] Step 2:

[0315] User: Input the message content to be replied to on LINE into the communication device.

[0316] Specific operation: Input a message such as "What are you doing tonight?" into the text box and click the "send" button.

[0317] Step 3:

[0318] Terminal: The emotion engine analyzes the user's emotions from the input message.

[0319] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., anticipation, joy, anxiety, etc.).

[0320] Step 4:

[0321] Terminal: Sends emotion data and messages to the server.

[0322] Specific operation: The analyzed sentiment data and message text are sent to the server as an HTTP request.

[0323] Step 5:

[0324] Server: Passes the received sentiment data and messages to the natural language processing engine to generate an appropriate reply.

[0325] Specific operation: The server transfers sentiment data and message content to a natural language processing engine, which then constructs the most appropriate reply based on the sentiment.

[0326] Step 6:

[0327] Server: Sends the generated reply message back to the terminal.

[0328] Specific action: The generated reply message is sent back to the user's terminal as an HTTP response.

[0329] Step 7:

[0330] Terminal: Displays the reply message to the user.

[0331] Specific action: Display the received reply on the screen so that the user can review it.

[0332] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0333] Program processing steps

[0334] Step 1:

[0335] User: Open the application on your smartphone or PC and access the screen for creating posts or scripts.

[0336] Specific steps: Launch the app and select the "Create Post / Script" option.

[0337] Step 2:

[0338] User: Enter keywords or themes related to the content you want to post into your communication device.

[0339] Specific action: Enter a topic such as "How to enjoy a host club" into the text box and click the "Submit" button.

[0340] Step 3:

[0341] Terminal: The emotion engine analyzes the user's emotions based on the keywords and themes entered.

[0342] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., excitement, joy, anxiety, etc.).

[0343] Step 4:

[0344] Terminal: Sends sentiment data and keywords / themes to the server.

[0345] Specific operation: The analyzed sentiment data and theme are sent to the server as an HTTP request.

[0346] Step 5:

[0347] Server: Passes the received sentiment data and keywords / themes to a natural language processing engine to generate the most suitable post text or script.

[0348] Specific operation: The server transfers sentiment data and theme content to a natural language processing engine, which then constructs the most suitable post text or script based on the sentiment.

[0349] Step 6:

[0350] Server: Sends generated posts and scripts back to the terminal.

[0351] Specific operation: The generated post text or script is sent back to the user's device as an HTTP response.

[0352] Step 7:

[0353] Terminal: Displays posts and scripts to the user.

[0354] Specific action: Display received posts and scripts on the screen for the user to review.

[0355] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0356] Program processing steps

[0357] Step 1:

[0358] User: Open the application on your smartphone or PC and access the manual creation screen.

[0359] Specific steps: Launch the app and select the "Create Manual" option.

[0360] Step 2:

[0361] User: Enter keywords related to the educational situation into the communication device.

[0362] Specific action: Enter keywords such as "How to interact with first-time customers" into the text box and click the "Send" button.

[0363] Step 3:

[0364] Terminal: The emotion engine analyzes the user's emotions based on the entered keywords.

[0365] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., anxiety, anticipation, joy, etc.).

[0366] Step 4:

[0367] Terminal: Sends sentiment data and keywords to the server.

[0368] Specific operation: The analyzed sentiment data and keywords are sent to the server as an HTTP request.

[0369] Step 5:

[0370] Server: Searches the database for received sentiment data and keywords, and extracts relevant know-how and success stories.

[0371] Specific operation: The server searches the database based on sentiment data and keywords, and extracts relevant know-how and case studies.

[0372] Step 6:

[0373] Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[0374] Specific actions: Based on the extracted information, create an optimal manual that addresses emotions.

[0375] Step 7:

[0376] Server: Sends the generated manual back to the terminal.

[0377] Specific action: The generated manual is sent back to the user's terminal as an HTTP response.

[0378] Step 8:

[0379] Terminal: Displays the manual to the user.

[0380] Specific action: Display the received manual on the screen so that it can be used for educational purposes.

[0381] (Example 2)

[0382] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0383] Conventional communication support systems and social media posting systems have the problem of not being able to generate replies and posts that take user emotions into consideration. Similarly, systems for creating educational manuals have the problem of not being able to provide optimal educational content based on emotions. There is a need for a system that solves these problems and automatically generates appropriate replies, posts, video scripts, and educational manuals that reflect user emotions.

[0384] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0385] In this invention, the server performs sentiment analysis on messages, themes, and keywords entered by the user into an electronic device, and provides the corresponding sentiment data and messages, themes, and keywords to a natural language processing device.

[0386] The system includes a natural language processing unit that generates optimal replies, posts, video scripts, and manuals, and a means for sending the generated content from the server to the user's electronic device. This enables the generation of appropriate replies, posts, and video scripts that take the user's emotions into consideration, as well as the automatic generation of emotion-based educational manuals.

[0387] A "user" is a person who operates an electronic device.

[0388] "Electronic devices" refer to devices that can connect to the internet, such as smartphones and personal computers.

[0389] The "reply message generation screen" is an interface that allows users to input messages via electronic devices and generate reply messages.

[0390] "Sentiment analysis" is the process of extracting sentiment data from messages, themes, and keywords entered by the user.

[0391] An "emotion engine" is software or hardware used to analyze a user's emotions from input messages, themes, and keywords.

[0392] A "server" is a remote computer system that receives and transmits data via a network and performs various processes.

[0393] A "natural language processing device" is software or hardware used to generate natural-sounding sentences based on input language data.

[0394] A "reply" is a written response to a message entered by a user.

[0395] A "theme" refers to the main topic or subject matter that a user sets when creating a post or video script.

[0396] A "keyword" is a specific word or phrase entered by the user and used as input data for analysis and generation processes.

[0397] A "post" is a text generated for posting on social media or similar platforms.

[0398] A "video script" is a scenario or script used when creating video content.

[0399] An "educational manual" is a document that describes teaching methods and know-how for specific situations.

[0400] "Database search" is the process by which a server extracts necessary information from stored data.

[0401] "Know-how" refers to successful case studies and specialized knowledge for specific tasks or situations.

[0402] The following describes in detail the operation of three systems using an emotion engine as embodiments for carrying out the present invention. Each system automatically generates reply text, SNS posts, video scripts, and educational manuals by having the user combine an emotion engine and a natural language processing device using an electronic device.

[0403] System (1): Suggested replies to customers via LINE

[0404] overview

[0405] This system provides a process where users use electronic devices (e.g., smartphones, computers) to generate appropriate LINE replies using an emotion engine.

[0406] operation

[0407] The user first opens the application on their electronic device and accesses the reply generation screen. After entering the message they wish to reply, the device uses an emotion engine (e.g., IBM Watson®, Microsoft® Azure® AI) to analyze the message and identify the user's emotion. The emotion data and message are sent from the device to the server, which passes the received data to a natural language processing unit (e.g., OpenAI GPT-4®) to generate an appropriate reply. The generated reply is then sent back from the server to the user's electronic device and displayed on it.

[0408] Specific example

[0409] When a user enters the message "What are you doing tonight?", the emotion engine recognizes it as "expectation." Based on that emotion and message, the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[0410] Example of a prompt

[0411] "Generate a reply that expresses anticipation in response to the message, 'What are you doing tonight?'"

[0412] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0413] overview

[0414] This system provides a process that allows users to use electronic devices to automatically generate social media posts and video scripts using an emotion engine.

[0415] operation

[0416] The user opens the application on their electronic device and accesses the screen for creating posts or scripts. After entering the theme and keywords for their post, the device uses an emotion engine to analyze them and identify the user's emotions. The emotion data, along with the theme and keywords, is sent from the device to the server, which then passes the received data to a natural language processing unit to generate the most suitable post or script. The generated post or script is then sent back from the server to the user's electronic device and displayed on it.

[0417] Specific example

[0418] When a user enters the theme "How to enjoy a host club" and the emotion engine recognizes it as "excitement," the server automatically generates a post based on that emotion and theme, saying, "Thanks to everyone who had fun with us today! There might be even more surprising things waiting for you next time you visit 😉," and displays it on the user's device.

[0419] Example of a prompt

[0420] "Please generate an exciting Twitter post on the theme of 'How to enjoy a host club.'"

[0421] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0422] overview

[0423] This system provides a process where users can use electronic devices to automatically generate educational manuals tailored to specific situations, utilizing an emotion engine.

[0424] operation

[0425] The user opens the application on their electronic device and accesses the manual creation screen. When they enter keywords related to the situation required for education, the device uses an emotion engine to analyze the keywords and identify the user's emotions. The emotion data and keywords are sent from the device to the server, where the server searches the received data in a database and extracts relevant know-how and success stories. Based on the extracted information, a natural language processing unit generates an optimal manual and sends it back from the server to the user's electronic device. The educational manual is displayed on the user's electronic device and used for education.

[0426] Specific example

[0427] When a user enters keywords related to the situation "how to interact with a customer for the first time," and the emotion engine recognizes this as "anxiety," the server automatically generates a manual based on that emotion and keyword, such as "When interacting with a customer for the first time, start by giving a thorough self-introduction and choosing a topic that will pique their interest. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device.

[0428] Example of a prompt

[0429] "Please create an educational manual that addresses the anxiety associated with the situation of 'how to interact with customers for the first time.'"

[0430] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0431] System (1): Suggested replies to customers via LINE

[0432] Processing steps

[0433] Step 1:

[0434] The user opens the application on an electronic device (smartphone or computer) and accesses the reply generation screen.

[0435] Specifically, you tap the application icon and select the reply generation screen from the menu.

[0436] Input: User actions

[0437] Output: Display of the reply generation screen

[0438] Step 2:

[0439] The user enters the message they want to reply to on LINE.

[0440] As a concrete example, you would enter a message such as "What are you doing tonight?" into the text box on the reply generation screen.

[0441] Input: Message content

[0442] Output: Input message data

[0443] Step 3:

[0444] The device uses an emotion engine (e.g., IBM Watson, Microsoft Azure AI) to analyze the input message and identify the user's emotions.

[0445] In terms of specific operations, the device sends message data to the emotion engine, which then extracts an emotion such as "expectation."

[0446] Input: Message data

[0447] Output: Analyzed sentiment data

[0448] Step 4:

[0449] The device sends the analyzed sentiment data and messages to the server.

[0450] Specifically, the device sends emotional data and messages to the server via the internet.

[0451] Input: emotion data, message data

[0452] Output: Notification of successful transmission to server

[0453] Step 5:

[0454] The server passes the received sentiment data and messages to a natural language processing unit (e.g., OpenAI GPT-4).

[0455] Specifically, the server sends sentiment data and messages to a natural language processing unit, which then generates an appropriate reply.

[0456] Input: emotion data, message data

[0457] Output: Generated reply data

[0458] Step 6:

[0459] The server sends the generated reply message back to the user's electronic device.

[0460] In terms of specific operations, the server sends the reply data back to the user's electronic device via the internet.

[0461] Input: Reply text data

[0462] Output: Notification that the reply message data has been sent to the user.

[0463] Step 7:

[0464] The device displays the reply message to the user.

[0465] Specifically, the device will display the received reply data on the screen so that the user can review it.

[0466] Input: Reply text data

[0467] Output: Display of reply text

[0468] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0469] Processing steps

[0470] Step 1:

[0471] The user opens the application on their electronic device and accesses the screen for creating posts and scripts.

[0472] Specifically, the user taps the application icon and selects the post or script creation screen from the menu.

[0473] Input: User actions

[0474] Output: Display of post text and script creation screen

[0475] Step 2:

[0476] The user enters the theme or keywords for the content they want to post.

[0477] As a concrete example, you would enter a theme such as "How to enjoy a host club" into the text box on the creation screen.

[0478] Input: Themes or keywords

[0479] Output: Input theme and keyword data

[0480] Step 3:

[0481] The device uses an emotion engine to analyze themes and keywords and identify the user's emotions.

[0482] Specifically, the device sends theme and keyword data to the emotion engine, which then extracts the emotion of "excitement."

[0483] Input: Theme and keyword data

[0484] Output: Analyzed sentiment data

[0485] Step 4:

[0486] The device sends the analyzed sentiment data, themes, and keywords to the server.

[0487] Specifically, the device sends sentiment data, themes, and keywords to the server via the internet.

[0488] Input: Sentiment data, theme and keyword data

[0489] Output: Notification of successful transmission to server

[0490] Step 5:

[0491] The server passes the received sentiment data, themes, and keywords to the natural language processing unit.

[0492] Specifically, the server sends sentiment data, themes, and keywords to a natural language processing unit to generate the most suitable post text or script.

[0493] Input: Sentiment data, theme and keyword data

[0494] Output: Generated post text and script data

[0495] Step 6:

[0496] The server sends the generated posts and scripts back to the user's electronic device.

[0497] Specifically, the server sends the posted text and script data back to the user's electronic device via the internet.

[0498] Input: Posted text or script data

[0499] Output: Notification that the user has completed sending the posted text and script data.

[0500] Step 7:

[0501] The device displays the posted text or script to the user.

[0502] Specifically, the device will display received posts and script data on the screen for the user to review.

[0503] Input: Posted text or script data

[0504] Output: Display of posted text and scripts

[0505] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0506] Processing steps

[0507] Step 1:

[0508] The user opens the application on their electronic device and accesses the manual creation screen.

[0509] Specifically, tap the application icon and select the manual creation screen from the menu.

[0510] Input: User actions

[0511] Output: Display of the manual creation screen

[0512] Step 2:

[0513] The user enters keywords related to the situation required for education.

[0514] As a concrete example, you would enter keywords such as "How to interact with first-time customers" into the text box on the creation screen.

[0515] Input: Keywords for the situation

[0516] Output: Input keyword data

[0517] Step 3:

[0518] The device uses an emotion engine to analyze keywords and identify the user's emotions.

[0519] Specifically, the device sends keyword data to the emotion engine, which then extracts the emotion "anxiety."

[0520] Input: Keyword data

[0521] Output: Analyzed sentiment data

[0522] Step 4:

[0523] The device sends the analyzed sentiment data and keywords to the server.

[0524] Specifically, the device sends sentiment data and keywords to the server via the internet.

[0525] Input: Sentiment data, keyword data

[0526] Output: Notification of successful transmission to server

[0527] Step 5:

[0528] The server processes the received sentiment data and keywords for database retrieval.

[0529] In terms of specific operations, the server searches the database and extracts relevant know-how and success stories.

[0530] Input: Sentiment data, keyword data

[0531] Output: Extracted know-how and success story data

[0532] Step 6:

[0533] Based on the extracted know-how and success stories, the server uses a natural language processing unit to generate the optimal manual.

[0534] Specifically, the server sends know-how and success story data to a natural language processing unit to generate an optimal training manual.

[0535] Input: Know-how and success story data

[0536] Output: Generated manual data

[0537] Step 7:

[0538] The server sends the generated manual back to the user's electronic device.

[0539] Specifically, the server sends manual data back to the user's electronic device via the internet.

[0540] Input: Manual data

[0541] Output: Notification that manual data has been successfully sent to the user.

[0542] Step 8:

[0543] The device displays an educational manual to the user.

[0544] Specifically, the device will display the received manual data on the screen so that the user can review it.

[0545] Input: Manual data

[0546] Output: Manual display

[0547] (Application Example 2)

[0548] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0549] In conventional communication devices, creating posts, video scripts, generating replies, and creating manuals made it difficult for users to generate text that reflected appropriate emotions based on themes and keywords. Furthermore, especially in social media posts and video scripts, content tended to be monotonous and lacked emotional depth, making it difficult to instantly generate engaging content. This increased the burden on users, creating a demand for more efficient content generation.

[0550] The specific processing performed 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 analyzing themes and keywords entered by the user into a communication device using an emotion engine, means for passing the analyzed emotion data and themes and keywords to a natural language processing engine, and means for the natural language processing engine to generate an optimal post or script. This makes it possible to automatically generate appropriate posts or scripts that reflect emotions based on themes and keywords entered by the user.

[0551] An "emotion engine" is an algorithm that analyzes emotions from text entered by a user and outputs that emotion data.

[0552] A "natural language processing engine" is an algorithm used to generate appropriate text based on sentiment data and input themes or keywords.

[0553] A "communication device" is a device that a user operates to input messages, themes, keywords, and send them to a server. Specific examples include smartphones and personal computers.

[0554] "Themes and keywords" refer to the topics and main words of the content that the user wants to generate, and the text is generated based on these.

[0555] A "server" is a computer system that receives data transmitted from a user's communication device and processes it in cooperation with an emotion engine and a natural language processing engine.

[0556] The "post text and script generation screen" is an interface that allows users to input the post text or script they want to generate by operating their communication devices.

[0557] "Analyzed emotion data" refers to information about emotions extracted from user input by the emotion engine.

[0558] "Generated posts and scripts" refer to text generated by a natural language processing engine that users can actually post or use.

[0559] The "reply message generation screen" is an interface that allows users to generate reply messages by operating their communication devices.

[0560] "Situational keywords" are words that users enter to describe a specific situation or context.

[0561] The "manual creation screen" is an interface that users operate to generate manuals for educational and instructional purposes.

[0562] "Know-how and success stories" refer to a collection of past successful achievements and knowledge, indicating the optimal method for specific situations.

[0563] System Overview

[0564] This invention is a system that utilizes an emotion engine and a natural language processing engine to automatically generate posts, scripts, replies, and educational manuals based on themes, keywords, and messages specified by the user. To realize this system, the user's communication equipment, server, emotion engine, and natural language processing engine work in coordination.

[0565] Required hardware and software

[0566] Communication devices: Devices such as smartphones and personal computers that users operate to input text.

[0567] Server: A computer system that receives and processes data transmitted from a user's communication device.

[0568] Emotion engine: An algorithm that analyzes emotions from text and outputs emotion data. A concrete example is the sentiment-analysis pipeline in Hugging Face.

[0569] Natural Language Processing Engine: An algorithm that generates appropriate sentences based on sentiment data and specified themes or keywords. A specific example is OpenAI's GPT-3.

[0570] Specific example of processing procedure

[0571] Automatic generation of posts and scripts

[0572] 1. User: Open the application on your smartphone or computer and access the screen for generating posts or scripts.

[0573] 2. User: Enter the theme or keywords you want to post into your communication device.

[0574] 3. Communication device: The emotion engine analyzes the user's emotions based on the input themes and keywords.

[0575] 4. Communication equipment: Transmits analyzed sentiment data, themes, and keywords to the server.

[0576] 5. Server: Passes the received sentiment data, themes, and keywords to a natural language processing engine to generate the most suitable post text or script.

[0577] 6. Server: Sends the generated posts and scripts back to the user's communication device.

[0578] 7. Communication equipment: Display posts and scripts to users so they can be used for actual social media and video posting.

[0579] Specific example

[0580] For example, if a user enters the theme "How I spend my holidays" and the emotion engine identifies "happiness," the generated post might look like this:

[0581] "Today we enjoyed a picnic in a beautiful park! The combination of the blue sky and green meadow was amazing 🌞"

[0582] Example of a prompt

[0583] Use the following prompt.

[0584] Users will generate social media posts and video scripts on the following theme: "How to spend your holidays," and the emotion is "happiness."

[0585] Automatically generate reply messages

[0586] 1. User: Open the application on your smartphone or computer and access the reply generation screen.

[0587] 2. User: Enter the message you want to reply to into your communication device.

[0588] 3. Communication device: The emotion engine analyzes the user's emotions from the input message.

[0589] 4. Communication equipment: Sends analyzed sentiment data and messages to the server.

[0590] 5. Server: Passes the received sentiment data and messages to the natural language processing engine to generate the most appropriate reply.

[0591] 6. Server: Sends the generated reply message back to the user's communication device.

[0592] 7. Communication devices: Display the reply text to the user and make it available for use as an actual message.

[0593] Generating educational manuals tailored to specific situations

[0594] 1. User: Open the application on your smartphone or computer and access the manual generation screen.

[0595] 2. User: Enter keywords related to the educational situation into the communication device.

[0596] 3. Communication device: The emotion engine analyzes the user's emotions from the input keywords.

[0597] 4. Communication device: Sends the analyzed sentiment data and situational keywords to the server.

[0598] 5. Server: Processes received sentiment data and keywords for database searching and extracts relevant know-how and success stories.

[0599] 6. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[0600] 7. Server: Sends the generated manual back to the user's communication device.

[0601] 8. Communication equipment: Display the manual to the user and make it available for educational purposes.

[0602] This allows users to quickly generate optimal, emotionally conscious text simply by specifying a theme or keywords.

[0603] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0604] Step 1:

[0605] Users open the application on their smartphones or computers and access the screen for generating posts and scripts.

[0606] Input: Themes or keywords you want to post about

[0607] Operation: The user enters a theme or keyword on the relevant screen of the application and presses the "Submit" button.

[0608] Step 2:

[0609] The communication device analyzes the input themes and keywords using an emotion engine.

[0610] Input: Themes and keywords entered by the user

[0611] Data processing: Themes and keywords are sent to an emotion engine (e.g., Hugging Face's sentiment-analysis) to perform sentiment analysis.

[0612] Output: Analyzed sentiment data

[0613] Operation: The emotion engine analyzes themes and keywords and generates emotion data.

[0614] Step 3:

[0615] The communication device sends the analyzed sentiment data, along with themes and keywords, to the server.

[0616] Input: Analyzed sentiment data and themes or keywords

[0617] Data transmission: Sentimental data, themes, and keywords are sent to the server as packets.

[0618] Output: Sentiment data, themes, and keywords that reached the server.

[0619] Operation: Communication devices send themes and keywords along with sentiment data to the server.

[0620] Step 4:

[0621] The server receives sentiment data, themes, and keywords, which are then passed to a natural language processing engine to generate the most suitable post text or script.

[0622] Input: Sentimental data, themes, and keywords that reached the server.

[0623] Data processing: Sentiment data, themes, and keywords are input into a natural language processing engine (e.g., OpenAI's GPT-3) to execute the text generation process.

[0624] Output: Generated post text and script

[0625] Operation: The server passes data to a natural language processing engine, which then generates the most suitable text.

[0626] Step 5:

[0627] The server sends the generated posts and scripts back to the user's communication device.

[0628] Input: Generated post text or script

[0629] Data transmission: The generated text is sent as a packet to the communication device.

[0630] Output: Generated text that reached the user's communication device

[0631] Operation: The server sends generated posts and scripts to the communication device.

[0632] Step 6:

[0633] The communication device displays the generated post text or script to the user.

[0634] Input: Generated text that reached the user's communication device

[0635] Data display: Displays the generated text in the display area within the application.

[0636] Output: Posts and scripts displayed to the user

[0637] Operation: The communication device displays the generated post text or script on the screen, allowing the user to review and use it.

[0638] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0639] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0640] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0641] [Second Embodiment]

[0642] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0643] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0644] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0645] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0646] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0647] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0648] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0649] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0650] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0651] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0652] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0653] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0654] Three main systems are required to implement the present invention. The embodiments are described below.

[0655] System (1): Suggested replies to customers via LINE

[0656] This system provides a process for users to generate appropriate LINE reply messages using communication equipment.

[0657] System operation

[0658] 1. User: Open the application on a communication device such as a smartphone or PC and access the reply generation screen.

[0659] 2. User: Enter the message you want to reply to on LINE into your communication device.

[0660] 3. Terminal: Send this entered message to the server.

[0661] 4. Server: Passes the received message to the natural language processing engine and generates an appropriate reply.

[0662] 5. Server: Sends the generated reply message back to the user's communication device.

[0663] 6. Terminal: Display the reply text to the user and make it usable as an actual LINE message.

[0664] Specific example

[0665] When a user types the message "What are you doing tonight?", the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[0666] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0667] This system provides a process that allows users to automatically generate social media posts and video scripts using communication devices.

[0668] System operation

[0669] 1. User: Open the application on a communication device such as a smartphone or PC and access the screen for creating posts or scripts.

[0670] 2. User: Enter keywords or themes related to the content you want to post into your communication device.

[0671] 3. Terminal: Sends the entered keywords and themes to the server.

[0672] 4. Server: Passes the received data to the natural language processing engine to generate the most suitable post text or script.

[0673] 5. Server: Sends the generated posts and scripts back to the user's communication device.

[0674] 6. Device: Displays posts and scripts to users, allowing them to use them for actual social media and video posting.

[0675] Specific example

[0676] When a user enters the theme "How to enjoy a host club," the server automatically generates a post saying, "Thanks to everyone who had fun with us today! There might be even more surprising things waiting for you next time you visit 😉," and displays it on the user's device.

[0677] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0678] This system provides a process for users to automatically generate educational manuals tailored to specific situations using communication devices.

[0679] System operation

[0680] 1. User: Open the application on a communication device such as a smartphone or PC and access the manual creation screen.

[0681] 2. User: Enter keywords related to the educational situation into the communication device.

[0682] 3. Terminal: Sends this entered keyword to the server.

[0683] 4. Server: Searches the database for the received keywords and extracts relevant know-how and success stories.

[0684] 5. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[0685] 6. Server: Sends the generated manual back to the user's communication device.

[0686] 7. Terminal: Display the manual to the user and make it available for educational purposes.

[0687] Specific example

[0688] When a user inputs a situation such as "How to interact with a customer for the first time," the server automatically generates a manual that says, "When interacting with a customer for the first time, first introduce yourself properly and choose a topic that will interest them. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device.

[0689] ---

[0690] The above describes the embodiments for carrying out the present invention. Each system combines the user's communication equipment, a server, and a natural language processing engine to provide replies, SNS posts, video scripts, and educational manuals that can be responded to quickly and effectively.

[0691] The following describes the processing flow.

[0692] System (1): Suggested replies to customers via LINE

[0693] Program processing steps

[0694] Step 1:

[0695] User: Open the application on your smartphone or PC and access the reply generation screen.

[0696] Specific steps: Launch the app and select the "Generate Reply" option.

[0697] Step 2:

[0698] User: Enter the message you want to reply to on LINE into your communication device.

[0699] Specific action: Enter a message such as "What are you doing tonight?" into the text box and click the "Send" button.

[0700] Step 3:

[0701] Terminal: Sends the entered message to the server.

[0702] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[0703] Step 4:

[0704] Server: Passes the received message to the natural language processing engine.

[0705] Specific operation: The server receives the HTTP request and forwards the message text to the natural language processing engine.

[0706] Step 5:

[0707] Server: The natural language processing engine generates the optimal reply.

[0708] Specific operation: The engine analyzes the input data and generates an appropriate reply while referring to past data and trend information.

[0709] Step 6:

[0710] Server: Sends the generated reply message back to the terminal.

[0711] Specific operation: The generated text is sent back to the user's terminal as an HTTP response.

[0712] Step 7:

[0713] Terminal: Displays the reply message to the user.

[0714] Specific action: Display the received reply on the screen so that the user can review it.

[0715] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0716] Program processing steps

[0717] Step 1:

[0718] User: Open the application on your smartphone or PC and access the screen for creating posts or scripts.

[0719] Specific steps: Launch the app and select the "Create Post / Script" option.

[0720] Step 2:

[0721] User: Enter keywords or themes related to the content you want to post into your communication device.

[0722] Specific action: Enter a topic such as "How to enjoy a host club" into the text box and click the "Submit" button.

[0723] Step 3:

[0724] Terminal: Sends the entered keywords and themes to the server.

[0725] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[0726] Step 4:

[0727] Server: Passes the received data to the natural language processing engine.

[0728] Specific operation: The server receives an HTTP request and transfers the input text to the natural language processing engine.

[0729] Step 5:

[0730] Server: A natural language processing engine generates the most suitable posts and scripts.

[0731] Specific operation: The engine analyzes the input data and generates appropriate social media posts and video scripts.

[0732] Step 6:

[0733] Server: Sends generated posts and scripts back to the terminal.

[0734] Specific operation: The generated text is sent back to the user's terminal as an HTTP response.

[0735] Step 7:

[0736] Terminal: Displays posts and scripts to the user.

[0737] Specific action: Display the received text on the screen so that the user can review it.

[0738] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0739] Program processing steps

[0740] Step 1:

[0741] User: Open the application on your smartphone or PC and access the manual creation screen.

[0742] Specific steps: Launch the app and select the "Create Manual" option.

[0743] Step 2:

[0744] User: Enter keywords related to the educational situation into the communication device.

[0745] Specific action: Enter keywords such as "How to interact with first-time customers" into the text box and click the "Send" button.

[0746] Step 3:

[0747] Terminal: Sends the entered keyword to the server.

[0748] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[0749] Step 4:

[0750] Server: Searches the database for the received keywords and extracts relevant know-how and success stories.

[0751] Specific operation: The server searches the database and extracts relevant know-how and case studies.

[0752] Step 5:

[0753] Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[0754] Specific operation: Based on the extracted information, generate a manual outlining the optimal response methods for specific situations.

[0755] Step 6:

[0756] Server: Sends the generated manual back to the terminal.

[0757] Specific action: The generated manual is sent back to the user's terminal as an HTTP response.

[0758] Step 7:

[0759] Terminal: Displays the manual to the user.

[0760] Specific action: Display the received manual on the screen so that the user can review it.

[0761] (Example 1)

[0762] Next, we will describe Example 1. 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".

[0763] In modern society, the use of communication tools and social media is rapidly expanding. However, generating appropriate replies, posts, and educational manuals requires considerable time and effort, necessitating efficient methods. Therefore, a system is needed that enables individual users to communicate quickly and accurately, providing consistent and high-quality content.

[0764] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0765] In this invention, the server includes means for the user to access a reply generation screen by operating a communication device, means for sending a message entered by the user on the communication device to the server, means for the server to pass the received message to a natural language processing engine, means for the natural language processing engine to generate an optimal reply, means for the server to return the generated reply to the user's communication device, means for the user's communication device to display the reply on a screen, means for automatically generating a reply based on prompt input using a generation AI model, and means for the user's communication device to make the generated reply usable as a LINE message. This enables the user to efficiently and quickly generate appropriate replies, posts, and educational manuals, saving effort and time.

[0766] A "user" refers to an individual or legal entity that operates communication devices to input messages, keywords, etc., and utilizes the generated replies, posts, manuals, etc.

[0767] "Communication equipment" refers to devices such as smartphones and personal computers that can connect to the internet and communicate with servers.

[0768] A "reply message generation screen" refers to an interface on a communication device that allows the user to input message content and generate a reply message.

[0769] A "message" refers to text data entered by a user into a communication device, which is sent to a server and used to generate a reply.

[0770] A "server" refers to a computer system that processes data received from communication devices via the internet, generates necessary information, and sends it back to the communication devices.

[0771] A "natural language processing engine" refers to software or algorithms that analyze input messages and keywords to generate human-readable text.

[0772] A "generative AI model" refers to an artificial intelligence model that generates text data using machine learning techniques.

[0773] A "prompt message" refers to the text input into the generative AI model, which then uses this text to generate replies, posts, and manuals.

[0774] The term "screen for creating posts and video scripts" refers to an interface on a communication device that allows users to input themes and keywords for posts and video scripts, and then displays the generated text based on those inputs.

[0775] The "Manual Creation Screen for Cast Training" refers to an interface on a communication device that allows users to input keywords describing situations necessary for training, and then displays the training manual generated based on those keywords.

[0776] A "database" refers to a collection of information managed by a server, containing specific information, know-how, success stories, and other data.

[0777] To implement this invention, three main systems are required. Each system consists of a combination of the user's communication equipment, a server, a natural language processing engine, and a generative AI model.

[0778] System (1): Suggested replies to customers via LINE

[0779] This system provides a process for users to generate appropriate LINE replies using their communication devices. Users open the application on their communication devices, such as smartphones or PCs, and access the reply generation screen. When the user enters the message they want to reply to on LINE into their communication device, the device sends this message to the server. The server passes the received message to a natural language processing engine (for example, OpenAI's GPT-3) and generates an appropriate reply. The generated reply is sent back from the server to the user's communication device, and the device displays the reply to the user. For example, if the user enters the message "What are you doing tonight?", the server will automatically generate a reply such as "I might be a little busy tonight, but I'd love to meet up" and display it on the user's device. An example of a prompt is "Generate an appropriate reply to the message 'What are you doing tonight?'"

[0780] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0781] This system provides a process for users to automatically generate social media posts and video scripts using their communication devices. Users open the application on their communication devices, such as smartphones or PCs, and access the post / script creation screen. When the user enters keywords or themes related to the content they want to post into their communication device, the device sends these keywords or themes to the server. The server passes the received data to a natural language processing engine, which generates the most suitable post or script. The generated post or script is sent back from the server to the user's communication device, and the device displays the post or script to the user. For example, if the user enters the theme "How to enjoy a host club," the server will automatically generate a post such as "Thanks to everyone who had fun with me today! There might be an even more surprising twist next time you visit 😉" and display it on the user's device. An example of a prompt is "Please generate a social media post on the theme 'How to enjoy a host club'."

[0782] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0783] This system provides a process for users to automatically generate educational manuals tailored to specific situations using communication devices. Users open the application on a communication device such as a smartphone or PC and access the manual creation screen. When the user enters keywords related to the situation required for education into the communication device, the device sends these keywords to the server. The server searches the database for the received keywords and extracts relevant know-how and success stories. Based on the extracted information, a natural language processing engine generates the optimal manual, and the generated manual is sent back from the server to the user's communication device. The device displays the manual to the user, making it available for educational use. For example, if the user enters the situation "How to interact with a first-time customer," the server automatically generates a manual such as "When interacting with a first-time customer, first introduce yourself properly and choose a topic that will interest them. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device. An example of a prompt is "Please generate an educational manual for the situation 'How to interact with a first-time customer'."

[0784] Through the system described above, users can efficiently and quickly generate appropriate replies, posts, and training manuals, saving effort and time. This enables users to provide consistent, high-quality content.

[0785] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0786] System (1): Suggested replies to customers via LINE

[0787] * We will refer to smartphones as "devices," servers as "servers," and customers as "users."

[0788] Step 1:

[0789] The user operates the device to launch the application and access the reply generation screen. The input is accessing the application's launch screen, and the output is the display of the reply generation screen. Specifically, the user taps the device icon, and the application launches.

[0790] Step 2:

[0791] The user enters the message they want to reply in the input field on their device. The input is the message the user has entered (e.g., "What are you doing tonight?"), and the output is the preparation for sending this message data to the server. Specifically, the user enters the message and presses the send button.

[0792] Step 3:

[0793] The terminal sends the message content entered by the user to the server. The input is the message content entered by the user, and the output is the HTTP request received by the server. Specifically, the terminal generates an HTTP request and sends the data to a specific API endpoint on the server.

[0794] Step 4:

[0795] The server parses the received message content and passes it to a natural language processing engine (e.g., OpenAI's GPT-3). The input is the message data of the received HTTP request, and the output is the input data for the natural language processing engine. Specifically, the server parses the request, extracts the necessary data, and passes it to the processing engine.

[0796] Step 5:

[0797] The natural language processing engine generates a reply based on the message content. The input is the message data provided by the server, and the output is the generated reply. Specifically, the natural language processing engine uses a generative AI model to analyze the prompt and generates a reply based on the results. The generated reply is temporarily stored on the server.

[0798] Step 6:

[0799] The server sends the generated reply back to the user's terminal. The input is the reply generated by the natural language processing engine, and the output is the HTTP response to the user's terminal. Specifically, the server sends an HTTP response containing the generated reply to the terminal.

[0800] Step 7:

[0801] The terminal analyzes the received reply data and displays it to the user. The input is the HTTP response received from the server, and the output is the display of the reply text on the screen. Specifically, the terminal analyzes the response and outputs the reply text to the display area. The user can then send the displayed reply text as a LINE message.

[0802] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0803] Step 1:

[0804] The user operates their device to launch the application and access the screen for creating posts and video scripts. Input is accessing the application's launch screen, and output is the display of the post or script creation screen. Specifically, the user taps the icon on their device, and the application launches.

[0805] Step 2:

[0806] The user enters keywords or themes related to the content they want to post into the input field on their device. The input is the keywords or themes entered by the user, and the output is the preparation for sending this data to the server. Specifically, the user enters keywords and presses the submit button.

[0807] Step 3:

[0808] The device sends keywords and themes entered by the user to the server. The input is the keywords and themes entered by the user, and the output is the HTTP request received by the server. Specifically, the device generates an HTTP request and sends the data to a specific API endpoint on the server.

[0809] Step 4:

[0810] The server analyzes the received keywords and themes and passes them to the natural language processing engine. The input is the data from the received HTTP request, and the output is the input data for the natural language processing engine. Specifically, the server analyzes the request, extracts the necessary data, and passes it to the processing engine.

[0811] Step 5:

[0812] The natural language processing engine generates optimal posts and scripts based on keywords and themes. The input is data provided by the server, and the output is the generated posts and scripts. Specifically, the natural language processing engine uses a generative AI model to analyze prompt text and generates posts and scripts based on the results. The generated documents are temporarily stored on the server.

[0813] Step 6:

[0814] The server sends the generated post text or script back to the user's terminal. The input is a document generated by a natural language processing engine, and the output is an HTTP response to the user's terminal. Specifically, the server sends an HTTP response containing the generated document to the terminal.

[0815] Step 7:

[0816] The terminal analyzes received posts and script data and displays them to the user. The input is the HTTP response received from the server, and the output is the display of the posts and scripts on the screen. Specifically, the terminal analyzes the response and outputs the document to the display area. The user can then use the displayed document for actual social media or video posting.

[0817] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0818] Step 1:

[0819] The user operates the device to launch the application and access the manual creation screen for cast training. The input is access to the application's launch screen, and the output is the display of the manual creation screen. Specifically, the user taps the icon on the device, and the application launches.

[0820] Step 2:

[0821] The user enters keywords representing the educational situation into the input field on the terminal. The input is the keywords entered by the user, and the output is a preparation for sending this data to the server. Specifically, the user enters keywords and presses the submit button.

[0822] Step 3:

[0823] The terminal sends the keywords entered by the user to the server. The input is the keywords entered by the user, and the output is the HTTP request received by the server. Specifically, the terminal generates an HTTP request and sends the data to a specific API endpoint on the server.

[0824] Step 4:

[0825] The server analyzes the received keywords and performs a search in the database. The input is the data from the received HTTP request, and the output is the database search query. Specifically, the server analyzes the request, searches the database based on the keywords, and extracts relevant know-how and success stories.

[0826] Step 5:

[0827] The server passes the extracted information to a natural language processing engine to generate the optimal manual. The input is information extracted from the database, and the output is the generated manual. Specifically, the server passes the extracted information to the natural language processing engine, which uses a generative AI model to analyze it into prompt sentences and generate the manual.

[0828] Step 6:

[0829] The server sends the generated manual back to the user's terminal. The input is the manual generated by the natural language processing engine, and the output is the HTTP response to the user's terminal. Specifically, the server sends the HTTP response containing the generated manual to the terminal.

[0830] Step 7:

[0831] The terminal analyzes the received manual data and displays it to the user. The input is the HTTP response received from the server, and the output is the display of the manual on the screen. Specifically, the terminal analyzes the response and outputs the manual to the display area. The user can then use the displayed manual for educational purposes.

[0832] (Application Example 1)

[0833] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0834] Traditional customer support systems have presented challenges, including the significant time and effort required for support staff to respond quickly and accurately to customer inquiries. Furthermore, inconsistent response quality due to varying skill levels among support staff led to variability in customer satisfaction. Additionally, there was a lack of efficient tools for instantly generating appropriate replies. To address these issues, there is a need to streamline customer support and achieve consistently high-quality responses.

[0835] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0836] In this invention, the server includes means for the user to access a customer response message generation screen by operating a communication device, means for the user to send an inquiry message entered into the communication device to the server, means for the server to pass the received inquiry message to a natural language processing engine, means for the natural language processing engine to generate an optimal response, means for the generated response to be sent back from the server to the user's communication device, and means for the user's communication device to display the response on a screen. This enables customer support personnel to generate fast, consistent, and high-quality response messages.

[0837] "A means by which a user operates a communication device to access a customer response message generation screen" refers to an operating interface that allows a user to access a specific application screen using a communication device such as a smartphone or personal computer.

[0838] "A means of sending inquiry messages entered by a user into a communication device to a server" refers to a function that allows a user to input a message using an input device (keyboard, touch panel, etc.) on a communication device and send it to a remote server via the internet.

[0839] "A means of passing query messages received by the server to the natural language processing engine" refers to a data transfer function that allows the server to pass received data to algorithms and models for natural language processing.

[0840] "A means by which a natural language processing engine generates the optimal reply" refers to the process of generating an appropriate reply to a received message using natural language processing technology (e.g., a natural language generation model).

[0841] "Means for sending the generated reply message back from the server to the user's communication device" refers to a function for sending data back from the server to the user's communication device after the reply message has been generated.

[0842] "Means for displaying the reply on the user's communication device screen" refers to a function that visually displays the generated reply on the user's smartphone or computer screen.

[0843] To implement this invention, it is necessary to combine a user, a server, and a natural language processing engine. The embodiments thereof are described in detail below.

[0844] First, the user accesses the customer response generation screen using a communication device such as a smartphone or PC. At this point, the user enters the customer inquiry message and sends it to the server. This communication process is achieved through two-way communication over the internet.

[0845] The server receives inquiry messages sent by users. These messages are passed to a natural language processing engine (e.g., OpenAI's GPT-3 model). The natural language processing engine generates the most appropriate reply based on the received message. This process utilizes a generative AI model to generate the reply using appropriate prompts.

[0846] The generated reply is sent back to the user's communication device via the server. The user's communication device displays the received reply on its screen, and the user can review and edit it.

[0847] For example, suppose a user receives the following customer inquiry message: "My delivery is delayed. When will it arrive?" The user enters this message and sends it to the server. The server passes this message to a natural language processing engine and generates a reply message like this: "Thank you for your inquiry. We are currently investigating the delivery delay. You can check the specific estimated arrival date using tracking number XYZ1234. We apologize for the inconvenience." This reply message is sent back from the server to the user's device and displayed on the user's screen.

[0848] In embodiments of this invention, the following specific hardware and software are used:

[0849] Hardware: Servers, smartphones, personal computers

[0850] Software: OpenAI GPT-3 API, Python

[0851] An example of a prompt statement is to use the following format:

[0852] Customer inquiry: My order is delayed. Please tell me when it will arrive.

[0853] Appropriate reply:

[0854] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0855] Step 1:

[0856] Users access the customer response generation screen by operating communication devices such as smartphones or personal computers. The screen the user views is displayed as an interface. The user enters their inquiry message into this interface. The entered message is then converted into the format required for subsequent processing.

[0857] Input: Customer inquiry message

[0858] Output: Query message converted to format

[0859] Step 2:

[0860] The terminal sends the entered query message to the server. This transmission is performed using network protocols such as HTTP requests. During this process, the input data is encoded into a format that is easily processed by the server.

[0861] Input: Inquiry message converted to format

[0862] Output: Query message sent to the server

[0863] Step 3:

[0864] The server receives the query message from the terminal. The server then performs the necessary preprocessing to pass the received message to the natural language processing engine. This preprocessing includes message cleansing and tokenization.

[0865] Input: Inquiry message sent to the server

[0866] Output: Preprocessed query message

[0867] Step 4:

[0868] The server passes the pre-processed query message to a natural language processing engine (e.g., OpenAI's GPT-3). The natural language processing engine generates the best possible response based on the received message. This process involves applying prompts and running a generative AI model.

[0869] Input: Preprocessed query message

[0870] Output: Generated reply

[0871] Step 5:

[0872] The server sends the generated reply message back to the terminal. This return is done using a network protocol such as an HTTP response. In this case, the generated reply message is encoded as needed.

[0873] Input: Generated reply

[0874] Output: Reply message sent back to the terminal

[0875] Step 6:

[0876] The terminal displays the reply received from the server on its screen. At this time, it decodes the message into a format that is easy for the user to understand and displays it on the interface.

[0877] Input: Reply sent back to the device

[0878] Output: Reply text displayed on the screen

[0879] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0880] The embodiments for carrying out the present invention will be described with explanations of the operation and specific examples of three systems that combine emotion engines.

[0881] System (1): Suggested replies to customers via LINE

[0882] This system provides a process where users use communication devices to generate appropriate LINE replies using an emotion engine.

[0883] System operation

[0884] 1. User: Open the application on a communication device such as a smartphone or PC and access the reply generation screen.

[0885] 2. User: Enter the message you want to reply to on LINE into your communication device.

[0886] 3. Terminal: The emotion engine analyzes the user's emotions from the input message.

[0887] 4. Terminal: Sends emotional data and messages to the server.

[0888] 5. Server: Passes the received sentiment data and messages to the natural language processing engine to generate an appropriate reply.

[0889] 6. Server: Sends the generated reply message back to the user's communication device.

[0890] 7. Terminal: Display the reply text to the user and make it available for use as an actual LINE message.

[0891] Specific example

[0892] When a user enters the message "What are you doing tonight?", the emotion engine analyzes the user's emotions and recognizes it as "expectation." Based on that emotion and message, the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[0893] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0894] This system provides a process that allows users to use communication devices to automatically generate social media posts and video scripts using an emotion engine.

[0895] System operation

[0896] 1. User: Open the application on a communication device such as a smartphone or PC and access the screen for creating posts or scripts.

[0897] 2. User: Enter keywords or themes related to the content you want to post into your communication device.

[0898] 3. Terminal: The emotion engine analyzes the user's emotions based on the keywords and themes entered.

[0899] 4. Terminal: Sends sentiment data and keywords / themes to the server.

[0900] 5. Server: Passes the received sentiment data and keywords / themes to a natural language processing engine to generate the most suitable post text or script.

[0901] 6. Server: Sends the generated posts and scripts back to the user's communication device.

[0902] 7. Device: Displays posts and scripts to users, allowing them to use them for actual social media and video posting.

[0903] Specific example

[0904] When a user enters the theme "How to enjoy a host club" and the emotion engine recognizes it as "excitement," the server automatically generates a post based on that emotion and theme, saying, "Thanks to everyone who had fun with us today! There might be even more surprising things waiting for you next time you visit 😉," and displays it on the user's device.

[0905] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0906] This system provides a process that allows users to use communication devices to automatically generate educational manuals tailored to specific situations, utilizing an emotion engine.

[0907] System operation

[0908] 1. User: Open the application on a communication device such as a smartphone or PC and access the manual creation screen.

[0909] 2. User: Enter keywords related to the educational situation into the communication device.

[0910] 3. Terminal: The emotion engine analyzes the user's emotions based on the entered keywords.

[0911] 4. Terminal: Sends sentiment data and keywords to the server.

[0912] 5. Server: Searches the database for received sentiment data and keywords, and extracts relevant know-how and success stories.

[0913] 6. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[0914] 7. Server: Sends the generated manual back to the user's communication device.

[0915] 8. Terminal: Display the manual to the user and make it available for educational purposes.

[0916] Specific example

[0917] When a user enters keywords related to the situation "how to interact with a customer for the first time," and the emotion engine recognizes this as "anxiety," the server automatically generates a manual based on that emotion and keyword, such as "When interacting with a customer for the first time, start by giving a thorough self-introduction and choosing a topic that will pique their interest. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device.

[0918] ---

[0919] The above describes the embodiments for carrying out the present invention. Each system combines the user's communication device, server, natural language processing engine, and emotion engine to provide quick and effective response messages, SNS posts, video scripts, and educational manuals.

[0920] The following describes the processing flow.

[0921] System (1): Suggested replies to customers via LINE

[0922] Program processing steps

[0923] Step 1:

[0924] User: Open the application on your smartphone or PC and access the reply generation screen.

[0925] Specific steps: Launch the app and select the "Generate Reply" option.

[0926] Step 2:

[0927] User: Enter the message you want to reply to on LINE into your communication device.

[0928] Specific action: Enter a message such as "What are you doing tonight?" into the text box and click the "Send" button.

[0929] Step 3:

[0930] Terminal: The emotion engine analyzes the user's emotions from the input message.

[0931] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., anticipation, joy, anxiety, etc.).

[0932] Step 4:

[0933] Terminal: Sends emotion data and messages to the server.

[0934] Specific operation: The analyzed sentiment data and message text are sent to the server as an HTTP request.

[0935] Step 5:

[0936] Server: Passes the received sentiment data and messages to the natural language processing engine to generate an appropriate reply.

[0937] Specific operation: The server transfers sentiment data and message content to a natural language processing engine, which then constructs the most appropriate reply based on the sentiment.

[0938] Step 6:

[0939] Server: Sends the generated reply message back to the terminal.

[0940] Specific action: The generated reply message is sent back to the user's terminal as an HTTP response.

[0941] Step 7:

[0942] Terminal: Displays the reply message to the user.

[0943] Specific action: Display the received reply on the screen so that the user can review it.

[0944] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[0945] Program processing steps

[0946] Step 1:

[0947] User: Open the application on your smartphone or PC and access the screen for creating posts or scripts.

[0948] Specific steps: Launch the app and select the "Create Post / Script" option.

[0949] Step 2:

[0950] User: Enter keywords or themes related to the content you want to post into your communication device.

[0951] Specific action: Enter a topic such as "How to enjoy a host club" into the text box and click the "Submit" button.

[0952] Step 3:

[0953] Terminal: The emotion engine analyzes the user's emotions based on the keywords and themes entered.

[0954] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., excitement, joy, anxiety, etc.).

[0955] Step 4:

[0956] Terminal: Sends sentiment data and keywords / themes to the server.

[0957] Specific operation: The analyzed sentiment data and theme are sent to the server as an HTTP request.

[0958] Step 5:

[0959] Server: Passes the received sentiment data and keywords / themes to a natural language processing engine to generate the most suitable post text or script.

[0960] Specific operation: The server transfers sentiment data and theme content to a natural language processing engine, which then constructs the most suitable post text or script based on the sentiment.

[0961] Step 6:

[0962] Server: Sends generated posts and scripts back to the terminal.

[0963] Specific operation: The generated post text or script is sent back to the user's device as an HTTP response.

[0964] Step 7:

[0965] Terminal: Displays posts and scripts to the user.

[0966] Specific action: Display received posts and scripts on the screen for the user to review.

[0967] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[0968] Program processing steps

[0969] Step 1:

[0970] User: Open the application on your smartphone or PC and access the manual creation screen.

[0971] Specific steps: Launch the app and select the "Create Manual" option.

[0972] Step 2:

[0973] User: Enter keywords related to the educational situation into the communication device.

[0974] Specific action: Enter keywords such as "How to interact with first-time customers" into the text box and click the "Send" button.

[0975] Step 3:

[0976] Terminal: The emotion engine analyzes the user's emotions based on the entered keywords.

[0977] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., anxiety, anticipation, joy, etc.).

[0978] Step 4:

[0979] Terminal: Sends sentiment data and keywords to the server.

[0980] Specific operation: The analyzed sentiment data and keywords are sent to the server as an HTTP request.

[0981] Step 5:

[0982] Server: Searches the database for received sentiment data and keywords, and extracts relevant know-how and success stories.

[0983] Specific operation: The server searches the database based on sentiment data and keywords, and extracts relevant know-how and case studies.

[0984] Step 6:

[0985] Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[0986] Specific actions: Based on the extracted information, create an optimal manual that addresses emotions.

[0987] Step 7:

[0988] Server: Sends the generated manual back to the terminal.

[0989] Specific action: The generated manual is sent back to the user's terminal as an HTTP response.

[0990] Step 8:

[0991] Terminal: Displays the manual to the user.

[0992] Specific action: Display the received manual on the screen so that it can be used for educational purposes.

[0993] (Example 2)

[0994] Next, we will describe Example 2. 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".

[0995] Conventional communication support systems and social media posting systems have the problem of not being able to generate replies and posts that take user emotions into consideration. Similarly, systems for creating educational manuals have the problem of not being able to provide optimal educational content based on emotions. There is a need for a system that solves these problems and automatically generates appropriate replies, posts, video scripts, and educational manuals that reflect user emotions.

[0996] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0997] In this invention, the server performs sentiment analysis on messages, themes, and keywords entered by the user into an electronic device, and provides the corresponding sentiment data and messages, themes, and keywords to a natural language processing device.

[0998] The system includes a natural language processing unit that generates optimal replies, posts, video scripts, and manuals, and a means for sending the generated content from the server to the user's electronic device. This enables the generation of appropriate replies, posts, and video scripts that take the user's emotions into consideration, as well as the automatic generation of emotion-based educational manuals.

[0999] A "user" is a person who operates an electronic device.

[1000] "Electronic devices" refer to devices that can connect to the internet, such as smartphones and personal computers.

[1001] The "reply message generation screen" is an interface that allows users to input messages via electronic devices and generate reply messages.

[1002] "Sentiment analysis" is the process of extracting sentiment data from messages, themes, and keywords entered by the user.

[1003] An "emotion engine" is software or hardware used to analyze a user's emotions from input messages, themes, and keywords.

[1004] A "server" is a remote computer system that receives and transmits data via a network and performs various processes.

[1005] A "natural language processing device" is software or hardware used to generate natural-sounding sentences based on input language data.

[1006] A "reply" is a written response to a message entered by a user.

[1007] A "theme" refers to the main topic or subject matter that a user sets when creating a post or video script.

[1008] A "keyword" is a specific word or phrase entered by the user and used as input data for analysis and generation processes.

[1009] A "post" is a text generated for posting on social media or similar platforms.

[1010] A "video script" is a scenario or script used when creating video content.

[1011] An "educational manual" is a document that describes teaching methods and know-how for specific situations.

[1012] "Database search" is the process by which a server extracts necessary information from stored data.

[1013] "Know-how" refers to successful case studies and specialized knowledge for specific tasks or situations.

[1014] The following describes in detail the operation of three systems using an emotion engine as embodiments for carrying out the present invention. Each system automatically generates reply text, SNS posts, video scripts, and educational manuals by having the user combine an emotion engine and a natural language processing device using an electronic device.

[1015] System (1): Suggested replies to customers via LINE

[1016] overview

[1017] This system provides a process where users use electronic devices (e.g., smartphones, computers) to generate appropriate LINE replies using an emotion engine.

[1018] operation

[1019] The user first opens the application on their electronic device and accesses the reply generation screen. After entering the message they wish to reply, the device uses an emotion engine (e.g., IBM Watson, Microsoft Azure AI) to analyze the message and identify the user's emotion. The emotion data and message are sent from the device to the server, which passes the received data to a natural language processing unit (e.g., OpenAI GPT-4) to generate an appropriate reply. The generated reply is then sent back from the server to the user's electronic device and displayed on it.

[1020] Specific example

[1021] When a user enters the message "What are you doing tonight?", the emotion engine recognizes it as "expectation." Based on that emotion and message, the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[1022] Example of a prompt

[1023] "Generate a reply that expresses anticipation in response to the message, 'What are you doing tonight?'"

[1024] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1025] overview

[1026] This system provides a process that allows users to use electronic devices to automatically generate social media posts and video scripts using an emotion engine.

[1027] operation

[1028] The user opens the application on their electronic device and accesses the screen for creating posts or scripts. After entering the theme and keywords for their post, the device uses an emotion engine to analyze them and identify the user's emotions. The emotion data, along with the theme and keywords, is sent from the device to the server, which then passes the received data to a natural language processing unit to generate the most suitable post or script. The generated post or script is then sent back from the server to the user's electronic device and displayed on it.

[1029] Specific example

[1030] When a user enters the theme "How to enjoy a host club" and the emotion engine recognizes it as "excitement," the server automatically generates a post based on that emotion and theme, saying, "Thanks to everyone who had fun with us today! There might be even more surprising things waiting for you next time you visit 😉," and displays it on the user's device.

[1031] Example of a prompt

[1032] "Please generate an exciting Twitter post on the theme of 'How to enjoy a host club.'"

[1033] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1034] overview

[1035] This system provides a process where users can use electronic devices to automatically generate educational manuals tailored to specific situations, utilizing an emotion engine.

[1036] operation

[1037] The user opens the application on their electronic device and accesses the manual creation screen. When they enter keywords related to the situation required for education, the device uses an emotion engine to analyze the keywords and identify the user's emotions. The emotion data and keywords are sent from the device to the server, where the server searches the received data in a database and extracts relevant know-how and success stories. Based on the extracted information, a natural language processing unit generates an optimal manual and sends it back from the server to the user's electronic device. The educational manual is displayed on the user's electronic device and used for education.

[1038] Specific example

[1039] When a user enters keywords related to the situation "how to interact with a customer for the first time," and the emotion engine recognizes this as "anxiety," the server automatically generates a manual based on that emotion and keyword, such as "When interacting with a customer for the first time, start by giving a thorough self-introduction and choosing a topic that will pique their interest. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device.

[1040] Example of a prompt

[1041] "Please create an educational manual that addresses the anxiety associated with the situation of 'how to interact with customers for the first time.'"

[1042] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1043] System (1): Suggested replies to customers via LINE

[1044] Processing steps

[1045] Step 1:

[1046] The user opens the application on an electronic device (smartphone or computer) and accesses the reply generation screen.

[1047] Specifically, you tap the application icon and select the reply generation screen from the menu.

[1048] Input: User actions

[1049] Output: Display of the reply generation screen

[1050] Step 2:

[1051] The user enters the message they want to reply to on LINE.

[1052] As a concrete example, you would enter a message such as "What are you doing tonight?" into the text box on the reply generation screen.

[1053] Input: Message content

[1054] Output: Input message data

[1055] Step 3:

[1056] The device uses an emotion engine (e.g., IBM Watson, Microsoft Azure AI) to analyze the input message and identify the user's emotions.

[1057] In terms of specific operations, the device sends message data to the emotion engine, which then extracts an emotion such as "expectation."

[1058] Input: Message data

[1059] Output: Analyzed sentiment data

[1060] Step 4:

[1061] The device sends the analyzed sentiment data and messages to the server.

[1062] Specifically, the device sends emotional data and messages to the server via the internet.

[1063] Input: emotion data, message data

[1064] Output: Notification of successful transmission to server

[1065] Step 5:

[1066] The server passes the received sentiment data and messages to a natural language processing unit (e.g., OpenAI GPT-4).

[1067] Specifically, the server sends sentiment data and messages to a natural language processing unit, which then generates an appropriate reply.

[1068] Input: emotion data, message data

[1069] Output: Generated reply data

[1070] Step 6:

[1071] The server sends the generated reply message back to the user's electronic device.

[1072] In terms of specific operations, the server sends the reply data back to the user's electronic device via the internet.

[1073] Input: Reply text data

[1074] Output: Notification that the reply message data has been sent to the user.

[1075] Step 7:

[1076] The device displays the reply message to the user.

[1077] Specifically, the device will display the received reply data on the screen so that the user can review it.

[1078] Input: Reply text data

[1079] Output: Display of reply text

[1080] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1081] Processing steps

[1082] Step 1:

[1083] The user opens the application on their electronic device and accesses the screen for creating posts and scripts.

[1084] Specifically, the user taps the application icon and selects the post or script creation screen from the menu.

[1085] Input: User actions

[1086] Output: Display of post text and script creation screen

[1087] Step 2:

[1088] The user enters the theme or keywords for the content they want to post.

[1089] As a concrete example, you would enter a theme such as "How to enjoy a host club" into the text box on the creation screen.

[1090] Input: Themes or keywords

[1091] Output: Input theme and keyword data

[1092] Step 3:

[1093] The device uses an emotion engine to analyze themes and keywords and identify the user's emotions.

[1094] Specifically, the device sends theme and keyword data to the emotion engine, which then extracts the emotion of "excitement."

[1095] Input: Theme and keyword data

[1096] Output: Analyzed sentiment data

[1097] Step 4:

[1098] The device sends the analyzed sentiment data, themes, and keywords to the server.

[1099] Specifically, the device sends sentiment data, themes, and keywords to the server via the internet.

[1100] Input: Sentiment data, theme and keyword data

[1101] Output: Notification of successful transmission to server

[1102] Step 5:

[1103] The server passes the received sentiment data, themes, and keywords to the natural language processing unit.

[1104] Specifically, the server sends sentiment data, themes, and keywords to a natural language processing unit to generate the most suitable post text or script.

[1105] Input: Sentiment data, theme and keyword data

[1106] Output: Generated post text and script data

[1107] Step 6:

[1108] The server sends the generated posts and scripts back to the user's electronic device.

[1109] Specifically, the server sends the posted text and script data back to the user's electronic device via the internet.

[1110] Input: Posted text or script data

[1111] Output: Notification that the user has completed sending the posted text and script data.

[1112] Step 7:

[1113] The device displays the posted text or script to the user.

[1114] Specifically, the device will display received posts and script data on the screen for the user to review.

[1115] Input: Posted text or script data

[1116] Output: Display of posted text and scripts

[1117] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1118] Processing steps

[1119] Step 1:

[1120] The user opens the application on their electronic device and accesses the manual creation screen.

[1121] Specifically, tap the application icon and select the manual creation screen from the menu.

[1122] Input: User actions

[1123] Output: Display of the manual creation screen

[1124] Step 2:

[1125] The user enters keywords related to the situation required for education.

[1126] As a concrete example, you would enter keywords such as "How to interact with first-time customers" into the text box on the creation screen.

[1127] Input: Keywords for the situation

[1128] Output: Input keyword data

[1129] Step 3:

[1130] The device uses an emotion engine to analyze keywords and identify the user's emotions.

[1131] Specifically, the device sends keyword data to the emotion engine, which then extracts the emotion "anxiety."

[1132] Input: Keyword data

[1133] Output: Analyzed sentiment data

[1134] Step 4:

[1135] The device sends the analyzed sentiment data and keywords to the server.

[1136] Specifically, the device sends sentiment data and keywords to the server via the internet.

[1137] Input: Sentiment data, keyword data

[1138] Output: Notification of successful transmission to server

[1139] Step 5:

[1140] The server processes the received sentiment data and keywords for database retrieval.

[1141] In terms of specific operations, the server searches the database and extracts relevant know-how and success stories.

[1142] Input: Sentiment data, keyword data

[1143] Output: Extracted know-how and success story data

[1144] Step 6:

[1145] Based on the extracted know-how and success stories, the server uses a natural language processing unit to generate the optimal manual.

[1146] Specifically, the server sends know-how and success story data to a natural language processing unit to generate an optimal training manual.

[1147] Input: Know-how and success story data

[1148] Output: Generated manual data

[1149] Step 7:

[1150] The server sends the generated manual back to the user's electronic device.

[1151] Specifically, the server sends manual data back to the user's electronic device via the internet.

[1152] Input: Manual data

[1153] Output: Notification that manual data has been successfully sent to the user.

[1154] Step 8:

[1155] The device displays an educational manual to the user.

[1156] Specifically, the device will display the received manual data on the screen so that the user can review it.

[1157] Input: Manual data

[1158] Output: Manual display

[1159] (Application Example 2)

[1160] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1161] In conventional communication devices, creating posts, video scripts, generating replies, and creating manuals made it difficult for users to generate text that reflected appropriate emotions based on themes and keywords. Furthermore, especially in social media posts and video scripts, content tended to be monotonous and lacked emotional depth, making it difficult to instantly generate engaging content. This increased the burden on users, creating a demand for more efficient content generation.

[1162] The specific processing performed 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 analyzing themes and keywords entered by the user into a communication device using an emotion engine, means for passing the analyzed emotion data and themes and keywords to a natural language processing engine, and means for the natural language processing engine to generate an optimal post or script. This makes it possible to automatically generate appropriate posts or scripts that reflect emotions based on themes and keywords entered by the user.

[1163] An "emotion engine" is an algorithm that analyzes emotions from text entered by a user and outputs that emotion data.

[1164] A "natural language processing engine" is an algorithm used to generate appropriate text based on sentiment data and input themes or keywords.

[1165] A "communication device" is a device that a user operates to input messages, themes, keywords, and send them to a server. Specific examples include smartphones and personal computers.

[1166] "Themes and keywords" refer to the topics and main words of the content that the user wants to generate, and the text is generated based on these.

[1167] A "server" is a computer system that receives data transmitted from a user's communication device and processes it in cooperation with an emotion engine and a natural language processing engine.

[1168] The "post text and script generation screen" is an interface that allows users to input the post text or script they want to generate by operating their communication devices.

[1169] "Analyzed emotion data" refers to information about emotions extracted from user input by the emotion engine.

[1170] "Generated posts and scripts" refer to text generated by a natural language processing engine that users can actually post or use.

[1171] The "reply message generation screen" is an interface that allows users to generate reply messages by operating their communication devices.

[1172] "Situational keywords" are words that users enter to describe a specific situation or context.

[1173] The "manual creation screen" is an interface that users operate to generate manuals for educational and instructional purposes.

[1174] "Know-how and success stories" refer to a collection of past successful achievements and knowledge, indicating the optimal method for specific situations.

[1175] System Overview

[1176] This invention is a system that utilizes an emotion engine and a natural language processing engine to automatically generate posts, scripts, replies, and educational manuals based on themes, keywords, and messages specified by the user. To realize this system, the user's communication equipment, server, emotion engine, and natural language processing engine work in coordination.

[1177] Required hardware and software

[1178] Communication devices: Devices such as smartphones and personal computers that users operate to input text.

[1179] Server: A computer system that receives and processes data transmitted from a user's communication device.

[1180] Emotion engine: An algorithm that analyzes emotions from text and outputs emotion data. A concrete example is the sentiment-analysis pipeline in Hugging Face.

[1181] Natural Language Processing Engine: An algorithm that generates appropriate sentences based on sentiment data and specified themes or keywords. A specific example is OpenAI's GPT-3.

[1182] Specific example of processing procedure

[1183] Automatic generation of posts and scripts

[1184] 1. User: Open the application on your smartphone or computer and access the screen for generating posts or scripts.

[1185] 2. User: Enter the theme or keywords you want to post into your communication device.

[1186] 3. Communication device: The emotion engine analyzes the user's emotions based on the input themes and keywords.

[1187] 4. Communication equipment: Transmits analyzed sentiment data, themes, and keywords to the server.

[1188] 5. Server: Passes the received sentiment data, themes, and keywords to a natural language processing engine to generate the most suitable post text or script.

[1189] 6. Server: Sends the generated posts and scripts back to the user's communication device.

[1190] 7. Communication equipment: Display posts and scripts to users so they can be used for actual social media and video posting.

[1191] Specific example

[1192] For example, if a user enters the theme "How I spend my holidays" and the emotion engine identifies "happiness," the generated post might look like this:

[1193] "Today we enjoyed a picnic in a beautiful park! The combination of the blue sky and green meadow was amazing 🌞"

[1194] Example of a prompt

[1195] Use the following prompt.

[1196] Users will generate social media posts and video scripts on the following theme: "How to spend your holidays," and the emotion is "happiness."

[1197] Automatically generate reply messages

[1198] 1. User: Open the application on your smartphone or computer and access the reply generation screen.

[1199] 2. User: Enter the message you want to reply to into your communication device.

[1200] 3. Communication device: The emotion engine analyzes the user's emotions from the input message.

[1201] 4. Communication equipment: Sends analyzed sentiment data and messages to the server.

[1202] 5. Server: Passes the received sentiment data and messages to the natural language processing engine to generate the most appropriate reply.

[1203] 6. Server: Sends the generated reply message back to the user's communication device.

[1204] 7. Communication devices: Display the reply text to the user and make it available for use as an actual message.

[1205] Generating educational manuals tailored to specific situations

[1206] 1. User: Open the application on your smartphone or computer and access the manual generation screen.

[1207] 2. User: Enter keywords related to the educational situation into the communication device.

[1208] 3. Communication device: The emotion engine analyzes the user's emotions from the input keywords.

[1209] 4. Communication device: Sends the analyzed sentiment data and situational keywords to the server.

[1210] 5. Server: Processes received sentiment data and keywords for database searching and extracts relevant know-how and success stories.

[1211] 6. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[1212] 7. Server: Sends the generated manual back to the user's communication device.

[1213] 8. Communication equipment: Display the manual to the user and make it available for educational purposes.

[1214] This allows users to quickly generate optimal, emotionally conscious text simply by specifying a theme or keywords.

[1215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1216] Step 1:

[1217] Users open the application on their smartphones or computers and access the screen for generating posts and scripts.

[1218] Input: Themes or keywords you want to post about

[1219] Operation: The user enters a theme or keyword on the relevant screen of the application and presses the "Submit" button.

[1220] Step 2:

[1221] The communication device analyzes the input themes and keywords using an emotion engine.

[1222] Input: Themes and keywords entered by the user

[1223] Data processing: Themes and keywords are sent to an emotion engine (e.g., Hugging Face's sentiment-analysis) to perform sentiment analysis.

[1224] Output: Analyzed sentiment data

[1225] Operation: The emotion engine analyzes themes and keywords and generates emotion data.

[1226] Step 3:

[1227] The communication device sends the analyzed sentiment data, along with themes and keywords, to the server.

[1228] Input: Analyzed sentiment data and themes or keywords

[1229] Data transmission: Sentimental data, themes, and keywords are sent to the server as packets.

[1230] Output: Sentiment data, themes, and keywords that reached the server.

[1231] Operation: Communication devices send themes and keywords along with sentiment data to the server.

[1232] Step 4:

[1233] The server receives sentiment data, themes, and keywords, which are then passed to a natural language processing engine to generate the most suitable post text or script.

[1234] Input: Sentimental data, themes, and keywords that reached the server.

[1235] Data processing: Sentiment data, themes, and keywords are input into a natural language processing engine (e.g., OpenAI's GPT-3) to execute the text generation process.

[1236] Output: Generated post text and script

[1237] Operation: The server passes data to a natural language processing engine, which then generates the most suitable text.

[1238] Step 5:

[1239] The server sends the generated posts and scripts back to the user's communication device.

[1240] Input: Generated post text or script

[1241] Data transmission: The generated text is sent as a packet to the communication device.

[1242] Output: Generated text that reached the user's communication device

[1243] Operation: The server sends generated posts and scripts to the communication device.

[1244] Step 6:

[1245] The communication device displays the generated post text or script to the user.

[1246] Input: Generated text that reached the user's communication device

[1247] Data display: Displays the generated text in the display area within the application.

[1248] Output: Posts and scripts displayed to the user

[1249] Operation: The communication device displays the generated post text or script on the screen, allowing the user to review and use it.

[1250] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1251] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1252] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1253] [Third Embodiment]

[1254] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1255] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1256] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1257] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1258] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1259] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1260] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1261] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1262] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1263] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1264] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1265] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1266] Three main systems are required to implement the present invention. The embodiments are described below.

[1267] System (1): Suggested replies to customers via LINE

[1268] This system provides a process for users to generate appropriate LINE reply messages using communication equipment.

[1269] System operation

[1270] 1. User: Open the application on a communication device such as a smartphone or PC and access the reply generation screen.

[1271] 2. User: Enter the message you want to reply to on LINE into your communication device.

[1272] 3. Terminal: Send this entered message to the server.

[1273] 4. Server: Passes the received message to the natural language processing engine and generates an appropriate reply.

[1274] 5. Server: Sends the generated reply message back to the user's communication device.

[1275] 6. Terminal: Display the reply text to the user and make it usable as an actual LINE message.

[1276] Specific example

[1277] When a user types the message "What are you doing tonight?", the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[1278] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1279] This system provides a process that allows users to automatically generate social media posts and video scripts using communication devices.

[1280] System operation

[1281] 1. User: Open the application on a communication device such as a smartphone or PC and access the screen for creating posts or scripts.

[1282] 2. User: Enter keywords or themes related to the content you want to post into your communication device.

[1283] 3. Terminal: Sends the entered keywords and themes to the server.

[1284] 4. Server: Passes the received data to the natural language processing engine to generate the most suitable post text or script.

[1285] 5. Server: Sends the generated posts and scripts back to the user's communication device.

[1286] 6. Device: Displays posts and scripts to users, allowing them to use them for actual social media and video posting.

[1287] Specific example

[1288] When a user enters the theme "How to enjoy a host club," the server automatically generates a post saying, "Thanks to everyone who had fun with us today! There might be even more surprising things waiting for you next time you visit 😉," and displays it on the user's device.

[1289] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1290] This system provides a process for users to automatically generate educational manuals tailored to specific situations using communication devices.

[1291] System operation

[1292] 1. User: Open the application on a communication device such as a smartphone or PC and access the manual creation screen.

[1293] 2. User: Enter keywords related to the educational situation into the communication device.

[1294] 3. Terminal: Sends this entered keyword to the server.

[1295] 4. Server: Searches the database for the received keywords and extracts relevant know-how and success stories.

[1296] 5. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[1297] 6. Server: Sends the generated manual back to the user's communication device.

[1298] 7. Terminal: Display the manual to the user and make it available for educational purposes.

[1299] Specific example

[1300] When a user inputs a situation such as "How to interact with a customer for the first time," the server automatically generates a manual that says, "When interacting with a customer for the first time, first introduce yourself properly and choose a topic that will interest them. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device.

[1301] ---

[1302] The above describes the embodiments for carrying out the present invention. Each system combines the user's communication equipment, a server, and a natural language processing engine to provide replies, SNS posts, video scripts, and educational manuals that can be responded to quickly and effectively.

[1303] The following describes the processing flow.

[1304] System (1): Suggested replies to customers via LINE

[1305] Program processing steps

[1306] Step 1:

[1307] User: Open the application on your smartphone or PC and access the reply generation screen.

[1308] Specific steps: Launch the app and select the "Generate Reply" option.

[1309] Step 2:

[1310] User: Enter the message you want to reply to on LINE into your communication device.

[1311] Specific action: Enter a message such as "What are you doing tonight?" into the text box and click the "Send" button.

[1312] Step 3:

[1313] Terminal: Sends the entered message to the server.

[1314] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[1315] Step 4:

[1316] Server: Passes the received message to the natural language processing engine.

[1317] Specific operation: The server receives the HTTP request and forwards the message text to the natural language processing engine.

[1318] Step 5:

[1319] Server: The natural language processing engine generates the optimal reply.

[1320] Specific operation: The engine analyzes the input data and generates an appropriate reply while referring to past data and trend information.

[1321] Step 6:

[1322] Server: Sends the generated reply message back to the terminal.

[1323] Specific operation: The generated text is sent back to the user's terminal as an HTTP response.

[1324] Step 7:

[1325] Terminal: Displays the reply message to the user.

[1326] Specific action: Display the received reply on the screen so that the user can review it.

[1327] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1328] Program processing steps

[1329] Step 1:

[1330] User: Open the application on your smartphone or PC and access the screen for creating posts or scripts.

[1331] Specific steps: Launch the app and select the "Create Post / Script" option.

[1332] Step 2:

[1333] User: Enter keywords or themes related to the content you want to post into your communication device.

[1334] Specific action: Enter a topic such as "How to enjoy a host club" into the text box and click the "Submit" button.

[1335] Step 3:

[1336] Terminal: Sends the entered keywords and themes to the server.

[1337] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[1338] Step 4:

[1339] Server: Passes the received data to the natural language processing engine.

[1340] Specific operation: The server receives an HTTP request and transfers the input text to the natural language processing engine.

[1341] Step 5:

[1342] Server: A natural language processing engine generates the most suitable posts and scripts.

[1343] Specific operation: The engine analyzes the input data and generates appropriate social media posts and video scripts.

[1344] Step 6:

[1345] Server: Sends generated posts and scripts back to the terminal.

[1346] Specific operation: The generated text is sent back to the user's terminal as an HTTP response.

[1347] Step 7:

[1348] Terminal: Displays posts and scripts to the user.

[1349] Specific action: Display the received text on the screen so that the user can review it.

[1350] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1351] Program processing steps

[1352] Step 1:

[1353] User: Open the application on your smartphone or PC and access the manual creation screen.

[1354] Specific steps: Launch the app and select the "Create Manual" option.

[1355] Step 2:

[1356] User: Enter keywords related to the educational situation into the communication device.

[1357] Specific action: Enter keywords such as "How to interact with first-time customers" into the text box and click the "Send" button.

[1358] Step 3:

[1359] Terminal: Sends the entered keyword to the server.

[1360] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[1361] Step 4:

[1362] Server: Searches the database for the received keywords and extracts relevant know-how and success stories.

[1363] Specific operation: The server searches the database and extracts relevant know-how and case studies.

[1364] Step 5:

[1365] Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[1366] Specific operation: Based on the extracted information, generate a manual outlining the optimal response methods for specific situations.

[1367] Step 6:

[1368] Server: Sends the generated manual back to the terminal.

[1369] Specific action: The generated manual is sent back to the user's terminal as an HTTP response.

[1370] Step 7:

[1371] Terminal: Displays the manual to the user.

[1372] Specific action: Display the received manual on the screen so that the user can review it.

[1373] (Example 1)

[1374] Next, we will describe Example 1. 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."

[1375] In modern society, the use of communication tools and social media is rapidly expanding. However, generating appropriate replies, posts, and educational manuals requires considerable time and effort, necessitating efficient methods. Therefore, a system is needed that enables individual users to communicate quickly and accurately, providing consistent and high-quality content.

[1376] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1377] In this invention, the server includes means for the user to access a reply generation screen by operating a communication device, means for sending a message entered by the user on the communication device to the server, means for the server to pass the received message to a natural language processing engine, means for the natural language processing engine to generate an optimal reply, means for the server to return the generated reply to the user's communication device, means for the user's communication device to display the reply on a screen, means for automatically generating a reply based on prompt input using a generation AI model, and means for the user's communication device to make the generated reply usable as a LINE message. This enables the user to efficiently and quickly generate appropriate replies, posts, and educational manuals, saving effort and time.

[1378] A "user" refers to an individual or legal entity that operates communication devices to input messages, keywords, etc., and utilizes the generated replies, posts, manuals, etc.

[1379] "Communication equipment" refers to devices such as smartphones and personal computers that can connect to the internet and communicate with servers.

[1380] A "reply message generation screen" refers to an interface on a communication device that allows the user to input message content and generate a reply message.

[1381] A "message" refers to text data entered by a user into a communication device, which is sent to a server and used to generate a reply.

[1382] A "server" refers to a computer system that processes data received from communication devices via the internet, generates necessary information, and sends it back to the communication devices.

[1383] A "natural language processing engine" refers to software or algorithms that analyze input messages and keywords to generate human-readable text.

[1384] A "generative AI model" refers to an artificial intelligence model that generates text data using machine learning techniques.

[1385] A "prompt message" refers to the text input into the generative AI model, which then uses this text to generate replies, posts, and manuals.

[1386] The term "screen for creating posts and video scripts" refers to an interface on a communication device that allows users to input themes and keywords for posts and video scripts, and then displays the generated text based on those inputs.

[1387] The "Manual Creation Screen for Cast Training" refers to an interface on a communication device that allows users to input keywords describing situations necessary for training, and then displays the training manual generated based on those keywords.

[1388] A "database" refers to a collection of information managed by a server, containing specific information, know-how, success stories, and other data.

[1389] To implement this invention, three main systems are required. Each system consists of a combination of the user's communication equipment, a server, a natural language processing engine, and a generative AI model.

[1390] System (1): Suggested replies to customers via LINE

[1391] This system provides a process for users to generate appropriate LINE replies using their communication devices. Users open the application on their communication devices, such as smartphones or PCs, and access the reply generation screen. When the user enters the message they want to reply to on LINE into their communication device, the device sends this message to the server. The server passes the received message to a natural language processing engine (for example, OpenAI's GPT-3) and generates an appropriate reply. The generated reply is sent back from the server to the user's communication device, and the device displays the reply to the user. For example, if the user enters the message "What are you doing tonight?", the server will automatically generate a reply such as "I might be a little busy tonight, but I'd love to meet up" and display it on the user's device. An example of a prompt is "Generate an appropriate reply to the message 'What are you doing tonight?'"

[1392] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1393] This system provides a process for users to automatically generate social media posts and video scripts using their communication devices. Users open the application on their communication devices, such as smartphones or PCs, and access the post / script creation screen. When the user enters keywords or themes related to the content they want to post into their communication device, the device sends these keywords or themes to the server. The server passes the received data to a natural language processing engine, which generates the most suitable post or script. The generated post or script is sent back from the server to the user's communication device, and the device displays the post or script to the user. For example, if the user enters the theme "How to enjoy a host club," the server will automatically generate a post such as "Thanks to everyone who had fun with me today! There might be an even more surprising twist next time you visit 😉" and display it on the user's device. An example of a prompt is "Please generate a social media post on the theme 'How to enjoy a host club'."

[1394] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1395] This system provides a process for users to automatically generate educational manuals tailored to specific situations using communication devices. Users open the application on a communication device such as a smartphone or PC and access the manual creation screen. When the user enters keywords related to the situation required for education into the communication device, the device sends these keywords to the server. The server searches the database for the received keywords and extracts relevant know-how and success stories. Based on the extracted information, a natural language processing engine generates the optimal manual, and the generated manual is sent back from the server to the user's communication device. The device displays the manual to the user, making it available for educational use. For example, if the user enters the situation "How to interact with a first-time customer," the server automatically generates a manual such as "When interacting with a first-time customer, first introduce yourself properly and choose a topic that will interest them. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device. An example of a prompt is "Please generate an educational manual for the situation 'How to interact with a first-time customer'."

[1396] Through the system described above, users can efficiently and quickly generate appropriate replies, posts, and training manuals, saving effort and time. This enables users to provide consistent, high-quality content.

[1397] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1398] System (1): Suggested replies to customers via LINE

[1399] * We will refer to smartphones as "devices," servers as "servers," and customers as "users."

[1400] Step 1:

[1401] The user operates the device to launch the application and access the reply generation screen. The input is accessing the application's launch screen, and the output is the display of the reply generation screen. Specifically, the user taps the device icon, and the application launches.

[1402] Step 2:

[1403] The user enters the message they want to reply in the input field on their device. The input is the message the user has entered (e.g., "What are you doing tonight?"), and the output is the preparation for sending this message data to the server. Specifically, the user enters the message and presses the send button.

[1404] Step 3:

[1405] The terminal sends the message content entered by the user to the server. The input is the message content entered by the user, and the output is the HTTP request received by the server. Specifically, the terminal generates an HTTP request and sends the data to a specific API endpoint on the server.

[1406] Step 4:

[1407] The server parses the received message content and passes it to a natural language processing engine (e.g., OpenAI's GPT-3). The input is the message data of the received HTTP request, and the output is the input data for the natural language processing engine. Specifically, the server parses the request, extracts the necessary data, and passes it to the processing engine.

[1408] Step 5:

[1409] The natural language processing engine generates a reply based on the message content. The input is the message data provided by the server, and the output is the generated reply. Specifically, the natural language processing engine uses a generative AI model to analyze the prompt and generates a reply based on the results. The generated reply is temporarily stored on the server.

[1410] Step 6:

[1411] The server sends the generated reply back to the user's terminal. The input is the reply generated by the natural language processing engine, and the output is the HTTP response to the user's terminal. Specifically, the server sends an HTTP response containing the generated reply to the terminal.

[1412] Step 7:

[1413] The terminal analyzes the received reply data and displays it to the user. The input is the HTTP response received from the server, and the output is the display of the reply text on the screen. Specifically, the terminal analyzes the response and outputs the reply text to the display area. The user can then send the displayed reply text as a LINE message.

[1414] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1415] Step 1:

[1416] The user operates their device to launch the application and access the screen for creating posts and video scripts. Input is accessing the application's launch screen, and output is the display of the post or script creation screen. Specifically, the user taps the icon on their device, and the application launches.

[1417] Step 2:

[1418] The user enters keywords or themes related to the content they want to post into the input field on their device. The input is the keywords or themes entered by the user, and the output is the preparation for sending this data to the server. Specifically, the user enters keywords and presses the submit button.

[1419] Step 3:

[1420] The device sends keywords and themes entered by the user to the server. The input is the keywords and themes entered by the user, and the output is the HTTP request received by the server. Specifically, the device generates an HTTP request and sends the data to a specific API endpoint on the server.

[1421] Step 4:

[1422] The server analyzes the received keywords and themes and passes them to the natural language processing engine. The input is the data from the received HTTP request, and the output is the input data for the natural language processing engine. Specifically, the server analyzes the request, extracts the necessary data, and passes it to the processing engine.

[1423] Step 5:

[1424] The natural language processing engine generates optimal posts and scripts based on keywords and themes. The input is data provided by the server, and the output is the generated posts and scripts. Specifically, the natural language processing engine uses a generative AI model to analyze prompt text and generates posts and scripts based on the results. The generated documents are temporarily stored on the server.

[1425] Step 6:

[1426] The server sends the generated post text or script back to the user's terminal. The input is a document generated by a natural language processing engine, and the output is an HTTP response to the user's terminal. Specifically, the server sends an HTTP response containing the generated document to the terminal.

[1427] Step 7:

[1428] The terminal analyzes received posts and script data and displays them to the user. The input is the HTTP response received from the server, and the output is the display of the posts and scripts on the screen. Specifically, the terminal analyzes the response and outputs the document to the display area. The user can then use the displayed document for actual social media or video posting.

[1429] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1430] Step 1:

[1431] The user operates the device to launch the application and access the manual creation screen for cast training. The input is access to the application's launch screen, and the output is the display of the manual creation screen. Specifically, the user taps the icon on the device, and the application launches.

[1432] Step 2:

[1433] The user enters keywords representing the educational situation into the input field on the terminal. The input is the keywords entered by the user, and the output is a preparation for sending this data to the server. Specifically, the user enters keywords and presses the submit button.

[1434] Step 3:

[1435] The terminal sends the keywords entered by the user to the server. The input is the keywords entered by the user, and the output is the HTTP request received by the server. Specifically, the terminal generates an HTTP request and sends the data to a specific API endpoint on the server.

[1436] Step 4:

[1437] The server analyzes the received keywords and performs a search in the database. The input is the data from the received HTTP request, and the output is the database search query. Specifically, the server analyzes the request, searches the database based on the keywords, and extracts relevant know-how and success stories.

[1438] Step 5:

[1439] The server passes the extracted information to a natural language processing engine to generate the optimal manual. The input is information extracted from the database, and the output is the generated manual. Specifically, the server passes the extracted information to the natural language processing engine, which uses a generative AI model to analyze it into prompt sentences and generate the manual.

[1440] Step 6:

[1441] The server sends the generated manual back to the user's terminal. The input is the manual generated by the natural language processing engine, and the output is the HTTP response to the user's terminal. Specifically, the server sends the HTTP response containing the generated manual to the terminal.

[1442] Step 7:

[1443] The terminal analyzes the received manual data and displays it to the user. The input is the HTTP response received from the server, and the output is the display of the manual on the screen. Specifically, the terminal analyzes the response and outputs the manual to the display area. The user can then use the displayed manual for educational purposes.

[1444] (Application Example 1)

[1445] Next, we will explain Application Example 1. In the following explanation, 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."

[1446] Traditional customer support systems have presented challenges, including the significant time and effort required for support staff to respond quickly and accurately to customer inquiries. Furthermore, inconsistent response quality due to varying skill levels among support staff led to variability in customer satisfaction. Additionally, there was a lack of efficient tools for instantly generating appropriate replies. To address these issues, there is a need to streamline customer support and achieve consistently high-quality responses.

[1447] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1448] In this invention, the server includes means for the user to access a customer response message generation screen by operating a communication device, means for the user to send an inquiry message entered into the communication device to the server, means for the server to pass the received inquiry message to a natural language processing engine, means for the natural language processing engine to generate an optimal response, means for the generated response to be sent back from the server to the user's communication device, and means for the user's communication device to display the response on a screen. This enables customer support personnel to generate fast, consistent, and high-quality response messages.

[1449] "A means by which a user operates a communication device to access a customer response message generation screen" refers to an operating interface that allows a user to access a specific application screen using a communication device such as a smartphone or personal computer.

[1450] "A means of sending inquiry messages entered by a user into a communication device to a server" refers to a function that allows a user to input a message using an input device (keyboard, touch panel, etc.) on a communication device and send it to a remote server via the internet.

[1451] "A means of passing query messages received by the server to the natural language processing engine" refers to a data transfer function that allows the server to pass received data to algorithms and models for natural language processing.

[1452] "A means by which a natural language processing engine generates the optimal reply" refers to the process of generating an appropriate reply to a received message using natural language processing technology (e.g., a natural language generation model).

[1453] "Means for sending the generated reply message back from the server to the user's communication device" refers to a function for sending data back from the server to the user's communication device after the reply message has been generated.

[1454] "Means for displaying the reply on the user's communication device screen" refers to a function that visually displays the generated reply on the user's smartphone or computer screen.

[1455] To implement this invention, it is necessary to combine a user, a server, and a natural language processing engine. The embodiments thereof are described in detail below.

[1456] First, the user accesses the customer response generation screen using a communication device such as a smartphone or PC. At this point, the user enters the customer inquiry message and sends it to the server. This communication process is achieved through two-way communication over the internet.

[1457] The server receives inquiry messages sent by users. These messages are passed to a natural language processing engine (e.g., OpenAI's GPT-3 model). The natural language processing engine generates the most appropriate reply based on the received message. This process utilizes a generative AI model to generate the reply using appropriate prompts.

[1458] The generated reply is sent back to the user's communication device via the server. The user's communication device displays the received reply on its screen, and the user can review and edit it.

[1459] For example, suppose a user receives the following customer inquiry message: "My delivery is delayed. When will it arrive?" The user enters this message and sends it to the server. The server passes this message to a natural language processing engine and generates a reply message like this: "Thank you for your inquiry. We are currently investigating the delivery delay. You can check the specific estimated arrival date using tracking number XYZ1234. We apologize for the inconvenience." This reply message is sent back from the server to the user's device and displayed on the user's screen.

[1460] In embodiments of this invention, the following specific hardware and software are used:

[1461] Hardware: Servers, smartphones, personal computers

[1462] Software: OpenAI GPT-3 API, Python

[1463] An example of a prompt statement is to use the following format:

[1464] Customer inquiry: My order is delayed. Please tell me when it will arrive.

[1465] Appropriate reply:

[1466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1467] Step 1:

[1468] Users access the customer response generation screen by operating communication devices such as smartphones or personal computers. The screen the user views is displayed as an interface. The user enters their inquiry message into this interface. The entered message is then converted into the format required for subsequent processing.

[1469] Input: Customer inquiry message

[1470] Output: Query message converted to format

[1471] Step 2:

[1472] The terminal sends the entered query message to the server. This transmission is performed using network protocols such as HTTP requests. During this process, the input data is encoded into a format that is easily processed by the server.

[1473] Input: Inquiry message converted to format

[1474] Output: Query message sent to the server

[1475] Step 3:

[1476] The server receives the query message from the terminal. The server then performs the necessary preprocessing to pass the received message to the natural language processing engine. This preprocessing includes message cleansing and tokenization.

[1477] Input: Inquiry message sent to the server

[1478] Output: Preprocessed query message

[1479] Step 4:

[1480] The server passes the pre-processed query message to a natural language processing engine (e.g., OpenAI's GPT-3). The natural language processing engine generates the best possible response based on the received message. This process involves applying prompts and running a generative AI model.

[1481] Input: Preprocessed query message

[1482] Output: Generated reply

[1483] Step 5:

[1484] The server sends the generated reply message back to the terminal. This return is done using a network protocol such as an HTTP response. In this case, the generated reply message is encoded as needed.

[1485] Input: Generated reply

[1486] Output: Reply message sent back to the terminal

[1487] Step 6:

[1488] The terminal displays the reply received from the server on its screen. At this time, it decodes the message into a format that is easy for the user to understand and displays it on the interface.

[1489] Input: Reply sent back to the device

[1490] Output: Reply text displayed on the screen

[1491] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1492] The embodiments for carrying out the present invention will be described with explanations of the operation and specific examples of three systems that combine emotion engines.

[1493] System (1): Suggested replies to customers via LINE

[1494] This system provides a process where users use communication devices to generate appropriate LINE replies using an emotion engine.

[1495] System operation

[1496] 1. User: Open the application on a communication device such as a smartphone or PC and access the reply generation screen.

[1497] 2. User: Enter the message you want to reply to on LINE into your communication device.

[1498] 3. Terminal: The emotion engine analyzes the user's emotions from the input message.

[1499] 4. Terminal: Sends emotional data and messages to the server.

[1500] 5. Server: Passes the received sentiment data and messages to the natural language processing engine to generate an appropriate reply.

[1501] 6. Server: Sends the generated reply message back to the user's communication device.

[1502] 7. Terminal: Display the reply text to the user and make it available for use as an actual LINE message.

[1503] Specific example

[1504] When a user enters the message "What are you doing tonight?", the emotion engine analyzes the user's emotions and recognizes it as "expectation." Based on that emotion and message, the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[1505] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1506] This system provides a process that allows users to use communication devices to automatically generate social media posts and video scripts using an emotion engine.

[1507] System operation

[1508] 1. User: Open the application on a communication device such as a smartphone or PC and access the screen for creating posts or scripts.

[1509] 2. User: Enter keywords or themes related to the content you want to post into your communication device.

[1510] 3. Terminal: The emotion engine analyzes the user's emotions based on the keywords and themes entered.

[1511] 4. Terminal: Sends sentiment data and keywords / themes to the server.

[1512] 5. Server: Passes the received sentiment data and keywords / themes to a natural language processing engine to generate the most suitable post text or script.

[1513] 6. Server: Sends the generated posts and scripts back to the user's communication device.

[1514] 7. Device: Displays posts and scripts to users, allowing them to use them for actual social media and video posting.

[1515] Specific example

[1516] When a user enters the theme "How to enjoy a host club" and the emotion engine recognizes it as "excitement," the server automatically generates a post based on that emotion and theme, saying, "Thanks to everyone who had fun with us today! There might be even more surprising things waiting for you next time you visit 😉," and displays it on the user's device.

[1517] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1518] This system provides a process that allows users to use communication devices to automatically generate educational manuals tailored to specific situations, utilizing an emotion engine.

[1519] System operation

[1520] 1. User: Open the application on a communication device such as a smartphone or PC and access the manual creation screen.

[1521] 2. User: Enter keywords related to the educational situation into the communication device.

[1522] 3. Terminal: The emotion engine analyzes the user's emotions based on the entered keywords.

[1523] 4. Terminal: Sends sentiment data and keywords to the server.

[1524] 5. Server: Searches the database for received sentiment data and keywords, and extracts relevant know-how and success stories.

[1525] 6. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[1526] 7. Server: Sends the generated manual back to the user's communication device.

[1527] 8. Terminal: Display the manual to the user and make it available for educational purposes.

[1528] Specific example

[1529] When a user enters keywords related to the situation "how to interact with a customer for the first time," and the emotion engine recognizes this as "anxiety," the server automatically generates a manual based on that emotion and keyword, such as "When interacting with a customer for the first time, start by giving a thorough self-introduction and choosing a topic that will pique their interest. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device.

[1530] ---

[1531] The above describes the embodiments for carrying out the present invention. Each system combines the user's communication device, server, natural language processing engine, and emotion engine to provide quick and effective response messages, SNS posts, video scripts, and educational manuals.

[1532] The following describes the processing flow.

[1533] System (1): Suggested replies to customers via LINE

[1534] Program processing steps

[1535] Step 1:

[1536] User: Open the application on your smartphone or PC and access the reply generation screen.

[1537] Specific steps: Launch the app and select the "Generate Reply" option.

[1538] Step 2:

[1539] User: Enter the message you want to reply to on LINE into your communication device.

[1540] Specific action: Enter a message such as "What are you doing tonight?" into the text box and click the "Send" button.

[1541] Step 3:

[1542] Terminal: The emotion engine analyzes the user's emotions from the input message.

[1543] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., anticipation, joy, anxiety, etc.).

[1544] Step 4:

[1545] Terminal: Sends emotion data and messages to the server.

[1546] Specific operation: The analyzed sentiment data and message text are sent to the server as an HTTP request.

[1547] Step 5:

[1548] Server: Passes the received sentiment data and messages to the natural language processing engine to generate an appropriate reply.

[1549] Specific operation: The server transfers sentiment data and message content to a natural language processing engine, which then constructs the most appropriate reply based on the sentiment.

[1550] Step 6:

[1551] Server: Sends the generated reply message back to the terminal.

[1552] Specific action: The generated reply message is sent back to the user's terminal as an HTTP response.

[1553] Step 7:

[1554] Terminal: Displays the reply message to the user.

[1555] Specific action: Display the received reply on the screen so that the user can review it.

[1556] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1557] Program processing steps

[1558] Step 1:

[1559] User: Open the application on your smartphone or PC and access the screen for creating posts or scripts.

[1560] Specific steps: Launch the app and select the "Create Post / Script" option.

[1561] Step 2:

[1562] User: Enter keywords or themes related to the content you want to post into your communication device.

[1563] Specific action: Enter a topic such as "How to enjoy a host club" into the text box and click the "Submit" button.

[1564] Step 3:

[1565] Terminal: The emotion engine analyzes the user's emotions based on the keywords and themes entered.

[1566] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., excitement, joy, anxiety, etc.).

[1567] Step 4:

[1568] Terminal: Sends sentiment data and keywords / themes to the server.

[1569] Specific operation: The analyzed sentiment data and theme are sent to the server as an HTTP request.

[1570] Step 5:

[1571] Server: Passes the received sentiment data and keywords / themes to a natural language processing engine to generate the most suitable post text or script.

[1572] Specific operation: The server transfers sentiment data and theme content to a natural language processing engine, which then constructs the most suitable post text or script based on the sentiment.

[1573] Step 6:

[1574] Server: Sends generated posts and scripts back to the terminal.

[1575] Specific operation: The generated post text or script is sent back to the user's device as an HTTP response.

[1576] Step 7:

[1577] Terminal: Displays posts and scripts to the user.

[1578] Specific action: Display received posts and scripts on the screen for the user to review.

[1579] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1580] Program processing steps

[1581] Step 1:

[1582] User: Open the application on your smartphone or PC and access the manual creation screen.

[1583] Specific steps: Launch the app and select the "Create Manual" option.

[1584] Step 2:

[1585] User: Enter keywords related to the educational situation into the communication device.

[1586] Specific action: Enter keywords such as "How to interact with first-time customers" into the text box and click the "Send" button.

[1587] Step 3:

[1588] Terminal: The emotion engine analyzes the user's emotions based on the entered keywords.

[1589] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., anxiety, anticipation, joy, etc.).

[1590] Step 4:

[1591] Terminal: Sends sentiment data and keywords to the server.

[1592] Specific operation: The analyzed sentiment data and keywords are sent to the server as an HTTP request.

[1593] Step 5:

[1594] Server: Searches the database for received sentiment data and keywords, and extracts relevant know-how and success stories.

[1595] Specific operation: The server searches the database based on sentiment data and keywords, and extracts relevant know-how and case studies.

[1596] Step 6:

[1597] Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[1598] Specific actions: Based on the extracted information, create an optimal manual that addresses emotions.

[1599] Step 7:

[1600] Server: Sends the generated manual back to the terminal.

[1601] Specific action: The generated manual is sent back to the user's terminal as an HTTP response.

[1602] Step 8:

[1603] Terminal: Displays the manual to the user.

[1604] Specific action: Display the received manual on the screen so that it can be used for educational purposes.

[1605] (Example 2)

[1606] Next, we will describe Example 2. 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."

[1607] Conventional communication support systems and social media posting systems have the problem of not being able to generate replies and posts that take user emotions into consideration. Similarly, systems for creating educational manuals have the problem of not being able to provide optimal educational content based on emotions. There is a need for a system that solves these problems and automatically generates appropriate replies, posts, video scripts, and educational manuals that reflect user emotions.

[1608] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1609] In this invention, the server performs sentiment analysis on messages, themes, and keywords entered by the user into an electronic device, and provides the corresponding sentiment data and messages, themes, and keywords to a natural language processing device.

[1610] The system includes a natural language processing unit that generates optimal replies, posts, video scripts, and manuals, and a means for sending the generated content from the server to the user's electronic device. This enables the generation of appropriate replies, posts, and video scripts that take the user's emotions into consideration, as well as the automatic generation of emotion-based educational manuals.

[1611] A "user" is a person who operates an electronic device.

[1612] "Electronic devices" refer to devices that can connect to the internet, such as smartphones and personal computers.

[1613] The "reply message generation screen" is an interface that allows users to input messages via electronic devices and generate reply messages.

[1614] "Sentiment analysis" is the process of extracting sentiment data from messages, themes, and keywords entered by the user.

[1615] An "emotion engine" is software or hardware used to analyze a user's emotions from input messages, themes, and keywords.

[1616] A "server" is a remote computer system that receives and transmits data via a network and performs various processes.

[1617] A "natural language processing device" is software or hardware used to generate natural-sounding sentences based on input language data.

[1618] A "reply" is a written response to a message entered by a user.

[1619] A "theme" refers to the main topic or subject matter that a user sets when creating a post or video script.

[1620] A "keyword" is a specific word or phrase entered by the user and used as input data for analysis and generation processes.

[1621] A "post" is a text generated for posting on social media or similar platforms.

[1622] A "video script" is a scenario or script used when creating video content.

[1623] An "educational manual" is a document that describes teaching methods and know-how for specific situations.

[1624] "Database search" is the process by which a server extracts necessary information from stored data.

[1625] "Know-how" refers to successful case studies and specialized knowledge for specific tasks or situations.

[1626] The following describes in detail the operation of three systems using an emotion engine as embodiments for carrying out the present invention. Each system automatically generates reply text, SNS posts, video scripts, and educational manuals by having the user combine an emotion engine and a natural language processing device using an electronic device.

[1627] System (1): Suggested replies to customers via LINE

[1628] overview

[1629] This system provides a process where users use electronic devices (e.g., smartphones, computers) to generate appropriate LINE replies using an emotion engine.

[1630] operation

[1631] The user first opens the application on their electronic device and accesses the reply generation screen. After entering the message they wish to reply, the device uses an emotion engine (e.g., IBM Watson, Microsoft Azure AI) to analyze the message and identify the user's emotion. The emotion data and message are sent from the device to the server, which passes the received data to a natural language processing unit (e.g., OpenAI GPT-4) to generate an appropriate reply. The generated reply is then sent back from the server to the user's electronic device and displayed on it.

[1632] Specific example

[1633] When a user enters the message "What are you doing tonight?", the emotion engine recognizes it as "expectation." Based on that emotion and message, the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[1634] Example of a prompt

[1635] "Generate a reply that expresses anticipation in response to the message, 'What are you doing tonight?'"

[1636] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1637] overview

[1638] This system provides a process that allows users to use electronic devices to automatically generate social media posts and video scripts using an emotion engine.

[1639] operation

[1640] The user opens the application on their electronic device and accesses the screen for creating posts or scripts. After entering the theme and keywords for their post, the device uses an emotion engine to analyze them and identify the user's emotions. The emotion data, along with the theme and keywords, is sent from the device to the server, which then passes the received data to a natural language processing unit to generate the most suitable post or script. The generated post or script is then sent back from the server to the user's electronic device and displayed on it.

[1641] Specific example

[1642] When a user enters the theme "How to enjoy a host club" and the emotion engine recognizes it as "excitement," the server automatically generates a post based on that emotion and theme, saying, "Thanks to everyone who had fun with us today! There might be even more surprising things waiting for you next time you visit 😉," and displays it on the user's device.

[1643] Example of a prompt

[1644] "Please generate an exciting Twitter post on the theme of 'How to enjoy a host club.'"

[1645] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1646] overview

[1647] This system provides a process where users can use electronic devices to automatically generate educational manuals tailored to specific situations, utilizing an emotion engine.

[1648] operation

[1649] The user opens the application on their electronic device and accesses the manual creation screen. When they enter keywords related to the situation required for education, the device uses an emotion engine to analyze the keywords and identify the user's emotions. The emotion data and keywords are sent from the device to the server, where the server searches the received data in a database and extracts relevant know-how and success stories. Based on the extracted information, a natural language processing unit generates an optimal manual and sends it back from the server to the user's electronic device. The educational manual is displayed on the user's electronic device and used for education.

[1650] Specific example

[1651] When a user enters keywords related to the situation "how to interact with a customer for the first time," and the emotion engine recognizes this as "anxiety," the server automatically generates a manual based on that emotion and keyword, such as "When interacting with a customer for the first time, start by giving a thorough self-introduction and choosing a topic that will pique their interest. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device.

[1652] Example of a prompt

[1653] "Please create an educational manual that addresses the anxiety associated with the situation of 'how to interact with customers for the first time.'"

[1654] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1655] System (1): Suggested replies to customers via LINE

[1656] Processing steps

[1657] Step 1:

[1658] The user opens the application on an electronic device (smartphone or computer) and accesses the reply generation screen.

[1659] Specifically, you tap the application icon and select the reply generation screen from the menu.

[1660] Input: User actions

[1661] Output: Display of the reply generation screen

[1662] Step 2:

[1663] The user enters the message they want to reply to on LINE.

[1664] As a concrete example, you would enter a message such as "What are you doing tonight?" into the text box on the reply generation screen.

[1665] Input: Message content

[1666] Output: Input message data

[1667] Step 3:

[1668] The device uses an emotion engine (e.g., IBM Watson, Microsoft Azure AI) to analyze the input message and identify the user's emotions.

[1669] In terms of specific operations, the device sends message data to the emotion engine, which then extracts an emotion such as "expectation."

[1670] Input: Message data

[1671] Output: Analyzed sentiment data

[1672] Step 4:

[1673] The device sends the analyzed sentiment data and messages to the server.

[1674] Specifically, the device sends emotional data and messages to the server via the internet.

[1675] Input: emotion data, message data

[1676] Output: Notification of successful transmission to server

[1677] Step 5:

[1678] The server passes the received sentiment data and messages to a natural language processing unit (e.g., OpenAI GPT-4).

[1679] Specifically, the server sends sentiment data and messages to a natural language processing unit, which then generates an appropriate reply.

[1680] Input: emotion data, message data

[1681] Output: Generated reply data

[1682] Step 6:

[1683] The server sends the generated reply message back to the user's electronic device.

[1684] In terms of specific operations, the server sends the reply data back to the user's electronic device via the internet.

[1685] Input: Reply text data

[1686] Output: Notification that the reply message data has been sent to the user.

[1687] Step 7:

[1688] The device displays the reply message to the user.

[1689] Specifically, the device will display the received reply data on the screen so that the user can review it.

[1690] Input: Reply text data

[1691] Output: Display of reply text

[1692] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1693] Processing steps

[1694] Step 1:

[1695] The user opens the application on their electronic device and accesses the screen for creating posts and scripts.

[1696] Specifically, the user taps the application icon and selects the post or script creation screen from the menu.

[1697] Input: User actions

[1698] Output: Display of post text and script creation screen

[1699] Step 2:

[1700] The user enters the theme or keywords for the content they want to post.

[1701] As a concrete example, you would enter a theme such as "How to enjoy a host club" into the text box on the creation screen.

[1702] Input: Themes or keywords

[1703] Output: Input theme and keyword data

[1704] Step 3:

[1705] The device uses an emotion engine to analyze themes and keywords and identify the user's emotions.

[1706] Specifically, the device sends theme and keyword data to the emotion engine, which then extracts the emotion of "excitement."

[1707] Input: Theme and keyword data

[1708] Output: Analyzed sentiment data

[1709] Step 4:

[1710] The device sends the analyzed sentiment data, themes, and keywords to the server.

[1711] Specifically, the device sends sentiment data, themes, and keywords to the server via the internet.

[1712] Input: Sentiment data, theme and keyword data

[1713] Output: Notification of successful transmission to server

[1714] Step 5:

[1715] The server passes the received sentiment data, themes, and keywords to the natural language processing unit.

[1716] Specifically, the server sends sentiment data, themes, and keywords to a natural language processing unit to generate the most suitable post text or script.

[1717] Input: Sentiment data, theme and keyword data

[1718] Output: Generated post text and script data

[1719] Step 6:

[1720] The server sends the generated posts and scripts back to the user's electronic device.

[1721] Specifically, the server sends the posted text and script data back to the user's electronic device via the internet.

[1722] Input: Posted text or script data

[1723] Output: Notification that the user has completed sending the posted text and script data.

[1724] Step 7:

[1725] The device displays the posted text or script to the user.

[1726] Specifically, the device will display received posts and script data on the screen for the user to review.

[1727] Input: Posted text or script data

[1728] Output: Display of posted text and scripts

[1729] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1730] Processing steps

[1731] Step 1:

[1732] The user opens the application on their electronic device and accesses the manual creation screen.

[1733] Specifically, tap the application icon and select the manual creation screen from the menu.

[1734] Input: User actions

[1735] Output: Display of the manual creation screen

[1736] Step 2:

[1737] The user enters keywords related to the situation required for education.

[1738] As a concrete example, you would enter keywords such as "How to interact with first-time customers" into the text box on the creation screen.

[1739] Input: Keywords for the situation

[1740] Output: Input keyword data

[1741] Step 3:

[1742] The device uses an emotion engine to analyze keywords and identify the user's emotions.

[1743] Specifically, the device sends keyword data to the emotion engine, which then extracts the emotion "anxiety."

[1744] Input: Keyword data

[1745] Output: Analyzed sentiment data

[1746] Step 4:

[1747] The device sends the analyzed sentiment data and keywords to the server.

[1748] Specifically, the device sends sentiment data and keywords to the server via the internet.

[1749] Input: Sentiment data, keyword data

[1750] Output: Notification of successful transmission to server

[1751] Step 5:

[1752] The server processes the received sentiment data and keywords for database retrieval.

[1753] In terms of specific operations, the server searches the database and extracts relevant know-how and success stories.

[1754] Input: Sentiment data, keyword data

[1755] Output: Extracted know-how and success story data

[1756] Step 6:

[1757] Based on the extracted know-how and success stories, the server uses a natural language processing unit to generate the optimal manual.

[1758] Specifically, the server sends know-how and success story data to a natural language processing unit to generate an optimal training manual.

[1759] Input: Know-how and success story data

[1760] Output: Generated manual data

[1761] Step 7:

[1762] The server sends the generated manual back to the user's electronic device.

[1763] Specifically, the server sends manual data back to the user's electronic device via the internet.

[1764] Input: Manual data

[1765] Output: Notification that manual data has been successfully sent to the user.

[1766] Step 8:

[1767] The device displays an educational manual to the user.

[1768] Specifically, the device will display the received manual data on the screen so that the user can review it.

[1769] Input: Manual data

[1770] Output: Manual display

[1771] (Application Example 2)

[1772] Next, we will explain application example 2. In the following explanation, 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."

[1773] In conventional communication devices, creating posts, video scripts, generating replies, and creating manuals made it difficult for users to generate text that reflected appropriate emotions based on themes and keywords. Furthermore, especially in social media posts and video scripts, content tended to be monotonous and lacked emotional depth, making it difficult to instantly generate engaging content. This increased the burden on users, creating a demand for more efficient content generation.

[1774] The specific processing performed 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 analyzing themes and keywords entered by the user into a communication device using an emotion engine, means for passing the analyzed emotion data and themes and keywords to a natural language processing engine, and means for the natural language processing engine to generate an optimal post or script. This makes it possible to automatically generate appropriate posts or scripts that reflect emotions based on themes and keywords entered by the user.

[1775] An "emotion engine" is an algorithm that analyzes emotions from text entered by a user and outputs that emotion data.

[1776] A "natural language processing engine" is an algorithm used to generate appropriate text based on sentiment data and input themes or keywords.

[1777] A "communication device" is a device that a user operates to input messages, themes, keywords, and send them to a server. Specific examples include smartphones and personal computers.

[1778] "Themes and keywords" refer to the topics and main words of the content that the user wants to generate, and the text is generated based on these.

[1779] A "server" is a computer system that receives data transmitted from a user's communication device and processes it in cooperation with an emotion engine and a natural language processing engine.

[1780] The "post text and script generation screen" is an interface that allows users to input the post text or script they want to generate by operating their communication devices.

[1781] "Analyzed emotion data" refers to information about emotions extracted from user input by the emotion engine.

[1782] "Generated posts and scripts" refer to text generated by a natural language processing engine that users can actually post or use.

[1783] The "reply message generation screen" is an interface that allows users to generate reply messages by operating their communication devices.

[1784] "Situational keywords" are words that users enter to describe a specific situation or context.

[1785] The "manual creation screen" is an interface that users operate to generate manuals for educational and instructional purposes.

[1786] "Know-how and success stories" refer to a collection of past successful achievements and knowledge, indicating the optimal method for specific situations.

[1787] System Overview

[1788] This invention is a system that utilizes an emotion engine and a natural language processing engine to automatically generate posts, scripts, replies, and educational manuals based on themes, keywords, and messages specified by the user. To realize this system, the user's communication equipment, server, emotion engine, and natural language processing engine work in coordination.

[1789] Required hardware and software

[1790] Communication devices: Devices such as smartphones and personal computers that users operate to input text.

[1791] Server: A computer system that receives and processes data transmitted from a user's communication device.

[1792] Emotion engine: An algorithm that analyzes emotions from text and outputs emotion data. A concrete example is the sentiment-analysis pipeline in Hugging Face.

[1793] Natural Language Processing Engine: An algorithm that generates appropriate sentences based on sentiment data and specified themes or keywords. A specific example is OpenAI's GPT-3.

[1794] Specific example of processing procedure

[1795] Automatic generation of posts and scripts

[1796] 1. User: Open the application on your smartphone or computer and access the screen for generating posts or scripts.

[1797] 2. User: Enter the theme or keywords you want to post into your communication device.

[1798] 3. Communication device: The emotion engine analyzes the user's emotions based on the input themes and keywords.

[1799] 4. Communication equipment: Transmits analyzed sentiment data, themes, and keywords to the server.

[1800] 5. Server: Passes the received sentiment data, themes, and keywords to a natural language processing engine to generate the most suitable post text or script.

[1801] 6. Server: Sends the generated posts and scripts back to the user's communication device.

[1802] 7. Communication equipment: Display posts and scripts to users so they can be used for actual social media and video posting.

[1803] Specific example

[1804] For example, if a user enters the theme "How I spend my holidays" and the emotion engine identifies "happiness," the generated post might look like this:

[1805] "Today we enjoyed a picnic in a beautiful park! The combination of the blue sky and green meadow was amazing 🌞"

[1806] Example of a prompt

[1807] Use the following prompt.

[1808] Users will generate social media posts and video scripts on the following theme: "How to spend your holidays," and the emotion is "happiness."

[1809] Automatically generate reply messages

[1810] 1. User: Open the application on your smartphone or computer and access the reply generation screen.

[1811] 2. User: Enter the message you want to reply to into your communication device.

[1812] 3. Communication device: The emotion engine analyzes the user's emotions from the input message.

[1813] 4. Communication equipment: Sends analyzed sentiment data and messages to the server.

[1814] 5. Server: Passes the received sentiment data and messages to the natural language processing engine to generate the most appropriate reply.

[1815] 6. Server: Sends the generated reply message back to the user's communication device.

[1816] 7. Communication devices: Display the reply text to the user and make it available for use as an actual message.

[1817] Generating educational manuals tailored to specific situations

[1818] 1. User: Open the application on your smartphone or computer and access the manual generation screen.

[1819] 2. User: Enter keywords related to the educational situation into the communication device.

[1820] 3. Communication device: The emotion engine analyzes the user's emotions from the input keywords.

[1821] 4. Communication device: Sends the analyzed sentiment data and situational keywords to the server.

[1822] 5. Server: Processes received sentiment data and keywords for database searching and extracts relevant know-how and success stories.

[1823] 6. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[1824] 7. Server: Sends the generated manual back to the user's communication device.

[1825] 8. Communication equipment: Display the manual to the user and make it available for educational purposes.

[1826] This allows users to quickly generate optimal, emotionally conscious text simply by specifying a theme or keywords.

[1827] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1828] Step 1:

[1829] Users open the application on their smartphones or computers and access the screen for generating posts and scripts.

[1830] Input: Themes or keywords you want to post about

[1831] Operation: The user enters a theme or keyword on the relevant screen of the application and presses the "Submit" button.

[1832] Step 2:

[1833] The communication device analyzes the input themes and keywords using an emotion engine.

[1834] Input: Themes and keywords entered by the user

[1835] Data processing: Themes and keywords are sent to an emotion engine (e.g., Hugging Face's sentiment-analysis) to perform sentiment analysis.

[1836] Output: Analyzed sentiment data

[1837] Operation: The emotion engine analyzes themes and keywords and generates emotion data.

[1838] Step 3:

[1839] The communication device sends the analyzed sentiment data, along with themes and keywords, to the server.

[1840] Input: Analyzed sentiment data and themes or keywords

[1841] Data transmission: Sentimental data, themes, and keywords are sent to the server as packets.

[1842] Output: Sentiment data, themes, and keywords that reached the server.

[1843] Operation: Communication devices send themes and keywords along with sentiment data to the server.

[1844] Step 4:

[1845] The server receives sentiment data, themes, and keywords, which are then passed to a natural language processing engine to generate the most suitable post text or script.

[1846] Input: Sentimental data, themes, and keywords that reached the server.

[1847] Data processing: Sentiment data, themes, and keywords are input into a natural language processing engine (e.g., OpenAI's GPT-3) to execute the text generation process.

[1848] Output: Generated post text and script

[1849] Operation: The server passes data to a natural language processing engine, which then generates the most suitable text.

[1850] Step 5:

[1851] The server sends the generated posts and scripts back to the user's communication device.

[1852] Input: Generated post text or script

[1853] Data transmission: The generated text is sent as a packet to the communication device.

[1854] Output: Generated text that reached the user's communication device

[1855] Operation: The server sends generated posts and scripts to the communication device.

[1856] Step 6:

[1857] The communication device displays the generated post text or script to the user.

[1858] Input: Generated text that reached the user's communication device

[1859] Data display: Displays the generated text in the display area within the application.

[1860] Output: Posts and scripts displayed to the user

[1861] Operation: The communication device displays the generated post text or script on the screen, allowing the user to review and use it.

[1862] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1863] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1864] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1865] [Fourth Embodiment]

[1866] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1867] As shown in Figure 7, the 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.

[1868] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1869] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1870] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1871] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1872] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1873] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1874] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1875] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1876] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1877] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1878] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1879] Three main systems are required to implement the present invention. The embodiments are described below.

[1880] System (1): Suggested replies to customers via LINE

[1881] This system provides a process for users to generate appropriate LINE reply messages using communication equipment.

[1882] System operation

[1883] 1. User: Open the application on a communication device such as a smartphone or PC and access the reply generation screen.

[1884] 2. User: Enter the message you want to reply to on LINE into your communication device.

[1885] 3. Terminal: Send this entered message to the server.

[1886] 4. Server: Passes the received message to the natural language processing engine and generates an appropriate reply.

[1887] 5. Server: Sends the generated reply message back to the user's communication device.

[1888] 6. Terminal: Display the reply text to the user and make it usable as an actual LINE message.

[1889] Specific example

[1890] When a user types the message "What are you doing tonight?", the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[1891] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1892] This system provides a process that allows users to automatically generate social media posts and video scripts using communication devices.

[1893] System operation

[1894] 1. User: Open the application on a communication device such as a smartphone or PC and access the screen for creating posts or scripts.

[1895] 2. User: Enter keywords or themes related to the content you want to post into your communication device.

[1896] 3. Terminal: Sends the entered keywords and themes to the server.

[1897] 4. Server: Passes the received data to the natural language processing engine to generate the most suitable post text or script.

[1898] 5. Server: Sends the generated posts and scripts back to the user's communication device.

[1899] 6. Device: Displays posts and scripts to users, allowing them to use them for actual social media and video posting.

[1900] Specific example

[1901] When a user enters the theme "How to enjoy a host club," the server automatically generates a post saying, "Thanks to everyone who had fun with us today! There might be even more surprising things waiting for you next time you visit 😉," and displays it on the user's device.

[1902] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1903] This system provides a process for users to automatically generate educational manuals tailored to specific situations using communication devices.

[1904] System operation

[1905] 1. User: Open the application on a communication device such as a smartphone or PC and access the manual creation screen.

[1906] 2. User: Enter keywords related to the educational situation into the communication device.

[1907] 3. Terminal: Sends this entered keyword to the server.

[1908] 4. Server: Searches the database for the received keywords and extracts relevant know-how and success stories.

[1909] 5. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[1910] 6. Server: Sends the generated manual back to the user's communication device.

[1911] 7. Terminal: Display the manual to the user and make it available for educational purposes.

[1912] Specific example

[1913] When a user inputs a situation such as "How to interact with a customer for the first time," the server automatically generates a manual that says, "When interacting with a customer for the first time, first introduce yourself properly and choose a topic that will interest them. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device.

[1914] ---

[1915] The above describes the embodiments for carrying out the present invention. Each system combines the user's communication equipment, a server, and a natural language processing engine to provide replies, SNS posts, video scripts, and educational manuals that can be responded to quickly and effectively.

[1916] The following describes the processing flow.

[1917] System (1): Suggested replies to customers via LINE

[1918] Program processing steps

[1919] Step 1:

[1920] User: Open the application on your smartphone or PC and access the reply generation screen.

[1921] Specific steps: Launch the app and select the "Generate Reply" option.

[1922] Step 2:

[1923] User: Enter the message you want to reply to on LINE into your communication device.

[1924] Specific action: Enter a message such as "What are you doing tonight?" into the text box and click the "Send" button.

[1925] Step 3:

[1926] Terminal: Sends the entered message to the server.

[1927] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[1928] Step 4:

[1929] Server: Passes the received message to the natural language processing engine.

[1930] Specific operation: The server receives the HTTP request and forwards the message text to the natural language processing engine.

[1931] Step 5:

[1932] Server: The natural language processing engine generates the optimal reply.

[1933] Specific operation: The engine analyzes the input data and generates an appropriate reply while referring to past data and trend information.

[1934] Step 6:

[1935] Server: Sends the generated reply message back to the terminal.

[1936] Specific operation: The generated text is sent back to the user's terminal as an HTTP response.

[1937] Step 7:

[1938] Terminal: Displays the reply message to the user.

[1939] Specific action: Display the received reply on the screen so that the user can review it.

[1940] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[1941] Program processing steps

[1942] Step 1:

[1943] User: Open the application on your smartphone or PC and access the screen for creating posts or scripts.

[1944] Specific steps: Launch the app and select the "Create Post / Script" option.

[1945] Step 2:

[1946] User: Enter keywords or themes related to the content you want to post into your communication device.

[1947] Specific action: Enter a topic such as "How to enjoy a host club" into the text box and click the "Submit" button.

[1948] Step 3:

[1949] Terminal: Sends the entered keywords and themes to the server.

[1950] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[1951] Step 4:

[1952] Server: Passes the received data to the natural language processing engine.

[1953] Specific operation: The server receives an HTTP request and transfers the input text to the natural language processing engine.

[1954] Step 5:

[1955] Server: A natural language processing engine generates the most suitable posts and scripts.

[1956] Specific operation: The engine analyzes the input data and generates appropriate social media posts and video scripts.

[1957] Step 6:

[1958] Server: Sends generated posts and scripts back to the terminal.

[1959] Specific operation: The generated text is sent back to the user's terminal as an HTTP response.

[1960] Step 7:

[1961] Terminal: Displays posts and scripts to the user.

[1962] Specific action: Display the received text on the screen so that the user can review it.

[1963] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[1964] Program processing steps

[1965] Step 1:

[1966] User: Open the application on your smartphone or PC and access the manual creation screen.

[1967] Specific steps: Launch the app and select the "Create Manual" option.

[1968] Step 2:

[1969] User: Enter keywords related to the educational situation into the communication device.

[1970] Specific action: Enter keywords such as "How to interact with first-time customers" into the text box and click the "Send" button.

[1971] Step 3:

[1972] Terminal: Sends the entered keyword to the server.

[1973] Specific operation: When the submit button is clicked, the entered text data is sent to the server as an HTTP request.

[1974] Step 4:

[1975] Server: Searches the database for the received keywords and extracts relevant know-how and success stories.

[1976] Specific operation: The server searches the database and extracts relevant know-how and case studies.

[1977] Step 5:

[1978] Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[1979] Specific operation: Based on the extracted information, generate a manual outlining the optimal response methods for specific situations.

[1980] Step 6:

[1981] Server: Sends the generated manual back to the terminal.

[1982] Specific action: The generated manual is sent back to the user's terminal as an HTTP response.

[1983] Step 7:

[1984] Terminal: Displays the manual to the user.

[1985] Specific action: Display the received manual on the screen so that the user can review it.

[1986] (Example 1)

[1987] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1988] In modern society, the use of communication tools and social media is rapidly expanding. However, generating appropriate replies, posts, and educational manuals requires considerable time and effort, necessitating efficient methods. Therefore, a system is needed that enables individual users to communicate quickly and accurately, providing consistent and high-quality content.

[1989] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1990] In this invention, the server includes means for the user to access a reply generation screen by operating a communication device, means for sending a message entered by the user on the communication device to the server, means for the server to pass the received message to a natural language processing engine, means for the natural language processing engine to generate an optimal reply, means for the server to return the generated reply to the user's communication device, means for the user's communication device to display the reply on a screen, means for automatically generating a reply based on prompt input using a generation AI model, and means for the user's communication device to make the generated reply usable as a LINE message. This enables the user to efficiently and quickly generate appropriate replies, posts, and educational manuals, saving effort and time.

[1991] A "user" refers to an individual or legal entity that operates communication devices to input messages, keywords, etc., and utilizes the generated replies, posts, manuals, etc.

[1992] "Communication equipment" refers to devices such as smartphones and personal computers that can connect to the internet and communicate with servers.

[1993] A "reply message generation screen" refers to an interface on a communication device that allows the user to input message content and generate a reply message.

[1994] A "message" refers to text data entered by a user into a communication device, which is sent to a server and used to generate a reply.

[1995] A "server" refers to a computer system that processes data received from communication devices via the internet, generates necessary information, and sends it back to the communication devices.

[1996] A "natural language processing engine" refers to software or algorithms that analyze input messages and keywords to generate human-readable text.

[1997] A "generative AI model" refers to an artificial intelligence model that generates text data using machine learning techniques.

[1998] A "prompt message" refers to the text input into the generative AI model, which then uses this text to generate replies, posts, and manuals.

[1999] The term "screen for creating posts and video scripts" refers to an interface on a communication device that allows users to input themes and keywords for posts and video scripts, and then displays the generated text based on those inputs.

[2000] The "Manual Creation Screen for Cast Training" refers to an interface on a communication device that allows users to input keywords describing situations necessary for training, and then displays the training manual generated based on those keywords.

[2001] A "database" refers to a collection of information managed by a server, containing specific information, know-how, success stories, and other data.

[2002] To implement this invention, three main systems are required. Each system consists of a combination of the user's communication equipment, a server, a natural language processing engine, and a generative AI model.

[2003] System (1): Suggested replies to customers via LINE

[2004] This system provides a process for users to generate appropriate LINE replies using their communication devices. Users open the application on their communication devices, such as smartphones or PCs, and access the reply generation screen. When the user enters the message they want to reply to on LINE into their communication device, the device sends this message to the server. The server passes the received message to a natural language processing engine (for example, OpenAI's GPT-3) and generates an appropriate reply. The generated reply is sent back from the server to the user's communication device, and the device displays the reply to the user. For example, if the user enters the message "What are you doing tonight?", the server will automatically generate a reply such as "I might be a little busy tonight, but I'd love to meet up" and display it on the user's device. An example of a prompt is "Generate an appropriate reply to the message 'What are you doing tonight?'"

[2005] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[2006] This system provides a process for users to automatically generate social media posts and video scripts using their communication devices. Users open the application on their communication devices, such as smartphones or PCs, and access the post / script creation screen. When the user enters keywords or themes related to the content they want to post into their communication device, the device sends these keywords or themes to the server. The server passes the received data to a natural language processing engine, which generates the most suitable post or script. The generated post or script is sent back from the server to the user's communication device, and the device displays the post or script to the user. For example, if the user enters the theme "How to enjoy a host club," the server will automatically generate a post such as "Thanks to everyone who had fun with me today! There might be an even more surprising twist next time you visit 😉" and display it on the user's device. An example of a prompt is "Please generate a social media post on the theme 'How to enjoy a host club'."

[2007] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[2008] This system provides a process for users to automatically generate educational manuals tailored to specific situations using communication devices. Users open the application on a communication device such as a smartphone or PC and access the manual creation screen. When the user enters keywords related to the situation required for education into the communication device, the device sends these keywords to the server. The server searches the database for the received keywords and extracts relevant know-how and success stories. Based on the extracted information, a natural language processing engine generates the optimal manual, and the generated manual is sent back from the server to the user's communication device. The device displays the manual to the user, making it available for educational use. For example, if the user enters the situation "How to interact with a first-time customer," the server automatically generates a manual such as "When interacting with a first-time customer, first introduce yourself properly and choose a topic that will interest them. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device. An example of a prompt is "Please generate an educational manual for the situation 'How to interact with a first-time customer'."

[2009] Through the system described above, users can efficiently and quickly generate appropriate replies, posts, and training manuals, saving effort and time. This enables users to provide consistent, high-quality content.

[2010] The flow of the specific processing in Example 1 will be explained using Figure 11.

[2011] System (1): Suggested replies to customers via LINE

[2012] * We will refer to smartphones as "devices," servers as "servers," and customers as "users."

[2013] Step 1:

[2014] The user operates the device to launch the application and access the reply generation screen. The input is accessing the application's launch screen, and the output is the display of the reply generation screen. Specifically, the user taps the device icon, and the application launches.

[2015] Step 2:

[2016] The user enters the message they want to reply in the input field on their device. The input is the message the user has entered (e.g., "What are you doing tonight?"), and the output is the preparation for sending this message data to the server. Specifically, the user enters the message and presses the send button.

[2017] Step 3:

[2018] The terminal sends the message content entered by the user to the server. The input is the message content entered by the user, and the output is the HTTP request received by the server. Specifically, the terminal generates an HTTP request and sends the data to a specific API endpoint on the server.

[2019] Step 4:

[2020] The server parses the received message content and passes it to a natural language processing engine (e.g., OpenAI's GPT-3). The input is the message data of the received HTTP request, and the output is the input data for the natural language processing engine. Specifically, the server parses the request, extracts the necessary data, and passes it to the processing engine.

[2021] Step 5:

[2022] The natural language processing engine generates a reply based on the message content. The input is the message data provided by the server, and the output is the generated reply. Specifically, the natural language processing engine uses a generative AI model to analyze the prompt and generates a reply based on the results. The generated reply is temporarily stored on the server.

[2023] Step 6:

[2024] The server sends the generated reply back to the user's terminal. The input is the reply generated by the natural language processing engine, and the output is the HTTP response to the user's terminal. Specifically, the server sends an HTTP response containing the generated reply to the terminal.

[2025] Step 7:

[2026] The terminal analyzes the received reply data and displays it to the user. The input is the HTTP response received from the server, and the output is the display of the reply text on the screen. Specifically, the terminal analyzes the response and outputs the reply text to the display area. The user can then send the displayed reply text as a LINE message.

[2027] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[2028] Step 1:

[2029] The user operates their device to launch the application and access the screen for creating posts and video scripts. Input is accessing the application's launch screen, and output is the display of the post or script creation screen. Specifically, the user taps the icon on their device, and the application launches.

[2030] Step 2:

[2031] The user enters keywords or themes related to the content they want to post into the input field on their device. The input is the keywords or themes entered by the user, and the output is the preparation for sending this data to the server. Specifically, the user enters keywords and presses the submit button.

[2032] Step 3:

[2033] The device sends keywords and themes entered by the user to the server. The input is the keywords and themes entered by the user, and the output is the HTTP request received by the server. Specifically, the device generates an HTTP request and sends the data to a specific API endpoint on the server.

[2034] Step 4:

[2035] The server analyzes the received keywords and themes and passes them to the natural language processing engine. The input is the data from the received HTTP request, and the output is the input data for the natural language processing engine. Specifically, the server analyzes the request, extracts the necessary data, and passes it to the processing engine.

[2036] Step 5:

[2037] The natural language processing engine generates optimal posts and scripts based on keywords and themes. The input is data provided by the server, and the output is the generated posts and scripts. Specifically, the natural language processing engine uses a generative AI model to analyze prompt text and generates posts and scripts based on the results. The generated documents are temporarily stored on the server.

[2038] Step 6:

[2039] The server sends the generated post text or script back to the user's terminal. The input is a document generated by a natural language processing engine, and the output is an HTTP response to the user's terminal. Specifically, the server sends an HTTP response containing the generated document to the terminal.

[2040] Step 7:

[2041] The terminal analyzes received posts and script data and displays them to the user. The input is the HTTP response received from the server, and the output is the display of the posts and scripts on the screen. Specifically, the terminal analyzes the response and outputs the document to the display area. The user can then use the displayed document for actual social media or video posting.

[2042] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[2043] Step 1:

[2044] The user operates the device to launch the application and access the manual creation screen for cast training. The input is access to the application's launch screen, and the output is the display of the manual creation screen. Specifically, the user taps the icon on the device, and the application launches.

[2045] Step 2:

[2046] The user enters keywords representing the educational situation into the input field on the terminal. The input is the keywords entered by the user, and the output is a preparation for sending this data to the server. Specifically, the user enters keywords and presses the submit button.

[2047] Step 3:

[2048] The terminal sends the keywords entered by the user to the server. The input is the keywords entered by the user, and the output is the HTTP request received by the server. Specifically, the terminal generates an HTTP request and sends the data to a specific API endpoint on the server.

[2049] Step 4:

[2050] The server analyzes the received keywords and performs a search in the database. The input is the data from the received HTTP request, and the output is the database search query. Specifically, the server analyzes the request, searches the database based on the keywords, and extracts relevant know-how and success stories.

[2051] Step 5:

[2052] The server passes the extracted information to a natural language processing engine to generate the optimal manual. The input is information extracted from the database, and the output is the generated manual. Specifically, the server passes the extracted information to the natural language processing engine, which uses a generative AI model to analyze it into prompt sentences and generate the manual.

[2053] Step 6:

[2054] The server sends the generated manual back to the user's terminal. The input is the manual generated by the natural language processing engine, and the output is the HTTP response to the user's terminal. Specifically, the server sends the HTTP response containing the generated manual to the terminal.

[2055] Step 7:

[2056] The terminal analyzes the received manual data and displays it to the user. The input is the HTTP response received from the server, and the output is the display of the manual on the screen. Specifically, the terminal analyzes the response and outputs the manual to the display area. The user can then use the displayed manual for educational purposes.

[2057] (Application Example 1)

[2058] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2059] Traditional customer support systems have presented challenges, including the significant time and effort required for support staff to respond quickly and accurately to customer inquiries. Furthermore, inconsistent response quality due to varying skill levels among support staff led to variability in customer satisfaction. Additionally, there was a lack of efficient tools for instantly generating appropriate replies. To address these issues, there is a need to streamline customer support and achieve consistently high-quality responses.

[2060] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[2061] In this invention, the server includes means for the user to access a customer response message generation screen by operating a communication device, means for the user to send an inquiry message entered into the communication device to the server, means for the server to pass the received inquiry message to a natural language processing engine, means for the natural language processing engine to generate an optimal response, means for the generated response to be sent back from the server to the user's communication device, and means for the user's communication device to display the response on a screen. This enables customer support personnel to generate fast, consistent, and high-quality response messages.

[2062] "A means by which a user operates a communication device to access a customer response message generation screen" refers to an operating interface that allows a user to access a specific application screen using a communication device such as a smartphone or personal computer.

[2063] "A means of sending inquiry messages entered by a user into a communication device to a server" refers to a function that allows a user to input a message using an input device (keyboard, touch panel, etc.) on a communication device and send it to a remote server via the internet.

[2064] "A means of passing query messages received by the server to the natural language processing engine" refers to a data transfer function that allows the server to pass received data to algorithms and models for natural language processing.

[2065] "A means by which a natural language processing engine generates the optimal reply" refers to the process of generating an appropriate reply to a received message using natural language processing technology (e.g., a natural language generation model).

[2066] "Means for sending the generated reply message back from the server to the user's communication device" refers to a function for sending data back from the server to the user's communication device after the reply message has been generated.

[2067] "Means for displaying the reply on the user's communication device screen" refers to a function that visually displays the generated reply on the user's smartphone or computer screen.

[2068] To implement this invention, it is necessary to combine a user, a server, and a natural language processing engine. The embodiments thereof are described in detail below.

[2069] First, the user accesses the customer response generation screen using a communication device such as a smartphone or PC. At this point, the user enters the customer inquiry message and sends it to the server. This communication process is achieved through two-way communication over the internet.

[2070] The server receives inquiry messages sent by users. These messages are passed to a natural language processing engine (e.g., OpenAI's GPT-3 model). The natural language processing engine generates the most appropriate reply based on the received message. This process utilizes a generative AI model to generate the reply using appropriate prompts.

[2071] The generated reply is sent back to the user's communication device via the server. The user's communication device displays the received reply on its screen, and the user can review and edit it.

[2072] For example, suppose a user receives the following customer inquiry message: "My delivery is delayed. When will it arrive?" The user enters this message and sends it to the server. The server passes this message to a natural language processing engine and generates a reply message like this: "Thank you for your inquiry. We are currently investigating the delivery delay. You can check the specific estimated arrival date using tracking number XYZ1234. We apologize for the inconvenience." This reply message is sent back from the server to the user's device and displayed on the user's screen.

[2073] In embodiments of this invention, the following specific hardware and software are used:

[2074] Hardware: Servers, smartphones, personal computers

[2075] Software: OpenAI GPT-3 API, Python

[2076] An example of a prompt statement is to use the following format:

[2077] Customer inquiry: My order is delayed. Please tell me when it will arrive.

[2078] Appropriate reply:

[2079] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[2080] Step 1:

[2081] Users access the customer response generation screen by operating communication devices such as smartphones or personal computers. The screen the user views is displayed as an interface. The user enters their inquiry message into this interface. The entered message is then converted into the format required for subsequent processing.

[2082] Input: Customer inquiry message

[2083] Output: Query message converted to format

[2084] Step 2:

[2085] The terminal sends the entered query message to the server. This transmission is performed using network protocols such as HTTP requests. During this process, the input data is encoded into a format that is easily processed by the server.

[2086] Input: Inquiry message converted to format

[2087] Output: Query message sent to the server

[2088] Step 3:

[2089] The server receives the query message from the terminal. The server then performs the necessary preprocessing to pass the received message to the natural language processing engine. This preprocessing includes message cleansing and tokenization.

[2090] Input: Inquiry message sent to the server

[2091] Output: Preprocessed query message

[2092] Step 4:

[2093] The server passes the pre-processed query message to a natural language processing engine (e.g., OpenAI's GPT-3). The natural language processing engine generates the best possible response based on the received message. This process involves applying prompts and running a generative AI model.

[2094] Input: Preprocessed query message

[2095] Output: Generated reply

[2096] Step 5:

[2097] The server sends the generated reply message back to the terminal. This return is done using a network protocol such as an HTTP response. In this case, the generated reply message is encoded as needed.

[2098] Input: Generated reply

[2099] Output: Reply message sent back to the terminal

[2100] Step 6:

[2101] The terminal displays the reply received from the server on its screen. At this time, it decodes the message into a format that is easy for the user to understand and displays it on the interface.

[2102] Input: Reply sent back to the device

[2103] Output: Reply text displayed on the screen

[2104] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[2105] The embodiments for carrying out the present invention will be described with explanations of the operation and specific examples of three systems that combine emotion engines.

[2106] System (1): Suggested replies to customers via LINE

[2107] This system provides a process where users use communication devices to generate appropriate LINE replies using an emotion engine.

[2108] System operation

[2109] 1. User: Open the application on a communication device such as a smartphone or PC and access the reply generation screen.

[2110] 2. User: Enter the message you want to reply to on LINE into your communication device.

[2111] 3. Terminal: The emotion engine analyzes the user's emotions from the input message.

[2112] 4. Terminal: Sends emotional data and messages to the server.

[2113] 5. Server: Passes the received sentiment data and messages to the natural language processing engine to generate an appropriate reply.

[2114] 6. Server: Sends the generated reply message back to the user's communication device.

[2115] 7. Terminal: Display the reply text to the user and make it available for use as an actual LINE message.

[2116] Specific example

[2117] When a user enters the message "What are you doing tonight?", the emotion engine analyzes the user's emotions and recognizes it as "expectation." Based on that emotion and message, the server automatically generates a reply such as "I might be a little busy tonight, but I'd love to meet up," and displays it on the user's device.

[2118] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[2119] This system provides a process that allows users to use communication devices to automatically generate social media posts and video scripts using an emotion engine.

[2120] System operation

[2121] 1. User: Open the application on a communication device such as a smartphone or PC and access the screen for creating posts or scripts.

[2122] 2. User: Enter keywords or themes related to the content you want to post into your communication device.

[2123] 3. Terminal: The emotion engine analyzes the user's emotions based on the keywords and themes entered.

[2124] 4. Terminal: Sends sentiment data and keywords / themes to the server.

[2125] 5. Server: Passes the received sentiment data and keywords / themes to a natural language processing engine to generate the most suitable post text or script.

[2126] 6. Server: Sends the generated posts and scripts back to the user's communication device.

[2127] 7. Device: Displays posts and scripts to users, allowing them to use them for actual social media and video posting.

[2128] Specific example

[2129] When a user enters the theme "How to enjoy a host club" and the emotion engine recognizes it as "excitement," the server automatically generates a post based on that emotion and theme, saying, "Thanks to everyone who had fun with us today! There might be even more surprising things waiting for you next time you visit 😉," and displays it on the user's device.

[2130] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[2131] This system provides a process that allows users to use communication devices to automatically generate educational manuals tailored to specific situations, utilizing an emotion engine.

[2132] System operation

[2133] 1. User: Open the application on a communication device such as a smartphone or PC and access the manual creation screen.

[2134] 2. User: Enter keywords related to the educational situation into the communication device.

[2135] 3. Terminal: The emotion engine analyzes the user's emotions based on the entered keywords.

[2136] 4. Terminal: Sends sentiment data and keywords to the server.

[2137] 5. Server: Searches the database for received sentiment data and keywords, and extracts relevant know-how and success stories.

[2138] 6. Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[2139] 7. Server: Sends the generated manual back to the user's communication device.

[2140] 8. Terminal: Display the manual to the user and make it available for educational purposes.

[2141] Specific example

[2142] When a user enters keywords related to the situation "how to interact with a customer for the first time," and the emotion engine recognizes this as "anxiety," the server automatically generates a manual based on that emotion and keyword, such as "When interacting with a customer for the first time, start by giving a thorough self-introduction and choosing a topic that will pique their interest. Example: 'My name is XX. I heard you like XX, what do you recommend these days?'" and displays it on the user's device.

[2143] ---

[2144] The above describes the embodiments for carrying out the present invention. Each system combines the user's communication device, server, natural language processing engine, and emotion engine to provide quick and effective response messages, SNS posts, video scripts, and educational manuals.

[2145] The following describes the processing flow.

[2146] System (1): Suggested replies to customers via LINE

[2147] Program processing steps

[2148] Step 1:

[2149] User: Open the application on your smartphone or PC and access the reply generation screen.

[2150] Specific steps: Launch the app and select the "Generate Reply" option.

[2151] Step 2:

[2152] User: Enter the message you want to reply to on LINE into your communication device.

[2153] Specific action: Enter a message such as "What are you doing tonight?" into the text box and click the "Send" button.

[2154] Step 3:

[2155] Terminal: The emotion engine analyzes the user's emotions from the input message.

[2156] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., anticipation, joy, anxiety, etc.).

[2157] Step 4:

[2158] Terminal: Sends emotion data and messages to the server.

[2159] Specific operation: The analyzed sentiment data and message text are sent to the server as an HTTP request.

[2160] Step 5:

[2161] Server: Passes the received sentiment data and messages to the natural language processing engine to generate an appropriate reply.

[2162] Specific operation: The server transfers sentiment data and message content to a natural language processing engine, which then constructs the most appropriate reply based on the sentiment.

[2163] Step 6:

[2164] Server: Sends the generated reply message back to the terminal.

[2165] Specific action: The generated reply message is sent back to the user's terminal as an HTTP response.

[2166] Step 7:

[2167] Terminal: Displays the reply message to the user.

[2168] Specific action: Display the received reply on the screen so that the user can review it.

[2169] System (2): Creating Twitter / Instagram posts and YouTube / TikTok scripts.

[2170] Program processing steps

[2171] Step 1:

[2172] User: Open the application on your smartphone or PC and access the screen for creating posts or scripts.

[2173] Specific steps: Launch the app and select the "Create Post / Script" option.

[2174] Step 2:

[2175] User: Enter keywords or themes related to the content you want to post into your communication device.

[2176] Specific action: Enter a topic such as "How to enjoy a host club" into the text box and click the "Submit" button.

[2177] Step 3:

[2178] Terminal: The emotion engine analyzes the user's emotions based on the keywords and themes entered.

[2179] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., excitement, joy, anxiety, etc.).

[2180] Step 4:

[2181] Terminal: Sends sentiment data and keywords / themes to the server.

[2182] Specific operation: The analyzed sentiment data and theme are sent to the server as an HTTP request.

[2183] Step 5:

[2184] Server: Passes the received sentiment data and keywords / themes to a natural language processing engine to generate the most suitable post text or script.

[2185] Specific operation: The server transfers sentiment data and theme content to a natural language processing engine, which then constructs the most suitable post text or script based on the sentiment.

[2186] Step 6:

[2187] Server: Sends generated posts and scripts back to the terminal.

[2188] Specific operation: The generated post text or script is sent back to the user's device as an HTTP response.

[2189] Step 7:

[2190] Terminal: Displays posts and scripts to the user.

[2191] Specific action: Display received posts and scripts on the screen for the user to review.

[2192] System (3): Create situation-specific manuals that compile the know-how of popular cast members and use them for cast member training.

[2193] Program processing steps

[2194] Step 1:

[2195] User: Open the application on your smartphone or PC and access the manual creation screen.

[2196] Specific steps: Launch the app and select the "Create Manual" option.

[2197] Step 2:

[2198] User: Enter keywords related to the educational situation into the communication device.

[2199] Specific action: Enter keywords such as "How to interact with first-time customers" into the text box and click the "Send" button.

[2200] Step 3:

[2201] Terminal: The emotion engine analyzes the user's emotions based on the entered keywords.

[2202] Specific operation: The emotion engine analyzes text data to identify the user's emotions (e.g., anxiety, anticipation, joy, etc.).

[2203] Step 4:

[2204] Terminal: Sends sentiment data and keywords to the server.

[2205] Specific operation: The analyzed sentiment data and keywords are sent to the server as an HTTP request.

[2206] Step 5:

[2207] Server: Searches the database for received sentiment data and keywords, and extracts relevant know-how and success stories.

[2208] Specific operation: The server searches the database based on sentiment data and keywords, and extracts relevant know-how and case studies.

[2209] Step 6:

[2210] Server: Based on the extracted information, a natural language processing engine generates the optimal manual.

[2211] Specific actions: Based on the extracted information, create an optimal manual that addresses emotions.

[2212] Step 7:

[2213] Server: Sends the generated manual back to the terminal.

[2214] Specific action: The generated manual is sent back to the user's terminal as an HTTP response.

[2215] Step 8:

[2216] Terminal: Displays the manual to the user.

[2217] Specific action: Display the received manual on the screen so that it can be used for educational purposes.

[2218] (Example 2)

[2219] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2220] Conventional communication support systems and social media posting systems have the problem of not being able to generate replies and posts that take user emotions into consideration. Similarly, systems for creating educational manuals have the problem of not being able to provide optimal educational content based on emotions. There is a need for a system that solves these problems and automatically generates appropriate replies, posts, video scripts, and educational manuals that reflect user emotions.

[2221] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[2222] In this invention, the server performs sentiment analysis on messages, themes, and keywords entered by the user into an electronic device, and provides the corresponding sentiment data and messages, themes, and keywords to a natural language processing device.

[2223] The system includes a natural language processing unit that generates optimal replies, posts, video scripts, and manuals, and a means for sending the generated content from the server to the user's electronic device. This enables the generation of appropriate replies, posts, and video scripts that take the user's emotions into consideration, as well as the automatic generation of emotion-based educational manuals.

[2224] A "user" is a person who operates an electronic device.

[2225] "Electronic devices" refer to devices that can connect to the internet, such as smartphones and personal computers.

[2226] The "reply message generation screen" is an interface that allows users to input messages via electronic devices and generate reply messages.

[2227] "Sentiment analysis" is the process of extracting sentiment data from messages, themes, and keywords entered by the user.

[2228] An "emotion engine" is software or hardware used to analyze a user's emotions from input messages, themes, and keywords.

[2229] A "server" is a remote computer system that receives and transmits data via a network and performs various processes.

[2230] A "...

Claims

1. A means by which the user operates a communication device to access the reply generation screen, A means of sending a message entered by a user into a communication device to a server, A means of passing messages received by the server to the natural language processing engine, A means by which a natural language processing engine generates the optimal reply, A means of sending the generated reply message back from the server to the user's communication device, A system that includes means for the user's communication device to display a reply message on a screen.

2. A means for users to operate communication devices to access the screen for creating posts and video scripts, A means of sending themes and keywords entered by the user into a communication device to a server, A means of passing themes and keywords received by the server to the natural language processing engine, A method for a natural language processing engine to generate optimal post text and video scripts, A means of sending the generated post text and video script back from the server to the user's communication device, The system according to claim 1, which includes means for the user's communication device to display posted text or video scripts on a display screen.

3. A means for the user to operate a communication device to access the manual creation screen for cast training, A means of sending keywords describing the situation entered by the user into a communication device to a server, A means for processing the keywords of the situation received by the server for database searching, A means for the server to extract relevant know-how and success stories, A means by which a natural language processing engine generates the optimal manual based on the extracted information, A means of sending the generated manual back from the server to the user's communication device, The system according to claim 1, which includes means for the user's communication device to display a manual on a display screen.

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