System

Abstract

A system is provided.SOLUTION: A system comprising: means for accepting inputs from a user; means for parsing the user's inputs; means for hosting a generative AI model that understands context, tone, and intent based on the parsed inputs and generates customized content; means for returning the customized content generated by the generative AI model to the user; and means for storing the customized content to aid in future generations.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, companies and individuals face the challenge of requiring advanced expertise and a significant amount of time for effective communication. Rapid, high-quality content generation is especially important in situations where personalized content is required, such as marketing, presentations, and document creation for everyday work. However, doing this manually requires significant costs and effort, so a system that can efficiently and automatically generate high-quality content is needed. [Means for solving the problem]

[0005] The present invention provides a system that accepts user input, analyzes the input, and generates customized content using a generative AI model based on an understanding of context, tone, and intent. Specifically, the system includes a means for obtaining necessary information from a database of user profiles and past content, and using that information to refine the generative AI model. The system also includes a means for providing a user interface that allows users to customize, review, and modify the input data. This system enables businesses and individuals to quickly and efficiently generate high-quality personalized content.

[0006] "User" refers to the individual or group that operates the system and provides input data.

[0007] "Input" refers to information or instructions provided by a user to a system.

[0008] "Means for accepting" refers to an interface or protocol for receiving input from a user.

[0009] "Means for parsing" refers to the processes or algorithms that understand input received from a user and analyze it for context, tone, and intent.

[0010] "Generative AI Model" refers to a machine learning model that uses artificial intelligence to generate customized content.

[0011] "Means for sending back" refers to a communication means for sending the generated customized content back to the user.

[0012] "Means for storing" refers to storage or a database that records the generated content and makes it available for subsequent generation.

[0013] "Database" refers to a system that systematically stores information about user profiles and past content.

[0014] "Measures to improve accuracy" refers to processes and algorithms used to improve the performance of generative AI models based on acquired information.

[0015] "User Interface" refers to an interface that allows a user to interact with the system and customize, review, and modify input data.

[0016] "Customized content" refers to content created by a generative AI model based on user input and analysis results that is tailored to a specific purpose or audience. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using an advanced generative AI model. Specific embodiments of the system are described below.

[0039] User operations

[0040] Users provide input to the device to generate content, such as a request like "Please write a script for a new product presentation," through an input form, along with information about the target audience and details about the desired tone and style.

[0041] Device Role

[0042] The device receives user input and processes it to send it to the server. Specifically, it converts the user's input into an appropriate data format, such as JSON, and forwards it to the server as an HTTP request. The request includes the user ID, details of the purpose, and information about the intended recipient.

[0043] Server Processing

[0044] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[0045] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics.

[0046] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[0047] The server then uses the acquired information to generate customized content by invoking a generative AI model that is designed to understand context, tone, and intent to generate high-quality content based on user requests.

[0048] The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[0049] Display by terminal

[0050] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[0051] Specific examples

[0052] Example 1: Creating a business presentation

[0053] 1. The user enters the "new product introduction presentation script" into the terminal.

[0054] 2. The terminal sends this request to the server, asking it to generate an appropriate script based on the context, tone, and characteristics of the target recipient.

[0055] 3. The server uses the generative AI model to generate a script and sends it back to the device.

[0056] 4. The user checks the generated script and modifies it as necessary before using it.

[0057] Example 2: Creating an email for a marketing campaign

[0058] 1. The user enters the "new campaign product introduction email" into the terminal.

[0059] 2. The device sends this request to the server, asking it to generate email content based on the user's intentions.

[0060] 3. The server uses the generative AI model to generate content and sends it back to the device.

[0061] 4. The user reviews the generated email, edits it as needed, and uses it in their marketing efforts.

[0062] In this way, users, terminals, and servers work together to realize an efficient content generation system using generative AI.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The user operates the terminal and inputs a request for specific content generation, for example, "Please create a script for a presentation introducing a new product."

[0066] Step 2:

[0067] The device receives user input and converts it into JSON format, which includes information about the user ID, input, tone preference, and target recipient.

[0068] Step 3:

[0069] The device sends JSON format data to the server as an HTTP request, sending a POST request to the server's endpoint.

[0070] Step 4:

[0071] The server receives an HTTP request from the terminal, analyzes the contents of the request, and extracts the user's input data.

[0072] Step 5:

[0073] The server extracts user-input data and analyzes it for context, tone, purpose, and target audience characteristics using natural language processing techniques.

[0074] Step 6:

[0075] The server retrieves user profile information and previously generated content from a database, which is used for analysis.

[0076] Step 7:

[0077] The server generates customized content based on user profile information and input data by invoking a generative AI model that understands context, tone, and intent to generate appropriate content.

[0078] Step 8:

[0079] The server formats the generated custom content and prepares it as response data, which is packaged in JSON.

[0080] Step 9:

[0081] The server sends the generated content in JSON format to the device as an HTTP response, and returns a POST request response to the device's endpoint.

[0082] Step 10:

[0083] The terminal receives the HTTP response from the server and displays the generated customized content on the user interface. The user can check the generated content and modify it as necessary.

[0084] Step 11:

[0085] Users can review the generated content, make any necessary edits, and then save or share the content as needed.

[0086] Step 12:

[0087] The device then sends the edited and saved content back to the server and stores it in a database, which can be used for future generation.

[0088] Example 1

[0089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0090] Conventional content generation systems simply process user input, making it difficult to generate customized content that fully understands the context, tone, and intent. As a result, users are forced to extensively edit the generated content, which is time-consuming and laborious. Furthermore, the quality of the generated content is inconsistent, often failing to meet user expectations.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0092] In this invention, the server includes means for accepting input from a user, means for analyzing the user's input, means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content, means for a terminal to convert the user's input into a data format and send it to the server, means for the server to return the customized content generated based on the analyzed information to the terminal, means for displaying the customized content generated by the generative AI model to the user, and means for saving the customized content and using it for future generation. This enables the generation of high-quality customized content with a deep understanding of the user's intent and context.

[0093] "User" means an individual or corporation that uses this system to create, review, or modify content.

[0094] A "terminal" is a device for a user to input information, and includes hardware and software that performs processing to transmit information to a server.

[0095] "Server" means a remote data processing device that analyzes requests received from users, hosts generative AI models, and generates customized content.

[0096] The "means for accepting input" is an interface for receiving data provided by a user for content generation at a terminal.

[0097] "Means for analyzing input" refers to algorithms and programs that analyze data received from a user based on context, tone, and intent.

[0098] A "generative AI model" is an artificial intelligence model that generates high-quality customized content based on analyzed data.

[0099] The "means for converting to a data format" is a processing method for converting user input into an appropriate format (e.g., JSON) to make it easier for the server to parse it.

[0100] The "means for transmitting to the server" is a communication means for transmitting the converted data from the terminal to the server.

[0101] "Means for returning" refers to the process of returning the generated customized content from the server to the terminal.

[0102] "Means for storage" refers to a method for storing the generated content in a database or the like and using it in future generation processes.

[0103] "User Interface" means the visual or operating environment that allows a user to review and modify input data.

[0104] The present invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using a generative AI model. Specific embodiments of the system are described below.

[0105] User operations

[0106] A user types a content generation request into the device, such as "Write a script for a new product launch presentation," along with information about the target audience and details about the desired tone and style.

[0107] Specific examples

[0108] Example prompt: "Write a script for a presentation to introduce a new product. The target audience is executives, and the tone should be professional and concise."

[0109] Device Role

[0110] The device receives user input, converts it into a data format, and sends it to the server. Specifically, it converts the user's input into JSON format and forwards it to the server as an HTTP request. The request includes the user ID, details of the purpose, and information about the target recipient.

[0111] Server Processing

[0112] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[0113] Input Analysis

[0114] The server analyzes the content of the user's request and extracts the context, tone, purpose, and characteristics of the intended audience.

[0115] Database Reference

[0116] The server retrieves user profile information and past content from the database and uses it for analysis. Based on this information, the server invokes a generative AI model (e.g., OpenAI GPT-4) to generate customized content based on the user's requests.

[0117] Returning generated content

[0118] The server formats the generated content into an appropriate format (e.g., text or HTML) and sends it back to the device as an HTTP response, where it is stored in a database for future personalization.

[0119] Display by terminal

[0120] The terminal receives the customized content returned from the server and displays it to the user through a user interface. The user can check the displayed content and make corrections or additions as necessary.

[0121] Specific examples

[0122] Examples of generated content:

[0123] "This new product is 20% more efficient than our previous product and will also help reduce costs. I believe that management in particular will recognize this as another new value we can provide to our customers."

[0124] In this way, users, devices, and servers work together to realize an efficient content generation system using generative AI models, allowing users to quickly and easily obtain high-quality customized content for business or personal use.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Explain the program's processing flow by dividing it into processing steps

[0127] Step 1: User Input

[0128] A user types a content generation request into the device. For example, the user might type, "Please create a script for a new product introduction presentation," along with other details such as the target audience and desired tone.

[0129] input

[0130] Request text (e.g., "Please write a script for a new product introduction presentation.")

[0131] Target Audience Information

[0132] Desired tone and style

[0133] output

[0134] User input data (request details, target audience information, desired tone and style)

[0135] Step 2: Converting input data on the terminal

[0136] The terminal receives input from the user and converts it into JSON format, including the user ID, purpose details, and information about the intended recipient.

[0137] input

[0138] User input data (request details, target audience information, desired tone and style)

[0139] Data Processing

[0140] Convert the data into the appropriate format (JSON)

[0141] output

[0142] JSON format data (e.g., "{"request": "Please create a script for a new product introduction presentation", "userID": "12345", "audience": "Management", "tone": "Professional"}")

[0143] Step 3: Send data from the device to the server

[0144] The device then sends the converted JSON data to the server as an HTTP request, along with the user ID and purpose details.

[0145] input

[0146] JSON format data (request details, target audience information, desired tone and style)

[0147] Data Calculation

[0148] Generating an HTTP Request

[0149] output

[0150] HTTP request (e.g. POST request)

[0151] Step 4: Parsing input on the server

[0152] The server receives the incoming HTTP request and parses its content, analyzing the request text and extracting the context, tone, purpose, and characteristics of the target audience.

[0153] input

[0154] HTTP request (JSON data)

[0155] Data Processing

[0156] Parsing the request statement

[0157] output

[0158] Analysis results (context, tone, purpose, target audience characteristics)

[0159] Step 5: Look up the database on the server

[0160] The server retrieves user profile information and past content from the database and uses it for analysis. It references the database to retrieve past generated content and user preference information.

[0161] input

[0162] Analysis results (context, tone, purpose, target audience characteristics)

[0163] Data Search

[0164] Extracting relevant information from a database

[0165] output

[0166] Information obtained (user profile, past content)

[0167] Step 6: Server-side content generation

[0168] The server calls a generative AI model based on the acquired information and generates high-quality customized content.

[0169] input

[0170] Information obtained (user profile, past content)

[0171] Analysis results (context, tone, purpose, target audience characteristics)

[0172] Data Calculation

[0173] Running generative AI models

[0174] output

[0175] Generated customized content

[0176] Step 7: Return from server to device

[0177] The generated customized content is formatted and sent back to the device as an HTTP response, and is stored in a database for future personalization.

[0178] input

[0179] Generated customized content

[0180] Data Processing

[0181] Applying Formatting

[0182] output

[0183] HTTP response (generated content)

[0184] Step 8: View in terminal

[0185] The terminal displays the customized content returned from the server through a user interface.

[0186] input

[0187] HTTP response (generated content)

[0188] Data Processing

[0189] Conversion to display format

[0190] output

[0191] What is displayed on the user interface

[0192] Step 9: User review and correction

[0193] The user checks the displayed customized content and makes corrections or additions as necessary.

[0194] input

[0195] What is displayed on the user interface

[0196] Data Processing

[0197] Corrections and additions

[0198] output

[0199] Modified content

[0200] Through this series of steps, a system is realized in which users, terminals, and servers cooperate to efficiently generate high-quality customized content.

[0201] (Application example 1)

[0202] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0203] Modern virtual stores require systems that allow users to quickly and effectively generate product introductions and descriptions. Without such systems, users must manually create content, which is time-consuming and labor-intensive, and the quality is inconsistent. Furthermore, without a means to instantly provide customized information, it becomes difficult to deliver appropriate information to potential customers. Therefore, the objective of this invention is to provide a system that uses generative AI models to generate high-quality content in real time and that can be immediately used within a virtual store.

[0204] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0205] In this invention, the server includes means for accepting input from a user, means for analyzing the input, means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content, means for allowing a user to input a request to generate product introductions and descriptions in a virtual store and provide customized information in real time, means for returning the generated customized content to the user, and means for storing the customized content and using it for future generation, thereby enabling users to generate and provide high-quality customized product introductions and descriptions in real time.

[0206] "User input" refers to user requests or instructions accepted by the system.

[0207] "Means for analyzing input" refers to a device or program that interprets the input received from the user and processes it to understand its context and intent.

[0208] "Generative AI model" refers to an artificial intelligence algorithm used to automatically generate new content based on specific rules and data.

[0209] A "virtual store" refers to a virtual shopping environment that provides products and services to users via the Internet.

[0210] A "request to generate product introductions or descriptions" refers to an order or request made by a user to a generative AI model to provide information about a product in a virtual store.

[0211] "Real-time customized information" refers to personalized content that is generated instantly and delivered in response to a user's request.

[0212] "Customized Content" refers to information or documentation that is optimized and generated to meet the needs and requirements of a particular user.

[0213] A "user profile" refers to a data set that compiles information about a specific user, such as their attributes, behavioral history, and preferences.

[0214] A "database" refers to a system or software that stores large amounts of information in a systematic manner and retrieves and uses it when needed.

[0215] A "user interface" is a means by which a user interacts with a system, and refers to input forms, display screens, etc.

[0216] The following describes an embodiment of the present invention: The present invention is a system in which a user, a terminal, and a server work together to generate customized content using an advanced generative AI model.

[0217] User operations

[0218] Users provide input to generate product descriptions and text within the virtual store, for example, by entering a request such as "Please write a description for our new product" through an input form, along with information about the target audience and details of the desired tone and style.

[0219] Device Role

[0220] The device receives input from the user and processes it to send it to the server. Specifically, it converts the user's input into the appropriate data format and forwards it to the server as an HTTP request. The request includes the user's ID, details of the purpose, and information about the intended recipient.

[0221] Server Processing

[0222] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[0223] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics.

[0224] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[0225] The server then uses the acquired information to invoke a generative AI model to generate customized content. The generative AI model is designed to understand context, tone, and intent to generate high-quality content based on the user's request. The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[0226] Display by terminal

[0227] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[0228] Specific examples

[0229] Example 1: Creating product descriptions for a virtual store

[0230] 1. The user inputs a request into the terminal: "Please write a description of a new product."

[0231] 2. The device sends this request to the server, asking it to generate an appropriate introduction based on the context, tone, and characteristics of the target recipient.

[0232] 3. The server uses a generative AI model (such as GPT-4) to generate a testimonial and sends it back to the device.

[0233] 4. The user reviews the generated introduction and modifies it as necessary.

[0234] Example of input prompt sentence:

[0235] "User ID: user123, Product: Smartwatch, Features: Heart rate monitor and GPS, Target: Non-technical beginners, Tone: Casual"

[0236] This invention enables users to generate and provide high-quality customized product descriptions in real time within a virtual store.

[0237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0238] Step 1:

[0239] User Input

[0240] The user provides a request to generate product descriptions and prompts within the virtual store, such as "Please create a description for our new product," along with information about the target audience and details about the desired tone and style, which then generates a specific prompt.

[0241] Inputs: "Write a description for our new product", target audience information, tone and style

[0242] Output: Specific prompt (e.g., User ID: user123, Product: Smartwatch, Features: Heart rate tracking and GPS, Target audience: Non-technical beginners, Tone: Casual)

[0243] Step 2:

[0244] Terminal data conversion

[0245] The device converts the request received from the user into an appropriate data format (e.g., JSON format) and sends it to the server as an HTTP request, including the user ID, details of the purpose, and information about the target recipient.

[0246] Input: prompt statement

[0247] Output: HTTP request in JSON format (e.g., {"user_id":"user123","prompt":"Please write a description for our new product","audience_info":"Beginner","tone_style":"Casual"})

[0248] Step 3:

[0249] Server input parsing

[0250] The server analyzes the HTTP request received from the device and extracts the context, tone, and characteristics of the target recipient. This analysis helps to understand the specific content of the request.

[0251] Input: HTTP request in JSON format

[0252] Output: Analyzed input data (context, tone, audience characteristics)

[0253] Step 4:

[0254] Database Reference

[0255] The server retrieves user profile information and past content from the database as needed and uses it for analysis, which enables more accurate content generation.

[0256] Input: Parsed input data

[0257] Output: Additional user profile data and historical content

[0258] Step 5:

[0259] Content generation using generative AI models

[0260] The server then calls a generative AI model based on the acquired information and analysis data to generate customized content. The generative AI model (e.g., GPT-4) generates high-quality introductions and descriptions based on the input prompt.

[0261] Input: User profile data, parsed input data

[0262] Output: Generated content (customized introduction, description)

[0263] Step 6:

[0264] Return from the server to the device

[0265] The server formats the generated customized content and sends it back to the device as an HTTP response, storing the generated content in a database for future personalization.

[0266] Input: Generated content

[0267] Output: Content formatted as an HTTP response

[0268] Step 7:

[0269] Display by terminal

[0270] The terminal receives the customized content sent back from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. Finally, the content is provided in a form that can be used in the virtual store.

[0271] Input: Content formatted as an HTTP response

[0272] Output: The customized content displayed to the user

[0273] These steps allow users to create and instantly deliver high-quality, customized product descriptions in real time within a virtual store.

[0274] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0275] This invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using an advanced generative AI model and an emotion engine. Specific embodiments of this system are described below.

[0276] User operations

[0277] The user provides input to the device to generate content, such as "Write a script for a new product introduction presentation." The user can also provide information about the target audience and details of the desired tone and style. The device can also accept voice or text input, which uses an emotion engine to analyze the user's emotional state.

[0278] Device Role

[0279] The device receives input from the user and converts the input data into JSON format, which includes the user ID, input content, tone preference, information about the target recipient, and emotional data analyzed by the emotion engine. This data is then sent to the server as an HTTP request.

[0280] Server Processing

[0281] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[0282] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics, including emotional data provided by the emotion engine.

[0283] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[0284] The server then uses the acquired information and emotional data to invoke a generative AI model to generate customized content. This generative AI model is designed to understand context, tone, intent, and emotional state to generate high-quality content based on user requests.

[0285] The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[0286] Display by terminal

[0287] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[0288] Specific examples

[0289] Example 1: Creating a business presentation

[0290] 1. The user inputs the "new product introduction presentation script" into the terminal and also inputs their current emotional state through voice input.

[0291] 2. The device sends this request and emotional data to the server, asking it to generate an appropriate script based on the context, tone, characteristics and emotional state of the target recipient.

[0292] 3. The server uses the generative AI model and emotion engine to generate a script and send it back to the device.

[0293] 4. The user checks the generated script and modifies it as necessary before using it.

[0294] Example 2: Creating an email for a marketing campaign

[0295] 1. The user enters the "new campaign product introduction email" into the terminal and inputs their current emotional state through the emotion engine.

[0296] 2. The device sends this request and emotional data to the server, asking it to generate email content based on the user's intentions and emotional state.

[0297] 3. The server uses the generative AI model and emotion engine to generate content and send it back to the device.

[0298] 4. The user reviews the generated email, edits it as needed, and uses it in their marketing efforts.

[0299] In this way, users, devices, and servers work together to realize an efficient content generation system using generative AI and an emotion engine.

[0300] The processing flow will be explained below.

[0301] Step 1:

[0302] The user operates the terminal and inputs a request for content generation, for example, "Please create a script for a presentation introducing a new product."

[0303] Step 2:

[0304] The user inputs emotion data by speaking aloud into the terminal, which allows the emotion engine to recognize the user's current emotional state.

[0305] Step 3:

[0306] The device receives user input as text data, and the emotion engine analyzes the voice data to detect the user's emotional state. The data, including the analysis results, is converted into JSON format. The converted data includes the user ID, input content, desired tone, information about the target recipient, and emotional data.

[0307] Step 4:

[0308] The device sends JSON format data to the server as an HTTP request, sending a POST request to the server's endpoint.

[0309] Step 5:

[0310] The server receives an HTTP request from the terminal, analyzes the contents of the request, and extracts the user's input data.

[0311] Step 6:

[0312] The server extracts user-input data and emotional data to analyze the context, tone, purpose, and target audience characteristics using natural language processing techniques and emotion recognition algorithms.

[0313] Step 7:

[0314] The server retrieves user profile information and previously generated content from the database and uses it for analysis, thereby improving the accuracy of the generative AI model.

[0315] Step 8:

[0316] Based on the acquired user profile information, input data, and emotional data, the server invokes a generative AI model to generate customized content. The generative AI model understands context, tone, intent, and emotional state to generate high-quality content based on user requests.

[0317] Step 9:

[0318] The server formats the generated custom content and prepares it as response data. The generated content is packaged in JSON format.

[0319] Step 10:

[0320] The server sends the generated content in JSON format to the device as an HTTP response, and returns a POST request response to the device's endpoint.

[0321] Step 11:

[0322] The terminal receives the HTTP response from the server and displays the generated customized content on the user interface. The user can check the generated content and modify it as necessary.

[0323] Step 12:

[0324] Users can review the generated content, make any necessary edits, and then save or share the content as needed.

[0325] Step 13:

[0326] The device then sends the edited and saved content back to the server and stores it in a database, which can be used for future generation.

[0327] Example 2

[0328] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0329] Conventional content generation systems have struggled to quickly and accurately provide customized content that fully reflects a user's context, tone, and intent. Furthermore, they lack the ability to generate content that takes into account emotional states, making it impossible to fully meet user needs. Furthermore, they lack a way to improve the accuracy of their generative AI models by utilizing past content and user profiles.

[0330] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for converting user input into JSON format; means for transferring the JSON-formatted data to the server; means for the server to analyze the user input and extract context, tone, intent, and emotional state; means for retrieving user profile information and past content from a database and integrating the analysis results; means for hosting a generative AI model that generates customized content based on the integrated information; means for formatting the generated content and returning it to the terminal; and means for saving the customized content and using it for future generation. This enables the rapid generation and provision of high-quality customized content that reflects the user's context, tone, intent, and emotional state. Furthermore, the accuracy of the generative AI model can be improved by utilizing past content and user profiles.

[0331] "User input" is information or instructions provided by a user to the system.

[0332] "JSON format" is an abbreviation for JavaScript Object Notation, and is a method of structuring and representing data in text format.

[0333] A "server" is a computer system that processes and manages data, receives requests from terminals, and returns appropriate responses.

[0334] "Context" refers to background or situational information related to the user's input.

[0335] "Tone" refers to the atmosphere or style of the content being generated, and can be professional, casual, or the like.

[0336] "Intention" refers to the purpose or goal that a user is trying to achieve with their input.

[0337] "Emotional state" refers to the emotion the user is feeling at the time of input, and includes joy, anger, sadness, etc.

[0338] A "database" is a system for managing a collection of data, enabling efficient data retrieval and storage.

[0339] "User profile information" refers to information about an individual user, such as the user's attributes and behavioral history.

[0340] "Past content" refers to previously generated content that is related to a user's past requests.

[0341] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate customized content based on input data.

[0342] "Hosting" means running a generative AI model on a server so that it can respond to requests from other systems and users.

[0343] "Formatting" means shaping the generated content into a particular structure or form.

[0344] A "terminal" is a device that is directly operated by a user, and includes a computer, a smartphone, a tablet, and the like.

[0345] This invention is a system in which users, terminals, and servers work together to efficiently generate customized content using advanced generative AI models and emotion engines.

[0346] Users input content generation requests through their devices. This input includes context, tone, information about the target audience, desired style, and the user's emotional state. For example, a user can input the prompt "Please write a script for a presentation introducing a new product," specifying "businessmen in their 30s and 40s" as the target audience and "professionals" as the tone. The user can also provide their current emotional state through voice or text input.

[0347] The device receives input from the user and converts the input data into JSON format, which includes the user ID, input content, desired tone, target recipient information, and emotion data analyzed by the emotion engine. The device then forwards this data to the server as an HTTP request.

[0348] The server analyzes the HTTP request received from the device. First, it analyzes the user's input to extract the context, tone, purpose, and characteristics of the target audience. It also includes emotional data provided by the emotion engine. Next, the server retrieves user profile information and past content from the database and integrates this information with the analysis results.

[0349] The server then invokes a generative AI model to generate customized content. This model is designed to understand context, tone, intent, and emotional state, allowing it to generate high-quality content. The generated content is then formatted and sent back to the device. At the same time, this generated content is stored in a database for future use.

[0350] The terminal receives the customized content returned from the server, and the user can review the content through the terminal's user interface and make any necessary modifications or additions.

[0351] As a concrete example, consider the case where a user inputs, "Please create a script for a presentation introducing a new product. The target audience is businessmen in their 30s and 40s, and the tone should be professional." At this time, let's assume that the user's emotional state is "neutral." The device converts this input into JSON format and forwards it to the server. The server analyzes this data and uses a generative AI model and emotion engine to generate a customized script based on the specified conditions and sends it back to the device. The user can review the returned script and modify it as necessary.

[0352] In this way, users, devices, and servers work together to quickly and efficiently generate customized content that meets the user's needs using advanced generative AI and emotion engines.

[0353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0354] Program processing flow

[0355] Step 1: User Input

[0356] A user uses a device to input a content generation request, for example, "Please write a script for a presentation introducing a new product." The user also provides details about the target audience (e.g., business people in their 30s and 40s), the desired tone (e.g., professional), and style. The user also provides their current emotional state through voice or text input.

[0357] Input: Content generation request, target audience information, desired tone and style, emotional state

[0358] Output: User input data

[0359] Step 2: Convert data via terminal

[0360] The device receives input data from the user and converts it into JSON format, which includes the user ID, input content, tone preference, target recipient information, and emotion data analyzed by the emotion engine.

[0361] Input: User-entered data

[0362] Output: JSON format data

[0363] Specific behavior:

[0364] Parse the input data and format it into JSON format.

[0365] For example, data in the format {"userID": "12345", "request": "New product introduction presentation script", "tone": "Professional", "audience": "Businessmen in their 30s and 40s", "emotion": "neutral"} is generated.

[0366] Step 3: Transfer data from your device to the server

[0367] The terminal transfers the data converted into JSON format to the server as an HTTP request.

[0368] Input: JSON format data

[0369] Output: HTTP request

[0370] Specific behavior:

[0371] Send JSON format data to the server as an HTTP request.

[0372] Step 4: Parsing the input by the server

[0373] The server receives and analyzes the HTTP request, analyzing the user's input to extract context, tone, purpose, and target audience characteristics, as well as emotional data provided by the emotion engine.

[0374] Input: HTTP request

[0375] Output: Analyzed data (context, tone, purpose, target audience characteristics, emotional state)

[0376] Specific behavior:

[0377] It parses HTTP request data to extract context, tone, purpose, target audience characteristics, and emotional state.

[0378] Step 5: Retrieving information from the database

[0379] The server retrieves user profile information and past content from the database, and combines this information with the analysis results.

[0380] Input: User profile information and past content requests

[0381] Output: Information retrieved from the database

[0382] Specific behavior:

[0383] User profile information and historical content is retrieved from the database and integrated with the analysis results.

[0384] Step 6: Content generation with generative AI models

[0385] The server then uses the integrated information to invoke a generative AI model to generate customized content, which understands context, tone, intent, and emotional state to generate high-quality content based on user requests.

[0386] Input: Integration information

[0387] Output: Generated content

[0388] Specific behavior:

[0389] It invokes a generative AI model, providing it with context, tone, intent, and emotional state as input.

[0390] A generative AI model generates specific scripts and email text.

[0391] Step 7: Server formats and returns content

[0392] The generated content is formatted and sent back to the device as an HTTP response, and is also stored in a database for future use.

[0393] Input: Generated content

[0394] Output: HTTP response, database update

[0395] Specific behavior:

[0396] The generated content is reformatted into JSON format and sent to the terminal as an HTTP response.

[0397] Store the generated content in a database.

[0398] Step 8: View and edit content using your device

[0399] The terminal receives the customized content returned from the server and displays it to the user through a user interface. The user can then check the displayed content and make corrections or additions as necessary.

[0400] Input: HTTP response

[0401] Output: The displayed customized content

[0402] Specific behavior:

[0403] The generated content is displayed on the user interface of the terminal.

[0404] The user reviews the displayed content and manually corrects or adds to it.

[0405] (Application example 2)

[0406] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0407] In modern content delivery services, it is important to quickly generate and provide personalized content that meets the diverse needs and emotional states of users. However, conventional systems often find it difficult to effectively analyze and reflect the user's current emotional state, and the generated content is often not sufficiently personalized. This makes it difficult to improve user satisfaction and prevents effective content delivery.

[0408] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting input from a user, means for analyzing the user input and generating emotional data, including an emotion analysis engine that analyzes the user's emotional state, and means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content. This enables highly personalized content generation based on the user's current emotional state.

[0409] A "user" is a person or organization that uses an information system or service.

[0410] "Input" is data or information that a user provides to an information system.

[0411] "Analysis" is the process of understanding input data or information and extracting specific meaning or intent.

[0412] A "generative AI model" is an artificial intelligence algorithm that automatically generates content based on user input data.

[0413] The "emotion analysis engine" is a component that analyzes the user's emotional state and generates emotional data.

[0414] "Emotion data" is data obtained by analyzing the user's emotional state using an emotion analysis engine.

[0415] "Customized content" is content that is generated based on the user's input data and emotional data and is optimized for an individual user.

[0416] "Context" is the meaning or relevance of a user's input data in a particular situation or condition.

[0417] "Tone" is a term that refers to the overall mood or style of content.

[0418] "Intent" is the purpose or goal inferred from the user's input.

[0419] A "server" is a computer system that receives and processes requests from users.

[0420] A "database" is a system for efficiently storing, searching, and managing structured information.

[0421] A "user profile" is a data set that compiles a user's attribute information, behavioral history, etc.

[0422] A "user interface" is the means or screen through which a user interacts with a system.

[0423] "Personalized" means optimized according to the attributes and status of each individual user.

[0424] This invention relates to a system for generating personalized content using user input data and emotion data. This system operates in cooperation with a user, a terminal, and a server, and utilizes a generative AI model and an emotion analysis engine. Specific embodiments are described below.

[0425] 1. User operations

[0426] A user requests content creation using a content distribution service application. For example, the user might input, "Please create a relaxing travel video." At this time, the user also provides emotional data using voice. The emotional data is information that indicates the user's current emotional state.

[0427] 2. Role of the terminal

[0428] The device receives text and voice input from the user and acquires this data. It converts the acquired data into JSON format and generates request data that includes the user ID, input content, tone preference, information about the recipient, and emotional data analyzed by the emotion analysis engine. It then sends this request data to the server as an HTTP request.

[0429] 3. Server Processing

[0430] The server analyzes the request data received from the device. First, it analyzes the input content to extract the context, tone, intent, and characteristics of the target recipient. It also analyzes the emotional data provided by the sentiment analysis engine. Next, the server retrieves user profile information and information about past content from the database and uses this information for analysis. Based on this information, it invokes a generative AI model to generate customized content according to the request. This generated content is formatted and sent back to the device.

[0431] 4. Display by terminal

[0432] The terminal receives the customized content from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality personalized content.

[0433] Hardware and software used

[0434] Hardware: Smartphone, Head-Mounted Display (HMD)

[0435] Software: Generative AI model, sentiment analysis engine, server (REST API for sending HTTP requests)

[0436] Specific examples

[0437] For example, you can enter the following prompt sentence into your application:

[0438] "Make a travel video that makes you feel relaxed. Your current emotional state is 'happy.'"

[0439] After sending the prompt to the server, the server analyzes the prompt and emotional data to generate a relaxing travel video for the user. For example, a video combining beautiful beach and mountain scenery in Hawaii is generated and displayed to the user. This application allows users to easily enjoy the ideal video content that matches their emotional state.

[0440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0441] Step 1:

[0442] A user uses a content delivery service application to request the generation of a specific piece of content. For example, the user inputs, "Please create a travel video that will make me feel relaxed." The user also provides voice input and their current emotional state (e.g., "happy"). The text request and emotional data are passed to the device as input data.

[0443] Step 2:

[0444] The device receives text input and emotion data from the user and converts this data into JSON format. At this time, it generates request data that includes the user ID, input content, tone preference, information about the target recipient, and emotion data analyzed by the emotion analysis engine. The generated JSON data is prepared as input for the HTTP request.

[0445] Step 3:

[0446] The device sends the generated HTTP request to the server. The request includes the user ID, input content, tone preference, information about the target recipient, and emotional data. The device sends the data to the server using the HTTP POST method.

[0447] Step 4:

[0448] The server analyzes the HTTP request data received from the device. First, the server analyzes the user's input and extracts the context, tone, purpose, and characteristics of the target recipient. At the same time, it also analyzes the emotional data analyzed by the emotion analysis engine. The analysis results are generated as intermediate data within the server.

[0449] Step 5:

[0450] The server retrieves user profile information and past content data from the database, which is then combined with the analyzed input data and sentiment data to further refine the intermediate data, enabling fine-grained personalization.

[0451] Step 6:

[0452] The server uses the intermediate data to call a generative AI model to generate customized content. The generative AI model generates optimal content based on user input, emotional data, past content history, etc. The generated content is saved as finished data on the server.

[0453] Step 7:

[0454] The server returns the completed data to the device, which prepares the received content for display and provides an interface for the user to review and modify. After receiving the completed data, the device displays the personalized content to the user.

[0455] Step 8:

[0456] The user checks the received content and makes corrections or additions as necessary. Finally, personalized content that satisfies the user is completed. User feedback is again entered into the terminal, completing the entire process.

[0457] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0458] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0459] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0460] [Second embodiment]

[0461] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0462] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0463] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0464] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0465] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0466] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0467] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0468] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0469] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0470] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0471] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0472] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0473] The present invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using an advanced generative AI model. Specific embodiments of the system are described below.

[0474] User operations

[0475] Users provide input to the device to generate content, such as a request like "Please write a script for a new product presentation," through an input form, along with information about the target audience and details about the desired tone and style.

[0476] Device Role

[0477] The device receives user input and processes it to send it to the server. Specifically, it converts the user's input into an appropriate data format, such as JSON, and forwards it to the server as an HTTP request. The request includes the user ID, details of the purpose, and information about the intended recipient.

[0478] Server Processing

[0479] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[0480] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics.

[0481] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[0482] The server then uses the acquired information to generate customized content by invoking a generative AI model that is designed to understand context, tone, and intent to generate high-quality content based on user requests.

[0483] The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[0484] Display by terminal

[0485] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[0486] Specific examples

[0487] Example 1: Creating a business presentation

[0488] 1. The user enters the "new product introduction presentation script" into the terminal.

[0489] 2. The terminal sends this request to the server, asking it to generate an appropriate script based on the context, tone, and characteristics of the target recipient.

[0490] 3. The server uses the generative AI model to generate a script and sends it back to the device.

[0491] 4. The user checks the generated script and modifies it as necessary before using it.

[0492] Example 2: Creating an email for a marketing campaign

[0493] 1. The user enters the "new campaign product introduction email" into the terminal.

[0494] 2. The device sends this request to the server, asking it to generate email content based on the user's intentions.

[0495] 3. The server uses the generative AI model to generate content and sends it back to the device.

[0496] 4. The user reviews the generated email, edits it as needed, and uses it in their marketing efforts.

[0497] In this way, users, terminals, and servers work together to realize an efficient content generation system using generative AI.

[0498] The processing flow will be explained below.

[0499] Step 1:

[0500] The user operates the terminal and inputs a request for specific content generation, for example, "Please create a script for a presentation introducing a new product."

[0501] Step 2:

[0502] The device receives user input and converts it into JSON format, which includes information about the user ID, input, tone preference, and target recipient.

[0503] Step 3:

[0504] The device sends JSON format data to the server as an HTTP request, sending a POST request to the server's endpoint.

[0505] Step 4:

[0506] The server receives an HTTP request from the terminal, analyzes the contents of the request, and extracts the user's input data.

[0507] Step 5:

[0508] The server extracts user-input data and analyzes it for context, tone, purpose, and target audience characteristics using natural language processing techniques.

[0509] Step 6:

[0510] The server retrieves user profile information and previously generated content from a database, which is used for analysis.

[0511] Step 7:

[0512] The server generates customized content based on user profile information and input data by invoking a generative AI model that understands context, tone, and intent to generate appropriate content.

[0513] Step 8:

[0514] The server formats the generated custom content and prepares it as response data, which is packaged in JSON.

[0515] Step 9:

[0516] The server sends the generated content in JSON format to the device as an HTTP response, and returns a POST request response to the device's endpoint.

[0517] Step 10:

[0518] The terminal receives the HTTP response from the server and displays the generated customized content on the user interface. The user can check the generated content and modify it as necessary.

[0519] Step 11:

[0520] Users can review the generated content, make any necessary edits, and then save or share the content as needed.

[0521] Step 12:

[0522] The device then sends the edited and saved content back to the server and stores it in a database, which can be used for future generation.

[0523] Example 1

[0524] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0525] Conventional content generation systems simply process user input, making it difficult to generate customized content that fully understands the context, tone, and intent. As a result, users are forced to extensively edit the generated content, which is time-consuming and laborious. Furthermore, the quality of the generated content is inconsistent, often failing to meet user expectations.

[0526] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0527] In this invention, the server includes means for accepting input from a user, means for analyzing the user's input, means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content, means for a terminal to convert the user's input into a data format and send it to the server, means for the server to return the customized content generated based on the analyzed information to the terminal, means for displaying the customized content generated by the generative AI model to the user, and means for saving the customized content and using it for future generation. This enables the generation of high-quality customized content with a deep understanding of the user's intent and context.

[0528] "User" means an individual or corporation that uses this system to create, review, or modify content.

[0529] A "terminal" is a device for a user to input information, and includes hardware and software that performs processing to transmit information to a server.

[0530] "Server" means a remote data processing device that analyzes requests received from users, hosts generative AI models, and generates customized content.

[0531] The "means for accepting input" is an interface for receiving data provided by a user for content generation at a terminal.

[0532] "Means for analyzing input" refers to algorithms and programs that analyze data received from a user based on context, tone, and intent.

[0533] A "generative AI model" is an artificial intelligence model that generates high-quality customized content based on analyzed data.

[0534] The "means for converting to a data format" is a processing method for converting user input into an appropriate format (e.g., JSON) to make it easier for the server to parse it.

[0535] The "means for transmitting to the server" is a communication means for transmitting the converted data from the terminal to the server.

[0536] "Means for returning" refers to the process of returning the generated customized content from the server to the terminal.

[0537] "Means for storage" refers to a method for storing the generated content in a database or the like and using it in future generation processes.

[0538] "User Interface" means the visual or operating environment that allows a user to review and modify input data.

[0539] The present invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using a generative AI model. Specific embodiments of the system are described below.

[0540] User operations

[0541] A user types a content generation request into the device, such as "Write a script for a new product launch presentation," along with information about the target audience and details about the desired tone and style.

[0542] Specific examples

[0543] Example prompt: "Write a script for a presentation to introduce a new product. The target audience is executives, and the tone should be professional and concise."

[0544] Device Role

[0545] The device receives user input, converts it into a data format, and sends it to the server. Specifically, it converts the user's input into JSON format and forwards it to the server as an HTTP request. The request includes the user ID, details of the purpose, and information about the target recipient.

[0546] Server Processing

[0547] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[0548] Input Analysis

[0549] The server analyzes the content of the user's request and extracts the context, tone, purpose, and characteristics of the intended audience.

[0550] Database Reference

[0551] The server retrieves user profile information and past content from the database and uses it for analysis. Based on this information, the server invokes a generative AI model (e.g., OpenAI GPT-4) to generate customized content based on the user's requests.

[0552] Returning generated content

[0553] The server formats the generated content into an appropriate format (e.g., text or HTML) and sends it back to the device as an HTTP response, where it is stored in a database for future personalization.

[0554] Display by terminal

[0555] The terminal receives the customized content returned from the server and displays it to the user through a user interface. The user can check the displayed content and make corrections or additions as necessary.

[0556] Specific examples

[0557] Examples of generated content:

[0558] "This new product is 20% more efficient than our previous product and will also help reduce costs. I believe that management in particular will recognize this as another new value we can provide to our customers."

[0559] In this way, users, devices, and servers work together to realize an efficient content generation system using generative AI models, allowing users to quickly and easily obtain high-quality customized content for business or personal use.

[0560] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0561] Explain the program's processing flow by dividing it into processing steps

[0562] Step 1: User Input

[0563] A user types a content generation request into the device. For example, the user might type, "Please create a script for a new product introduction presentation," along with other details such as the target audience and desired tone.

[0564] input

[0565] Request text (e.g., "Please write a script for a new product introduction presentation.")

[0566] Target Audience Information

[0567] Desired tone and style

[0568] output

[0569] User input data (request details, target audience information, desired tone and style)

[0570] Step 2: Converting input data on the terminal

[0571] The terminal receives input from the user and converts it into JSON format, including the user ID, purpose details, and information about the intended recipient.

[0572] input

[0573] User input data (request details, target audience information, desired tone and style)

[0574] Data Processing

[0575] Convert the data into the appropriate format (JSON)

[0576] output

[0577] JSON format data (e.g., "{"request": "Please create a script for a new product introduction presentation", "userID": "12345", "audience": "Management", "tone": "Professional"}")

[0578] Step 3: Send data from the device to the server

[0579] The device then sends the converted JSON data to the server as an HTTP request, along with the user ID and purpose details.

[0580] input

[0581] JSON format data (request details, target audience information, desired tone and style)

[0582] Data Calculation

[0583] Generating an HTTP Request

[0584] output

[0585] HTTP request (e.g. POST request)

[0586] Step 4: Parsing input on the server

[0587] The server receives the incoming HTTP request and parses its content, analyzing the request text and extracting the context, tone, purpose, and characteristics of the target audience.

[0588] input

[0589] HTTP request (JSON data)

[0590] Data Processing

[0591] Parsing the request statement

[0592] output

[0593] Analysis results (context, tone, purpose, target audience characteristics)

[0594] Step 5: Look up the database on the server

[0595] The server retrieves user profile information and past content from the database and uses it for analysis. It references the database to retrieve past generated content and user preference information.

[0596] input

[0597] Analysis results (context, tone, purpose, target audience characteristics)

[0598] Data Search

[0599] Extracting relevant information from a database

[0600] output

[0601] Information obtained (user profile, past content)

[0602] Step 6: Server-side content generation

[0603] The server calls a generative AI model based on the acquired information and generates high-quality customized content.

[0604] input

[0605] Information obtained (user profile, past content)

[0606] Analysis results (context, tone, purpose, target audience characteristics)

[0607] Data Calculation

[0608] Running generative AI models

[0609] output

[0610] Generated customized content

[0611] Step 7: Return from server to device

[0612] The generated customized content is formatted and sent back to the device as an HTTP response, and is stored in a database for future personalization.

[0613] input

[0614] Generated customized content

[0615] Data Processing

[0616] Applying Formatting

[0617] output

[0618] HTTP response (generated content)

[0619] Step 8: View in terminal

[0620] The terminal displays the customized content returned from the server through a user interface.

[0621] input

[0622] HTTP response (generated content)

[0623] Data Processing

[0624] Conversion to display format

[0625] output

[0626] What is displayed on the user interface

[0627] Step 9: User review and correction

[0628] The user checks the displayed customized content and makes corrections or additions as necessary.

[0629] input

[0630] What is displayed on the user interface

[0631] Data Processing

[0632] Corrections and additions

[0633] output

[0634] Modified content

[0635] Through this series of steps, a system is realized in which users, terminals, and servers cooperate to efficiently generate high-quality customized content.

[0636] (Application example 1)

[0637] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0638] Modern virtual stores require systems that allow users to quickly and effectively generate product introductions and descriptions. Without such systems, users must manually create content, which is time-consuming and labor-intensive, and the quality is inconsistent. Furthermore, without a means to instantly provide customized information, it becomes difficult to deliver appropriate information to potential customers. Therefore, the objective of this invention is to provide a system that uses generative AI models to generate high-quality content in real time and that can be immediately used within a virtual store.

[0639] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0640] In this invention, the server includes means for accepting input from a user, means for analyzing the input, means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content, means for allowing a user to input a request to generate product introductions and descriptions in a virtual store and provide customized information in real time, means for returning the generated customized content to the user, and means for storing the customized content and using it for future generation, thereby enabling users to generate and provide high-quality customized product introductions and descriptions in real time.

[0641] "User input" refers to user requests or instructions accepted by the system.

[0642] "Means for analyzing input" refers to a device or program that interprets the input received from the user and processes it to understand its context and intent.

[0643] "Generative AI model" refers to an artificial intelligence algorithm used to automatically generate new content based on specific rules and data.

[0644] A "virtual store" refers to a virtual shopping environment that provides products and services to users via the Internet.

[0645] A "request to generate product introductions or descriptions" refers to an order or request made by a user to a generative AI model to provide information about a product in a virtual store.

[0646] "Real-time customized information" refers to personalized content that is generated instantly and delivered in response to a user's request.

[0647] "Customized Content" refers to information or documentation that is optimized and generated to meet the needs and requirements of a particular user.

[0648] A "user profile" refers to a data set that compiles information about a specific user, such as their attributes, behavioral history, and preferences.

[0649] A "database" refers to a system or software that stores large amounts of information in a systematic manner and retrieves and uses it when needed.

[0650] A "user interface" is a means by which a user interacts with a system, and refers to input forms, display screens, etc.

[0651] The following describes an embodiment of the present invention: The present invention is a system in which a user, a terminal, and a server work together to generate customized content using an advanced generative AI model.

[0652] User operations

[0653] Users provide input to generate product descriptions and text within the virtual store, for example, by entering a request such as "Please write a description for our new product" through an input form, along with information about the target audience and details of the desired tone and style.

[0654] Device Role

[0655] The device receives input from the user and processes it to send it to the server. Specifically, it converts the user's input into the appropriate data format and forwards it to the server as an HTTP request. The request includes the user's ID, details of the purpose, and information about the intended recipient.

[0656] Server Processing

[0657] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[0658] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics.

[0659] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[0660] The server then uses the acquired information to invoke a generative AI model to generate customized content. The generative AI model is designed to understand context, tone, and intent to generate high-quality content based on the user's request. The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[0661] Display by terminal

[0662] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[0663] Specific examples

[0664] Example 1: Creating product descriptions for a virtual store

[0665] 1. The user inputs a request into the terminal: "Please write a description of a new product."

[0666] 2. The device sends this request to the server, asking it to generate an appropriate introduction based on the context, tone, and characteristics of the target recipient.

[0667] 3. The server uses a generative AI model (such as GPT-4) to generate a testimonial and sends it back to the device.

[0668] 4. The user reviews the generated introduction and modifies it as necessary.

[0669] Example of input prompt sentence:

[0670] "User ID: user123, Product: Smartwatch, Features: Heart rate monitor and GPS, Target: Non-technical beginners, Tone: Casual"

[0671] This invention enables users to generate and provide high-quality customized product descriptions in real time within a virtual store.

[0672] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0673] Step 1:

[0674] User Input

[0675] The user provides a request to generate product descriptions and prompts within the virtual store, such as "Please create a description for our new product," along with information about the target audience and details about the desired tone and style, which then generates a specific prompt.

[0676] Inputs: "Write a description for our new product", target audience information, tone and style

[0677] Output: Specific prompt (e.g., User ID: user123, Product: Smartwatch, Features: Heart rate tracking and GPS, Target audience: Non-technical beginners, Tone: Casual)

[0678] Step 2:

[0679] Terminal data conversion

[0680] The device converts the request received from the user into an appropriate data format (e.g., JSON format) and sends it to the server as an HTTP request, including the user ID, details of the purpose, and information about the target recipient.

[0681] Input: prompt statement

[0682] Output: HTTP request in JSON format (e.g., {"user_id":"user123","prompt":"Please write a description for our new product","audience_info":"Beginner","tone_style":"Casual"})

[0683] Step 3:

[0684] Server input parsing

[0685] The server analyzes the HTTP request received from the device and extracts the context, tone, and characteristics of the target recipient. This analysis helps to understand the specific content of the request.

[0686] Input: HTTP request in JSON format

[0687] Output: Analyzed input data (context, tone, audience characteristics)

[0688] Step 4:

[0689] Database Reference

[0690] The server retrieves user profile information and past content from the database as needed and uses it for analysis, which enables more accurate content generation.

[0691] Input: Parsed input data

[0692] Output: Additional user profile data and historical content

[0693] Step 5:

[0694] Content generation using generative AI models

[0695] The server then calls a generative AI model based on the acquired information and analysis data to generate customized content. The generative AI model (e.g., GPT-4) generates high-quality introductions and descriptions based on the input prompt.

[0696] Input: User profile data, parsed input data

[0697] Output: Generated content (customized introduction, description)

[0698] Step 6:

[0699] Return from the server to the device

[0700] The server formats the generated customized content and sends it back to the device as an HTTP response, storing the generated content in a database for future personalization.

[0701] Input: Generated content

[0702] Output: Content formatted as an HTTP response

[0703] Step 7:

[0704] Display by terminal

[0705] The terminal receives the customized content sent back from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. Finally, the content is provided in a form that can be used in the virtual store.

[0706] Input: Content formatted as an HTTP response

[0707] Output: The customized content displayed to the user

[0708] These steps allow users to create and instantly deliver high-quality, customized product descriptions in real time within a virtual store.

[0709] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0710] This invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using an advanced generative AI model and an emotion engine. Specific embodiments of this system are described below.

[0711] User operations

[0712] The user provides input to the device to generate content, such as "Write a script for a new product introduction presentation." The user can also provide information about the target audience and details of the desired tone and style. The device can also accept voice or text input, which uses an emotion engine to analyze the user's emotional state.

[0713] Device Role

[0714] The device receives input from the user and converts the input data into JSON format, which includes the user ID, input content, tone preference, information about the target recipient, and emotional data analyzed by the emotion engine. This data is then sent to the server as an HTTP request.

[0715] Server Processing

[0716] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[0717] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics, including emotional data provided by the emotion engine.

[0718] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[0719] The server then uses the acquired information and emotional data to invoke a generative AI model to generate customized content. This generative AI model is designed to understand context, tone, intent, and emotional state to generate high-quality content based on user requests.

[0720] The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[0721] Display by terminal

[0722] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[0723] Specific examples

[0724] Example 1: Creating a business presentation

[0725] 1. The user inputs the "new product introduction presentation script" into the terminal and also inputs their current emotional state through voice input.

[0726] 2. The device sends this request and emotional data to the server, asking it to generate an appropriate script based on the context, tone, characteristics and emotional state of the target recipient.

[0727] 3. The server uses the generative AI model and emotion engine to generate a script and send it back to the device.

[0728] 4. The user checks the generated script and modifies it as necessary before using it.

[0729] Example 2: Creating an email for a marketing campaign

[0730] 1. The user enters the "new campaign product introduction email" into the terminal and inputs their current emotional state through the emotion engine.

[0731] 2. The device sends this request and emotional data to the server, asking it to generate email content based on the user's intentions and emotional state.

[0732] 3. The server uses the generative AI model and emotion engine to generate content and send it back to the device.

[0733] 4. The user reviews the generated email, edits it as needed, and uses it in their marketing efforts.

[0734] In this way, users, devices, and servers work together to realize an efficient content generation system using generative AI and an emotion engine.

[0735] The processing flow will be explained below.

[0736] Step 1:

[0737] The user operates the terminal and inputs a request for content generation, for example, "Please create a script for a presentation introducing a new product."

[0738] Step 2:

[0739] The user inputs emotion data by speaking aloud into the terminal, which allows the emotion engine to recognize the user's current emotional state.

[0740] Step 3:

[0741] The device receives user input as text data, and the emotion engine analyzes the voice data to detect the user's emotional state. The data, including the analysis results, is converted into JSON format. The converted data includes the user ID, input content, desired tone, information about the target recipient, and emotional data.

[0742] Step 4:

[0743] The device sends JSON format data to the server as an HTTP request, sending a POST request to the server's endpoint.

[0744] Step 5:

[0745] The server receives an HTTP request from the terminal, analyzes the contents of the request, and extracts the user's input data.

[0746] Step 6:

[0747] The server extracts user-input data and emotional data to analyze the context, tone, purpose, and target audience characteristics using natural language processing techniques and emotion recognition algorithms.

[0748] Step 7:

[0749] The server retrieves user profile information and previously generated content from the database and uses it for analysis, thereby improving the accuracy of the generative AI model.

[0750] Step 8:

[0751] Based on the acquired user profile information, input data, and emotional data, the server invokes a generative AI model to generate customized content. The generative AI model understands context, tone, intent, and emotional state to generate high-quality content based on user requests.

[0752] Step 9:

[0753] The server formats the generated custom content and prepares it as response data. The generated content is packaged in JSON format.

[0754] Step 10:

[0755] The server sends the generated content in JSON format to the device as an HTTP response, and returns a POST request response to the device's endpoint.

[0756] Step 11:

[0757] The terminal receives the HTTP response from the server and displays the generated customized content on the user interface. The user can check the generated content and modify it as necessary.

[0758] Step 12:

[0759] Users can review the generated content, make any necessary edits, and then save or share the content as needed.

[0760] Step 13:

[0761] The device then sends the edited and saved content back to the server and stores it in a database, which can be used for future generation.

[0762] Example 2

[0763] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0764] Conventional content generation systems have struggled to quickly and accurately provide customized content that fully reflects a user's context, tone, and intent. Furthermore, they lack the ability to generate content that takes into account emotional states, making it impossible to fully meet user needs. Furthermore, they lack a way to improve the accuracy of their generative AI models by utilizing past content and user profiles.

[0765] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for converting user input into JSON format; means for transferring the JSON-formatted data to the server; means for the server to analyze the user input and extract context, tone, intent, and emotional state; means for retrieving user profile information and past content from a database and integrating the analysis results; means for hosting a generative AI model that generates customized content based on the integrated information; means for formatting the generated content and returning it to the terminal; and means for saving the customized content and using it for future generation. This enables the rapid generation and provision of high-quality customized content that reflects the user's context, tone, intent, and emotional state. Furthermore, the accuracy of the generative AI model can be improved by utilizing past content and user profiles.

[0766] "User input" is information or instructions provided by a user to the system.

[0767] "JSON format" is an abbreviation for JavaScript Object Notation, and is a method of structuring and representing data in text format.

[0768] A "server" is a computer system that processes and manages data, receives requests from terminals, and returns appropriate responses.

[0769] "Context" refers to background or situational information related to the user's input.

[0770] "Tone" refers to the atmosphere or style of the content being generated, and can be professional, casual, or the like.

[0771] "Intention" refers to the purpose or goal that a user is trying to achieve with their input.

[0772] "Emotional state" refers to the emotion the user is feeling at the time of input, and includes joy, anger, sadness, etc.

[0773] A "database" is a system for managing a collection of data, enabling efficient data retrieval and storage.

[0774] "User profile information" refers to information about an individual user, such as the user's attributes and behavioral history.

[0775] "Past content" refers to previously generated content that is related to a user's past requests.

[0776] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate customized content based on input data.

[0777] "Hosting" means running a generative AI model on a server so that it can respond to requests from other systems and users.

[0778] "Formatting" means shaping the generated content into a particular structure or form.

[0779] A "terminal" is a device that is directly operated by a user, and includes a computer, a smartphone, a tablet, and the like.

[0780] This invention is a system in which users, terminals, and servers work together to efficiently generate customized content using advanced generative AI models and emotion engines.

[0781] Users input content generation requests through their devices. This input includes context, tone, information about the target audience, desired style, and the user's emotional state. For example, a user can input the prompt "Please write a script for a presentation introducing a new product," specifying "businessmen in their 30s and 40s" as the target audience and "professionals" as the tone. The user can also provide their current emotional state through voice or text input.

[0782] The device receives input from the user and converts the input data into JSON format, which includes the user ID, input content, desired tone, target recipient information, and emotion data analyzed by the emotion engine. The device then forwards this data to the server as an HTTP request.

[0783] The server analyzes the HTTP request received from the device. First, it analyzes the user's input to extract the context, tone, purpose, and characteristics of the target audience. It also includes emotional data provided by the emotion engine. Next, the server retrieves user profile information and past content from the database and integrates this information with the analysis results.

[0784] The server then invokes a generative AI model to generate customized content. This model is designed to understand context, tone, intent, and emotional state, allowing it to generate high-quality content. The generated content is then formatted and sent back to the device. At the same time, this generated content is stored in a database for future use.

[0785] The terminal receives the customized content returned from the server, and the user can review the content through the terminal's user interface and make any necessary modifications or additions.

[0786] As a concrete example, consider the case where a user inputs, "Please create a script for a presentation introducing a new product. The target audience is businessmen in their 30s and 40s, and the tone should be professional." At this time, let's assume that the user's emotional state is "neutral." The device converts this input into JSON format and forwards it to the server. The server analyzes this data and uses a generative AI model and emotion engine to generate a customized script based on the specified conditions and sends it back to the device. The user can review the returned script and modify it as necessary.

[0787] In this way, users, devices, and servers work together to quickly and efficiently generate customized content that meets the user's needs using advanced generative AI and emotion engines.

[0788] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0789] Program processing flow

[0790] Step 1: User Input

[0791] A user uses a device to input a content generation request, for example, "Please write a script for a presentation introducing a new product." The user also provides details about the target audience (e.g., business people in their 30s and 40s), the desired tone (e.g., professional), and style. The user also provides their current emotional state through voice or text input.

[0792] Input: Content generation request, target audience information, desired tone and style, emotional state

[0793] Output: User input data

[0794] Step 2: Convert data via terminal

[0795] The device receives input data from the user and converts it into JSON format, which includes the user ID, input content, tone preference, target recipient information, and emotion data analyzed by the emotion engine.

[0796] Input: User-entered data

[0797] Output: JSON format data

[0798] Specific behavior:

[0799] Parse the input data and format it into JSON format.

[0800] For example, data in the format {"userID": "12345", "request": "New product introduction presentation script", "tone": "Professional", "audience": "Businessmen in their 30s and 40s", "emotion": "neutral"} is generated.

[0801] Step 3: Transfer data from your device to the server

[0802] The terminal transfers the data converted into JSON format to the server as an HTTP request.

[0803] Input: JSON format data

[0804] Output: HTTP request

[0805] Specific behavior:

[0806] Send JSON format data to the server as an HTTP request.

[0807] Step 4: Parsing the input by the server

[0808] The server receives and analyzes the HTTP request, analyzing the user's input to extract context, tone, purpose, and target audience characteristics, as well as emotional data provided by the emotion engine.

[0809] Input: HTTP request

[0810] Output: Analyzed data (context, tone, purpose, target audience characteristics, emotional state)

[0811] Specific behavior:

[0812] It parses HTTP request data to extract context, tone, purpose, target audience characteristics, and emotional state.

[0813] Step 5: Retrieving information from the database

[0814] The server retrieves user profile information and past content from the database, and combines this information with the analysis results.

[0815] Input: User profile information and past content requests

[0816] Output: Information retrieved from the database

[0817] Specific behavior:

[0818] User profile information and historical content is retrieved from the database and integrated with the analysis results.

[0819] Step 6: Content generation with generative AI models

[0820] The server then uses the integrated information to invoke a generative AI model to generate customized content, which understands context, tone, intent, and emotional state to generate high-quality content based on user requests.

[0821] Input: Integration information

[0822] Output: Generated content

[0823] Specific behavior:

[0824] It invokes a generative AI model, providing it with context, tone, intent, and emotional state as input.

[0825] A generative AI model generates specific scripts and email text.

[0826] Step 7: Server formats and returns content

[0827] The generated content is formatted and sent back to the device as an HTTP response, and is also stored in a database for future use.

[0828] Input: Generated content

[0829] Output: HTTP response, database update

[0830] Specific behavior:

[0831] The generated content is reformatted into JSON format and sent to the terminal as an HTTP response.

[0832] Store the generated content in a database.

[0833] Step 8: View and edit content using your device

[0834] The terminal receives the customized content returned from the server and displays it to the user through a user interface. The user can then check the displayed content and make corrections or additions as necessary.

[0835] Input: HTTP response

[0836] Output: The displayed customized content

[0837] Specific behavior:

[0838] The generated content is displayed on the user interface of the terminal.

[0839] The user reviews the displayed content and manually corrects or adds to it.

[0840] (Application example 2)

[0841] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0842] In modern content delivery services, it is important to quickly generate and provide personalized content that meets the diverse needs and emotional states of users. However, conventional systems often find it difficult to effectively analyze and reflect the user's current emotional state, and the generated content is often not sufficiently personalized. This makes it difficult to improve user satisfaction and prevents effective content delivery.

[0843] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting input from a user, means for analyzing the user input and generating emotional data, including an emotion analysis engine that analyzes the user's emotional state, and means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content. This enables highly personalized content generation based on the user's current emotional state.

[0844] A "user" is a person or organization that uses an information system or service.

[0845] "Input" is data or information that a user provides to an information system.

[0846] "Analysis" is the process of understanding input data or information and extracting specific meaning or intent.

[0847] A "generative AI model" is an artificial intelligence algorithm that automatically generates content based on user input data.

[0848] The "emotion analysis engine" is a component that analyzes the user's emotional state and generates emotional data.

[0849] "Emotion data" is data obtained by analyzing the user's emotional state using an emotion analysis engine.

[0850] "Customized content" is content that is generated based on the user's input data and emotional data and is optimized for an individual user.

[0851] "Context" is the meaning or relevance of a user's input data in a particular situation or condition.

[0852] "Tone" is a term that refers to the overall mood or style of content.

[0853] "Intent" is the purpose or goal inferred from the user's input.

[0854] A "server" is a computer system that receives and processes requests from users.

[0855] A "database" is a system for efficiently storing, searching, and managing structured information.

[0856] A "user profile" is a data set that compiles a user's attribute information, behavioral history, etc.

[0857] A "user interface" is the means or screen through which a user interacts with a system.

[0858] "Personalized" means optimized according to the attributes and status of each individual user.

[0859] This invention relates to a system for generating personalized content using user input data and emotion data. This system operates in cooperation with a user, a terminal, and a server, and utilizes a generative AI model and an emotion analysis engine. Specific embodiments are described below.

[0860] 1. User operations

[0861] A user requests content creation using a content distribution service application. For example, the user might input, "Please create a relaxing travel video." At this time, the user also provides emotional data using voice. The emotional data is information that indicates the user's current emotional state.

[0862] 2. Role of the terminal

[0863] The device receives text and voice input from the user and acquires this data. It converts the acquired data into JSON format and generates request data that includes the user ID, input content, tone preference, information about the recipient, and emotional data analyzed by the emotion analysis engine. It then sends this request data to the server as an HTTP request.

[0864] 3. Server Processing

[0865] The server analyzes the request data received from the device. First, it analyzes the input content to extract the context, tone, intent, and characteristics of the target recipient. It also analyzes the emotional data provided by the sentiment analysis engine. Next, the server retrieves user profile information and information about past content from the database and uses this information for analysis. Based on this information, it invokes a generative AI model to generate customized content according to the request. This generated content is formatted and sent back to the device.

[0866] 4. Display by terminal

[0867] The terminal receives the customized content from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality personalized content.

[0868] Hardware and software used

[0869] Hardware: Smartphone, Head-Mounted Display (HMD)

[0870] Software: Generative AI model, sentiment analysis engine, server (REST API for sending HTTP requests)

[0871] Specific examples

[0872] For example, you can enter the following prompt sentence into your application:

[0873] "Make a travel video that makes you feel relaxed. Your current emotional state is 'happy.'"

[0874] After sending the prompt to the server, the server analyzes the prompt and emotional data to generate a relaxing travel video for the user. For example, a video combining beautiful beach and mountain scenery in Hawaii is generated and displayed to the user. This application allows users to easily enjoy the ideal video content that matches their emotional state.

[0875] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0876] Step 1:

[0877] A user uses a content delivery service application to request the generation of a specific piece of content. For example, the user inputs, "Please create a travel video that will make me feel relaxed." The user also provides voice input and their current emotional state (e.g., "happy"). The text request and emotional data are passed to the device as input data.

[0878] Step 2:

[0879] The device receives text input and emotion data from the user and converts this data into JSON format. At this time, it generates request data that includes the user ID, input content, tone preference, information about the target recipient, and emotion data analyzed by the emotion analysis engine. The generated JSON data is prepared as input for the HTTP request.

[0880] Step 3:

[0881] The device sends the generated HTTP request to the server. The request includes the user ID, input content, tone preference, information about the target recipient, and emotional data. The device sends the data to the server using the HTTP POST method.

[0882] Step 4:

[0883] The server analyzes the HTTP request data received from the device. First, the server analyzes the user's input and extracts the context, tone, purpose, and characteristics of the target recipient. At the same time, it also analyzes the emotional data analyzed by the emotion analysis engine. The analysis results are generated as intermediate data within the server.

[0884] Step 5:

[0885] The server retrieves user profile information and past content data from the database, which is then combined with the analyzed input data and sentiment data to further refine the intermediate data, enabling fine-grained personalization.

[0886] Step 6:

[0887] The server uses the intermediate data to call a generative AI model to generate customized content. The generative AI model generates optimal content based on user input, emotional data, past content history, etc. The generated content is saved as finished data on the server.

[0888] Step 7:

[0889] The server returns the completed data to the device, which prepares the received content for display and provides an interface for the user to review and modify. After receiving the completed data, the device displays the personalized content to the user.

[0890] Step 8:

[0891] The user checks the received content and makes corrections or additions as necessary. Finally, personalized content that satisfies the user is completed. User feedback is again entered into the terminal, completing the entire process.

[0892] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0893] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0894] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0895] [Third embodiment]

[0896] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0897] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0898] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0899] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0900] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0901] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0902] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0903] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0904] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0905] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0906] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0907] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0908] The present invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using an advanced generative AI model. Specific embodiments of the system are described below.

[0909] User operations

[0910] Users provide input to the device to generate content, such as a request like "Please write a script for a new product presentation," through an input form, along with information about the target audience and details about the desired tone and style.

[0911] Device Role

[0912] The device receives user input and processes it to send it to the server. Specifically, it converts the user's input into an appropriate data format, such as JSON, and forwards it to the server as an HTTP request. The request includes the user ID, details of the purpose, and information about the intended recipient.

[0913] Server Processing

[0914] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[0915] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics.

[0916] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[0917] The server then uses the acquired information to generate customized content by invoking a generative AI model that is designed to understand context, tone, and intent to generate high-quality content based on user requests.

[0918] The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[0919] Display by terminal

[0920] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[0921] Specific examples

[0922] Example 1: Creating a business presentation

[0923] 1. The user enters the "new product introduction presentation script" into the terminal.

[0924] 2. The terminal sends this request to the server, asking it to generate an appropriate script based on the context, tone, and characteristics of the target recipient.

[0925] 3. The server uses the generative AI model to generate a script and sends it back to the device.

[0926] 4. The user checks the generated script and modifies it as necessary before using it.

[0927] Example 2: Creating an email for a marketing campaign

[0928] 1. The user enters the "new campaign product introduction email" into the terminal.

[0929] 2. The device sends this request to the server, asking it to generate email content based on the user's intentions.

[0930] 3. The server uses the generative AI model to generate content and sends it back to the device.

[0931] 4. The user reviews the generated email, edits it as needed, and uses it in their marketing efforts.

[0932] In this way, users, terminals, and servers work together to realize an efficient content generation system using generative AI.

[0933] The processing flow will be explained below.

[0934] Step 1:

[0935] The user operates the terminal and inputs a request for specific content generation, for example, "Please create a script for a presentation introducing a new product."

[0936] Step 2:

[0937] The device receives user input and converts it into JSON format, which includes information about the user ID, input, tone preference, and target recipient.

[0938] Step 3:

[0939] The device sends JSON format data to the server as an HTTP request, sending a POST request to the server's endpoint.

[0940] Step 4:

[0941] The server receives an HTTP request from the terminal, analyzes the contents of the request, and extracts the user's input data.

[0942] Step 5:

[0943] The server extracts user-input data and analyzes it for context, tone, purpose, and target audience characteristics using natural language processing techniques.

[0944] Step 6:

[0945] The server retrieves user profile information and previously generated content from a database, which is used for analysis.

[0946] Step 7:

[0947] The server generates customized content based on user profile information and input data by invoking a generative AI model that understands context, tone, and intent to generate appropriate content.

[0948] Step 8:

[0949] The server formats the generated custom content and prepares it as response data, which is packaged in JSON.

[0950] Step 9:

[0951] The server sends the generated content in JSON format to the device as an HTTP response, and returns a POST request response to the device's endpoint.

[0952] Step 10:

[0953] The terminal receives the HTTP response from the server and displays the generated customized content on the user interface. The user can check the generated content and modify it as necessary.

[0954] Step 11:

[0955] Users can review the generated content, make any necessary edits, and then save or share the content as needed.

[0956] Step 12:

[0957] The device then sends the edited and saved content back to the server and stores it in a database, which can be used for future generation.

[0958] Example 1

[0959] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0960] Conventional content generation systems simply process user input, making it difficult to generate customized content that fully understands the context, tone, and intent. As a result, users are forced to extensively edit the generated content, which is time-consuming and laborious. Furthermore, the quality of the generated content is inconsistent, often failing to meet user expectations.

[0961] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0962] In this invention, the server includes means for accepting input from a user, means for analyzing the user's input, means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content, means for a terminal to convert the user's input into a data format and send it to the server, means for the server to return the customized content generated based on the analyzed information to the terminal, means for displaying the customized content generated by the generative AI model to the user, and means for saving the customized content and using it for future generation. This enables the generation of high-quality customized content with a deep understanding of the user's intent and context.

[0963] "User" means an individual or corporation that uses this system to create, review, or modify content.

[0964] A "terminal" is a device for a user to input information, and includes hardware and software that performs processing to transmit information to a server.

[0965] "Server" means a remote data processing device that analyzes requests received from users, hosts generative AI models, and generates customized content.

[0966] The "means for accepting input" is an interface for receiving data provided by a user for content generation at a terminal.

[0967] "Means for analyzing input" refers to algorithms and programs that analyze data received from a user based on context, tone, and intent.

[0968] A "generative AI model" is an artificial intelligence model that generates high-quality customized content based on analyzed data.

[0969] The "means for converting to a data format" is a processing method for converting user input into an appropriate format (e.g., JSON) to make it easier for the server to parse it.

[0970] The "means for transmitting to the server" is a communication means for transmitting the converted data from the terminal to the server.

[0971] "Means for returning" refers to the process of returning the generated customized content from the server to the terminal.

[0972] "Means for storage" refers to a method for storing the generated content in a database or the like and using it in future generation processes.

[0973] "User Interface" means the visual or operating environment that allows a user to review and modify input data.

[0974] The present invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using a generative AI model. Specific embodiments of the system are described below.

[0975] User operations

[0976] A user types a content generation request into the device, such as "Write a script for a new product launch presentation," along with information about the target audience and details about the desired tone and style.

[0977] Specific examples

[0978] Example prompt: "Write a script for a presentation to introduce a new product. The target audience is executives, and the tone should be professional and concise."

[0979] Device Role

[0980] The device receives user input, converts it into a data format, and sends it to the server. Specifically, it converts the user's input into JSON format and forwards it to the server as an HTTP request. The request includes the user ID, details of the purpose, and information about the target recipient.

[0981] Server Processing

[0982] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[0983] Input Analysis

[0984] The server analyzes the content of the user's request and extracts the context, tone, purpose, and characteristics of the intended audience.

[0985] Database Reference

[0986] The server retrieves user profile information and past content from the database and uses it for analysis. Based on this information, the server invokes a generative AI model (e.g., OpenAI GPT-4) to generate customized content based on the user's requests.

[0987] Returning generated content

[0988] The server formats the generated content into an appropriate format (e.g., text or HTML) and sends it back to the device as an HTTP response, where it is stored in a database for future personalization.

[0989] Display by terminal

[0990] The terminal receives the customized content returned from the server and displays it to the user through a user interface. The user can check the displayed content and make corrections or additions as necessary.

[0991] Specific examples

[0992] Examples of generated content:

[0993] "This new product is 20% more efficient than our previous product and will also help reduce costs. I believe that management in particular will recognize this as another new value we can provide to our customers."

[0994] In this way, users, devices, and servers work together to realize an efficient content generation system using generative AI models, allowing users to quickly and easily obtain high-quality customized content for business or personal use.

[0995] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0996] Explain the program's processing flow by dividing it into processing steps

[0997] Step 1: User Input

[0998] A user types a content generation request into the device. For example, the user might type, "Please create a script for a new product introduction presentation," along with other details such as the target audience and desired tone.

[0999] input

[1000] Request text (e.g., "Please write a script for a new product introduction presentation.")

[1001] Target Audience Information

[1002] Desired tone and style

[1003] output

[1004] User input data (request details, target audience information, desired tone and style)

[1005] Step 2: Converting input data on the terminal

[1006] The terminal receives input from the user and converts it into JSON format, including the user ID, purpose details, and information about the intended recipient.

[1007] input

[1008] User input data (request details, target audience information, desired tone and style)

[1009] Data Processing

[1010] Convert the data into the appropriate format (JSON)

[1011] output

[1012] JSON format data (e.g., "{"request": "Please create a script for a new product introduction presentation", "userID": "12345", "audience": "Management", "tone": "Professional"}")

[1013] Step 3: Send data from the device to the server

[1014] The device then sends the converted JSON data to the server as an HTTP request, along with the user ID and purpose details.

[1015] input

[1016] JSON format data (request details, target audience information, desired tone and style)

[1017] Data Calculation

[1018] Generating an HTTP Request

[1019] output

[1020] HTTP request (e.g. POST request)

[1021] Step 4: Parsing input on the server

[1022] The server receives the incoming HTTP request and parses its content, analyzing the request text and extracting the context, tone, purpose, and characteristics of the target audience.

[1023] input

[1024] HTTP request (JSON data)

[1025] Data Processing

[1026] Parsing the request statement

[1027] output

[1028] Analysis results (context, tone, purpose, target audience characteristics)

[1029] Step 5: Look up the database on the server

[1030] The server retrieves user profile information and past content from the database and uses it for analysis. It references the database to retrieve past generated content and user preference information.

[1031] input

[1032] Analysis results (context, tone, purpose, target audience characteristics)

[1033] Data Search

[1034] Extracting relevant information from a database

[1035] output

[1036] Information obtained (user profile, past content)

[1037] Step 6: Server-side content generation

[1038] The server calls a generative AI model based on the acquired information and generates high-quality customized content.

[1039] input

[1040] Information obtained (user profile, past content)

[1041] Analysis results (context, tone, purpose, target audience characteristics)

[1042] Data Calculation

[1043] Running generative AI models

[1044] output

[1045] Generated customized content

[1046] Step 7: Return from server to device

[1047] The generated customized content is formatted and sent back to the device as an HTTP response, and is stored in a database for future personalization.

[1048] input

[1049] Generated customized content

[1050] Data Processing

[1051] Applying Formatting

[1052] output

[1053] HTTP response (generated content)

[1054] Step 8: View in terminal

[1055] The terminal displays the customized content returned from the server through a user interface.

[1056] input

[1057] HTTP response (generated content)

[1058] Data Processing

[1059] Conversion to display format

[1060] output

[1061] What is displayed on the user interface

[1062] Step 9: User review and correction

[1063] The user checks the displayed customized content and makes corrections or additions as necessary.

[1064] input

[1065] What is displayed on the user interface

[1066] Data Processing

[1067] Corrections and additions

[1068] output

[1069] Modified content

[1070] Through this series of steps, a system is realized in which users, terminals, and servers cooperate to efficiently generate high-quality customized content.

[1071] (Application example 1)

[1072] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1073] Modern virtual stores require systems that allow users to quickly and effectively generate product introductions and descriptions. Without such systems, users must manually create content, which is time-consuming and labor-intensive, and the quality is inconsistent. Furthermore, without a means to instantly provide customized information, it becomes difficult to deliver appropriate information to potential customers. Therefore, the objective of this invention is to provide a system that uses generative AI models to generate high-quality content in real time and that can be immediately used within a virtual store.

[1074] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1075] In this invention, the server includes means for accepting input from a user, means for analyzing the input, means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content, means for allowing a user to input a request to generate product introductions and descriptions in a virtual store and provide customized information in real time, means for returning the generated customized content to the user, and means for storing the customized content and using it for future generation, thereby enabling users to generate and provide high-quality customized product introductions and descriptions in real time.

[1076] "User input" refers to user requests or instructions accepted by the system.

[1077] "Means for analyzing input" refers to a device or program that interprets the input received from the user and processes it to understand its context and intent.

[1078] "Generative AI model" refers to an artificial intelligence algorithm used to automatically generate new content based on specific rules and data.

[1079] A "virtual store" refers to a virtual shopping environment that provides products and services to users via the Internet.

[1080] A "request to generate product introductions or descriptions" refers to an order or request made by a user to a generative AI model to provide information about a product in a virtual store.

[1081] "Real-time customized information" refers to personalized content that is generated instantly and delivered in response to a user's request.

[1082] "Customized Content" refers to information or documentation that is optimized and generated to meet the needs and requirements of a particular user.

[1083] A "user profile" refers to a data set that compiles information about a specific user, such as their attributes, behavioral history, and preferences.

[1084] A "database" refers to a system or software that stores large amounts of information in a systematic manner and retrieves and uses it when needed.

[1085] A "user interface" is a means by which a user interacts with a system, and refers to input forms, display screens, etc.

[1086] The following describes an embodiment of the present invention: The present invention is a system in which a user, a terminal, and a server work together to generate customized content using an advanced generative AI model.

[1087] User operations

[1088] Users provide input to generate product descriptions and text within the virtual store, for example, by entering a request such as "Please write a description for our new product" through an input form, along with information about the target audience and details of the desired tone and style.

[1089] Device Role

[1090] The device receives input from the user and processes it to send it to the server. Specifically, it converts the user's input into the appropriate data format and forwards it to the server as an HTTP request. The request includes the user's ID, details of the purpose, and information about the intended recipient.

[1091] Server Processing

[1092] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[1093] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics.

[1094] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[1095] The server then uses the acquired information to invoke a generative AI model to generate customized content. The generative AI model is designed to understand context, tone, and intent to generate high-quality content based on the user's request. The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[1096] Display by terminal

[1097] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[1098] Specific examples

[1099] Example 1: Creating product descriptions for a virtual store

[1100] 1. The user inputs a request into the terminal: "Please write a description of a new product."

[1101] 2. The device sends this request to the server, asking it to generate an appropriate introduction based on the context, tone, and characteristics of the target recipient.

[1102] 3. The server uses a generative AI model (such as GPT-4) to generate a testimonial and sends it back to the device.

[1103] 4. The user reviews the generated introduction and modifies it as necessary.

[1104] Example of input prompt sentence:

[1105] "User ID: user123, Product: Smartwatch, Features: Heart rate monitor and GPS, Target: Non-technical beginners, Tone: Casual"

[1106] This invention enables users to generate and provide high-quality customized product descriptions in real time within a virtual store.

[1107] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1108] Step 1:

[1109] User Input

[1110] The user provides a request to generate product descriptions and prompts within the virtual store, such as "Please create a description for our new product," along with information about the target audience and details about the desired tone and style, which then generates a specific prompt.

[1111] Inputs: "Write a description for our new product", target audience information, tone and style

[1112] Output: Specific prompt (e.g., User ID: user123, Product: Smartwatch, Features: Heart rate tracking and GPS, Target audience: Non-technical beginners, Tone: Casual)

[1113] Step 2:

[1114] Terminal data conversion

[1115] The device converts the request received from the user into an appropriate data format (e.g., JSON format) and sends it to the server as an HTTP request, including the user ID, details of the purpose, and information about the target recipient.

[1116] Input: prompt statement

[1117] Output: HTTP request in JSON format (e.g., {"user_id":"user123","prompt":"Please write a description for our new product","audience_info":"Beginner","tone_style":"Casual"})

[1118] Step 3:

[1119] Server input parsing

[1120] The server analyzes the HTTP request received from the device and extracts the context, tone, and characteristics of the target recipient. This analysis helps to understand the specific content of the request.

[1121] Input: HTTP request in JSON format

[1122] Output: Analyzed input data (context, tone, audience characteristics)

[1123] Step 4:

[1124] Database Reference

[1125] The server retrieves user profile information and past content from the database as needed and uses it for analysis, which enables more accurate content generation.

[1126] Input: Parsed input data

[1127] Output: Additional user profile data and historical content

[1128] Step 5:

[1129] Content generation using generative AI models

[1130] The server then calls a generative AI model based on the acquired information and analysis data to generate customized content. The generative AI model (e.g., GPT-4) generates high-quality introductions and descriptions based on the input prompt.

[1131] Input: User profile data, parsed input data

[1132] Output: Generated content (customized introduction, description)

[1133] Step 6:

[1134] Return from the server to the device

[1135] The server formats the generated customized content and sends it back to the device as an HTTP response, storing the generated content in a database for future personalization.

[1136] Input: Generated content

[1137] Output: Content formatted as an HTTP response

[1138] Step 7:

[1139] Display by terminal

[1140] The terminal receives the customized content sent back from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. Finally, the content is provided in a form that can be used in the virtual store.

[1141] Input: Content formatted as an HTTP response

[1142] Output: The customized content displayed to the user

[1143] These steps allow users to create and instantly deliver high-quality, customized product descriptions in real time within a virtual store.

[1144] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1145] This invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using an advanced generative AI model and an emotion engine. Specific embodiments of this system are described below.

[1146] User operations

[1147] The user provides input to the device to generate content, such as "Write a script for a new product introduction presentation." The user can also provide information about the target audience and details of the desired tone and style. The device can also accept voice or text input, which uses an emotion engine to analyze the user's emotional state.

[1148] Device Role

[1149] The device receives input from the user and converts the input data into JSON format, which includes the user ID, input content, tone preference, information about the target recipient, and emotional data analyzed by the emotion engine. This data is then sent to the server as an HTTP request.

[1150] Server Processing

[1151] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[1152] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics, including emotional data provided by the emotion engine.

[1153] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[1154] The server then uses the acquired information and emotional data to invoke a generative AI model to generate customized content. This generative AI model is designed to understand context, tone, intent, and emotional state to generate high-quality content based on user requests.

[1155] The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[1156] Display by terminal

[1157] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[1158] Specific examples

[1159] Example 1: Creating a business presentation

[1160] 1. The user inputs the "new product introduction presentation script" into the terminal and also inputs their current emotional state through voice input.

[1161] 2. The device sends this request and emotional data to the server, asking it to generate an appropriate script based on the context, tone, characteristics and emotional state of the target recipient.

[1162] 3. The server uses the generative AI model and emotion engine to generate a script and send it back to the device.

[1163] 4. The user checks the generated script and modifies it as necessary before using it.

[1164] Example 2: Creating an email for a marketing campaign

[1165] 1. The user enters the "new campaign product introduction email" into the terminal and inputs their current emotional state through the emotion engine.

[1166] 2. The device sends this request and emotional data to the server, asking it to generate email content based on the user's intentions and emotional state.

[1167] 3. The server uses the generative AI model and emotion engine to generate content and send it back to the device.

[1168] 4. The user reviews the generated email, edits it as needed, and uses it in their marketing efforts.

[1169] In this way, users, devices, and servers work together to realize an efficient content generation system using generative AI and an emotion engine.

[1170] The processing flow will be explained below.

[1171] Step 1:

[1172] The user operates the terminal and inputs a request for content generation, for example, "Please create a script for a presentation introducing a new product."

[1173] Step 2:

[1174] The user inputs emotion data by speaking aloud into the terminal, which allows the emotion engine to recognize the user's current emotional state.

[1175] Step 3:

[1176] The device receives user input as text data, and the emotion engine analyzes the voice data to detect the user's emotional state. The data, including the analysis results, is converted into JSON format. The converted data includes the user ID, input content, desired tone, information about the target recipient, and emotional data.

[1177] Step 4:

[1178] The device sends JSON format data to the server as an HTTP request, sending a POST request to the server's endpoint.

[1179] Step 5:

[1180] The server receives an HTTP request from the terminal, analyzes the contents of the request, and extracts the user's input data.

[1181] Step 6:

[1182] The server extracts user-input data and emotional data to analyze the context, tone, purpose, and target audience characteristics using natural language processing techniques and emotion recognition algorithms.

[1183] Step 7:

[1184] The server retrieves user profile information and previously generated content from the database and uses it for analysis, thereby improving the accuracy of the generative AI model.

[1185] Step 8:

[1186] Based on the acquired user profile information, input data, and emotional data, the server invokes a generative AI model to generate customized content. The generative AI model understands context, tone, intent, and emotional state to generate high-quality content based on user requests.

[1187] Step 9:

[1188] The server formats the generated custom content and prepares it as response data. The generated content is packaged in JSON format.

[1189] Step 10:

[1190] The server sends the generated content in JSON format to the device as an HTTP response, and returns a POST request response to the device's endpoint.

[1191] Step 11:

[1192] The terminal receives the HTTP response from the server and displays the generated customized content on the user interface. The user can check the generated content and modify it as necessary.

[1193] Step 12:

[1194] Users can review the generated content, make any necessary edits, and then save or share the content as needed.

[1195] Step 13:

[1196] The device then sends the edited and saved content back to the server and stores it in a database, which can be used for future generation.

[1197] Example 2

[1198] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1199] Conventional content generation systems have struggled to quickly and accurately provide customized content that fully reflects a user's context, tone, and intent. Furthermore, they lack the ability to generate content that takes into account emotional states, making it impossible to fully meet user needs. Furthermore, they lack a way to improve the accuracy of their generative AI models by utilizing past content and user profiles.

[1200] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for converting user input into JSON format; means for transferring the JSON-formatted data to the server; means for the server to analyze the user input and extract context, tone, intent, and emotional state; means for retrieving user profile information and past content from a database and integrating the analysis results; means for hosting a generative AI model that generates customized content based on the integrated information; means for formatting the generated content and returning it to the terminal; and means for saving the customized content and using it for future generation. This enables the rapid generation and provision of high-quality customized content that reflects the user's context, tone, intent, and emotional state. Furthermore, the accuracy of the generative AI model can be improved by utilizing past content and user profiles.

[1201] "User input" is information or instructions provided by a user to the system.

[1202] "JSON format" is an abbreviation for JavaScript Object Notation, and is a method of structuring and representing data in text format.

[1203] A "server" is a computer system that processes and manages data, receives requests from terminals, and returns appropriate responses.

[1204] "Context" refers to background or situational information related to the user's input.

[1205] "Tone" refers to the atmosphere or style of the content being generated, and can be professional, casual, or the like.

[1206] "Intention" refers to the purpose or goal that a user is trying to achieve with their input.

[1207] "Emotional state" refers to the emotion the user is feeling at the time of input, and includes joy, anger, sadness, etc.

[1208] A "database" is a system for managing a collection of data, enabling efficient data retrieval and storage.

[1209] "User profile information" refers to information about an individual user, such as the user's attributes and behavioral history.

[1210] "Past content" refers to previously generated content that is related to a user's past requests.

[1211] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate customized content based on input data.

[1212] "Hosting" means running a generative AI model on a server so that it can respond to requests from other systems and users.

[1213] "Formatting" means shaping the generated content into a particular structure or form.

[1214] A "terminal" is a device that is directly operated by a user, and includes a computer, a smartphone, a tablet, and the like.

[1215] This invention is a system in which users, terminals, and servers work together to efficiently generate customized content using advanced generative AI models and emotion engines.

[1216] Users input content generation requests through their devices. This input includes context, tone, information about the target audience, desired style, and the user's emotional state. For example, a user can input the prompt "Please write a script for a presentation introducing a new product," specifying "businessmen in their 30s and 40s" as the target audience and "professionals" as the tone. The user can also provide their current emotional state through voice or text input.

[1217] The device receives input from the user and converts the input data into JSON format, which includes the user ID, input content, desired tone, target recipient information, and emotion data analyzed by the emotion engine. The device then forwards this data to the server as an HTTP request.

[1218] The server analyzes the HTTP request received from the device. First, it analyzes the user's input to extract the context, tone, purpose, and characteristics of the target audience. It also includes emotional data provided by the emotion engine. Next, the server retrieves user profile information and past content from the database and integrates this information with the analysis results.

[1219] The server then invokes a generative AI model to generate customized content. This model is designed to understand context, tone, intent, and emotional state, allowing it to generate high-quality content. The generated content is then formatted and sent back to the device. At the same time, this generated content is stored in a database for future use.

[1220] The terminal receives the customized content returned from the server, and the user can review the content through the terminal's user interface and make any necessary modifications or additions.

[1221] As a concrete example, consider the case where a user inputs, "Please create a script for a presentation introducing a new product. The target audience is businessmen in their 30s and 40s, and the tone should be professional." At this time, let's assume that the user's emotional state is "neutral." The device converts this input into JSON format and forwards it to the server. The server analyzes this data and uses a generative AI model and emotion engine to generate a customized script based on the specified conditions and sends it back to the device. The user can review the returned script and modify it as necessary.

[1222] In this way, users, devices, and servers work together to quickly and efficiently generate customized content that meets the user's needs using advanced generative AI and emotion engines.

[1223] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1224] Program processing flow

[1225] Step 1: User Input

[1226] A user uses a device to input a content generation request, for example, "Please write a script for a presentation introducing a new product." The user also provides details about the target audience (e.g., business people in their 30s and 40s), the desired tone (e.g., professional), and style. The user also provides their current emotional state through voice or text input.

[1227] Input: Content generation request, target audience information, desired tone and style, emotional state

[1228] Output: User input data

[1229] Step 2: Convert data via terminal

[1230] The device receives input data from the user and converts it into JSON format, which includes the user ID, input content, tone preference, target recipient information, and emotion data analyzed by the emotion engine.

[1231] Input: User-entered data

[1232] Output: JSON format data

[1233] Specific behavior:

[1234] Parse the input data and format it into JSON format.

[1235] For example, data in the format {"userID": "12345", "request": "New product introduction presentation script", "tone": "Professional", "audience": "Businessmen in their 30s and 40s", "emotion": "neutral"} is generated.

[1236] Step 3: Transfer data from your device to the server

[1237] The terminal transfers the data converted into JSON format to the server as an HTTP request.

[1238] Input: JSON format data

[1239] Output: HTTP request

[1240] Specific behavior:

[1241] Send JSON format data to the server as an HTTP request.

[1242] Step 4: Parsing the input by the server

[1243] The server receives and analyzes the HTTP request, analyzing the user's input to extract context, tone, purpose, and target audience characteristics, as well as emotional data provided by the emotion engine.

[1244] Input: HTTP request

[1245] Output: Analyzed data (context, tone, purpose, target audience characteristics, emotional state)

[1246] Specific behavior:

[1247] It parses HTTP request data to extract context, tone, purpose, target audience characteristics, and emotional state.

[1248] Step 5: Retrieving information from the database

[1249] The server retrieves user profile information and past content from the database, and combines this information with the analysis results.

[1250] Input: User profile information and past content requests

[1251] Output: Information retrieved from the database

[1252] Specific behavior:

[1253] User profile information and historical content is retrieved from the database and integrated with the analysis results.

[1254] Step 6: Content generation with generative AI models

[1255] The server then uses the integrated information to invoke a generative AI model to generate customized content, which understands context, tone, intent, and emotional state to generate high-quality content based on user requests.

[1256] Input: Integration information

[1257] Output: Generated content

[1258] Specific behavior:

[1259] It invokes a generative AI model, providing it with context, tone, intent, and emotional state as input.

[1260] A generative AI model generates specific scripts and email text.

[1261] Step 7: Server formats and returns content

[1262] The generated content is formatted and sent back to the device as an HTTP response, and is also stored in a database for future use.

[1263] Input: Generated content

[1264] Output: HTTP response, database update

[1265] Specific behavior:

[1266] The generated content is reformatted into JSON format and sent to the terminal as an HTTP response.

[1267] Store the generated content in a database.

[1268] Step 8: View and edit content using your device

[1269] The terminal receives the customized content returned from the server and displays it to the user through a user interface. The user can then check the displayed content and make corrections or additions as necessary.

[1270] Input: HTTP response

[1271] Output: The displayed customized content

[1272] Specific behavior:

[1273] The generated content is displayed on the user interface of the terminal.

[1274] The user reviews the displayed content and manually corrects or adds to it.

[1275] (Application example 2)

[1276] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1277] In modern content delivery services, it is important to quickly generate and provide personalized content that meets the diverse needs and emotional states of users. However, conventional systems often find it difficult to effectively analyze and reflect the user's current emotional state, and the generated content is often not sufficiently personalized. This makes it difficult to improve user satisfaction and prevents effective content delivery.

[1278] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting input from a user, means for analyzing the user input and generating emotional data, including an emotion analysis engine that analyzes the user's emotional state, and means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content. This enables highly personalized content generation based on the user's current emotional state.

[1279] A "user" is a person or organization that uses an information system or service.

[1280] "Input" is data or information that a user provides to an information system.

[1281] "Analysis" is the process of understanding input data or information and extracting specific meaning or intent.

[1282] A "generative AI model" is an artificial intelligence algorithm that automatically generates content based on user input data.

[1283] The "emotion analysis engine" is a component that analyzes the user's emotional state and generates emotional data.

[1284] "Emotion data" is data obtained by analyzing the user's emotional state using an emotion analysis engine.

[1285] "Customized content" is content that is generated based on the user's input data and emotional data and is optimized for an individual user.

[1286] "Context" is the meaning or relevance of a user's input data in a particular situation or condition.

[1287] "Tone" is a term that refers to the overall mood or style of content.

[1288] "Intent" is the purpose or goal inferred from the user's input.

[1289] A "server" is a computer system that receives and processes requests from users.

[1290] A "database" is a system for efficiently storing, searching, and managing structured information.

[1291] A "user profile" is a data set that compiles a user's attribute information, behavioral history, etc.

[1292] A "user interface" is the means or screen through which a user interacts with a system.

[1293] "Personalized" means optimized according to the attributes and status of each individual user.

[1294] This invention relates to a system for generating personalized content using user input data and emotion data. This system operates in cooperation with a user, a terminal, and a server, and utilizes a generative AI model and an emotion analysis engine. Specific embodiments are described below.

[1295] 1. User operations

[1296] A user requests content creation using a content distribution service application. For example, the user might input, "Please create a relaxing travel video." At this time, the user also provides emotional data using voice. The emotional data is information that indicates the user's current emotional state.

[1297] 2. Role of the terminal

[1298] The device receives text and voice input from the user and acquires this data. It converts the acquired data into JSON format and generates request data that includes the user ID, input content, tone preference, information about the recipient, and emotional data analyzed by the emotion analysis engine. It then sends this request data to the server as an HTTP request.

[1299] 3. Server Processing

[1300] The server analyzes the request data received from the device. First, it analyzes the input content to extract the context, tone, intent, and characteristics of the target recipient. It also analyzes the emotional data provided by the sentiment analysis engine. Next, the server retrieves user profile information and information about past content from the database and uses this information for analysis. Based on this information, it invokes a generative AI model to generate customized content according to the request. This generated content is formatted and sent back to the device.

[1301] 4. Display by terminal

[1302] The terminal receives the customized content from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality personalized content.

[1303] Hardware and software used

[1304] Hardware: Smartphone, Head-Mounted Display (HMD)

[1305] Software: Generative AI model, sentiment analysis engine, server (REST API for sending HTTP requests)

[1306] Specific examples

[1307] For example, you can enter the following prompt sentence into your application:

[1308] "Make a travel video that makes you feel relaxed. Your current emotional state is 'happy.'"

[1309] After sending the prompt to the server, the server analyzes the prompt and emotional data to generate a relaxing travel video for the user. For example, a video combining beautiful beach and mountain scenery in Hawaii is generated and displayed to the user. This application allows users to easily enjoy the ideal video content that matches their emotional state.

[1310] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1311] Step 1:

[1312] A user uses a content delivery service application to request the generation of a specific piece of content. For example, the user inputs, "Please create a travel video that will make me feel relaxed." The user also provides voice input and their current emotional state (e.g., "happy"). The text request and emotional data are passed to the device as input data.

[1313] Step 2:

[1314] The device receives text input and emotion data from the user and converts this data into JSON format. At this time, it generates request data that includes the user ID, input content, tone preference, information about the target recipient, and emotion data analyzed by the emotion analysis engine. The generated JSON data is prepared as input for the HTTP request.

[1315] Step 3:

[1316] The device sends the generated HTTP request to the server. The request includes the user ID, input content, tone preference, information about the target recipient, and emotional data. The device sends the data to the server using the HTTP POST method.

[1317] Step 4:

[1318] The server analyzes the HTTP request data received from the device. First, the server analyzes the user's input and extracts the context, tone, purpose, and characteristics of the target recipient. At the same time, it also analyzes the emotional data analyzed by the emotion analysis engine. The analysis results are generated as intermediate data within the server.

[1319] Step 5:

[1320] The server retrieves user profile information and past content data from the database, which is then combined with the analyzed input data and sentiment data to further refine the intermediate data, enabling fine-grained personalization.

[1321] Step 6:

[1322] The server uses the intermediate data to call a generative AI model to generate customized content. The generative AI model generates optimal content based on user input, emotional data, past content history, etc. The generated content is saved as finished data on the server.

[1323] Step 7:

[1324] The server returns the completed data to the device, which prepares the received content for display and provides an interface for the user to review and modify. After receiving the completed data, the device displays the personalized content to the user.

[1325] Step 8:

[1326] The user checks the received content and makes corrections or additions as necessary. Finally, personalized content that satisfies the user is completed. User feedback is again entered into the terminal, completing the entire process.

[1327] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1328] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1329] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1330] [Fourth embodiment]

[1331] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1332] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1333] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1334] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1335] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1336] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1337] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1338] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1339] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1340] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1341] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1342] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1343] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1344] The present invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using an advanced generative AI model. Specific embodiments of the system are described below.

[1345] User operations

[1346] Users provide input to the device to generate content, such as a request like "Please write a script for a new product presentation," through an input form, along with information about the target audience and details about the desired tone and style.

[1347] Device Role

[1348] The device receives user input and processes it to send it to the server. Specifically, it converts the user's input into an appropriate data format, such as JSON, and forwards it to the server as an HTTP request. The request includes the user ID, details of the purpose, and information about the intended recipient.

[1349] Server Processing

[1350] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[1351] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics.

[1352] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[1353] The server then uses the acquired information to generate customized content by invoking a generative AI model that is designed to understand context, tone, and intent to generate high-quality content based on user requests.

[1354] The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[1355] Display by terminal

[1356] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[1357] Specific examples

[1358] Example 1: Creating a business presentation

[1359] 1. The user enters the "new product introduction presentation script" into the terminal.

[1360] 2. The terminal sends this request to the server, asking it to generate an appropriate script based on the context, tone, and characteristics of the target recipient.

[1361] 3. The server uses the generative AI model to generate a script and sends it back to the device.

[1362] 4. The user checks the generated script and modifies it as necessary before using it.

[1363] Example 2: Creating an email for a marketing campaign

[1364] 1. The user enters the "new campaign product introduction email" into the terminal.

[1365] 2. The device sends this request to the server, asking it to generate email content based on the user's intentions.

[1366] 3. The server uses the generative AI model to generate content and sends it back to the device.

[1367] 4. The user reviews the generated email, edits it as needed, and uses it in their marketing efforts.

[1368] In this way, users, terminals, and servers work together to realize an efficient content generation system using generative AI.

[1369] The processing flow will be explained below.

[1370] Step 1:

[1371] The user operates the terminal and inputs a request for specific content generation, for example, "Please create a script for a presentation introducing a new product."

[1372] Step 2:

[1373] The device receives user input and converts it into JSON format, which includes information about the user ID, input, tone preference, and target recipient.

[1374] Step 3:

[1375] The device sends JSON format data to the server as an HTTP request, sending a POST request to the server's endpoint.

[1376] Step 4:

[1377] The server receives an HTTP request from the terminal, analyzes the contents of the request, and extracts the user's input data.

[1378] Step 5:

[1379] The server extracts user-input data and analyzes it for context, tone, purpose, and target audience characteristics using natural language processing techniques.

[1380] Step 6:

[1381] The server retrieves user profile information and previously generated content from a database, which is used for analysis.

[1382] Step 7:

[1383] The server generates customized content based on user profile information and input data by invoking a generative AI model that understands context, tone, and intent to generate appropriate content.

[1384] Step 8:

[1385] The server formats the generated custom content and prepares it as response data, which is packaged in JSON.

[1386] Step 9:

[1387] The server sends the generated content in JSON format to the device as an HTTP response, and returns a POST request response to the device's endpoint.

[1388] Step 10:

[1389] The terminal receives the HTTP response from the server and displays the generated customized content on the user interface. The user can check the generated content and modify it as necessary.

[1390] Step 11:

[1391] Users can review the generated content, make any necessary edits, and then save or share the content as needed.

[1392] Step 12:

[1393] The device then sends the edited and saved content back to the server and stores it in a database, which can be used for future generation.

[1394] Example 1

[1395] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1396] Conventional content generation systems simply process user input, making it difficult to generate customized content that fully understands the context, tone, and intent. As a result, users are forced to extensively edit the generated content, which is time-consuming and laborious. Furthermore, the quality of the generated content is inconsistent, often failing to meet user expectations.

[1397] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1398] In this invention, the server includes means for accepting input from a user, means for analyzing the user's input, means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content, means for a terminal to convert the user's input into a data format and send it to the server, means for the server to return the customized content generated based on the analyzed information to the terminal, means for displaying the customized content generated by the generative AI model to the user, and means for saving the customized content and using it for future generation. This enables the generation of high-quality customized content with a deep understanding of the user's intent and context.

[1399] "User" means an individual or corporation that uses this system to create, review, or modify content.

[1400] A "terminal" is a device for a user to input information, and includes hardware and software that performs processing to transmit information to a server.

[1401] "Server" means a remote data processing device that analyzes requests received from users, hosts generative AI models, and generates customized content.

[1402] The "means for accepting input" is an interface for receiving data provided by a user for content generation at a terminal.

[1403] "Means for analyzing input" refers to algorithms and programs that analyze data received from a user based on context, tone, and intent.

[1404] A "generative AI model" is an artificial intelligence model that generates high-quality customized content based on analyzed data.

[1405] The "means for converting to a data format" is a processing method for converting user input into an appropriate format (e.g., JSON) to make it easier for the server to parse it.

[1406] The "means for transmitting to the server" is a communication means for transmitting the converted data from the terminal to the server.

[1407] "Means for returning" refers to the process of returning the generated customized content from the server to the terminal.

[1408] "Means for storage" refers to a method for storing the generated content in a database or the like and using it in future generation processes.

[1409] "User Interface" means the visual or operating environment that allows a user to review and modify input data.

[1410] The present invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using a generative AI model. Specific embodiments of the system are described below.

[1411] User operations

[1412] A user types a content generation request into the device, such as "Write a script for a new product launch presentation," along with information about the target audience and details about the desired tone and style.

[1413] Specific examples

[1414] Example prompt: "Write a script for a presentation to introduce a new product. The target audience is executives, and the tone should be professional and concise."

[1415] Device Role

[1416] The device receives user input, converts it into a data format, and sends it to the server. Specifically, it converts the user's input into JSON format and forwards it to the server as an HTTP request. The request includes the user ID, details of the purpose, and information about the target recipient.

[1417] Server Processing

[1418] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[1419] Input Analysis

[1420] The server analyzes the content of the user's request and extracts the context, tone, purpose, and characteristics of the intended audience.

[1421] Database Reference

[1422] The server retrieves user profile information and past content from the database and uses it for analysis. Based on this information, the server invokes a generative AI model (e.g., OpenAI GPT-4) to generate customized content based on the user's requests.

[1423] Returning generated content

[1424] The server formats the generated content into an appropriate format (e.g., text or HTML) and sends it back to the device as an HTTP response, where it is stored in a database for future personalization.

[1425] Display by terminal

[1426] The terminal receives the customized content returned from the server and displays it to the user through a user interface. The user can check the displayed content and make corrections or additions as necessary.

[1427] Specific examples

[1428] Examples of generated content:

[1429] "This new product is 20% more efficient than our previous product and will also help reduce costs. I believe that management in particular will recognize this as another new value we can provide to our customers."

[1430] In this way, users, devices, and servers work together to realize an efficient content generation system using generative AI models, allowing users to quickly and easily obtain high-quality customized content for business or personal use.

[1431] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1432] Explain the program's processing flow by dividing it into processing steps

[1433] Step 1: User Input

[1434] A user types a content generation request into the device. For example, the user might type, "Please create a script for a new product introduction presentation," along with other details such as the target audience and desired tone.

[1435] input

[1436] Request text (e.g., "Please write a script for a new product introduction presentation.")

[1437] Target Audience Information

[1438] Desired tone and style

[1439] output

[1440] User input data (request details, target audience information, desired tone and style)

[1441] Step 2: Converting input data on the terminal

[1442] The terminal receives input from the user and converts it into JSON format, including the user ID, purpose details, and information about the intended recipient.

[1443] input

[1444] User input data (request details, target audience information, desired tone and style)

[1445] Data Processing

[1446] Convert the data into the appropriate format (JSON)

[1447] output

[1448] JSON format data (e.g., "{"request": "Please create a script for a new product introduction presentation", "userID": "12345", "audience": "Management", "tone": "Professional"}")

[1449] Step 3: Send data from the device to the server

[1450] The device then sends the converted JSON data to the server as an HTTP request, along with the user ID and purpose details.

[1451] input

[1452] JSON format data (request details, target audience information, desired tone and style)

[1453] Data Calculation

[1454] Generating an HTTP Request

[1455] output

[1456] HTTP request (e.g. POST request)

[1457] Step 4: Parsing input on the server

[1458] The server receives the incoming HTTP request and parses its content, analyzing the request text and extracting the context, tone, purpose, and characteristics of the target audience.

[1459] input

[1460] HTTP request (JSON data)

[1461] Data Processing

[1462] Parsing the request statement

[1463] output

[1464] Analysis results (context, tone, purpose, target audience characteristics)

[1465] Step 5: Look up the database on the server

[1466] The server retrieves user profile information and past content from the database and uses it for analysis. It references the database to retrieve past generated content and user preference information.

[1467] input

[1468] Analysis results (context, tone, purpose, target audience characteristics)

[1469] Data Search

[1470] Extracting relevant information from a database

[1471] output

[1472] Information obtained (user profile, past content)

[1473] Step 6: Server-side content generation

[1474] The server calls a generative AI model based on the acquired information and generates high-quality customized content.

[1475] input

[1476] Information obtained (user profile, past content)

[1477] Analysis results (context, tone, purpose, target audience characteristics)

[1478] Data Calculation

[1479] Running generative AI models

[1480] output

[1481] Generated customized content

[1482] Step 7: Return from server to device

[1483] The generated customized content is formatted and sent back to the device as an HTTP response, and is stored in a database for future personalization.

[1484] input

[1485] Generated customized content

[1486] Data Processing

[1487] Applying Formatting

[1488] output

[1489] HTTP response (generated content)

[1490] Step 8: View in terminal

[1491] The terminal displays the customized content returned from the server through a user interface.

[1492] input

[1493] HTTP response (generated content)

[1494] Data Processing

[1495] Conversion to display format

[1496] output

[1497] What is displayed on the user interface

[1498] Step 9: User review and correction

[1499] The user checks the displayed customized content and makes corrections or additions as necessary.

[1500] input

[1501] What is displayed on the user interface

[1502] Data Processing

[1503] Corrections and additions

[1504] output

[1505] Modified content

[1506] Through this series of steps, a system is realized in which users, terminals, and servers cooperate to efficiently generate high-quality customized content.

[1507] (Application example 1)

[1508] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1509] Modern virtual stores require systems that allow users to quickly and effectively generate product introductions and descriptions. Without such systems, users must manually create content, which is time-consuming and labor-intensive, and the quality is inconsistent. Furthermore, without a means to instantly provide customized information, it becomes difficult to deliver appropriate information to potential customers. Therefore, the objective of this invention is to provide a system that uses generative AI models to generate high-quality content in real time and that can be immediately used within a virtual store.

[1510] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1511] In this invention, the server includes means for accepting input from a user, means for analyzing the input, means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content, means for allowing a user to input a request to generate product introductions and descriptions in a virtual store and provide customized information in real time, means for returning the generated customized content to the user, and means for storing the customized content and using it for future generation, thereby enabling users to generate and provide high-quality customized product introductions and descriptions in real time.

[1512] "User input" refers to user requests or instructions accepted by the system.

[1513] "Means for analyzing input" refers to a device or program that interprets the input received from the user and processes it to understand its context and intent.

[1514] "Generative AI model" refers to an artificial intelligence algorithm used to automatically generate new content based on specific rules and data.

[1515] A "virtual store" refers to a virtual shopping environment that provides products and services to users via the Internet.

[1516] A "request to generate product introductions or descriptions" refers to an order or request made by a user to a generative AI model to provide information about a product in a virtual store.

[1517] "Real-time customized information" refers to personalized content that is generated instantly and delivered in response to a user's request.

[1518] "Customized Content" refers to information or documentation that is optimized and generated to meet the needs and requirements of a particular user.

[1519] A "user profile" refers to a data set that compiles information about a specific user, such as their attributes, behavioral history, and preferences.

[1520] A "database" refers to a system or software that stores large amounts of information in a systematic manner and retrieves and uses it when needed.

[1521] A "user interface" is a means by which a user interacts with a system, and refers to input forms, display screens, etc.

[1522] The following describes an embodiment of the present invention: The present invention is a system in which a user, a terminal, and a server work together to generate customized content using an advanced generative AI model.

[1523] User operations

[1524] Users provide input to generate product descriptions and text within the virtual store, for example, by entering a request such as "Please write a description for our new product" through an input form, along with information about the target audience and details of the desired tone and style.

[1525] Device Role

[1526] The device receives input from the user and processes it to send it to the server. Specifically, it converts the user's input into the appropriate data format and forwards it to the server as an HTTP request. The request includes the user's ID, details of the purpose, and information about the intended recipient.

[1527] Server Processing

[1528] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[1529] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics.

[1530] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[1531] The server then uses the acquired information to invoke a generative AI model to generate customized content. The generative AI model is designed to understand context, tone, and intent to generate high-quality content based on the user's request. The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[1532] Display by terminal

[1533] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[1534] Specific examples

[1535] Example 1: Creating product descriptions for a virtual store

[1536] 1. The user inputs a request into the terminal: "Please write a description of a new product."

[1537] 2. The device sends this request to the server, asking it to generate an appropriate introduction based on the context, tone, and characteristics of the target recipient.

[1538] 3. The server uses a generative AI model (such as GPT-4) to generate a testimonial and sends it back to the device.

[1539] 4. The user reviews the generated introduction and modifies it as necessary.

[1540] Example of input prompt sentence:

[1541] "User ID: user123, Product: Smartwatch, Features: Heart rate monitor and GPS, Target: Non-technical beginners, Tone: Casual"

[1542] This invention enables users to generate and provide high-quality customized product descriptions in real time within a virtual store.

[1543] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1544] Step 1:

[1545] User Input

[1546] The user provides a request to generate product descriptions and prompts within the virtual store, such as "Please create a description for our new product," along with information about the target audience and details about the desired tone and style, which then generates a specific prompt.

[1547] Inputs: "Write a description for our new product", target audience information, tone and style

[1548] Output: Specific prompt (e.g., User ID: user123, Product: Smartwatch, Features: Heart rate tracking and GPS, Target audience: Non-technical beginners, Tone: Casual)

[1549] Step 2:

[1550] Terminal data conversion

[1551] The device converts the request received from the user into an appropriate data format (e.g., JSON format) and sends it to the server as an HTTP request, including the user ID, details of the purpose, and information about the target recipient.

[1552] Input: prompt statement

[1553] Output: HTTP request in JSON format (e.g., {"user_id":"user123","prompt":"Please write a description for our new product","audience_info":"Beginner","tone_style":"Casual"})

[1554] Step 3:

[1555] Server input parsing

[1556] The server analyzes the HTTP request received from the device and extracts the context, tone, and characteristics of the target recipient. This analysis helps to understand the specific content of the request.

[1557] Input: HTTP request in JSON format

[1558] Output: Analyzed input data (context, tone, audience characteristics)

[1559] Step 4:

[1560] Database Reference

[1561] The server retrieves user profile information and past content from the database as needed and uses it for analysis, which enables more accurate content generation.

[1562] Input: Parsed input data

[1563] Output: Additional user profile data and historical content

[1564] Step 5:

[1565] Content generation using generative AI models

[1566] The server then calls a generative AI model based on the acquired information and analysis data to generate customized content. The generative AI model (e.g., GPT-4) generates high-quality introductions and descriptions based on the input prompt.

[1567] Input: User profile data, parsed input data

[1568] Output: Generated content (customized introduction, description)

[1569] Step 6:

[1570] Return from the server to the device

[1571] The server formats the generated customized content and sends it back to the device as an HTTP response, storing the generated content in a database for future personalization.

[1572] Input: Generated content

[1573] Output: Content formatted as an HTTP response

[1574] Step 7:

[1575] Display by terminal

[1576] The terminal receives the customized content sent back from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. Finally, the content is provided in a form that can be used in the virtual store.

[1577] Input: Content formatted as an HTTP response

[1578] Output: The customized content displayed to the user

[1579] These steps allow users to create and instantly deliver high-quality, customized product descriptions in real time within a virtual store.

[1580] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1581] This invention relates to a system in which a user, a terminal, and a server cooperate to generate customized content using an advanced generative AI model and an emotion engine. Specific embodiments of this system are described below.

[1582] User operations

[1583] The user provides input to the device to generate content, such as "Write a script for a new product introduction presentation." The user can also provide information about the target audience and details of the desired tone and style. The device can also accept voice or text input, which uses an emotion engine to analyze the user's emotional state.

[1584] Device Role

[1585] The device receives input from the user and converts the input data into JSON format, which includes the user ID, input content, tone preference, information about the target recipient, and emotional data analyzed by the emotion engine. This data is then sent to the server as an HTTP request.

[1586] Server Processing

[1587] The server analyzes the request received from the device and performs any necessary pre-processing, which includes the following steps:

[1588] Input Analysis: The server analyzes the user's input to extract context, tone, purpose, and target audience characteristics, including emotional data provided by the emotion engine.

[1589] Database lookup: The server retrieves user profile information and past content from the database and uses it for analysis.

[1590] The server then uses the acquired information and emotional data to invoke a generative AI model to generate customized content. This generative AI model is designed to understand context, tone, intent, and emotional state to generate high-quality content based on user requests.

[1591] The generated content is formatted and sent back to the device, where it is stored in a database for future personalization.

[1592] Display by terminal

[1593] The terminal receives the customized content returned from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality customized content.

[1594] Specific examples

[1595] Example 1: Creating a business presentation

[1596] 1. The user inputs the "new product introduction presentation script" into the terminal and also inputs their current emotional state through voice input.

[1597] 2. The device sends this request and emotional data to the server, asking it to generate an appropriate script based on the context, tone, characteristics and emotional state of the target recipient.

[1598] 3. The server uses the generative AI model and emotion engine to generate a script and send it back to the device.

[1599] 4. The user checks the generated script and modifies it as necessary before using it.

[1600] Example 2: Creating an email for a marketing campaign

[1601] 1. The user enters the "new campaign product introduction email" into the terminal and inputs their current emotional state through the emotion engine.

[1602] 2. The device sends this request and emotional data to the server, asking it to generate email content based on the user's intentions and emotional state.

[1603] 3. The server uses the generative AI model and emotion engine to generate content and send it back to the device.

[1604] 4. The user reviews the generated email, edits it as needed, and uses it in their marketing efforts.

[1605] In this way, users, devices, and servers work together to realize an efficient content generation system using generative AI and an emotion engine.

[1606] The processing flow will be explained below.

[1607] Step 1:

[1608] The user operates the terminal and inputs a request for content generation, for example, "Please create a script for a presentation introducing a new product."

[1609] Step 2:

[1610] The user inputs emotion data by speaking aloud into the terminal, which allows the emotion engine to recognize the user's current emotional state.

[1611] Step 3:

[1612] The device receives user input as text data, and the emotion engine analyzes the voice data to detect the user's emotional state. The data, including the analysis results, is converted into JSON format. The converted data includes the user ID, input content, desired tone, information about the target recipient, and emotional data.

[1613] Step 4:

[1614] The device sends JSON format data to the server as an HTTP request, sending a POST request to the server's endpoint.

[1615] Step 5:

[1616] The server receives an HTTP request from the terminal, analyzes the contents of the request, and extracts the user's input data.

[1617] Step 6:

[1618] The server extracts user-input data and emotional data to analyze the context, tone, purpose, and target audience characteristics using natural language processing techniques and emotion recognition algorithms.

[1619] Step 7:

[1620] The server retrieves user profile information and previously generated content from the database and uses it for analysis, thereby improving the accuracy of the generative AI model.

[1621] Step 8:

[1622] Based on the acquired user profile information, input data, and emotional data, the server invokes a generative AI model to generate customized content. The generative AI model understands context, tone, intent, and emotional state to generate high-quality content based on user requests.

[1623] Step 9:

[1624] The server formats the generated custom content and prepares it as response data. The generated content is packaged in JSON format.

[1625] Step 10:

[1626] The server sends the generated content in JSON format to the device as an HTTP response, and returns a POST request response to the device's endpoint.

[1627] Step 11:

[1628] The terminal receives the HTTP response from the server and displays the generated customized content on the user interface. The user can check the generated content and modify it as necessary.

[1629] Step 12:

[1630] Users can review the generated content, make any necessary edits, and then save or share the content as needed.

[1631] Step 13:

[1632] The device then sends the edited and saved content back to the server and stores it in a database, which can be used for future generation.

[1633] Example 2

[1634] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1635] Conventional content generation systems have struggled to quickly and accurately provide customized content that fully reflects a user's context, tone, and intent. Furthermore, they lack the ability to generate content that takes into account emotional states, making it impossible to fully meet user needs. Furthermore, they lack a way to improve the accuracy of their generative AI models by utilizing past content and user profiles.

[1636] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for converting user input into JSON format; means for transferring the JSON-formatted data to the server; means for the server to analyze the user input and extract context, tone, intent, and emotional state; means for retrieving user profile information and past content from a database and integrating the analysis results; means for hosting a generative AI model that generates customized content based on the integrated information; means for formatting the generated content and returning it to the terminal; and means for saving the customized content and using it for future generation. This enables the rapid generation and provision of high-quality customized content that reflects the user's context, tone, intent, and emotional state. Furthermore, the accuracy of the generative AI model can be improved by utilizing past content and user profiles.

[1637] "User input" is information or instructions provided by a user to the system.

[1638] "JSON format" is an abbreviation for JavaScript Object Notation, and is a method of structuring and representing data in text format.

[1639] A "server" is a computer system that processes and manages data, receives requests from terminals, and returns appropriate responses.

[1640] "Context" refers to background or situational information related to the user's input.

[1641] "Tone" refers to the atmosphere or style of the content being generated, and can be professional, casual, or the like.

[1642] "Intention" refers to the purpose or goal that a user is trying to achieve with their input.

[1643] "Emotional state" refers to the emotion the user is feeling at the time of input, and includes joy, anger, sadness, etc.

[1644] A "database" is a system for managing a collection of data, enabling efficient data retrieval and storage.

[1645] "User profile information" refers to information about an individual user, such as the user's attributes and behavioral history.

[1646] "Past content" refers to previously generated content that is related to a user's past requests.

[1647] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate customized content based on input data.

[1648] "Hosting" means running a generative AI model on a server so that it can respond to requests from other systems and users.

[1649] "Formatting" means shaping the generated content into a particular structure or form.

[1650] A "terminal" is a device that is directly operated by a user, and includes a computer, a smartphone, a tablet, and the like.

[1651] This invention is a system in which users, terminals, and servers work together to efficiently generate customized content using advanced generative AI models and emotion engines.

[1652] Users input content generation requests through their devices. This input includes context, tone, information about the target audience, desired style, and the user's emotional state. For example, a user can input the prompt "Please write a script for a presentation introducing a new product," specifying "businessmen in their 30s and 40s" as the target audience and "professionals" as the tone. The user can also provide their current emotional state through voice or text input.

[1653] The device receives input from the user and converts the input data into JSON format, which includes the user ID, input content, desired tone, target recipient information, and emotion data analyzed by the emotion engine. The device then forwards this data to the server as an HTTP request.

[1654] The server analyzes the HTTP request received from the device. First, it analyzes the user's input to extract the context, tone, purpose, and characteristics of the target audience. It also includes emotional data provided by the emotion engine. Next, the server retrieves user profile information and past content from the database and integrates this information with the analysis results.

[1655] The server then invokes a generative AI model to generate customized content. This model is designed to understand context, tone, intent, and emotional state, allowing it to generate high-quality content. The generated content is then formatted and sent back to the device. At the same time, this generated content is stored in a database for future use.

[1656] The terminal receives the customized content returned from the server, and the user can review the content through the terminal's user interface and make any necessary modifications or additions.

[1657] As a concrete example, consider the case where a user inputs, "Please create a script for a presentation introducing a new product. The target audience is businessmen in their 30s and 40s, and the tone should be professional." At this time, let's assume that the user's emotional state is "neutral." The device converts this input into JSON format and forwards it to the server. The server analyzes this data and uses a generative AI model and emotion engine to generate a customized script based on the specified conditions and sends it back to the device. The user can review the returned script and modify it as necessary.

[1658] In this way, users, devices, and servers work together to quickly and efficiently generate customized content that meets the user's needs using advanced generative AI and emotion engines.

[1659] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1660] Program processing flow

[1661] Step 1: User Input

[1662] A user uses a device to input a content generation request, for example, "Please write a script for a presentation introducing a new product." The user also provides details about the target audience (e.g., business people in their 30s and 40s), the desired tone (e.g., professional), and style. The user also provides their current emotional state through voice or text input.

[1663] Input: Content generation request, target audience information, desired tone and style, emotional state

[1664] Output: User input data

[1665] Step 2: Convert data via terminal

[1666] The device receives input data from the user and converts it into JSON format, which includes the user ID, input content, tone preference, target recipient information, and emotion data analyzed by the emotion engine.

[1667] Input: User-entered data

[1668] Output: JSON format data

[1669] Specific behavior:

[1670] Parse the input data and format it into JSON format.

[1671] For example, data in the format {"userID": "12345", "request": "New product introduction presentation script", "tone": "Professional", "audience": "Businessmen in their 30s and 40s", "emotion": "neutral"} is generated.

[1672] Step 3: Transfer data from your device to the server

[1673] The terminal transfers the data converted into JSON format to the server as an HTTP request.

[1674] Input: JSON format data

[1675] Output: HTTP request

[1676] Specific behavior:

[1677] Send JSON format data to the server as an HTTP request.

[1678] Step 4: Parsing the input by the server

[1679] The server receives and analyzes the HTTP request, analyzing the user's input to extract context, tone, purpose, and target audience characteristics, as well as emotional data provided by the emotion engine.

[1680] Input: HTTP request

[1681] Output: Analyzed data (context, tone, purpose, target audience characteristics, emotional state)

[1682] Specific behavior:

[1683] It parses HTTP request data to extract context, tone, purpose, target audience characteristics, and emotional state.

[1684] Step 5: Retrieving information from the database

[1685] The server retrieves user profile information and past content from the database, and combines this information with the analysis results.

[1686] Input: User profile information and past content requests

[1687] Output: Information retrieved from the database

[1688] Specific behavior:

[1689] User profile information and historical content is retrieved from the database and integrated with the analysis results.

[1690] Step 6: Content generation with generative AI models

[1691] The server then uses the integrated information to invoke a generative AI model to generate customized content, which understands context, tone, intent, and emotional state to generate high-quality content based on user requests.

[1692] Input: Integration information

[1693] Output: Generated content

[1694] Specific behavior:

[1695] It invokes a generative AI model, providing it with context, tone, intent, and emotional state as input.

[1696] A generative AI model generates specific scripts and email text.

[1697] Step 7: Server formats and returns content

[1698] The generated content is formatted and sent back to the device as an HTTP response, and is also stored in a database for future use.

[1699] Input: Generated content

[1700] Output: HTTP response, database update

[1701] Specific behavior:

[1702] The generated content is reformatted into JSON format and sent to the terminal as an HTTP response.

[1703] Store the generated content in a database.

[1704] Step 8: View and edit content using your device

[1705] The terminal receives the customized content returned from the server and displays it to the user through a user interface. The user can then check the displayed content and make corrections or additions as necessary.

[1706] Input: HTTP response

[1707] Output: The displayed customized content

[1708] Specific behavior:

[1709] The generated content is displayed on the user interface of the terminal.

[1710] The user reviews the displayed content and manually corrects or adds to it.

[1711] (Application example 2)

[1712] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1713] In modern content delivery services, it is important to quickly generate and provide personalized content that meets the diverse needs and emotional states of users. However, conventional systems often find it difficult to effectively analyze and reflect the user's current emotional state, and the generated content is often not sufficiently personalized. This makes it difficult to improve user satisfaction and prevents effective content delivery.

[1714] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting input from a user, means for analyzing the user input and generating emotional data, including an emotion analysis engine that analyzes the user's emotional state, and means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content. This enables highly personalized content generation based on the user's current emotional state.

[1715] A "user" is a person or organization that uses an information system or service.

[1716] "Input" is data or information that a user provides to an information system.

[1717] "Analysis" is the process of understanding input data or information and extracting specific meaning or intent.

[1718] A "generative AI model" is an artificial intelligence algorithm that automatically generates content based on user input data.

[1719] The "emotion analysis engine" is a component that analyzes the user's emotional state and generates emotional data.

[1720] "Emotion data" is data obtained by analyzing the user's emotional state using an emotion analysis engine.

[1721] "Customized content" is content that is generated based on the user's input data and emotional data and is optimized for an individual user.

[1722] "Context" is the meaning or relevance of a user's input data in a particular situation or condition.

[1723] "Tone" is a term that refers to the overall mood or style of content.

[1724] "Intent" is the purpose or goal inferred from the user's input.

[1725] A "server" is a computer system that receives and processes requests from users.

[1726] A "database" is a system for efficiently storing, searching, and managing structured information.

[1727] A "user profile" is a data set that compiles a user's attribute information, behavioral history, etc.

[1728] A "user interface" is the means or screen through which a user interacts with a system.

[1729] "Personalized" means optimized according to the attributes and status of each individual user.

[1730] This invention relates to a system for generating personalized content using user input data and emotion data. This system operates in cooperation with a user, a terminal, and a server, and utilizes a generative AI model and an emotion analysis engine. Specific embodiments are described below.

[1731] 1. User operations

[1732] A user requests content creation using a content distribution service application. For example, the user might input, "Please create a relaxing travel video." At this time, the user also provides emotional data using voice. The emotional data is information that indicates the user's current emotional state.

[1733] 2. Role of the terminal

[1734] The device receives text and voice input from the user and acquires this data. It converts the acquired data into JSON format and generates request data that includes the user ID, input content, tone preference, information about the recipient, and emotional data analyzed by the emotion analysis engine. It then sends this request data to the server as an HTTP request.

[1735] 3. Server Processing

[1736] The server analyzes the request data received from the device. First, it analyzes the input content to extract the context, tone, intent, and characteristics of the target recipient. It also analyzes the emotional data provided by the sentiment analysis engine. Next, the server retrieves user profile information and information about past content from the database and uses this information for analysis. Based on this information, it invokes a generative AI model to generate customized content according to the request. This generated content is formatted and sent back to the device.

[1737] 4. Display by terminal

[1738] The terminal receives the customized content from the server and displays it to the user. The user can check the displayed content and make corrections or additions as necessary. In this way, the user can quickly and easily obtain and use high-quality personalized content.

[1739] Hardware and software used

[1740] Hardware: Smartphone, Head-Mounted Display (HMD)

[1741] Software: Generative AI model, sentiment analysis engine, server (REST API for sending HTTP requests)

[1742] Specific examples

[1743] For example, you can enter the following prompt sentence into your application:

[1744] "Make a travel video that makes you feel relaxed. Your current emotional state is 'happy.'"

[1745] After sending the prompt to the server, the server analyzes the prompt and emotional data to generate a relaxing travel video for the user. For example, a video combining beautiful beach and mountain scenery in Hawaii is generated and displayed to the user. This application allows users to easily enjoy the ideal video content that matches their emotional state.

[1746] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1747] Step 1:

[1748] A user uses a content delivery service application to request the generation of a specific piece of content. For example, the user inputs, "Please create a travel video that will make me feel relaxed." The user also provides voice input and their current emotional state (e.g., "happy"). The text request and emotional data are passed to the device as input data.

[1749] Step 2:

[1750] The device receives text input and emotion data from the user and converts this data into JSON format. At this time, it generates request data that includes the user ID, input content, tone preference, information about the target recipient, and emotion data analyzed by the emotion analysis engine. The generated JSON data is prepared as input for the HTTP request.

[1751] Step 3:

[1752] The device sends the generated HTTP request to the server. The request includes the user ID, input content, tone preference, information about the target recipient, and emotional data. The device sends the data to the server using the HTTP POST method.

[1753] Step 4:

[1754] The server analyzes the HTTP request data received from the device. First, the server analyzes the user's input and extracts the context, tone, purpose, and characteristics of the target recipient. At the same time, it also analyzes the emotional data analyzed by the emotion analysis engine. The analysis results are generated as intermediate data within the server.

[1755] Step 5:

[1756] The server retrieves user profile information and past content data from the database, which is then combined with the analyzed input data and sentiment data to further refine the intermediate data, enabling fine-grained personalization.

[1757] Step 6:

[1758] The server uses the intermediate data to call a generative AI model to generate customized content. The generative AI model generates optimal content based on user input, emotional data, past content history, etc. The generated content is saved as finished data on the server.

[1759] Step 7:

[1760] The server returns the completed data to the device, which prepares the received content for display and provides an interface for the user to review and modify. After receiving the completed data, the device displays the personalized content to the user.

[1761] Step 8:

[1762] The user checks the received content and makes corrections or additions as necessary. Finally, personalized content that satisfies the user is completed. User feedback is again entered into the terminal, completing the entire process.

[1763] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1764] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1765] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1766] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1767] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1768] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1769] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1770] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1771] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1772] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1773] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1774] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1775] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1776] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1777] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1778] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1779] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1780] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1781] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1782] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1783] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1784] The following is further disclosed regarding the above embodiment.

[1785] (Claim 1)

[1786] means for accepting input from a user;

[1787] means for analyzing said user input;

[1788] a means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content;

[1789] means for returning customized content generated by the generative AI model to the user;

[1790] means for storing the customized content for subsequent generation;

[1791] A system including:

[1792] (Claim 2)

[1793] means for retrieving the required information from a database of user profiles and past content;

[1794] and means for using the acquired information to improve the accuracy of the generative AI model.

[1795] 10. The system of claim 1.

[1796] (Claim 3)

[1797] further comprising means for providing a user interface and allowing a user to customize, review, and modify input data;

[1798] 10. The system of claim 1.

[1799] "Example 1"

[1800] (Claim 1)

[1801] means for accepting input from a user;

[1802] means for analyzing said user input;

[1803] a means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content;

[1804] means for converting the user's input into a data format and transmitting the data to a server;

[1805] A means for returning customized content generated based on the analyzed information by the server to the terminal;

[1806] means for displaying customized content generated by the generative AI model to the user;

[1807] means for storing the customized content for subsequent generation;

[1808] A system including:

[1809] (Claim 2)

[1810] means for retrieving the required information from a database of user profiles and past content;

[1811] and means for using the acquired information to improve the accuracy of the generative AI model.

[1812] 10. The system of claim 1.

[1813] (Claim 3)

[1814] further comprising means for providing a user interface and allowing a user to customize, review, and modify input data;

[1815] 10. The system of claim 1.

[1816] "Application Example 1"

[1817] (Claim 1)

[1818] means for accepting input from a user;

[1819] means for analyzing said user input;

[1820] a means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content;

[1821] A means for allowing a user to input a request to generate product introductions and descriptions in a virtual store and provide customized information in real time;

[1822] means for returning customized content generated by the generative AI model to the user;

[1823] means for storing the customized content for subsequent generation;

[1824] A system including:

[1825] (Claim 2)

[1826] means for retrieving the required information from a database of user profiles and past content;

[1827] and means for using the acquired information to improve the accuracy of the generative AI model.

[1828] 10. The system of claim 1.

[1829] (Claim 3)

[1830] further comprising means for providing a user interface and allowing a user to customize, review, and modify input data;

[1831] 10. The system of claim 1.

[1832] "Example 2: Combining Emotion Engines"

[1833] (Claim 1)

[1834] means for accepting input from a user;

[1835] means for converting the user's input into a JSON format;

[1836] means for transferring the JSON format data to a server;

[1837] means by the server for analyzing the user's input and extracting context, tone, intent, and emotional state;

[1838] means for retrieving user profile information and past content from a database and integrating it with the analysis results;

[1839] means for hosting a generative AI model that generates customized content based on the integrated information;

[1840] means for formatting the generated content and transmitting it back to the terminal;

[1841] means for storing the customized content for subsequent generation;

[1842] A system including:

[1843] (Claim 2)

[1844] Further comprising means for obtaining necessary information from a database and improving the accuracy of the generative AI model;

[1845] 10. The system of claim 1.

[1846] (Claim 3)

[1847] further comprising means for providing a user interface and allowing a user to customize, review, and modify input data;

[1848] 10. The system of claim 1.

[1849] "Application example 2 when combining emotion engines"

[1850] New Claims

[1851] (Claim 1)

[1852] means for accepting input from a user;

[1853] means for analyzing said user input;

[1854] a means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content;

[1855] means for generating emotion data, the emotion data including an emotion analysis engine for analyzing an emotional state of a user;

[1856] means for returning customized content generated by the generative AI model to the user;

[1857] means for storing the customized content for subsequent generation;

[1858] A system including:

[1859] (Claim 2)

[1860] means for retrieving the required information from a database of user profiles and past content;

[1861] and means for improving the accuracy of the generative AI model using the acquired information and emotion data.

[1862] 10. The system of claim 1.

[1863] (Claim 3)

[1864] and means for providing a user interface and allowing a user to customize, review, and modify the input data and emotion data.

[1865] 10. The system of claim 1. [Explanation of symbols]

[1866] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for accepting input from a user; means for analyzing said user input; a means for hosting a generative AI model that understands context, tone, and intent based on the analyzed input data and generates customized content; means for returning customized content generated by the generative AI model to the user; means for storing the customized content for subsequent generation; A system including:

2. means for retrieving the required information from a database of user profiles and past content; and means for using the acquired information to improve the accuracy of the generative AI model. The system of claim 1 .

3. further comprising means for providing a user interface and allowing a user to customize, review, and modify input data; The system of claim 1 .

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