System

Abstract

A system is provided.SOLUTION: A system including means for receiving input data from a user, means for analyzing the received input data and extracting a keyword, means for generating content using generative artificial intelligence based on the extracted keyword, means for personalizing the generated content based on a past behavior history of the user, means for transmitting the personalized content to a terminal of the user, means for receiving feedback from the user and storing the feedback in a database, and means for utilizing the feedback when content is generated next time.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] Currently, content creators, educational institutions, and marketing departments spend a lot of time and effort generating personalized content for individual users. Furthermore, a lack of a system that effectively incorporates user feedback into future content generation hinders user satisfaction. Furthermore, there is no efficient, integrated system to solve these issues, and organizations often rely on individual solutions. There is a need to improve this situation and achieve more efficient and effective content generation and personalization. [Means for solving the problem]

[0005] The present invention provides a means for receiving input data (text, audio, images, and videos) from users, analyzing it, and extracting keywords. It also provides a means for generating content using generative artificial intelligence based on the extracted keywords, a means for personalizing the generated content based on the user's past behavioral history, and a means for sending this personalized content to the user's terminal. Furthermore, the present invention proposes a system that includes a means for receiving feedback from users, storing that feedback in a database, and utilizing it the next time content is generated. This system enables content creators, educational institutions, and marketing departments to efficiently generate personalized content, thereby increasing user satisfaction.

[0006] "User" means any person or entity that uses the System to make content requests and receive generated content.

[0007] "Input Data" means text, audio, image, or video information provided by a User to a System.

[0008] The term "means" refers to a method or device provided for performing each function of the system.

[0009] A "server" is a computer system that receives input data, analyzes it, generates content, handles personalization, and manages feedback.

[0010] "Keywords" are important words and phrases that are extracted from the analyzed input data and that form the basis of content generation.

[0011] "Generative AI" stands for generative artificial intelligence, and refers to algorithms or models that automatically generate unique content based on given data.

[0012] "Behavioral history" refers to information such as data and feedback from when a user used the system in the past, and the history of content viewed.

[0013] "Personalization" is the process of tailoring and optimizing content for individual users.

[0014] "Terminal" means a device used by a user to send input data or receive and display generated content, such as a smartphone, tablet, or PC.

[0015] "Feedback" refers to reactions such as opinions, impressions, requests, etc. that users provide in response to generated content.

[0016] "Database" means an internal information management system used to store received feedback and other relevant information and use it for future content generation. [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] This invention is a system that combines generative AI and artificial general intelligence (AGI) to efficiently process a wide variety of input data (text, audio, images, and videos) from users and generate and provide personalized content. The system aims to provide high-quality content that matches the user's interests and preferences, improving the user experience.

[0039] System configuration

[0040] The system mainly consists of the following components:

[0041] 1. User terminal: A device that receives user input data and sends it to the server. Examples include smartphones, tablets, and PCs.

[0042] 2. Server: Analyzes input data, generates content using generative AI and AGI, and provides personalized content.

[0043] 3. Database: Stores users' past behavioral history and feedback and uses it when generating content.

[0044] Program processing

[0045] (1) Receiving user input

[0046] The user inputs a content generation request into the terminal. For example, if the user wants an article about "travel," the user inputs text such as "Generate a guide article about travel."

[0047] (2) Data preprocessing

[0048] The terminal receives this input, converts the data into an appropriate format, and then sends it to the server.

[0049] (3) Data analysis

[0050] The server analyzes the received data and extracts keywords, such as "travel," "guide," and "recommended spots."

[0051] (4) Content generation

[0052] The server uses generative AI to generate content based on the extracted keywords and the user's past behavioral history. For example, it creates specific articles on "recommended travel spots" and "how to plan a trip."

[0053] (5) Personalization

[0054] The server then further personalizes the generated content to match the user's interests. If the user has previously viewed content related to "beach resorts," the server will take that information into account and include "recommended beach resorts" in the article.

[0055] (6) Provision of content

[0056] The server formats the final personalized content and sends it to the device, which displays it to the user, for example, displaying an article in a layout tailored to the screen of a smartphone.

[0057] (7) Feedback collection

[0058] After users have finished viewing the content provided, they can provide feedback, such as leaving a comment like "I found the article helpful" or "I'd like more information."

[0059] (8) Processing Feedback

[0060] The device receives the user's feedback and sends it to the server, which stores it in a database and takes it into account when generating content next time.

[0061] Specific examples

[0062] As a specific scenario, we will explain the behavior when a user wants articles about "summer travel plans."

[0063] 1. The user enters the text "I want an article generated about summer travel plans."

[0064] 2. The device sends this text to the server.

[0065] 3. The server analyzes the text and extracts keywords such as "summer," "travel plans," and "recommended spots."

[0066] 4. The server uses generative AI to create articles on "recommended spots for summer travel" and "how to plan an effective trip."

[0067] 5. The server adds specific information, such as "beach resort" or "mountainous area," based on the user's past behavioral history.

[0068] 6. The server formats the final article and sends it to the device.

[0069] 7. The device displays personalized content to the user.

[0070] 8. Users view the content and provide feedback such as "Very helpful" or "I'd like more detailed information."

[0071] 9. The device sends the feedback to the server, which stores it in a database.

[0072] In this way, the system can efficiently provide high-quality personalized content that meets the user's needs.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user uses the device to make a content generation request. For example, the user enters text such as "I would like an article about travel."

[0076] Step 2:

[0077] The terminal receives the input data, converts it into a communication format, and sends the converted data to the server.

[0078] Step 3:

[0079] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[0080] Step 4:

[0081] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "travel," "guide," and "recommended spots."

[0082] Step 5:

[0083] The server considers the extracted keywords and the user's past behavioral history and activates a generative AI model, which generates new content (e.g., a travel guide article) based on the analyzed information.

[0084] Step 6:

[0085] The server-generated content is personalized based on the user's past behavior and interests. For example, if the user has previously expressed an interest in "beach resorts," the server will create personalized content that includes that information.

[0086] Step 7:

[0087] The server formats the personalized content and sends it to the user's device.

[0088] Step 8:

[0089] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[0090] Step 9:

[0091] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[0092] Step 10:

[0093] The device collects feedback from the user and sends it to the server.

[0094] Step 11:

[0095] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[0096] In this way, the system can efficiently generate and provide personalized content that meets the user's needs.

[0097] Example 1

[0098] 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."

[0099] In today's information society, users demand a wide variety of content, and they also demand a high level of quality. However, conventional content generation systems have difficulty providing personalized content tailored to individual users' interests and preferences, resulting in a poor user experience. Furthermore, they lack the ability to reflect user feedback in the next generation of content, making continuous improvement difficult.

[0100] 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.

[0101] In this invention, the server includes means for converting input data from a user into an appropriate format, means for transmitting the converted data to the server, means for analyzing the data received by the server and extracting keywords, means for generating content using a generative AI model, means for personalizing the generated content based on the user's past behavioral history, means for transmitting the personalized content to the user's terminal and displaying it, means for receiving feedback from the user and storing the feedback in a database, and means for utilizing the feedback when generating content next time. This makes it possible to provide high-quality personalized content that meets the individual interests and preferences of the user, improving the user experience and enabling continuous improvement of the content based on the feedback.

[0102] "Input Data" means information provided by a user to a system, and may be in the form of text, audio, images, or video.

[0103] "Terminal" refers to the device used by the user, including smartphones, tablets, and personal computers.

[0104] A "server" is a central processing unit that handles input data analysis, content generation, personalization, and feedback.

[0105] The "means for converting into a format" is a function for carrying out a process of converting input data into a format that is easy to analyze.

[0106] The "means for analyzing and extracting keywords" is a function for carrying out the process of identifying and extracting key keywords from received input data.

[0107] "Means for generating content using a generative AI model" refers to a function for carrying out a process of generating information using an artificial intelligence model based on extracted keywords.

[0108] "Personalized content" is content that is generated and personalized by adding specific information based on the user's past behavioral history and interests.

[0109] "Feedback" refers to ratings and comments made by users on provided content.

[0110] A "database" is a system for storing user feedback and past behavioral history.

[0111] "Means to be utilized the next time content is generated" is a function that performs a process to reflect saved feedback and past behavioral history in newly generated content.

[0112] "Means for displaying" refers to the function of displaying personalized content in a format appropriate for the user's device.

[0113] The present invention provides a system for generating and providing high-quality personalized content that meets the individual interests and preferences of users based on a wide variety of input data. A detailed description of specific embodiments of the present invention is provided below.

[0114] The system is broadly composed of a user terminal, a server, and a database. These elements communicate with each other to efficiently generate content and perform personalization.

[0115] A user device is a device that receives user input. Examples include smartphones, tablets, and PCs. Users use these devices to request desired content by typing text or speaking. In the case of speech input, speech recognition software on the device converts speech into text.

[0116] The server is a central processing unit that receives input data sent by users, analyzes it, and generates content. The server has the following functions:

[0117] 1. Data preprocessing: The server converts the data received from the terminal into an appropriate format (e.g., JSON format, XML format). This conversion is performed using various programming languages ​​(e.g., Python, Java).

[0118] 2. Data analysis: The server analyzes the received data using natural language processing tools (e.g., spaCy, NLTK) and extracts keywords. For example, from a request such as "Generate travel guide articles," it extracts keywords such as "travel," "guide," and "recommended spots."

[0119] 3. Content generation: The server generates content based on the extracted keywords using a generative AI model (e.g., GPT-4). By inputting specific prompts into the generative AI model, detailed content such as "Please write an article about recommended travel spots" can be automatically generated.

[0120] 4. Personalization: The server personalizes the generated content based on the user's past behavior and interests. For example, if the user has previously viewed a lot of information about "beach resorts," the server may include "recommended beach resorts" in the generated article.

[0121] 5. Providing content: The server sends the final personalized content to the device, which receives it and displays it in a format appropriate for the user (e.g., a smartphone screen layout).

[0122] 6. Feedback collection and utilization: After viewing the provided content, users can enter their feedback in text. The device receives this feedback and sends it to the server. The server stores the feedback in a database and utilizes it the next time content is generated.

[0123] This allows the system to generate high-quality content tailored to each user's individual interests and preferences, and to continuously improve it. For example, if a user requests an article about summer travel plans, the server extracts keywords such as "summer," "travel plans," and "recommended spots," and uses a generative AI model to create a detailed travel guide, personalizing it based on the user's past preferences. As a result, users can easily obtain the optimal travel plan that best suits them.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1: Accepting User Input

[0126] A user inputs a content generation request into a device. For example, the user inputs the text "Please generate a travel guide article." This input data is sent to the device.

[0127] Input: Text data containing the user's preferences.

[0128] Output: The input data in text format.

[0129] Step 2: Preprocessing the data

[0130] The device converts the input data received from the user into an appropriate format (e.g., JSON format, XML format), and sends this converted data to the server.

[0131] Input: Text data entered by the user.

[0132] Output: Pretty-printed JSON or XML data.

[0133] Step 3: Receiving and analyzing data

[0134] The server analyzes the data received from the device using an analysis tool (e.g., spaCy, NLTK) to extract key keywords, such as "travel," "guide," and "recommended spots."

[0135] Input: Well-formed JSON or XML data.

[0136] Output: The extracted keyword list.

[0137] Step 4: Content Generation

[0138] The server inputs the extracted keywords as a prompt into a generative AI model (e.g., GPT-4), and uses the generative AI model to generate specific content. For example, the prompt could be "Please write an article about recommended travel spots."

[0139] Input: The extracted keyword list.

[0140] Output: The generated content (e.g., a text article).

[0141] Step 5: Personalization

[0142] The server personalizes the generated content based on the user's past behavior. For example, if a user has frequently viewed information about "beach resorts," that information will be included in the article.

[0143] Input: Generated content and user past behavior.

[0144] Output: Personalized content.

[0145] Step 6: Providing content

[0146] The server sends the final personalized content to the device, which receives the data and displays it in a format appropriate for the user (e.g., a smartphone screen layout).

[0147] Input: Personalized content.

[0148] Output: The content that is displayed on the user's device.

[0149] Step 7: Gather feedback

[0150] After users have finished viewing the provided content, they can enter their feedback in text, such as "The article was helpful" or "I'd like more information," and send it to their device.

[0151] Input: Feedback data, including user ratings and comments.

[0152] Output: Formatted feedback data.

[0153] Step 8: Processing feedback

[0154] The device receives feedback from the user and sends it to the server, which stores the received feedback in a database and uses it the next time content is generated.

[0155] Input: Formatted feedback data.

[0156] Output: Feedback data stored in a database.

[0157] In this way, through the specific operations and inputs / outputs at each step, the system can provide high-quality personalized content that meets the individual interests and preferences of users.

[0158] (Application example 1)

[0159] 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."

[0160] Conventional personalized content generation systems were limited in the types of input data and the way feedback was utilized, making them unable to adequately meet diverse user needs. Furthermore, the content generation process using generative AI models was inefficient, resulting in low quality content and low user satisfaction. Furthermore, the level of personalization based on user preferences was insufficient, leaving the improvement of the user experience a challenge.

[0161] 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.

[0162] In this invention, the server includes means for receiving input data from a user, means for analyzing the received input data and extracting keywords, means for generating content using artificial intelligence based on the extracted keywords, means for personalizing the generated content based on the user's past behavior history, means for displaying the generated content on a display terminal, means for receiving feedback from the user and saving the feedback in a database, means for utilizing the feedback when generating content next time, and means for generating prompt sentences and inputting the prompt sentences to a generative AI model. This makes it possible to respond to the diversity of user input data and efficiently utilize the generative AI model to provide high-quality personalized content.

[0163] A "user terminal" is a device that receives input data from a user and sends it to a server, and includes smartphones, tablets, and personal computers.

[0164] The "server" is a central device that analyzes data sent by users, generates content using generative AI models and prompts, and sends personalized content to user devices.

[0165] The "database" is an information management system that stores users' past behavioral history and feedback information and uses it when generating content the next time.

[0166] A "generative AI model" is an artificial intelligence model used to generate high-quality content based on user input data.

[0167] A "prompt" is an input text for a generative AI model, and is an instruction sentence that specifically expresses the user's intentions and wishes.

[0168] "Personalized content" refers to content that is customized based on a user's past behavioral history and preferences, and includes information tailored to individual needs.

[0169] "Feedback" refers to reactions and comments made by users to the content provided, and is information that is used to improve the quality of the next content generated.

[0170] "Input Data" refers to information provided by a User in the form of text, audio, images, video, or other information.

[0171] "Keywords" are important words and phrases extracted from input data through analysis, and play a central role in content generation.

[0172] "Analysis" is the process of analyzing input data from users and extracting important elements and keywords.

[0173] "Content generation" is the process of using a generative AI model to create information based on input data and keywords.

[0174] "Personalization" is the process of tailoring generated content to meet individual needs, based on the user's interests and past behavior.

[0175] A "display terminal" is a device that displays personalized content to users, such as a smartphone, tablet, or PC.

[0176] MODE FOR CARRYING OUT THE INVENTION

[0177] This invention is a system that efficiently processes various user input data (text, voice, images, videos) and generates and provides high-quality personalized content using generative AI models and prompts. This system mainly consists of the following elements:

[0178] User Device

[0179] A user terminal is a device that includes a smartphone, tablet, PC, etc., and serves to receive input data from a user and transmit that data to a server.

[0180] server

[0181] The server has the following main roles:

[0182] 1. Receiving and parsing input data:

[0183] It analyzes input data such as text, voice, images, and videos sent by users and extracts important keywords. This analysis process is carried out using natural language processing (NLP) technology.

[0184] 2. Content Generation:

[0185] Based on the extracted keywords and the user's past behavioral history, a generative AI model is used to generate content, and a prompt sentence is generated and input into the generative AI model.

[0186] 3. Personalization:

[0187] The generated content is further personalized based on the user's interests and preferences, referencing past behavioral history and feedback information from a database to provide customized information for each user.

[0188] Database

[0189] The database stores users' past behavioral history and feedback information, which the server uses to generate and personalize content.

[0190] Specific examples

[0191] Let's take the example of a user entering text such as "Tell me the latest movie recommendations." The user's device receives this text and sends it to the server. The server analyzes the entered text and extracts keywords such as "latest," "movie," and "recommended." The server then generates a prompt for the generative AI model. Specifically, it generates the following prompt:

[0192] Prompt: "The user has previously expressed an interest in psychological thrillers. Provide a list of current popular movies, including recommendations with a particular focus on psychological thrillers."

[0193] This prompt is passed as input to a generative AI model, which generates a list of "Top 5 New Movies," along with detailed information about psychological thriller movies the user has previously been interested in. The final content is then displayed and served to the user on their device, such as a smartphone or tablet.

[0194] Hardware and software used

[0195] Hardware: User devices (smartphones, tablets, PCs), servers (cloud servers or on-premise servers)

[0196] Software: Natural Language Processing (NLP) models, generative AI models (e.g., GPT-3), database management systems (DBMS)

[0197] This significantly improves the user experience by allowing users to receive high-quality personalized content based on their interests and preferences, and by using collected feedback to generate new content, the quality of the content provided can be continuously improved.

[0198] As a result, the present invention is able to accommodate the diversity of user input data and efficiently utilize generative AI models to provide high-quality personalized content.

[0199] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0200] Step 1:

[0201] The user inputs input data (e.g., the text "What are your recommendations for the latest movies?") into the device. The device receives this input data and sends it to the server.

[0202] Input: User input data (text, audio, images, video)

[0203] Processing: The device receives the input data

[0204] Output: Sends the received input data to the server

[0205] Step 2:

[0206] The server analyzes the received input data and extracts important keywords using natural language processing (NLP) techniques.

[0207] Input: The input data submitted (e.g., the text "What are your recommendations for the latest movies?")

[0208] Processing: Analyzes text using NLP techniques to extract keywords (e.g., "latest," "movies," "recommended")

[0209] Output: A list of extracted keywords

[0210] Step 3:

[0211] The server generates a prompt based on the extracted keywords, which is then fed directly into the generative AI model.

[0212] Input: A list of extracted keywords

[0213] Processing: Generate a prompt based on keywords (e.g., "The user has previously expressed an interest in psychological thrillers. Provide a list of current popular movies, including recommendations with a particular focus on psychological thrillers.")

[0214] Output: Generated prompt statement

[0215] Step 4:

[0216] The server inputs a prompt sentence into the generative AI model to generate content. The generative AI model generates content with the specified content based on the prompt sentence.

[0217] Input: Generated prompt statement

[0218] Processing: Input a prompt into the generative AI model to generate content (e.g., "Top 5 new movies").

[0219] Output: Generated content

[0220] Step 5:

[0221] The server personalizes the generated content based on the user's past behavior history, referencing the past behavior history and feedback information stored in the database to customize it for each user.

[0222] Input: Generated content, user history

[0223] Processing: Tailoring and personalizing content based on past behavior

[0224] Output: personalized content

[0225] Step 6:

[0226] The server sends the final personalized content to the user's device, which displays the content.

[0227] Input: Personalized content

[0228] Processing: Send the content to the user's device, and the device displays the content

[0229] Output: The personalized content that is displayed to the user

[0230] Step 7:

[0231] The user views the provided content and provides feedback (e.g., "Very helpful" or "I'd like more details"). The device receives this feedback and sends it to the server.

[0232] Input: User feedback

[0233] Processing: The device receives the feedback and sends it to the server.

[0234] Output: Feedback is sent to the server

[0235] Step 8:

[0236] The server stores the received feedback in a database and uses this feedback the next time content is generated.

[0237] Input: User feedback

[0238] Processing: Store the feedback in a database and use it the next time you generate content.

[0239] Output: Improved next content generation process

[0240] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0241] This invention combines a system that uses generative AI and artificial general intelligence (AGI) to process input data from users and generate and provide personalized content with an emotion engine that recognizes user emotions. This system aims to achieve even higher user satisfaction by providing content that takes into account the user's emotional state.

[0242] System configuration

[0243] The system mainly consists of the following components:

[0244] 1. User terminal: A device that receives user input data and sends it to the server. Examples include smartphones, tablets, and PCs.

[0245] 2. Emotion engine: Analyzes emotions from user input data (voice, text) and uses that information to generate content.

[0246] 3. Server: Analyzes input data, generates content using generative AI and AGI, and provides personalized content, taking into account emotional information from the emotion engine.

[0247] 4. Database: Stores user behavior history and feedback and uses it when generating content.

[0248] Program processing

[0249] (1) Receiving user input

[0250] The user inputs a content generation request into the device. For example, the user inputs the text "I want you to generate an article about travel."

[0251] (2) Data preprocessing

[0252] The terminal receives this input data, converts the data into an appropriate format, and then transmits it to the server.

[0253] (3) Data analysis and emotion recognition

[0254] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[0255] The emotion engine analyzes the user's emotions from text or voice data and provides the emotional state to the server. For example, emotions such as "curious" or "excited" are detected.

[0256] (4) Keyword extraction

[0257] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "travel," "guide," and "recommended spots."

[0258] (5) Content generation

[0259] The server launches a generative AI model based on the extracted keywords, emotional information from the emotion engine, and the user's past behavioral history. The generative AI model generates new content (e.g., a travel guide article) based on the analyzed information. If the emotional information is "curious," the article will be written in an adventurous tone.

[0260] (6) Personalization

[0261] The server then further personalizes the generated content to match the user's interests. If the user has previously shown interest in "beach resorts," the server will provide personalized content that includes that information.

[0262] (7) Provision of content

[0263] The server formats the final personalized content and sends it to the device.

[0264] (8) Display of content

[0265] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[0266] (9) Feedback collection

[0267] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[0268] (10) Processing Feedback

[0269] The device collects feedback from the user and sends it to the server.

[0270] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[0271] Specific examples

[0272] As a specific scenario, we will explain the behavior when a user wants articles about "summer travel plans."

[0273] 1. The user enters the text "I want an article generated about summer travel plans."

[0274] 2. The device sends this text to the server.

[0275] 3. The server analyzes the text and extracts keywords such as "summer," "travel plans," and "recommended spots."

[0276] 4. The emotion engine analyzes the user's emotions from the text and detects emotions such as "high expectations."

[0277] 5. The server uses generative AI to create articles about "recommended summer travel spots" and "how to plan an effective trip." It takes into account emotional information and creates articles with a tone that builds anticipation.

[0278] 6. The server adds specific information, such as "beach resort" or "mountainous area," based on the user's past behavioral history.

[0279] 7. The server formats the final article and sends it to the device.

[0280] 8. The device displays personalized content to the user.

[0281] 9. Users view the content and provide feedback such as "Very helpful" or "I'd like more information."

[0282] 10. The device sends the feedback to the server, which stores it in a database.

[0283] In this way, the system can efficiently provide high-quality personalized content that responds to the user's needs and emotional state.

[0284] The processing flow will be explained below.

[0285] Step 1:

[0286] The user uses the device to make a content generation request. For example, the user inputs text such as "I want an article about summer travel plans to be generated."

[0287] Step 2:

[0288] The terminal receives input data, converts the data into a communication format, and sends the converted data to the server.

[0289] Step 3:

[0290] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[0291] Step 4:

[0292] The server starts an emotion engine and analyzes the user's emotions from the text data. For example, the emotion engine detects the emotion "high expectations."

[0293] Step 5:

[0294] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "summer," "travel plans," and "recommended spots."

[0295] Step 6:

[0296] The server then activates a generative AI model based on the extracted keywords, emotional information, and the user's past behavioral history. The generative AI model generates new content (e.g., a travel guide article) based on the analyzed information.

[0297] Step 7:

[0298] The server tailors the generated content based on the user's emotional state, for example, if it detects a high sense of anticipation, it will create an article with an adventurous and fun tone.

[0299] Step 8:

[0300] The server then further personalizes the generated content to match the user's interests. For example, if the user has previously shown interest in "beach resorts," the server will provide personalized content that includes that information.

[0301] Step 9:

[0302] The server formats the final personalized content and sends it to the device.

[0303] Step 10:

[0304] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[0305] Step 11:

[0306] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[0307] Step 12:

[0308] The device collects feedback from the user and sends it to the server.

[0309] Step 13:

[0310] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[0311] In this way, by performing specific actions at each step, the system is able to efficiently provide personalized content that matches the user's emotions and needs.

[0312] Example 2

[0313] 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."

[0314] Conventional content generation systems analyze user input data and provide content without taking the user's emotions into account, resulting in low user satisfaction. In particular, it is difficult to efficiently provide personalized content that accurately reflects the user's emotional information. Furthermore, there is a lack of a means to continuously utilize user feedback to improve the quality of the content.

[0315] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0316] In this invention, the server includes means for receiving input data from a user, means for converting the received input data into an appropriate format, means for analyzing the converted input data and identifying its format, means for acquiring user emotional information from the input data using an emotion analysis engine, means for extracting keywords based on the analyzed format and emotional information, means for generating content based on the extracted keywords and emotional information using a generative artificial intelligence model, means for personalizing the generated content based on the user's past behavior history, means for transmitting the personalized content to the user's terminal, means for receiving feedback from the user and storing the feedback in a database, and means for utilizing the stored feedback when generating content next time. This makes it possible to efficiently provide high-quality personalized content that reflects the user's emotional information and improve user satisfaction.

[0317] "User" means a person or entity that utilizes the System to enter content generation requests and receive personalized content.

[0318] "Input Data" means information provided by a User to a System for content generation, including in the form of text, audio, images, video, or other information.

[0319] "Device" means a device used by a User to enter Input Data and view Generated Content, including a smartphone, tablet, or PC.

[0320] "Server" means a computer system that analyzes input data and performs content generation and personalization.

[0321] "Format" refers to the type and structure of data, and identifies whether the input data is text, audio, image, or video.

[0322] An "emotion analysis engine" is software or algorithms used to analyze and extract user emotional information from input data.

[0323] "Keywords" refer to important words and phrases extracted by analyzing the content of input data, and form the basis for content generation.

[0324] A "generative artificial intelligence model" is a type of AI technology used to generate new content based on extracted keywords and emotional information.

[0325] "Content" refers to information such as articles, reports, guides, etc. that are generated based on user input data and sentiment information.

[0326] "Personalization" is the process of optimizing content for individual users based on their past behavior and interests.

[0327] "Feedback" refers to input data such as user evaluations, opinions, and requests for improvement regarding the content provided.

[0328] A "database" is an information storage system for storing feedback received from users and past behavioral history.

[0329] This invention relates to a system that processes input data from users using generative AI and an emotion analysis engine to generate and provide personalized content. The purpose of this invention is to achieve higher user satisfaction by taking into account the user's emotional state.

[0330] System configuration

[0331] The system consists of the following elements:

[0332] 1. User Device

[0333] A device that receives user input data and sends it to a server. Examples include smartphones, tablets, and PCs. It provides an interface for users to enter input data.

[0334] 2. Sentiment Analysis Engine

[0335] Software or algorithms that analyze emotions from user input data (text, voice) and reflect that information in content generation. For example, Microsoft Azure's Text Analytics for Sentiment Analysis is used.

[0336] 3. Server

[0337] A computer system that analyzes input data and generates and personalizes content based on information from generative AI models and sentiment analysis engines, and delivers it to users. It also uses text analysis engines (e.g., SpaCy, NLTK).

[0338] 4. Database

[0339] An information storage system that stores users' past behavioral history and feedback and utilizes it when generating content.

[0340] Program processing

[0341] Receiving user input

[0342] A user enters a request via text, for example, "I want an article about summer travel plans."

[0343] Data Preprocessing

[0344] The terminal receives the input data, converts it to text, and then formats it appropriately, such as JSON, before sending it to the server.

[0345] Data analysis and emotion recognition

[0346] The server parses the received JSON data and analyzes the strings using a text analysis engine (e.g., SpaCy or NLTK). At the same time, a sentiment analysis engine analyzes the user's sentiment from the text and extracts emotional information such as "high expectations."

[0347] Keyword extraction

[0348] The server uses a text analysis engine to extract key keywords such as "travel," "summer," "guide," and "recommended spots" from the input text.

[0349] Content Generation

[0350] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate content based on the extracted keywords and sentiment information. For example, it generates articles about "recommended spots for summer travel" and "effective travel planning." It also crafts prompts to be written in a tone that builds anticipation.

[0351] Personalization

[0352] The server then personalizes the content it generates based on the user's past behavior. For example, if a user has previously shown interest in "beach resorts," that information will be added to the article.

[0353] Content provision

[0354] The server formats the final personalized content into HTML format and sends it to the device.

[0355] View content

[0356] The device then displays the received content to the user, for example, displaying an article in a format optimized for a smartphone screen.

[0357] Feedback collection

[0358] Users view the displayed article and enter their feedback, such as their impressions or suggestions for improvement, into the on-screen form. For example, they can enter feedback such as "This article was very helpful" or "I would like more detailed information."

[0359] Processing Feedback

[0360] The device receives feedback from the user, converts it into JSON format, and sends it to the server. The server stores the received feedback in a database and uses it for future content generation.

[0361] Examples of specific examples and prompts

[0362] As a concrete example, consider the case where a user enters the text "I want an article generated about summer travel plans." By entering this text into an application and submitting it, the system will perform the following actions:

[0363] 1. The user enters the text "I want an article generated about summer travel plans."

[0364] 2. The device converts the input text into JSON format and sends it to the server.

[0365] 3. The server performs text analysis and sentiment analysis to obtain the sentiment information of "high expectations" and extract key keywords.

[0366] 4. The server uses the generative AI model to generate content such as "Recommended spots for summer travel" and "How to plan an effective trip." The prompt includes elements that build anticipation: "Generate an article about travel. I'd especially like to know about recommended spots related to summer. Please also reflect my past interest in beach resorts."

[0367] 5. The server creates personalized articles based on the user's past behavioral history and sends them to the device.

[0368] 6. The device displays the article in a format optimized for the smartphone screen.

[0369] This mechanism enables the system to efficiently provide high-quality personalized content that reflects the user's emotional information, thereby improving user satisfaction.

[0370] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0371] Step 1:

[0372] The user inputs a content generation request. For example, they input text such as "I want you to generate an article about summer travel plans." The text is obtained as input data.

[0373] Step 2:

[0374] The terminal receives this input data. The terminal converts the received text data into an appropriate format and formats it in JSON format. The converted data is then output.

[0375] Step 3:

[0376] The terminal sends formatted input data to the server, which then passes the data to the server in JSON format.

[0377] Step 4:

[0378] The server parses the received JSON data. The server uses a text analysis engine (e.g. SpaCy, NLTK) to break the text into tokens and parse it. The parsed data is obtained.

[0379] Step 5:

[0380] The server starts the emotion analysis engine and obtains the user's emotional information from the analyzed text data. The emotion analysis engine analyzes the text data and extracts emotional information such as "high expectations." The emotional information is output.

[0381] Step 6:

[0382] The server uses a text analysis engine to extract key keywords from the analysis results. For example, keywords such as "travel," "summer," "guide," and "recommended spots" are extracted. The extracted keywords are then output.

[0383] Step 7:

[0384] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate content based on the extracted keywords and sentiment information. The generative AI model receives a specific prompt (e.g., "Please generate an article about travel. I'd especially like to know about recommended spots for summer. Please also reflect past interest in beach resorts.") as input and generates the content. The generated content is output.

[0385] Step 8:

[0386] The server personalizes the generated content based on the user's past behavioral history. For example, if the user's past behavior indicates an interest in "beach resorts," that information is added to the article. The personalized content is then output.

[0387] Step 9:

[0388] The server formats the final personalized content into a display format such as HTML and sends it to the device. The formatted content is output and sent to the device.

[0389] Step 10:

[0390] The device displays the personalized content received. The article is displayed in a format optimized for the smartphone screen. The content to be displayed is output.

[0391] Step 11:

[0392] Users can view the displayed article and enter feedback such as their impressions and requests for improvement. For example, they can enter feedback such as "This article was very helpful" or "I would like more detailed information." The entered feedback can then be obtained.

[0393] Step 12:

[0394] The device receives feedback from the user, converts it into JSON format, outputs the converted feedback in JSON format, and sends it to the server.

[0395] Step 13:

[0396] The server stores the received feedback in a database. The stored feedback is used the next time content is generated. The feedback data to be used in the next generation is output.

[0397] (Application example 2)

[0398] 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."

[0399] Conventional personalized content generation systems generate content solely based on past behavioral history and input data without considering the user's emotional state, often resulting in insufficient empathy between the user and the content. Furthermore, advertising content also fails to reflect the user's emotions, resulting in a lack of ability to attract the user's interest. The objective of this invention is to solve these problems and provide personalized content and advertisements that are tailored to the user's emotional state and have a higher level of engagement.

[0400] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input data from a user, means for analyzing the received input data and extracting keywords, means for generating content using a generation artificial intelligence based on the extracted keywords, means for personalizing the generated content based on the user's past behavior history, means for transmitting the personalized content to the user's terminal, means for receiving feedback from the user and saving the feedback in a database, means for utilizing the feedback the next time content is generated, means for analyzing emotions from the user's input data using an emotion engine and reflecting the emotion information in content generation, means for analyzing the user's gaze and voice in real time to collect emotion information, and means for generating personalized advertising content based on the emotion information and displaying it to the user. This makes it possible to provide highly accurate personalized content and advertising content that takes the user's emotional state into consideration.

[0401] "Means for receiving input data from the user" refers to a function for receiving data such as text, audio, images, and videos input by the user through the terminal.

[0402] The "means for analyzing received input data and extracting keywords" is a function for automatically extracting important keywords by analyzing received data.

[0403] "Means for generating content using generative artificial intelligence based on extracted keywords" refers to a function for generating appropriate content using generative artificial intelligence (AI) based on extracted keywords.

[0404] "Means for personalizing content generated based on a user's past behavioral history" refers to a function for optimizing content generated for individual users based on the user's past behavioral data.

[0405] "Means for sending personalized content to a user's device" refers to a function for transferring personalized content to a device used by a user.

[0406] "Means for receiving feedback from users and storing the feedback in a database" is a function for receiving opinions and evaluations from users and storing them in a database.

[0407] The "means for utilizing feedback when generating content next time" is a function for utilizing saved feedback information when generating content next time to provide more appropriate content.

[0408] "Means for analyzing emotions from user input data using an emotion engine and reflecting that emotional information in content generation" refers to a function for analyzing emotions from user input data using an emotion engine and generating content based on that emotional information.

[0409] "Means for analyzing a user's gaze and voice in real time to collect emotional information" is a function for analyzing a user's gaze and voice in real time to collect emotional information.

[0410] "Means for generating personalized advertising content based on the emotional information and displaying it to the user" refers to a function for individually optimizing advertising content based on the collected emotional information and displaying it on the user's device.

[0411] This invention is a system that generates and provides personalized content and advertising content that takes user emotions into consideration. This system is mainly composed of a user terminal, a server, an emotion engine, a generative AI model, and a database.

[0412] System configuration

[0413] User Device

[0414] A user terminal is a device used to receive input data from a user, such as a smartphone, tablet, personal computer, or smart glasses.

[0415] server

[0416] The server is the central unit that analyzes the input data, incorporates the results of the emotion engine, and uses generative AI models to generate personalized content and advertisements. The server also:

[0417] Receives user input data and converts the data into the appropriate format.

[0418] The emotion engine analyzes voice and gaze data from the user to obtain emotional information.

[0419] Keywords are extracted using a text analysis engine and input into a generative AI model.

[0420] Generate and personalize content based on emotional data and past behavioral history.

[0421] The generated content is transmitted to the user terminal.

[0422] Specific hardware includes cloud servers (e.g., Amazon AWS, Google Cloud), and software includes emotion engines (e.g., Microsoft Azure Emotion API) and generative AI models (e.g., OpenAI's GPT-4).

[0423] Emotion Engine

[0424] The emotion engine is software that analyzes emotions from user input data (voice, text, gaze, etc.). The analyzed emotional information is reflected when generating content.

[0425] Generative AI Models

[0426] A generative AI model is an artificial intelligence that automatically generates new content based on extracted keywords and emotional information. In particular, OpenAI's GPT-4 performs this function.

[0427] Database

[0428] The database stores the user's past behavior history and feedback, which is referenced the next time content is generated, resulting in content that is optimized for the user.

[0429] Examples and prompts

[0430] Assuming a user is wearing smart glasses, if the user verbally inputs a request such as "Tell me about recommended summer travel spots," the system will operate as follows:

[0431] 1. The user device (smart glasses) receives voice input and sends the data to the server.

[0432] 2. The server uses an emotion engine to analyze emotions such as "excitement" and "anticipation" from the user's voice data.

[0433] 3. Next, the server uses a text analysis engine to extract keywords such as "summer" and "travel."

[0434] 4. Use the generative AI model to create a prompt like this:

[0435] "Generate a personalized advertisement for a summer vacation. The user is feeling excited and curious. They have shown interest in resort beaches and hiking trails in the past."

[0436] 5. The generative AI model (GPT-4) generates content based on the prompt above.

[0437] 6. The server sends the generated content to the user's smart glasses for display.

[0438] In this way, the present invention analyzes the user's emotional state in real time and provides highly accurate personalized content and advertising content based on the analysis.

[0439] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0440] Step 1:

[0441] The user makes a voice input through the smart glasses, "Tell me about recommended summer travel spots." This voice data is received by the terminal and sent to the server. The input is the user's voice data, and the output is the transmission of the voice data to the server.

[0442] Step 2:

[0443] The server uses an emotion engine to analyze the received voice data and extract the user's emotional information. Specifically, emotional states such as "excitement" and "expectation" are analyzed from the voice data. The input is the received voice data, and the output is the extracted emotional information.

[0444] Step 3:

[0445] The server uses a text analysis engine to extract keywords from the user's voice data. For example, keywords such as "summer," "travel," and "recommended spots" are extracted. The input is the user's voice data, and the output is the extracted keywords.

[0446] Step 4:

[0447] The server sends a prompt to the generative AI model based on the emotional information, extracted keywords, and the user's past behavioral history. The specific prompt is, "Generate a personalized advertisement for a summer vacation. The user is feeling excited and curious. They have shown interest in beach resorts and hiking trails in the past." The input is emotional information, keywords, and past behavioral history, and the output is the generated prompt.

[0448] Step 5:

[0449] A generative AI model (e.g., GPT-4) generates content based on a prompt and returns the generated results to the server. The input is the prompt, and the output is the generated content (e.g., an article about recommended summer travel spots).

[0450] Step 6:

[0451] The server further personalizes the generated content based on the user's individual information and sends it to the user's device. Specifically, if the user has previously shown interest in "beach resorts" or "mountainous areas," that information is included. The input is the generated content and the user's individual information, and the output is the transmission of personalized content to the device.

[0452] Step 7:

[0453] The device (smart glasses) displays personalized content to the user. The user watches the displayed content and provides feedback as needed. The input is the personalized content, and the output is the display of the content and the generation of feedback.

[0454] Step 8:

[0455] Feedback from the user is received by the device and sent to the server. The server stores this feedback in a database and uses it the next time content is generated. The input is the user's feedback, and the output is the storage of the feedback in the database.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] [Second embodiment]

[0460] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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."

[0472] This invention is a system that combines generative AI and artificial general intelligence (AGI) to efficiently process a wide variety of input data (text, audio, images, and videos) from users and generate and provide personalized content. The system aims to provide high-quality content that matches the user's interests and preferences, improving the user experience.

[0473] System configuration

[0474] The system mainly consists of the following components:

[0475] 1. User terminal: A device that receives user input data and sends it to the server. Examples include smartphones, tablets, and PCs.

[0476] 2. Server: Analyzes input data, generates content using generative AI and AGI, and provides personalized content.

[0477] 3. Database: Stores users' past behavioral history and feedback and uses it when generating content.

[0478] Program processing

[0479] (1) Receiving user input

[0480] The user inputs a content generation request into the terminal. For example, if the user wants an article about "travel," the user inputs text such as "Generate a guide article about travel."

[0481] (2) Data preprocessing

[0482] The terminal receives this input, converts the data into an appropriate format, and then sends it to the server.

[0483] (3) Data analysis

[0484] The server analyzes the received data and extracts keywords, such as "travel," "guide," and "recommended spots."

[0485] (4) Content generation

[0486] The server uses generative AI to generate content based on the extracted keywords and the user's past behavioral history. For example, it creates specific articles on "recommended travel spots" and "how to plan a trip."

[0487] (5) Personalization

[0488] The server then further personalizes the generated content to match the user's interests. If the user has previously viewed content related to "beach resorts," the server will take that information into account and include "recommended beach resorts" in the article.

[0489] (6) Provision of content

[0490] The server formats the final personalized content and sends it to the device, which displays it to the user, for example, displaying an article in a layout tailored to the screen of a smartphone.

[0491] (7) Feedback collection

[0492] After users have finished viewing the content provided, they can provide feedback, such as leaving a comment like "I found the article helpful" or "I'd like more information."

[0493] (8) Processing Feedback

[0494] The device receives the user's feedback and sends it to the server, which stores it in a database and takes it into account when generating content next time.

[0495] Specific examples

[0496] As a specific scenario, we will explain the behavior when a user wants articles about "summer travel plans."

[0497] 1. The user enters the text "I want an article generated about summer travel plans."

[0498] 2. The device sends this text to the server.

[0499] 3. The server analyzes the text and extracts keywords such as "summer," "travel plans," and "recommended spots."

[0500] 4. The server uses generative AI to create articles on "recommended spots for summer travel" and "how to plan an effective trip."

[0501] 5. The server adds specific information, such as "beach resort" or "mountainous area," based on the user's past behavioral history.

[0502] 6. The server formats the final article and sends it to the device.

[0503] 7. The device displays personalized content to the user.

[0504] 8. Users view the content and provide feedback such as "Very helpful" or "I'd like more detailed information."

[0505] 9. The device sends the feedback to the server, which stores it in a database.

[0506] In this way, the system can efficiently provide high-quality personalized content that meets the user's needs.

[0507] The processing flow will be explained below.

[0508] Step 1:

[0509] The user uses the device to make a content generation request. For example, the user enters text such as "I would like an article about travel."

[0510] Step 2:

[0511] The terminal receives the input data, converts it into a communication format, and sends the converted data to the server.

[0512] Step 3:

[0513] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[0514] Step 4:

[0515] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "travel," "guide," and "recommended spots."

[0516] Step 5:

[0517] The server considers the extracted keywords and the user's past behavioral history and activates a generative AI model, which generates new content (e.g., a travel guide article) based on the analyzed information.

[0518] Step 6:

[0519] The server-generated content is personalized based on the user's past behavior and interests. For example, if the user has previously expressed an interest in "beach resorts," the server will create personalized content that includes that information.

[0520] Step 7:

[0521] The server formats the personalized content and sends it to the user's device.

[0522] Step 8:

[0523] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[0524] Step 9:

[0525] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[0526] Step 10:

[0527] The device collects feedback from the user and sends it to the server.

[0528] Step 11:

[0529] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[0530] In this way, the system can efficiently generate and provide personalized content that meets the user's needs.

[0531] Example 1

[0532] 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."

[0533] In today's information society, users demand a wide variety of content, and they also demand a high level of quality. However, conventional content generation systems have difficulty providing personalized content tailored to individual users' interests and preferences, resulting in a poor user experience. Furthermore, they lack the ability to reflect user feedback in the next generation of content, making continuous improvement difficult.

[0534] 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.

[0535] In this invention, the server includes means for converting input data from a user into an appropriate format, means for transmitting the converted data to the server, means for analyzing the data received by the server and extracting keywords, means for generating content using a generative AI model, means for personalizing the generated content based on the user's past behavioral history, means for transmitting the personalized content to the user's terminal and displaying it, means for receiving feedback from the user and storing the feedback in a database, and means for utilizing the feedback when generating content next time. This makes it possible to provide high-quality personalized content that meets the individual interests and preferences of the user, improving the user experience and enabling continuous improvement of the content based on the feedback.

[0536] "Input Data" means information provided by a user to a system, and may be in the form of text, audio, images, or video.

[0537] "Terminal" refers to the device used by the user, including smartphones, tablets, and personal computers.

[0538] A "server" is a central processing unit that handles input data analysis, content generation, personalization, and feedback.

[0539] The "means for converting into a format" is a function for carrying out a process of converting input data into a format that is easy to analyze.

[0540] The "means for analyzing and extracting keywords" is a function for carrying out the process of identifying and extracting key keywords from received input data.

[0541] "Means for generating content using a generative AI model" refers to a function for carrying out a process of generating information using an artificial intelligence model based on extracted keywords.

[0542] "Personalized content" is content that is generated and personalized by adding specific information based on the user's past behavioral history and interests.

[0543] "Feedback" refers to ratings and comments made by users on provided content.

[0544] A "database" is a system for storing user feedback and past behavioral history.

[0545] "Means to be utilized the next time content is generated" is a function that performs a process to reflect saved feedback and past behavioral history in newly generated content.

[0546] "Means for displaying" refers to the function of displaying personalized content in a format appropriate for the user's device.

[0547] The present invention provides a system for generating and providing high-quality personalized content that meets the individual interests and preferences of users based on a wide variety of input data. A detailed description of specific embodiments of the present invention is provided below.

[0548] The system is broadly composed of a user terminal, a server, and a database. These elements communicate with each other to efficiently generate content and perform personalization.

[0549] A user device is a device that receives user input. Examples include smartphones, tablets, and PCs. Users use these devices to request desired content by typing text or speaking. In the case of speech input, speech recognition software on the device converts speech into text.

[0550] The server is a central processing unit that receives input data sent by users, analyzes it, and generates content. The server has the following functions:

[0551] 1. Data preprocessing: The server converts the data received from the terminal into an appropriate format (e.g., JSON format, XML format). This conversion is performed using various programming languages ​​(e.g., Python, Java).

[0552] 2. Data analysis: The server analyzes the received data using natural language processing tools (e.g., spaCy, NLTK) and extracts keywords. For example, from a request such as "Generate travel guide articles," it extracts keywords such as "travel," "guide," and "recommended spots."

[0553] 3. Content generation: The server generates content based on the extracted keywords using a generative AI model (e.g., GPT-4). By inputting specific prompts into the generative AI model, detailed content such as "Please write an article about recommended travel spots" can be automatically generated.

[0554] 4. Personalization: The server personalizes the generated content based on the user's past behavior and interests. For example, if the user has previously viewed a lot of information about "beach resorts," the server may include "recommended beach resorts" in the generated article.

[0555] 5. Providing content: The server sends the final personalized content to the device, which receives it and displays it in a format appropriate for the user (e.g., a smartphone screen layout).

[0556] 6. Feedback collection and utilization: After viewing the provided content, users can enter their feedback in text. The device receives this feedback and sends it to the server. The server stores the feedback in a database and utilizes it the next time content is generated.

[0557] This allows the system to generate high-quality content tailored to each user's individual interests and preferences, and to continuously improve it. For example, if a user requests an article about summer travel plans, the server extracts keywords such as "summer," "travel plans," and "recommended spots," and uses a generative AI model to create a detailed travel guide, personalizing it based on the user's past preferences. As a result, users can easily obtain the optimal travel plan that best suits them.

[0558] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0559] Step 1: Accepting User Input

[0560] A user inputs a content generation request into a device. For example, the user inputs the text "Please generate a travel guide article." This input data is sent to the device.

[0561] Input: Text data containing the user's preferences.

[0562] Output: The input data in text format.

[0563] Step 2: Preprocessing the data

[0564] The device converts the input data received from the user into an appropriate format (e.g., JSON format, XML format), and sends this converted data to the server.

[0565] Input: Text data entered by the user.

[0566] Output: Pretty-printed JSON or XML data.

[0567] Step 3: Receiving and analyzing data

[0568] The server analyzes the data received from the device using an analysis tool (e.g., spaCy, NLTK) to extract key keywords, such as "travel," "guide," and "recommended spots."

[0569] Input: Well-formed JSON or XML data.

[0570] Output: The extracted keyword list.

[0571] Step 4: Content Generation

[0572] The server inputs the extracted keywords as a prompt into a generative AI model (e.g., GPT-4), and uses the generative AI model to generate specific content. For example, the prompt could be "Please write an article about recommended travel spots."

[0573] Input: The extracted keyword list.

[0574] Output: The generated content (e.g., a text article).

[0575] Step 5: Personalization

[0576] The server personalizes the generated content based on the user's past behavior. For example, if a user has frequently viewed information about "beach resorts," that information will be included in the article.

[0577] Input: Generated content and user past behavior.

[0578] Output: Personalized content.

[0579] Step 6: Providing content

[0580] The server sends the final personalized content to the device, which receives the data and displays it in a format appropriate for the user (e.g., a smartphone screen layout).

[0581] Input: Personalized content.

[0582] Output: The content that is displayed on the user's device.

[0583] Step 7: Gather feedback

[0584] After users have finished viewing the provided content, they can enter their feedback in text, such as "The article was helpful" or "I'd like more information," and send it to their device.

[0585] Input: Feedback data, including user ratings and comments.

[0586] Output: Formatted feedback data.

[0587] Step 8: Processing feedback

[0588] The device receives feedback from the user and sends it to the server, which stores the received feedback in a database and uses it the next time content is generated.

[0589] Input: Formatted feedback data.

[0590] Output: Feedback data stored in a database.

[0591] In this way, through the specific operations and inputs / outputs at each step, the system can provide high-quality personalized content that meets the individual interests and preferences of users.

[0592] (Application example 1)

[0593] 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."

[0594] Conventional personalized content generation systems were limited in the types of input data and the way feedback was utilized, making them unable to adequately meet diverse user needs. Furthermore, the content generation process using generative AI models was inefficient, resulting in low quality content and low user satisfaction. Furthermore, the level of personalization based on user preferences was insufficient, leaving the improvement of the user experience a challenge.

[0595] 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.

[0596] In this invention, the server includes means for receiving input data from a user, means for analyzing the received input data and extracting keywords, means for generating content using artificial intelligence based on the extracted keywords, means for personalizing the generated content based on the user's past behavior history, means for displaying the generated content on a display terminal, means for receiving feedback from the user and saving the feedback in a database, means for utilizing the feedback when generating content next time, and means for generating prompt sentences and inputting the prompt sentences to a generative AI model. This makes it possible to respond to the diversity of user input data and efficiently utilize the generative AI model to provide high-quality personalized content.

[0597] A "user terminal" is a device that receives input data from a user and sends it to a server, and includes smartphones, tablets, and personal computers.

[0598] The "server" is a central device that analyzes data sent by users, generates content using generative AI models and prompts, and sends personalized content to user devices.

[0599] The "database" is an information management system that stores users' past behavioral history and feedback information and uses it when generating content the next time.

[0600] A "generative AI model" is an artificial intelligence model used to generate high-quality content based on user input data.

[0601] A "prompt" is an input text for a generative AI model, and is an instruction sentence that specifically expresses the user's intentions and wishes.

[0602] "Personalized content" refers to content that is customized based on a user's past behavioral history and preferences, and includes information tailored to individual needs.

[0603] "Feedback" refers to reactions and comments made by users to the content provided, and is information that is used to improve the quality of the next content generated.

[0604] "Input Data" refers to information provided by a User in the form of text, audio, images, video, or other information.

[0605] "Keywords" are important words and phrases extracted from input data through analysis, and play a central role in content generation.

[0606] "Analysis" is the process of analyzing input data from users and extracting important elements and keywords.

[0607] "Content generation" is the process of using a generative AI model to create information based on input data and keywords.

[0608] "Personalization" is the process of tailoring generated content to meet individual needs, based on the user's interests and past behavior.

[0609] A "display terminal" is a device that displays personalized content to users, such as a smartphone, tablet, or PC.

[0610] MODE FOR CARRYING OUT THE INVENTION

[0611] This invention is a system that efficiently processes various user input data (text, voice, images, videos) and generates and provides high-quality personalized content using generative AI models and prompts. This system mainly consists of the following elements:

[0612] User Device

[0613] A user terminal is a device that includes a smartphone, tablet, PC, etc., and serves to receive input data from a user and transmit that data to a server.

[0614] server

[0615] The server has the following main roles:

[0616] 1. Receiving and parsing input data:

[0617] It analyzes input data such as text, voice, images, and videos sent by users and extracts important keywords. This analysis process is carried out using natural language processing (NLP) technology.

[0618] 2. Content Generation:

[0619] Based on the extracted keywords and the user's past behavioral history, a generative AI model is used to generate content, and a prompt sentence is generated and input into the generative AI model.

[0620] 3. Personalization:

[0621] The generated content is further personalized based on the user's interests and preferences, referencing past behavioral history and feedback information from a database to provide customized information for each user.

[0622] Database

[0623] The database stores users' past behavioral history and feedback information, which the server uses to generate and personalize content.

[0624] Specific examples

[0625] Let's take the example of a user entering text such as "Tell me the latest movie recommendations." The user's device receives this text and sends it to the server. The server analyzes the entered text and extracts keywords such as "latest," "movie," and "recommended." The server then generates a prompt for the generative AI model. Specifically, it generates the following prompt:

[0626] Prompt: "The user has previously expressed an interest in psychological thrillers. Provide a list of current popular movies, including recommendations with a particular focus on psychological thrillers."

[0627] This prompt is passed as input to a generative AI model, which generates a list of "Top 5 New Movies," along with detailed information about psychological thriller movies the user has previously been interested in. The final content is then displayed and served to the user on their device, such as a smartphone or tablet.

[0628] Hardware and software used

[0629] Hardware: User devices (smartphones, tablets, PCs), servers (cloud servers or on-premise servers)

[0630] Software: Natural Language Processing (NLP) models, generative AI models (e.g., GPT-3), database management systems (DBMS)

[0631] This significantly improves the user experience by allowing users to receive high-quality personalized content based on their interests and preferences, and by using collected feedback to generate new content, the quality of the content provided can be continuously improved.

[0632] As a result, the present invention is able to accommodate the diversity of user input data and efficiently utilize generative AI models to provide high-quality personalized content.

[0633] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0634] Step 1:

[0635] The user inputs input data (e.g., the text "What are your recommendations for the latest movies?") into the device. The device receives this input data and sends it to the server.

[0636] Input: User input data (text, audio, images, video)

[0637] Processing: The device receives the input data

[0638] Output: Sends the received input data to the server

[0639] Step 2:

[0640] The server analyzes the received input data and extracts important keywords using natural language processing (NLP) techniques.

[0641] Input: The input data submitted (e.g., the text "What are your recommendations for the latest movies?")

[0642] Processing: Analyzes text using NLP techniques to extract keywords (e.g., "latest," "movies," "recommended")

[0643] Output: A list of extracted keywords

[0644] Step 3:

[0645] The server generates a prompt based on the extracted keywords, which is then fed directly into the generative AI model.

[0646] Input: A list of extracted keywords

[0647] Processing: Generate a prompt based on keywords (e.g., "The user has previously expressed an interest in psychological thrillers. Provide a list of current popular movies, including recommendations with a particular focus on psychological thrillers.")

[0648] Output: Generated prompt statement

[0649] Step 4:

[0650] The server inputs a prompt sentence into the generative AI model to generate content. The generative AI model generates content with the specified content based on the prompt sentence.

[0651] Input: Generated prompt statement

[0652] Processing: Input a prompt into the generative AI model to generate content (e.g., "Top 5 new movies").

[0653] Output: Generated content

[0654] Step 5:

[0655] The server personalizes the generated content based on the user's past behavior history, referencing the past behavior history and feedback information stored in the database to customize it for each user.

[0656] Input: Generated content, user history

[0657] Processing: Tailoring and personalizing content based on past behavior

[0658] Output: personalized content

[0659] Step 6:

[0660] The server sends the final personalized content to the user's device, which displays the content.

[0661] Input: Personalized content

[0662] Processing: Send the content to the user's device, and the device displays the content

[0663] Output: The personalized content that is displayed to the user

[0664] Step 7:

[0665] The user views the provided content and provides feedback (e.g., "Very helpful" or "I'd like more details"). The device receives this feedback and sends it to the server.

[0666] Input: User feedback

[0667] Processing: The device receives the feedback and sends it to the server.

[0668] Output: Feedback is sent to the server

[0669] Step 8:

[0670] The server stores the received feedback in a database and uses this feedback the next time content is generated.

[0671] Input: User feedback

[0672] Processing: Store the feedback in a database and use it the next time you generate content.

[0673] Output: Improved next content generation process

[0674] 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.

[0675] This invention combines a system that uses generative AI and artificial general intelligence (AGI) to process input data from users and generate and provide personalized content with an emotion engine that recognizes user emotions. This system aims to achieve even higher user satisfaction by providing content that takes into account the user's emotional state.

[0676] System configuration

[0677] The system mainly consists of the following components:

[0678] 1. User terminal: A device that receives user input data and sends it to the server. Examples include smartphones, tablets, and PCs.

[0679] 2. Emotion engine: Analyzes emotions from user input data (voice, text) and uses that information to generate content.

[0680] 3. Server: Analyzes input data, generates content using generative AI and AGI, and provides personalized content, taking into account emotional information from the emotion engine.

[0681] 4. Database: Stores user behavior history and feedback and uses it when generating content.

[0682] Program processing

[0683] (1) Receiving user input

[0684] The user inputs a content generation request into the device. For example, the user inputs the text "I want you to generate an article about travel."

[0685] (2) Data preprocessing

[0686] The terminal receives this input data, converts the data into an appropriate format, and then transmits it to the server.

[0687] (3) Data analysis and emotion recognition

[0688] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[0689] The emotion engine analyzes the user's emotions from text or voice data and provides the emotional state to the server. For example, emotions such as "curious" or "excited" are detected.

[0690] (4) Keyword extraction

[0691] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "travel," "guide," and "recommended spots."

[0692] (5) Content generation

[0693] The server launches a generative AI model based on the extracted keywords, emotional information from the emotion engine, and the user's past behavioral history. The generative AI model generates new content (e.g., a travel guide article) based on the analyzed information. If the emotional information is "curious," the article will be written in an adventurous tone.

[0694] (6) Personalization

[0695] The server then further personalizes the generated content to match the user's interests. If the user has previously shown interest in "beach resorts," the server will provide personalized content that includes that information.

[0696] (7) Provision of content

[0697] The server formats the final personalized content and sends it to the device.

[0698] (8) Display of content

[0699] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[0700] (9) Feedback collection

[0701] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[0702] (10) Processing Feedback

[0703] The device collects feedback from the user and sends it to the server.

[0704] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[0705] Specific examples

[0706] As a specific scenario, we will explain the behavior when a user wants articles about "summer travel plans."

[0707] 1. The user enters the text "I want an article generated about summer travel plans."

[0708] 2. The device sends this text to the server.

[0709] 3. The server analyzes the text and extracts keywords such as "summer," "travel plans," and "recommended spots."

[0710] 4. The emotion engine analyzes the user's emotions from the text and detects emotions such as "high expectations."

[0711] 5. The server uses generative AI to create articles about "recommended summer travel spots" and "how to plan an effective trip." It takes into account emotional information and creates articles with a tone that builds anticipation.

[0712] 6. The server adds specific information, such as "beach resort" or "mountainous area," based on the user's past behavioral history.

[0713] 7. The server formats the final article and sends it to the device.

[0714] 8. The device displays personalized content to the user.

[0715] 9. Users view the content and provide feedback such as "Very helpful" or "I'd like more information."

[0716] 10. The device sends the feedback to the server, which stores it in a database.

[0717] In this way, the system can efficiently provide high-quality personalized content that responds to the user's needs and emotional state.

[0718] The processing flow will be explained below.

[0719] Step 1:

[0720] The user uses the device to make a content generation request. For example, the user inputs text such as "I want an article about summer travel plans to be generated."

[0721] Step 2:

[0722] The terminal receives input data, converts the data into a communication format, and sends the converted data to the server.

[0723] Step 3:

[0724] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[0725] Step 4:

[0726] The server starts an emotion engine and analyzes the user's emotions from the text data. For example, the emotion engine detects the emotion "high expectations."

[0727] Step 5:

[0728] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "summer," "travel plans," and "recommended spots."

[0729] Step 6:

[0730] The server then activates a generative AI model based on the extracted keywords, emotional information, and the user's past behavioral history. The generative AI model generates new content (e.g., a travel guide article) based on the analyzed information.

[0731] Step 7:

[0732] The server tailors the generated content based on the user's emotional state, for example, if it detects a high sense of anticipation, it will create an article with an adventurous and fun tone.

[0733] Step 8:

[0734] The server then further personalizes the generated content to match the user's interests. For example, if the user has previously shown interest in "beach resorts," the server will provide personalized content that includes that information.

[0735] Step 9:

[0736] The server formats the final personalized content and sends it to the device.

[0737] Step 10:

[0738] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[0739] Step 11:

[0740] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[0741] Step 12:

[0742] The device collects feedback from the user and sends it to the server.

[0743] Step 13:

[0744] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[0745] In this way, by performing specific actions at each step, the system is able to efficiently provide personalized content that matches the user's emotions and needs.

[0746] Example 2

[0747] 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."

[0748] Conventional content generation systems analyze user input data and provide content without taking the user's emotions into account, resulting in low user satisfaction. In particular, it is difficult to efficiently provide personalized content that accurately reflects the user's emotional information. Furthermore, there is a lack of a means to continuously utilize user feedback to improve the quality of the content.

[0749] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0750] In this invention, the server includes means for receiving input data from a user, means for converting the received input data into an appropriate format, means for analyzing the converted input data and identifying its format, means for acquiring user emotional information from the input data using an emotion analysis engine, means for extracting keywords based on the analyzed format and emotional information, means for generating content based on the extracted keywords and emotional information using a generative artificial intelligence model, means for personalizing the generated content based on the user's past behavior history, means for transmitting the personalized content to the user's terminal, means for receiving feedback from the user and storing the feedback in a database, and means for utilizing the stored feedback when generating content next time. This makes it possible to efficiently provide high-quality personalized content that reflects the user's emotional information and improve user satisfaction.

[0751] "User" means a person or entity that utilizes the System to enter content generation requests and receive personalized content.

[0752] "Input Data" means information provided by a User to a System for content generation, including in the form of text, audio, images, video, or other information.

[0753] "Device" means a device used by a User to enter Input Data and view Generated Content, including a smartphone, tablet, or PC.

[0754] "Server" means a computer system that analyzes input data and performs content generation and personalization.

[0755] "Format" refers to the type and structure of data, and identifies whether the input data is text, audio, image, or video.

[0756] An "emotion analysis engine" is software or algorithms used to analyze and extract user emotional information from input data.

[0757] "Keywords" refer to important words and phrases extracted by analyzing the content of input data, and form the basis for content generation.

[0758] A "generative artificial intelligence model" is a type of AI technology used to generate new content based on extracted keywords and emotional information.

[0759] "Content" refers to information such as articles, reports, guides, etc. that are generated based on user input data and sentiment information.

[0760] "Personalization" is the process of optimizing content for individual users based on their past behavior and interests.

[0761] "Feedback" refers to input data such as user evaluations, opinions, and requests for improvement regarding the content provided.

[0762] A "database" is an information storage system for storing feedback received from users and past behavioral history.

[0763] This invention relates to a system that processes input data from users using generative AI and an emotion analysis engine to generate and provide personalized content. The purpose of this invention is to achieve higher user satisfaction by taking into account the user's emotional state.

[0764] System configuration

[0765] The system consists of the following elements:

[0766] 1. User Device

[0767] A device that receives user input data and sends it to a server. Examples include smartphones, tablets, and PCs. It provides an interface for users to enter input data.

[0768] 2. Sentiment Analysis Engine

[0769] Software or algorithms that analyze emotions from user input data (text, voice) and reflect that information in content generation. For example, Microsoft Azure's Text Analytics for Sentiment Analysis is used.

[0770] 3. Server

[0771] A computer system that analyzes input data and generates and personalizes content based on information from generative AI models and sentiment analysis engines, and delivers it to users. It also uses text analysis engines (e.g., SpaCy, NLTK).

[0772] 4. Database

[0773] An information storage system that stores users' past behavioral history and feedback and utilizes it when generating content.

[0774] Program processing

[0775] Receiving user input

[0776] A user enters a request via text, for example, "I want an article about summer travel plans."

[0777] Data Preprocessing

[0778] The terminal receives the input data, converts it to text, and then formats it appropriately, such as JSON, before sending it to the server.

[0779] Data analysis and emotion recognition

[0780] The server parses the received JSON data and analyzes the strings using a text analysis engine (e.g., SpaCy or NLTK). At the same time, a sentiment analysis engine analyzes the user's sentiment from the text and extracts emotional information such as "high expectations."

[0781] Keyword extraction

[0782] The server uses a text analysis engine to extract key keywords such as "travel," "summer," "guide," and "recommended spots" from the input text.

[0783] Content Generation

[0784] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate content based on the extracted keywords and sentiment information. For example, it generates articles about "recommended spots for summer travel" and "effective travel planning." It also crafts prompts to be written in a tone that builds anticipation.

[0785] Personalization

[0786] The server then personalizes the content it generates based on the user's past behavior. For example, if a user has previously shown interest in "beach resorts," that information will be added to the article.

[0787] Content provision

[0788] The server formats the final personalized content into HTML format and sends it to the device.

[0789] View content

[0790] The device then displays the received content to the user, for example, displaying an article in a format optimized for a smartphone screen.

[0791] Feedback collection

[0792] Users view the displayed article and enter their feedback, such as their impressions or suggestions for improvement, into the on-screen form. For example, they can enter feedback such as "This article was very helpful" or "I would like more detailed information."

[0793] Processing Feedback

[0794] The device receives feedback from the user, converts it into JSON format, and sends it to the server. The server stores the received feedback in a database and uses it for future content generation.

[0795] Examples of specific examples and prompts

[0796] As a concrete example, consider the case where a user enters the text "I want an article generated about summer travel plans." By entering this text into an application and submitting it, the system will perform the following actions:

[0797] 1. The user enters the text "I want an article generated about summer travel plans."

[0798] 2. The device converts the input text into JSON format and sends it to the server.

[0799] 3. The server performs text analysis and sentiment analysis to obtain the sentiment information of "high expectations" and extract key keywords.

[0800] 4. The server uses the generative AI model to generate content such as "Recommended spots for summer travel" and "How to plan an effective trip." The prompt includes elements that build anticipation: "Generate an article about travel. I'd especially like to know about recommended spots related to summer. Please also reflect my past interest in beach resorts."

[0801] 5. The server creates personalized articles based on the user's past behavioral history and sends them to the device.

[0802] 6. The device displays the article in a format optimized for the smartphone screen.

[0803] This mechanism enables the system to efficiently provide high-quality personalized content that reflects the user's emotional information, thereby improving user satisfaction.

[0804] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0805] Step 1:

[0806] The user inputs a content generation request. For example, they input text such as "I want you to generate an article about summer travel plans." The text is obtained as input data.

[0807] Step 2:

[0808] The terminal receives this input data. The terminal converts the received text data into an appropriate format and formats it in JSON format. The converted data is then output.

[0809] Step 3:

[0810] The terminal sends formatted input data to the server, which then passes the data to the server in JSON format.

[0811] Step 4:

[0812] The server parses the received JSON data. The server uses a text analysis engine (e.g. SpaCy, NLTK) to break the text into tokens and parse it. The parsed data is obtained.

[0813] Step 5:

[0814] The server starts the emotion analysis engine and obtains the user's emotional information from the analyzed text data. The emotion analysis engine analyzes the text data and extracts emotional information such as "high expectations." The emotional information is output.

[0815] Step 6:

[0816] The server uses a text analysis engine to extract key keywords from the analysis results. For example, keywords such as "travel," "summer," "guide," and "recommended spots" are extracted. The extracted keywords are then output.

[0817] Step 7:

[0818] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate content based on the extracted keywords and sentiment information. The generative AI model receives a specific prompt (e.g., "Please generate an article about travel. I'd especially like to know about recommended spots for summer. Please also reflect past interest in beach resorts.") as input and generates the content. The generated content is output.

[0819] Step 8:

[0820] The server personalizes the generated content based on the user's past behavioral history. For example, if the user's past behavior indicates an interest in "beach resorts," that information is added to the article. The personalized content is then output.

[0821] Step 9:

[0822] The server formats the final personalized content into a display format such as HTML and sends it to the device. The formatted content is output and sent to the device.

[0823] Step 10:

[0824] The device displays the personalized content received. The article is displayed in a format optimized for the smartphone screen. The content to be displayed is output.

[0825] Step 11:

[0826] Users can view the displayed article and enter feedback such as their impressions and requests for improvement. For example, they can enter feedback such as "This article was very helpful" or "I would like more detailed information." The entered feedback can then be obtained.

[0827] Step 12:

[0828] The device receives feedback from the user, converts it into JSON format, outputs the converted feedback in JSON format, and sends it to the server.

[0829] Step 13:

[0830] The server stores the received feedback in a database. The stored feedback is used the next time content is generated. The feedback data to be used in the next generation is output.

[0831] (Application example 2)

[0832] 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."

[0833] Conventional personalized content generation systems generate content solely based on past behavioral history and input data without considering the user's emotional state, often resulting in insufficient empathy between the user and the content. Furthermore, advertising content also fails to reflect the user's emotions, resulting in a lack of ability to attract the user's interest. The objective of this invention is to solve these problems and provide personalized content and advertisements that are tailored to the user's emotional state and have a higher level of engagement.

[0834] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input data from a user, means for analyzing the received input data and extracting keywords, means for generating content using a generation artificial intelligence based on the extracted keywords, means for personalizing the generated content based on the user's past behavior history, means for transmitting the personalized content to the user's terminal, means for receiving feedback from the user and saving the feedback in a database, means for utilizing the feedback the next time content is generated, means for analyzing emotions from the user's input data using an emotion engine and reflecting the emotion information in content generation, means for analyzing the user's gaze and voice in real time to collect emotion information, and means for generating personalized advertising content based on the emotion information and displaying it to the user. This makes it possible to provide highly accurate personalized content and advertising content that takes the user's emotional state into consideration.

[0835] "Means for receiving input data from the user" refers to a function for receiving data such as text, audio, images, and videos input by the user through the terminal.

[0836] The "means for analyzing received input data and extracting keywords" is a function for automatically extracting important keywords by analyzing received data.

[0837] "Means for generating content using generative artificial intelligence based on extracted keywords" refers to a function for generating appropriate content using generative artificial intelligence (AI) based on extracted keywords.

[0838] "Means for personalizing content generated based on a user's past behavioral history" refers to a function for optimizing content generated for individual users based on the user's past behavioral data.

[0839] "Means for sending personalized content to a user's device" refers to a function for transferring personalized content to a device used by a user.

[0840] "Means for receiving feedback from users and storing the feedback in a database" is a function for receiving opinions and evaluations from users and storing them in a database.

[0841] The "means for utilizing feedback when generating content next time" is a function for utilizing saved feedback information when generating content next time to provide more appropriate content.

[0842] "Means for analyzing emotions from user input data using an emotion engine and reflecting that emotional information in content generation" refers to a function for analyzing emotions from user input data using an emotion engine and generating content based on that emotional information.

[0843] "Means for analyzing a user's gaze and voice in real time to collect emotional information" is a function for analyzing a user's gaze and voice in real time to collect emotional information.

[0844] "Means for generating personalized advertising content based on the emotional information and displaying it to the user" refers to a function for individually optimizing advertising content based on the collected emotional information and displaying it on the user's device.

[0845] This invention is a system that generates and provides personalized content and advertising content that takes user emotions into consideration. This system is mainly composed of a user terminal, a server, an emotion engine, a generative AI model, and a database.

[0846] System configuration

[0847] User Device

[0848] A user terminal is a device used to receive input data from a user, such as a smartphone, tablet, personal computer, or smart glasses.

[0849] server

[0850] The server is the central unit that analyzes the input data, incorporates the results of the emotion engine, and uses generative AI models to generate personalized content and advertisements. The server also:

[0851] Receives user input data and converts the data into the appropriate format.

[0852] The emotion engine analyzes voice and gaze data from the user to obtain emotional information.

[0853] Keywords are extracted using a text analysis engine and input into a generative AI model.

[0854] Generate and personalize content based on emotional data and past behavioral history.

[0855] The generated content is transmitted to the user terminal.

[0856] Specific hardware includes cloud servers (e.g., Amazon AWS, Google Cloud), and software includes emotion engines (e.g., Microsoft Azure Emotion API) and generative AI models (e.g., OpenAI's GPT-4).

[0857] Emotion Engine

[0858] The emotion engine is software that analyzes emotions from user input data (voice, text, gaze, etc.). The analyzed emotional information is reflected when generating content.

[0859] Generative AI Models

[0860] A generative AI model is an artificial intelligence that automatically generates new content based on extracted keywords and emotional information. In particular, OpenAI's GPT-4 performs this function.

[0861] Database

[0862] The database stores the user's past behavior history and feedback, which is referenced the next time content is generated, resulting in content that is optimized for the user.

[0863] Examples and prompts

[0864] Assuming a user is wearing smart glasses, if the user verbally inputs a request such as "Tell me about recommended summer travel spots," the system will operate as follows:

[0865] 1. The user device (smart glasses) receives voice input and sends the data to the server.

[0866] 2. The server uses an emotion engine to analyze emotions such as "excitement" and "anticipation" from the user's voice data.

[0867] 3. Next, the server uses a text analysis engine to extract keywords such as "summer" and "travel."

[0868] 4. Use the generative AI model to create a prompt like this:

[0869] "Generate a personalized advertisement for a summer vacation. The user is feeling excited and curious. They have shown interest in resort beaches and hiking trails in the past."

[0870] 5. The generative AI model (GPT-4) generates content based on the prompt above.

[0871] 6. The server sends the generated content to the user's smart glasses for display.

[0872] In this way, the present invention analyzes the user's emotional state in real time and provides highly accurate personalized content and advertising content based on the analysis.

[0873] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0874] Step 1:

[0875] The user makes a voice input through the smart glasses, "Tell me about recommended summer travel spots." This voice data is received by the terminal and sent to the server. The input is the user's voice data, and the output is the transmission of the voice data to the server.

[0876] Step 2:

[0877] The server uses an emotion engine to analyze the received voice data and extract the user's emotional information. Specifically, emotional states such as "excitement" and "expectation" are analyzed from the voice data. The input is the received voice data, and the output is the extracted emotional information.

[0878] Step 3:

[0879] The server uses a text analysis engine to extract keywords from the user's voice data. For example, keywords such as "summer," "travel," and "recommended spots" are extracted. The input is the user's voice data, and the output is the extracted keywords.

[0880] Step 4:

[0881] The server sends a prompt to the generative AI model based on the emotional information, extracted keywords, and the user's past behavioral history. The specific prompt is, "Generate a personalized advertisement for a summer vacation. The user is feeling excited and curious. They have shown interest in beach resorts and hiking trails in the past." The input is emotional information, keywords, and past behavioral history, and the output is the generated prompt.

[0882] Step 5:

[0883] A generative AI model (e.g., GPT-4) generates content based on a prompt and returns the generated results to the server. The input is the prompt, and the output is the generated content (e.g., an article about recommended summer travel spots).

[0884] Step 6:

[0885] The server further personalizes the generated content based on the user's individual information and sends it to the user's device. Specifically, if the user has previously shown interest in "beach resorts" or "mountainous areas," that information is included. The input is the generated content and the user's individual information, and the output is the transmission of personalized content to the device.

[0886] Step 7:

[0887] The device (smart glasses) displays personalized content to the user. The user watches the displayed content and provides feedback as needed. The input is the personalized content, and the output is the display of the content and the generation of feedback.

[0888] Step 8:

[0889] Feedback from the user is received by the device and sent to the server. The server stores this feedback in a database and uses it the next time content is generated. The input is the user's feedback, and the output is the storage of the feedback in the database.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] [Third embodiment]

[0894] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0895] 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.

[0896] 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).

[0897] 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.

[0898] 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.

[0899] 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).

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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."

[0906] This invention is a system that combines generative AI and artificial general intelligence (AGI) to efficiently process a wide variety of input data (text, audio, images, and videos) from users and generate and provide personalized content. The system aims to provide high-quality content that matches the user's interests and preferences, improving the user experience.

[0907] System configuration

[0908] The system mainly consists of the following components:

[0909] 1. User terminal: A device that receives user input data and sends it to the server. Examples include smartphones, tablets, and PCs.

[0910] 2. Server: Analyzes input data, generates content using generative AI and AGI, and provides personalized content.

[0911] 3. Database: Stores users' past behavioral history and feedback and uses it when generating content.

[0912] Program processing

[0913] (1) Receiving user input

[0914] The user inputs a content generation request into the terminal. For example, if the user wants an article about "travel," the user inputs text such as "Generate a guide article about travel."

[0915] (2) Data preprocessing

[0916] The terminal receives this input, converts the data into an appropriate format, and then sends it to the server.

[0917] (3) Data analysis

[0918] The server analyzes the received data and extracts keywords, such as "travel," "guide," and "recommended spots."

[0919] (4) Content generation

[0920] The server uses generative AI to generate content based on the extracted keywords and the user's past behavioral history. For example, it creates specific articles on "recommended travel spots" and "how to plan a trip."

[0921] (5) Personalization

[0922] The server then further personalizes the generated content to match the user's interests. If the user has previously viewed content related to "beach resorts," the server will take that information into account and include "recommended beach resorts" in the article.

[0923] (6) Provision of content

[0924] The server formats the final personalized content and sends it to the device, which displays it to the user, for example, displaying an article in a layout tailored to the screen of a smartphone.

[0925] (7) Feedback collection

[0926] After users have finished viewing the content provided, they can provide feedback, such as leaving a comment like "I found the article helpful" or "I'd like more information."

[0927] (8) Processing Feedback

[0928] The device receives the user's feedback and sends it to the server, which stores it in a database and takes it into account when generating content next time.

[0929] Specific examples

[0930] As a specific scenario, we will explain the behavior when a user wants articles about "summer travel plans."

[0931] 1. The user enters the text "I want an article generated about summer travel plans."

[0932] 2. The device sends this text to the server.

[0933] 3. The server analyzes the text and extracts keywords such as "summer," "travel plans," and "recommended spots."

[0934] 4. The server uses generative AI to create articles on "recommended spots for summer travel" and "how to plan an effective trip."

[0935] 5. The server adds specific information, such as "beach resort" or "mountainous area," based on the user's past behavioral history.

[0936] 6. The server formats the final article and sends it to the device.

[0937] 7. The device displays personalized content to the user.

[0938] 8. Users view the content and provide feedback such as "Very helpful" or "I'd like more detailed information."

[0939] 9. The device sends the feedback to the server, which stores it in a database.

[0940] In this way, the system can efficiently provide high-quality personalized content that meets the user's needs.

[0941] The processing flow will be explained below.

[0942] Step 1:

[0943] The user uses the device to make a content generation request. For example, the user enters text such as "I would like an article about travel."

[0944] Step 2:

[0945] The terminal receives the input data, converts it into a communication format, and sends the converted data to the server.

[0946] Step 3:

[0947] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[0948] Step 4:

[0949] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "travel," "guide," and "recommended spots."

[0950] Step 5:

[0951] The server considers the extracted keywords and the user's past behavioral history and activates a generative AI model, which generates new content (e.g., a travel guide article) based on the analyzed information.

[0952] Step 6:

[0953] The server-generated content is personalized based on the user's past behavior and interests. For example, if the user has previously expressed an interest in "beach resorts," the server will create personalized content that includes that information.

[0954] Step 7:

[0955] The server formats the personalized content and sends it to the user's device.

[0956] Step 8:

[0957] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[0958] Step 9:

[0959] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[0960] Step 10:

[0961] The device collects feedback from the user and sends it to the server.

[0962] Step 11:

[0963] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[0964] In this way, the system can efficiently generate and provide personalized content that meets the user's needs.

[0965] Example 1

[0966] 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."

[0967] In today's information society, users demand a wide variety of content, and they also demand a high level of quality. However, conventional content generation systems have difficulty providing personalized content tailored to individual users' interests and preferences, resulting in a poor user experience. Furthermore, they lack the ability to reflect user feedback in the next generation of content, making continuous improvement difficult.

[0968] 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.

[0969] In this invention, the server includes means for converting input data from a user into an appropriate format, means for transmitting the converted data to the server, means for analyzing the data received by the server and extracting keywords, means for generating content using a generative AI model, means for personalizing the generated content based on the user's past behavioral history, means for transmitting the personalized content to the user's terminal and displaying it, means for receiving feedback from the user and storing the feedback in a database, and means for utilizing the feedback when generating content next time. This makes it possible to provide high-quality personalized content that meets the individual interests and preferences of the user, improving the user experience and enabling continuous improvement of the content based on the feedback.

[0970] "Input Data" means information provided by a user to a system, and may be in the form of text, audio, images, or video.

[0971] "Terminal" refers to the device used by the user, including smartphones, tablets, and personal computers.

[0972] A "server" is a central processing unit that handles input data analysis, content generation, personalization, and feedback.

[0973] The "means for converting into a format" is a function for carrying out a process of converting input data into a format that is easy to analyze.

[0974] The "means for analyzing and extracting keywords" is a function for carrying out the process of identifying and extracting key keywords from received input data.

[0975] "Means for generating content using a generative AI model" refers to a function for carrying out a process of generating information using an artificial intelligence model based on extracted keywords.

[0976] "Personalized content" is content that is generated and personalized by adding specific information based on the user's past behavioral history and interests.

[0977] "Feedback" refers to ratings and comments made by users on provided content.

[0978] A "database" is a system for storing user feedback and past behavioral history.

[0979] "Means to be utilized the next time content is generated" is a function that performs a process to reflect saved feedback and past behavioral history in newly generated content.

[0980] "Means for displaying" refers to the function of displaying personalized content in a format appropriate for the user's device.

[0981] The present invention provides a system for generating and providing high-quality personalized content that meets the individual interests and preferences of users based on a wide variety of input data. A detailed description of specific embodiments of the present invention is provided below.

[0982] The system is broadly composed of a user terminal, a server, and a database. These elements communicate with each other to efficiently generate content and perform personalization.

[0983] A user device is a device that receives user input. Examples include smartphones, tablets, and PCs. Users use these devices to request desired content by typing text or speaking. In the case of speech input, speech recognition software on the device converts speech into text.

[0984] The server is a central processing unit that receives input data sent by users, analyzes it, and generates content. The server has the following functions:

[0985] 1. Data preprocessing: The server converts the data received from the terminal into an appropriate format (e.g., JSON format, XML format). This conversion is performed using various programming languages ​​(e.g., Python, Java).

[0986] 2. Data analysis: The server analyzes the received data using natural language processing tools (e.g., spaCy, NLTK) and extracts keywords. For example, from a request such as "Generate travel guide articles," it extracts keywords such as "travel," "guide," and "recommended spots."

[0987] 3. Content generation: The server generates content based on the extracted keywords using a generative AI model (e.g., GPT-4). By inputting specific prompts into the generative AI model, detailed content such as "Please write an article about recommended travel spots" can be automatically generated.

[0988] 4. Personalization: The server personalizes the generated content based on the user's past behavior and interests. For example, if the user has previously viewed a lot of information about "beach resorts," the server may include "recommended beach resorts" in the generated article.

[0989] 5. Providing content: The server sends the final personalized content to the device, which receives it and displays it in a format appropriate for the user (e.g., a smartphone screen layout).

[0990] 6. Feedback collection and utilization: After viewing the provided content, users can enter their feedback in text. The device receives this feedback and sends it to the server. The server stores the feedback in a database and utilizes it the next time content is generated.

[0991] This allows the system to generate high-quality content tailored to each user's individual interests and preferences, and to continuously improve it. For example, if a user requests an article about summer travel plans, the server extracts keywords such as "summer," "travel plans," and "recommended spots," and uses a generative AI model to create a detailed travel guide, personalizing it based on the user's past preferences. As a result, users can easily obtain the optimal travel plan that best suits them.

[0992] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0993] Step 1: Accepting User Input

[0994] A user inputs a content generation request into a device. For example, the user inputs the text "Please generate a travel guide article." This input data is sent to the device.

[0995] Input: Text data containing the user's preferences.

[0996] Output: The input data in text format.

[0997] Step 2: Preprocessing the data

[0998] The device converts the input data received from the user into an appropriate format (e.g., JSON format, XML format), and sends this converted data to the server.

[0999] Input: Text data entered by the user.

[1000] Output: Pretty-printed JSON or XML data.

[1001] Step 3: Receiving and analyzing data

[1002] The server analyzes the data received from the device using an analysis tool (e.g., spaCy, NLTK) to extract key keywords, such as "travel," "guide," and "recommended spots."

[1003] Input: Well-formed JSON or XML data.

[1004] Output: The extracted keyword list.

[1005] Step 4: Content Generation

[1006] The server inputs the extracted keywords as a prompt into a generative AI model (e.g., GPT-4), and uses the generative AI model to generate specific content. For example, the prompt could be "Please write an article about recommended travel spots."

[1007] Input: The extracted keyword list.

[1008] Output: The generated content (e.g., a text article).

[1009] Step 5: Personalization

[1010] The server personalizes the generated content based on the user's past behavior. For example, if a user has frequently viewed information about "beach resorts," that information will be included in the article.

[1011] Input: Generated content and user past behavior.

[1012] Output: Personalized content.

[1013] Step 6: Providing content

[1014] The server sends the final personalized content to the device, which receives the data and displays it in a format appropriate for the user (e.g., a smartphone screen layout).

[1015] Input: Personalized content.

[1016] Output: The content that is displayed on the user's device.

[1017] Step 7: Gather feedback

[1018] After users have finished viewing the provided content, they can enter their feedback in text, such as "The article was helpful" or "I'd like more information," and send it to their device.

[1019] Input: Feedback data, including user ratings and comments.

[1020] Output: Formatted feedback data.

[1021] Step 8: Processing feedback

[1022] The device receives feedback from the user and sends it to the server, which stores the received feedback in a database and uses it the next time content is generated.

[1023] Input: Formatted feedback data.

[1024] Output: Feedback data stored in a database.

[1025] In this way, through the specific operations and inputs / outputs at each step, the system can provide high-quality personalized content that meets the individual interests and preferences of users.

[1026] (Application example 1)

[1027] 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."

[1028] Conventional personalized content generation systems were limited in the types of input data and the way feedback was utilized, making them unable to adequately meet diverse user needs. Furthermore, the content generation process using generative AI models was inefficient, resulting in low quality content and low user satisfaction. Furthermore, the level of personalization based on user preferences was insufficient, leaving the improvement of the user experience a challenge.

[1029] 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.

[1030] In this invention, the server includes means for receiving input data from a user, means for analyzing the received input data and extracting keywords, means for generating content using artificial intelligence based on the extracted keywords, means for personalizing the generated content based on the user's past behavior history, means for displaying the generated content on a display terminal, means for receiving feedback from the user and saving the feedback in a database, means for utilizing the feedback when generating content next time, and means for generating prompt sentences and inputting the prompt sentences to a generative AI model. This makes it possible to respond to the diversity of user input data and efficiently utilize the generative AI model to provide high-quality personalized content.

[1031] A "user terminal" is a device that receives input data from a user and sends it to a server, and includes smartphones, tablets, and personal computers.

[1032] The "server" is a central device that analyzes data sent by users, generates content using generative AI models and prompts, and sends personalized content to user devices.

[1033] The "database" is an information management system that stores users' past behavioral history and feedback information and uses it when generating content the next time.

[1034] A "generative AI model" is an artificial intelligence model used to generate high-quality content based on user input data.

[1035] A "prompt" is an input text for a generative AI model, and is an instruction sentence that specifically expresses the user's intentions and wishes.

[1036] "Personalized content" refers to content that is customized based on a user's past behavioral history and preferences, and includes information tailored to individual needs.

[1037] "Feedback" refers to reactions and comments made by users to the content provided, and is information that is used to improve the quality of the next content generated.

[1038] "Input Data" refers to information provided by a User in the form of text, audio, images, video, or other information.

[1039] "Keywords" are important words and phrases extracted from input data through analysis, and play a central role in content generation.

[1040] "Analysis" is the process of analyzing input data from users and extracting important elements and keywords.

[1041] "Content generation" is the process of using a generative AI model to create information based on input data and keywords.

[1042] "Personalization" is the process of tailoring generated content to meet individual needs, based on the user's interests and past behavior.

[1043] A "display terminal" is a device that displays personalized content to users, such as a smartphone, tablet, or PC.

[1044] MODE FOR CARRYING OUT THE INVENTION

[1045] This invention is a system that efficiently processes various user input data (text, voice, images, videos) and generates and provides high-quality personalized content using generative AI models and prompts. This system mainly consists of the following elements:

[1046] User Device

[1047] A user terminal is a device that includes a smartphone, tablet, PC, etc., and serves to receive input data from a user and transmit that data to a server.

[1048] server

[1049] The server has the following main roles:

[1050] 1. Receiving and parsing input data:

[1051] It analyzes input data such as text, voice, images, and videos sent by users and extracts important keywords. This analysis process is carried out using natural language processing (NLP) technology.

[1052] 2. Content Generation:

[1053] Based on the extracted keywords and the user's past behavioral history, a generative AI model is used to generate content, and a prompt sentence is generated and input into the generative AI model.

[1054] 3. Personalization:

[1055] The generated content is further personalized based on the user's interests and preferences, referencing past behavioral history and feedback information from a database to provide customized information for each user.

[1056] Database

[1057] The database stores users' past behavioral history and feedback information, which the server uses to generate and personalize content.

[1058] Specific examples

[1059] Let's take the example of a user entering text such as "Tell me the latest movie recommendations." The user's device receives this text and sends it to the server. The server analyzes the entered text and extracts keywords such as "latest," "movie," and "recommended." The server then generates a prompt for the generative AI model. Specifically, it generates the following prompt:

[1060] Prompt: "The user has previously expressed an interest in psychological thrillers. Provide a list of current popular movies, including recommendations with a particular focus on psychological thrillers."

[1061] This prompt is passed as input to a generative AI model, which generates a list of "Top 5 New Movies," along with detailed information about psychological thriller movies the user has previously been interested in. The final content is then displayed and served to the user on their device, such as a smartphone or tablet.

[1062] Hardware and software used

[1063] Hardware: User devices (smartphones, tablets, PCs), servers (cloud servers or on-premise servers)

[1064] Software: Natural Language Processing (NLP) models, generative AI models (e.g., GPT-3), database management systems (DBMS)

[1065] This significantly improves the user experience by allowing users to receive high-quality personalized content based on their interests and preferences, and by using collected feedback to generate new content, the quality of the content provided can be continuously improved.

[1066] As a result, the present invention is able to accommodate the diversity of user input data and efficiently utilize generative AI models to provide high-quality personalized content.

[1067] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1068] Step 1:

[1069] The user inputs input data (e.g., the text "What are your recommendations for the latest movies?") into the device. The device receives this input data and sends it to the server.

[1070] Input: User input data (text, audio, images, video)

[1071] Processing: The device receives the input data

[1072] Output: Sends the received input data to the server

[1073] Step 2:

[1074] The server analyzes the received input data and extracts important keywords using natural language processing (NLP) techniques.

[1075] Input: The input data submitted (e.g., the text "What are your recommendations for the latest movies?")

[1076] Processing: Analyzes text using NLP techniques to extract keywords (e.g., "latest," "movies," "recommended")

[1077] Output: A list of extracted keywords

[1078] Step 3:

[1079] The server generates a prompt based on the extracted keywords, which is then fed directly into the generative AI model.

[1080] Input: A list of extracted keywords

[1081] Processing: Generate a prompt based on keywords (e.g., "The user has previously expressed an interest in psychological thrillers. Provide a list of current popular movies, including recommendations with a particular focus on psychological thrillers.")

[1082] Output: Generated prompt statement

[1083] Step 4:

[1084] The server inputs a prompt sentence into the generative AI model to generate content. The generative AI model generates content with the specified content based on the prompt sentence.

[1085] Input: Generated prompt statement

[1086] Processing: Input a prompt into the generative AI model to generate content (e.g., "Top 5 new movies").

[1087] Output: Generated content

[1088] Step 5:

[1089] The server personalizes the generated content based on the user's past behavior history, referencing the past behavior history and feedback information stored in the database to customize it for each user.

[1090] Input: Generated content, user history

[1091] Processing: Tailoring and personalizing content based on past behavior

[1092] Output: personalized content

[1093] Step 6:

[1094] The server sends the final personalized content to the user's device, which displays the content.

[1095] Input: Personalized content

[1096] Processing: Send the content to the user's device, and the device displays the content

[1097] Output: The personalized content that is displayed to the user

[1098] Step 7:

[1099] The user views the provided content and provides feedback (e.g., "Very helpful" or "I'd like more details"). The device receives this feedback and sends it to the server.

[1100] Input: User feedback

[1101] Processing: The device receives the feedback and sends it to the server.

[1102] Output: Feedback is sent to the server

[1103] Step 8:

[1104] The server stores the received feedback in a database and uses this feedback the next time content is generated.

[1105] Input: User feedback

[1106] Processing: Store the feedback in a database and use it the next time you generate content.

[1107] Output: Improved next content generation process

[1108] 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.

[1109] This invention combines a system that uses generative AI and artificial general intelligence (AGI) to process input data from users and generate and provide personalized content with an emotion engine that recognizes user emotions. This system aims to achieve even higher user satisfaction by providing content that takes into account the user's emotional state.

[1110] System configuration

[1111] The system mainly consists of the following components:

[1112] 1. User terminal: A device that receives user input data and sends it to the server. Examples include smartphones, tablets, and PCs.

[1113] 2. Emotion engine: Analyzes emotions from user input data (voice, text) and uses that information to generate content.

[1114] 3. Server: Analyzes input data, generates content using generative AI and AGI, and provides personalized content, taking into account emotional information from the emotion engine.

[1115] 4. Database: Stores user behavior history and feedback and uses it when generating content.

[1116] Program processing

[1117] (1) Receiving user input

[1118] The user inputs a content generation request into the device. For example, the user inputs the text "I want you to generate an article about travel."

[1119] (2) Data preprocessing

[1120] The terminal receives this input data, converts the data into an appropriate format, and then transmits it to the server.

[1121] (3) Data analysis and emotion recognition

[1122] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[1123] The emotion engine analyzes the user's emotions from text or voice data and provides the emotional state to the server. For example, emotions such as "curious" or "excited" are detected.

[1124] (4) Keyword extraction

[1125] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "travel," "guide," and "recommended spots."

[1126] (5) Content generation

[1127] The server launches a generative AI model based on the extracted keywords, emotional information from the emotion engine, and the user's past behavioral history. The generative AI model generates new content (e.g., a travel guide article) based on the analyzed information. If the emotional information is "curious," the article will be written in an adventurous tone.

[1128] (6) Personalization

[1129] The server then further personalizes the generated content to match the user's interests. If the user has previously shown interest in "beach resorts," the server will provide personalized content that includes that information.

[1130] (7) Provision of content

[1131] The server formats the final personalized content and sends it to the device.

[1132] (8) Display of content

[1133] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[1134] (9) Feedback collection

[1135] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[1136] (10) Processing Feedback

[1137] The device collects feedback from the user and sends it to the server.

[1138] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[1139] Specific examples

[1140] As a specific scenario, we will explain the behavior when a user wants articles about "summer travel plans."

[1141] 1. The user enters the text "I want an article generated about summer travel plans."

[1142] 2. The device sends this text to the server.

[1143] 3. The server analyzes the text and extracts keywords such as "summer," "travel plans," and "recommended spots."

[1144] 4. The emotion engine analyzes the user's emotions from the text and detects emotions such as "high expectations."

[1145] 5. The server uses generative AI to create articles about "recommended summer travel spots" and "how to plan an effective trip." It takes into account emotional information and creates articles with a tone that builds anticipation.

[1146] 6. The server adds specific information, such as "beach resort" or "mountainous area," based on the user's past behavioral history.

[1147] 7. The server formats the final article and sends it to the device.

[1148] 8. The device displays personalized content to the user.

[1149] 9. Users view the content and provide feedback such as "Very helpful" or "I'd like more information."

[1150] 10. The device sends the feedback to the server, which stores it in a database.

[1151] In this way, the system can efficiently provide high-quality personalized content that responds to the user's needs and emotional state.

[1152] The processing flow will be explained below.

[1153] Step 1:

[1154] The user uses the device to make a content generation request. For example, the user inputs text such as "I want an article about summer travel plans to be generated."

[1155] Step 2:

[1156] The terminal receives input data, converts the data into a communication format, and sends the converted data to the server.

[1157] Step 3:

[1158] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[1159] Step 4:

[1160] The server starts an emotion engine and analyzes the user's emotions from the text data. For example, the emotion engine detects the emotion "high expectations."

[1161] Step 5:

[1162] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "summer," "travel plans," and "recommended spots."

[1163] Step 6:

[1164] The server then activates a generative AI model based on the extracted keywords, emotional information, and the user's past behavioral history. The generative AI model generates new content (e.g., a travel guide article) based on the analyzed information.

[1165] Step 7:

[1166] The server tailors the generated content based on the user's emotional state, for example, if it detects a high sense of anticipation, it will create an article with an adventurous and fun tone.

[1167] Step 8:

[1168] The server then further personalizes the generated content to match the user's interests. For example, if the user has previously shown interest in "beach resorts," the server will provide personalized content that includes that information.

[1169] Step 9:

[1170] The server formats the final personalized content and sends it to the device.

[1171] Step 10:

[1172] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[1173] Step 11:

[1174] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[1175] Step 12:

[1176] The device collects feedback from the user and sends it to the server.

[1177] Step 13:

[1178] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[1179] In this way, by performing specific actions at each step, the system is able to efficiently provide personalized content that matches the user's emotions and needs.

[1180] Example 2

[1181] 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."

[1182] Conventional content generation systems analyze user input data and provide content without taking the user's emotions into account, resulting in low user satisfaction. In particular, it is difficult to efficiently provide personalized content that accurately reflects the user's emotional information. Furthermore, there is a lack of a means to continuously utilize user feedback to improve the quality of the content.

[1183] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1184] In this invention, the server includes means for receiving input data from a user, means for converting the received input data into an appropriate format, means for analyzing the converted input data and identifying its format, means for acquiring user emotional information from the input data using an emotion analysis engine, means for extracting keywords based on the analyzed format and emotional information, means for generating content based on the extracted keywords and emotional information using a generative artificial intelligence model, means for personalizing the generated content based on the user's past behavior history, means for transmitting the personalized content to the user's terminal, means for receiving feedback from the user and storing the feedback in a database, and means for utilizing the stored feedback when generating content next time. This makes it possible to efficiently provide high-quality personalized content that reflects the user's emotional information and improve user satisfaction.

[1185] "User" means a person or entity that utilizes the System to enter content generation requests and receive personalized content.

[1186] "Input Data" means information provided by a User to a System for content generation, including in the form of text, audio, images, video, or other information.

[1187] "Device" means a device used by a User to enter Input Data and view Generated Content, including a smartphone, tablet, or PC.

[1188] "Server" means a computer system that analyzes input data and performs content generation and personalization.

[1189] "Format" refers to the type and structure of data, and identifies whether the input data is text, audio, image, or video.

[1190] An "emotion analysis engine" is software or algorithms used to analyze and extract user emotional information from input data.

[1191] "Keywords" refer to important words and phrases extracted by analyzing the content of input data, and form the basis for content generation.

[1192] A "generative artificial intelligence model" is a type of AI technology used to generate new content based on extracted keywords and emotional information.

[1193] "Content" refers to information such as articles, reports, guides, etc. that are generated based on user input data and sentiment information.

[1194] "Personalization" is the process of optimizing content for individual users based on their past behavior and interests.

[1195] "Feedback" refers to input data such as user evaluations, opinions, and requests for improvement regarding the content provided.

[1196] A "database" is an information storage system for storing feedback received from users and past behavioral history.

[1197] This invention relates to a system that processes input data from users using generative AI and an emotion analysis engine to generate and provide personalized content. The purpose of this invention is to achieve higher user satisfaction by taking into account the user's emotional state.

[1198] System configuration

[1199] The system consists of the following elements:

[1200] 1. User Device

[1201] A device that receives user input data and sends it to a server. Examples include smartphones, tablets, and PCs. It provides an interface for users to enter input data.

[1202] 2. Sentiment Analysis Engine

[1203] Software or algorithms that analyze emotions from user input data (text, voice) and reflect that information in content generation. For example, Microsoft Azure's Text Analytics for Sentiment Analysis is used.

[1204] 3. Server

[1205] A computer system that analyzes input data and generates and personalizes content based on information from generative AI models and sentiment analysis engines, and delivers it to users. It also uses text analysis engines (e.g., SpaCy, NLTK).

[1206] 4. Database

[1207] An information storage system that stores users' past behavioral history and feedback and utilizes it when generating content.

[1208] Program processing

[1209] Receiving user input

[1210] A user enters a request via text, for example, "I want an article about summer travel plans."

[1211] Data Preprocessing

[1212] The terminal receives the input data, converts it to text, and then formats it appropriately, such as JSON, before sending it to the server.

[1213] Data analysis and emotion recognition

[1214] The server parses the received JSON data and analyzes the strings using a text analysis engine (e.g., SpaCy or NLTK). At the same time, a sentiment analysis engine analyzes the user's sentiment from the text and extracts emotional information such as "high expectations."

[1215] Keyword extraction

[1216] The server uses a text analysis engine to extract key keywords such as "travel," "summer," "guide," and "recommended spots" from the input text.

[1217] Content Generation

[1218] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate content based on the extracted keywords and sentiment information. For example, it generates articles about "recommended spots for summer travel" and "effective travel planning." It also crafts prompts to be written in a tone that builds anticipation.

[1219] Personalization

[1220] The server then personalizes the content it generates based on the user's past behavior. For example, if a user has previously shown interest in "beach resorts," that information will be added to the article.

[1221] Content provision

[1222] The server formats the final personalized content into HTML format and sends it to the device.

[1223] View content

[1224] The device then displays the received content to the user, for example, displaying an article in a format optimized for a smartphone screen.

[1225] Feedback collection

[1226] Users view the displayed article and enter their feedback, such as their impressions or suggestions for improvement, into the on-screen form. For example, they can enter feedback such as "This article was very helpful" or "I would like more detailed information."

[1227] Processing Feedback

[1228] The device receives feedback from the user, converts it into JSON format, and sends it to the server. The server stores the received feedback in a database and uses it for future content generation.

[1229] Examples of specific examples and prompts

[1230] As a concrete example, consider the case where a user enters the text "I want an article generated about summer travel plans." By entering this text into an application and submitting it, the system will perform the following actions:

[1231] 1. The user enters the text "I want an article generated about summer travel plans."

[1232] 2. The device converts the input text into JSON format and sends it to the server.

[1233] 3. The server performs text analysis and sentiment analysis to obtain the sentiment information of "high expectations" and extract key keywords.

[1234] 4. The server uses the generative AI model to generate content such as "Recommended spots for summer travel" and "How to plan an effective trip." The prompt includes elements that build anticipation: "Generate an article about travel. I'd especially like to know about recommended spots related to summer. Please also reflect my past interest in beach resorts."

[1235] 5. The server creates personalized articles based on the user's past behavioral history and sends them to the device.

[1236] 6. The device displays the article in a format optimized for the smartphone screen.

[1237] This mechanism enables the system to efficiently provide high-quality personalized content that reflects the user's emotional information, thereby improving user satisfaction.

[1238] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1239] Step 1:

[1240] The user inputs a content generation request. For example, they input text such as "I want you to generate an article about summer travel plans." The text is obtained as input data.

[1241] Step 2:

[1242] The terminal receives this input data. The terminal converts the received text data into an appropriate format and formats it in JSON format. The converted data is then output.

[1243] Step 3:

[1244] The terminal sends formatted input data to the server, which then passes the data to the server in JSON format.

[1245] Step 4:

[1246] The server parses the received JSON data. The server uses a text analysis engine (e.g. SpaCy, NLTK) to break the text into tokens and parse it. The parsed data is obtained.

[1247] Step 5:

[1248] The server starts the emotion analysis engine and obtains the user's emotional information from the analyzed text data. The emotion analysis engine analyzes the text data and extracts emotional information such as "high expectations." The emotional information is output.

[1249] Step 6:

[1250] The server uses a text analysis engine to extract key keywords from the analysis results. For example, keywords such as "travel," "summer," "guide," and "recommended spots" are extracted. The extracted keywords are then output.

[1251] Step 7:

[1252] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate content based on the extracted keywords and sentiment information. The generative AI model receives a specific prompt (e.g., "Please generate an article about travel. I'd especially like to know about recommended spots for summer. Please also reflect past interest in beach resorts.") as input and generates the content. The generated content is output.

[1253] Step 8:

[1254] The server personalizes the generated content based on the user's past behavioral history. For example, if the user's past behavior indicates an interest in "beach resorts," that information is added to the article. The personalized content is then output.

[1255] Step 9:

[1256] The server formats the final personalized content into a display format such as HTML and sends it to the device. The formatted content is output and sent to the device.

[1257] Step 10:

[1258] The device displays the personalized content received. The article is displayed in a format optimized for the smartphone screen. The content to be displayed is output.

[1259] Step 11:

[1260] Users can view the displayed article and enter feedback such as their impressions and requests for improvement. For example, they can enter feedback such as "This article was very helpful" or "I would like more detailed information." The entered feedback can then be obtained.

[1261] Step 12:

[1262] The device receives feedback from the user, converts it into JSON format, outputs the converted feedback in JSON format, and sends it to the server.

[1263] Step 13:

[1264] The server stores the received feedback in a database. The stored feedback is used the next time content is generated. The feedback data to be used in the next generation is output.

[1265] (Application example 2)

[1266] 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."

[1267] Conventional personalized content generation systems generate content solely based on past behavioral history and input data without considering the user's emotional state, often resulting in insufficient empathy between the user and the content. Furthermore, advertising content also fails to reflect the user's emotions, resulting in a lack of ability to attract the user's interest. The objective of this invention is to solve these problems and provide personalized content and advertisements that are tailored to the user's emotional state and have a higher level of engagement.

[1268] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input data from a user, means for analyzing the received input data and extracting keywords, means for generating content using a generation artificial intelligence based on the extracted keywords, means for personalizing the generated content based on the user's past behavior history, means for transmitting the personalized content to the user's terminal, means for receiving feedback from the user and saving the feedback in a database, means for utilizing the feedback the next time content is generated, means for analyzing emotions from the user's input data using an emotion engine and reflecting the emotion information in content generation, means for analyzing the user's gaze and voice in real time to collect emotion information, and means for generating personalized advertising content based on the emotion information and displaying it to the user. This makes it possible to provide highly accurate personalized content and advertising content that takes the user's emotional state into consideration.

[1269] "Means for receiving input data from the user" refers to a function for receiving data such as text, audio, images, and videos input by the user through the terminal.

[1270] The "means for analyzing received input data and extracting keywords" is a function for automatically extracting important keywords by analyzing received data.

[1271] "Means for generating content using generative artificial intelligence based on extracted keywords" refers to a function for generating appropriate content using generative artificial intelligence (AI) based on extracted keywords.

[1272] "Means for personalizing content generated based on a user's past behavioral history" refers to a function for optimizing content generated for individual users based on the user's past behavioral data.

[1273] "Means for sending personalized content to a user's device" refers to a function for transferring personalized content to a device used by a user.

[1274] "Means for receiving feedback from users and storing the feedback in a database" is a function for receiving opinions and evaluations from users and storing them in a database.

[1275] The "means for utilizing feedback when generating content next time" is a function for utilizing saved feedback information when generating content next time to provide more appropriate content.

[1276] "Means for analyzing emotions from user input data using an emotion engine and reflecting that emotional information in content generation" refers to a function for analyzing emotions from user input data using an emotion engine and generating content based on that emotional information.

[1277] "Means for analyzing a user's gaze and voice in real time to collect emotional information" is a function for analyzing a user's gaze and voice in real time to collect emotional information.

[1278] "Means for generating personalized advertising content based on the emotional information and displaying it to the user" refers to a function for individually optimizing advertising content based on the collected emotional information and displaying it on the user's device.

[1279] This invention is a system that generates and provides personalized content and advertising content that takes user emotions into consideration. This system is mainly composed of a user terminal, a server, an emotion engine, a generative AI model, and a database.

[1280] System configuration

[1281] User Device

[1282] A user terminal is a device used to receive input data from a user, such as a smartphone, tablet, personal computer, or smart glasses.

[1283] server

[1284] The server is the central unit that analyzes the input data, incorporates the results of the emotion engine, and uses generative AI models to generate personalized content and advertisements. The server also:

[1285] Receives user input data and converts the data into the appropriate format.

[1286] The emotion engine analyzes voice and gaze data from the user to obtain emotional information.

[1287] Keywords are extracted using a text analysis engine and input into a generative AI model.

[1288] Generate and personalize content based on emotional data and past behavioral history.

[1289] The generated content is transmitted to the user terminal.

[1290] Specific hardware includes cloud servers (e.g., Amazon AWS, Google Cloud), and software includes emotion engines (e.g., Microsoft Azure Emotion API) and generative AI models (e.g., OpenAI's GPT-4).

[1291] Emotion Engine

[1292] The emotion engine is software that analyzes emotions from user input data (voice, text, gaze, etc.). The analyzed emotional information is reflected when generating content.

[1293] Generative AI Models

[1294] A generative AI model is an artificial intelligence that automatically generates new content based on extracted keywords and emotional information. In particular, OpenAI's GPT-4 performs this function.

[1295] Database

[1296] The database stores the user's past behavior history and feedback, which is referenced the next time content is generated, resulting in content that is optimized for the user.

[1297] Examples and prompts

[1298] Assuming a user is wearing smart glasses, if the user verbally inputs a request such as "Tell me about recommended summer travel spots," the system will operate as follows:

[1299] 1. The user device (smart glasses) receives voice input and sends the data to the server.

[1300] 2. The server uses an emotion engine to analyze emotions such as "excitement" and "anticipation" from the user's voice data.

[1301] 3. Next, the server uses a text analysis engine to extract keywords such as "summer" and "travel."

[1302] 4. Use the generative AI model to create a prompt like this:

[1303] "Generate a personalized advertisement for a summer vacation. The user is feeling excited and curious. They have shown interest in resort beaches and hiking trails in the past."

[1304] 5. The generative AI model (GPT-4) generates content based on the prompt above.

[1305] 6. The server sends the generated content to the user's smart glasses for display.

[1306] In this way, the present invention analyzes the user's emotional state in real time and provides highly accurate personalized content and advertising content based on the analysis.

[1307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1308] Step 1:

[1309] The user makes a voice input through the smart glasses, "Tell me about recommended summer travel spots." This voice data is received by the terminal and sent to the server. The input is the user's voice data, and the output is the transmission of the voice data to the server.

[1310] Step 2:

[1311] The server uses an emotion engine to analyze the received voice data and extract the user's emotional information. Specifically, emotional states such as "excitement" and "expectation" are analyzed from the voice data. The input is the received voice data, and the output is the extracted emotional information.

[1312] Step 3:

[1313] The server uses a text analysis engine to extract keywords from the user's voice data. For example, keywords such as "summer," "travel," and "recommended spots" are extracted. The input is the user's voice data, and the output is the extracted keywords.

[1314] Step 4:

[1315] The server sends a prompt to the generative AI model based on the emotional information, extracted keywords, and the user's past behavioral history. The specific prompt is, "Generate a personalized advertisement for a summer vacation. The user is feeling excited and curious. They have shown interest in beach resorts and hiking trails in the past." The input is emotional information, keywords, and past behavioral history, and the output is the generated prompt.

[1316] Step 5:

[1317] A generative AI model (e.g., GPT-4) generates content based on a prompt and returns the generated results to the server. The input is the prompt, and the output is the generated content (e.g., an article about recommended summer travel spots).

[1318] Step 6:

[1319] The server further personalizes the generated content based on the user's individual information and sends it to the user's device. Specifically, if the user has previously shown interest in "beach resorts" or "mountainous areas," that information is included. The input is the generated content and the user's individual information, and the output is the transmission of personalized content to the device.

[1320] Step 7:

[1321] The device (smart glasses) displays personalized content to the user. The user watches the displayed content and provides feedback as needed. The input is the personalized content, and the output is the display of the content and the generation of feedback.

[1322] Step 8:

[1323] Feedback from the user is received by the device and sent to the server. The server stores this feedback in a database and uses it the next time content is generated. The input is the user's feedback, and the output is the storage of the feedback in the database.

[1324] 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.

[1325] 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.

[1326] 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.

[1327] [Fourth embodiment]

[1328] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1329] 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.

[1330] 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).

[1331] 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.

[1332] 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.

[1333] 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).

[1334] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1335] 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.

[1336] 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.

[1337] 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.

[1338] 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.

[1339] 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.

[1340] 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."

[1341] This invention is a system that combines generative AI and artificial general intelligence (AGI) to efficiently process a wide variety of input data (text, audio, images, and videos) from users and generate and provide personalized content. The system aims to provide high-quality content that matches the user's interests and preferences, improving the user experience.

[1342] System configuration

[1343] The system mainly consists of the following components:

[1344] 1. User terminal: A device that receives user input data and sends it to the server. Examples include smartphones, tablets, and PCs.

[1345] 2. Server: Analyzes input data, generates content using generative AI and AGI, and provides personalized content.

[1346] 3. Database: Stores user behavior history and feedback and uses it when generating content.

[1347] Program processing

[1348] (1) Receiving user input

[1349] The user inputs a content generation request into the terminal. For example, if the user wants an article about "travel," the user inputs text such as "Generate a guide article about travel."

[1350] (2) Data preprocessing

[1351] The terminal receives this input, converts the data into an appropriate format, and then sends it to the server.

[1352] (3) Data analysis

[1353] The server analyzes the received data and extracts keywords, such as "travel," "guide," and "recommended spots."

[1354] (4) Content generation

[1355] The server uses generative AI to generate content based on the extracted keywords and the user's past behavioral history. For example, it creates specific articles on "recommended travel spots" and "how to plan a trip."

[1356] (5) Personalization

[1357] The server then further personalizes the generated content to match the user's interests. If the user has previously viewed content related to "beach resorts," the server will take that information into account and include "recommended beach resorts" in the article.

[1358] (6) Provision of content

[1359] The server formats the final personalized content and sends it to the device, which displays it to the user, for example, displaying an article in a layout tailored to the screen of a smartphone.

[1360] (7) Feedback collection

[1361] After users have finished viewing the content provided, they can provide feedback, such as leaving a comment like "I found the article helpful" or "I'd like more information."

[1362] (8) Processing Feedback

[1363] The device receives the user's feedback and sends it to the server, which stores it in a database and takes it into account when generating content next time.

[1364] Specific examples

[1365] As a specific scenario, we will explain the behavior when a user wants articles about "summer travel plans."

[1366] 1. The user enters the text "I want an article generated about summer travel plans."

[1367] 2. The device sends this text to the server.

[1368] 3. The server analyzes the text and extracts keywords such as "summer," "travel plans," and "recommended spots."

[1369] 4. The server uses generative AI to create articles on "recommended spots for summer travel" and "how to plan an effective trip."

[1370] 5. The server adds specific information, such as "beach resort" or "mountainous area," based on the user's past behavioral history.

[1371] 6. The server formats the final article and sends it to the device.

[1372] 7. The device displays personalized content to the user.

[1373] 8. Users view the content and provide feedback such as "Very helpful" or "I'd like more detailed information."

[1374] 9. The device sends the feedback to the server, which stores it in a database.

[1375] In this way, the system can efficiently provide high-quality personalized content that meets the user's needs.

[1376] The processing flow will be explained below.

[1377] Step 1:

[1378] The user uses the device to make a content generation request. For example, the user enters text such as "I would like an article about travel."

[1379] Step 2:

[1380] The terminal receives the input data, converts it into a communication format, and sends the converted data to the server.

[1381] Step 3:

[1382] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[1383] Step 4:

[1384] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "travel," "guide," and "recommended spots."

[1385] Step 5:

[1386] The server considers the extracted keywords and the user's past behavioral history and activates a generative AI model, which generates new content (e.g., a travel guide article) based on the analyzed information.

[1387] Step 6:

[1388] The server-generated content is personalized based on the user's past behavior and interests. For example, if the user has previously expressed an interest in "beach resorts," the server will create personalized content that includes that information.

[1389] Step 7:

[1390] The server formats the personalized content and sends it to the user's device.

[1391] Step 8:

[1392] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[1393] Step 9:

[1394] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[1395] Step 10:

[1396] The device collects feedback from the user and sends it to the server.

[1397] Step 11:

[1398] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[1399] In this way, the system can efficiently generate and provide personalized content that meets the user's needs.

[1400] Example 1

[1401] 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."

[1402] In today's information society, users demand a wide variety of content, and they also demand a high level of quality. However, conventional content generation systems have difficulty providing personalized content tailored to individual users' interests and preferences, resulting in a poor user experience. Furthermore, they lack the ability to reflect user feedback in the next generation of content, making continuous improvement difficult.

[1403] 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.

[1404] In this invention, the server includes means for converting input data from a user into an appropriate format, means for transmitting the converted data to the server, means for analyzing the data received by the server and extracting keywords, means for generating content using a generative AI model, means for personalizing the generated content based on the user's past behavioral history, means for transmitting the personalized content to the user's terminal and displaying it, means for receiving feedback from the user and storing the feedback in a database, and means for utilizing the feedback when generating content next time. This makes it possible to provide high-quality personalized content that meets the individual interests and preferences of the user, improving the user experience and enabling continuous improvement of the content based on the feedback.

[1405] "Input Data" means information provided by a user to a system, and may be in the form of text, audio, images, or video.

[1406] "Terminal" refers to the device used by the user, including smartphones, tablets, and personal computers.

[1407] A "server" is a central processing unit that handles input data analysis, content generation, personalization, and feedback.

[1408] The "means for converting into a format" is a function for carrying out a process of converting input data into a format that is easy to analyze.

[1409] The "means for analyzing and extracting keywords" is a function for carrying out the process of identifying and extracting key keywords from received input data.

[1410] "Means for generating content using a generative AI model" refers to a function for carrying out a process of generating information using an artificial intelligence model based on extracted keywords.

[1411] "Personalized content" is content that is generated and personalized by adding specific information based on the user's past behavioral history and interests.

[1412] "Feedback" refers to ratings and comments made by users on provided content.

[1413] A "database" is a system for storing user feedback and past behavioral history.

[1414] "Means to be utilized when generating content next time" refers to a function that performs a process to reflect saved feedback and past behavioral history in newly generated content.

[1415] "Means for displaying" refers to the function of displaying personalized content in a format appropriate for the user's device.

[1416] The present invention is a system for generating and providing high-quality personalized content that responds to individual interests and preferences based on a wide variety of input data from users. Specific embodiments of the present invention will be described in detail below.

[1417] The system is broadly composed of a user terminal, a server, and a database. These elements communicate with each other to efficiently generate content and perform personalization.

[1418] A user device is a device that receives user input. Examples include smartphones, tablets, and PCs. Users use these devices to request desired content by typing text or speaking. In the case of speech input, speech recognition software on the device converts speech into text.

[1419] The server is a central processing unit that receives input data sent by users, analyzes it, and generates content. The server has the following functions:

[1420] 1. Data preprocessing: The server converts the data received from the terminal into an appropriate format (e.g., JSON format, XML format). This conversion is performed using various programming languages ​​(e.g., Python, Java).

[1421] 2. Data analysis: The server analyzes the received data using natural language processing tools (e.g., spaCy, NLTK) and extracts keywords. For example, from a request such as "Generate travel guide articles," it extracts keywords such as "travel," "guide," and "recommended spots."

[1422] 3. Content generation: The server generates content based on the extracted keywords using a generative AI model (e.g., GPT-4). By inputting specific prompts into the generative AI model, detailed content such as "Please write an article about recommended travel spots" can be automatically generated.

[1423] 4. Personalization: The server personalizes the generated content based on the user's past behavior and interests. For example, if the user has previously viewed a lot of information about "beach resorts," the server may include "recommended beach resorts" in the generated article.

[1424] 5. Providing content: The server sends the final personalized content to the device, which receives it and displays it in a format appropriate for the user (e.g., a smartphone screen layout).

[1425] 6. Feedback collection and utilization: After viewing the provided content, users can enter their feedback in text. The device receives this feedback and sends it to the server. The server stores the feedback in a database and utilizes it the next time content is generated.

[1426] This allows the system to generate high-quality content tailored to each user's individual interests and preferences, and to continuously improve it. For example, if a user requests an article about summer travel plans, the server extracts keywords such as "summer," "travel plans," and "recommended spots," and uses a generative AI model to create a detailed travel guide, personalizing it based on the user's past preferences. As a result, users can easily obtain the optimal travel plan that best suits them.

[1427] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1428] Step 1: Accepting User Input

[1429] A user inputs a content generation request into a device. For example, the user inputs the text "Please generate a travel guide article." This input data is sent to the device.

[1430] Input: Text data containing the user's preferences.

[1431] Output: The input data in text format.

[1432] Step 2: Preprocessing the data

[1433] The device converts the input data received from the user into an appropriate format (e.g., JSON format, XML format), and sends this converted data to the server.

[1434] Input: Text data entered by the user.

[1435] Output: Pretty-printed JSON or XML data.

[1436] Step 3: Receiving and analyzing data

[1437] The server analyzes the data received from the device using an analysis tool (e.g., spaCy, NLTK) to extract key keywords, such as "travel," "guide," and "recommended spots."

[1438] Input: Well-formed JSON or XML data.

[1439] Output: The extracted keyword list.

[1440] Step 4: Content Generation

[1441] The server inputs the extracted keywords as a prompt into a generative AI model (e.g., GPT-4), and uses the generative AI model to generate specific content. For example, the prompt could be "Please write an article about recommended travel spots."

[1442] Input: The extracted keyword list.

[1443] Output: The generated content (e.g., a text article).

[1444] Step 5: Personalization

[1445] The server personalizes the generated content based on the user's past behavior. For example, if the user has frequently viewed information about "beach resorts," that information will be included in the article.

[1446] Input: Generated content and user past behavior.

[1447] Output: Personalized content.

[1448] Step 6: Providing content

[1449] The server sends the final personalized content to the device, which receives the data and displays it in a format appropriate for the user (e.g., a smartphone screen layout).

[1450] Input: Personalized content.

[1451] Output: The content that is displayed on the user's device.

[1452] Step 7: Gather feedback

[1453] After users have finished viewing the provided content, they can enter their feedback in text, such as "The article was helpful" or "I'd like more information," and send it to their device.

[1454] Input: Feedback data, including user ratings and comments.

[1455] Output: Formatted feedback data.

[1456] Step 8: Processing feedback

[1457] The device receives feedback from the user and sends it to the server, which stores the received feedback in a database and uses it the next time content is generated.

[1458] Input: Formatted feedback data.

[1459] Output: Feedback data stored in a database.

[1460] In this way, through the specific operations and inputs / outputs at each step, the system can provide high-quality personalized content that meets the individual interests and preferences of users.

[1461] (Application example 1)

[1462] 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."

[1463] Conventional personalized content generation systems were limited in the types of input data and the way feedback was utilized, making them unable to adequately meet diverse user needs. Furthermore, the content generation process using generative AI models was inefficient, resulting in low quality content and low user satisfaction. Furthermore, the level of personalization based on user preferences was insufficient, leaving the improvement of the user experience a challenge.

[1464] 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.

[1465] In this invention, the server includes means for receiving input data from a user, means for analyzing the received input data and extracting keywords, means for generating content using artificial intelligence based on the extracted keywords, means for personalizing the generated content based on the user's past behavior history, means for displaying the generated content on a display terminal, means for receiving feedback from the user and saving the feedback in a database, means for utilizing the feedback when generating content next time, and means for generating prompt sentences and inputting the prompt sentences to a generative AI model. This makes it possible to respond to the diversity of user input data and efficiently utilize the generative AI model to provide high-quality personalized content.

[1466] A "user terminal" is a device that receives input data from a user and sends it to a server, and includes smartphones, tablets, and personal computers.

[1467] The "server" is a central device that analyzes data sent by users, generates content using generative AI models and prompts, and sends personalized content to user devices.

[1468] The "database" is an information management system that stores users' past behavioral history and feedback information and uses it when generating content the next time.

[1469] A "generative AI model" is an artificial intelligence model used to generate high-quality content based on user input data.

[1470] A "prompt" is an input text for a generative AI model, and is an instruction sentence that specifically expresses the user's intentions and wishes.

[1471] "Personalized content" refers to content that is customized based on a user's past behavioral history and preferences, and includes information tailored to individual needs.

[1472] "Feedback" refers to reactions and comments made by users to the content provided, and is information that is used to improve the quality of the next content generated.

[1473] "Input Data" refers to information provided by a User in the form of text, audio, images, video, or other information.

[1474] "Keywords" are important words and phrases extracted from input data through analysis, and play a central role in content generation.

[1475] "Analysis" is the process of analyzing input data from users and extracting important elements and keywords.

[1476] "Content generation" is the process of using a generative AI model to create information based on input data and keywords.

[1477] "Personalization" is the process of tailoring generated content to meet individual needs, based on the user's interests and past behavior.

[1478] A "display terminal" is a device that displays personalized content to users, such as a smartphone, tablet, or PC.

[1479] MODE FOR CARRYING OUT THE INVENTION

[1480] This invention is a system that efficiently processes various user input data (text, voice, images, videos) and generates and provides high-quality personalized content using generative AI models and prompts. This system mainly consists of the following elements:

[1481] User Device

[1482] A user terminal is a device that includes a smartphone, tablet, PC, etc., and serves to receive input data from the user and transmit that data to a server.

[1483] server

[1484] The server has the following main roles:

[1485] 1. Receiving and parsing input data:

[1486] It analyzes input data such as text, voice, images, and videos sent by users and extracts important keywords. This analysis process is carried out using natural language processing (NLP) technology.

[1487] 2. Content Generation:

[1488] Based on the extracted keywords and the user's past behavioral history, a generative AI model is used to generate content, and a prompt sentence is generated and input into the generative AI model.

[1489] 3. Personalization:

[1490] The generated content is further personalized based on the user's interests and preferences, referencing past behavioral history and feedback information from a database to provide customized information for each user.

[1491] Database

[1492] The database stores users' past behavioral history and feedback information, which the server uses to generate and personalize content.

[1493] Specific examples

[1494] Let's take the example of a user entering text such as "Tell me the latest movie recommendations." The user's device receives this text and sends it to the server. The server analyzes the entered text and extracts keywords such as "latest," "movie," and "recommended." The server then generates a prompt for the generative AI model. Specifically, it generates the following prompt:

[1495] Prompt: "The user has previously expressed an interest in psychological thrillers. Provide a list of current popular movies, including recommendations with a particular focus on psychological thrillers."

[1496] This prompt is passed as input to a generative AI model, which generates a list of "Top 5 New Movies," along with detailed information about psychological thriller movies the user has previously been interested in. The final content is then displayed and served to the user on their device, such as a smartphone or tablet.

[1497] Hardware and software used

[1498] Hardware: User devices (smartphones, tablets, PCs), servers (cloud servers or on-premise servers)

[1499] Software: Natural Language Processing (NLP) models, generative AI models (e.g., GPT-3), database management systems (DBMS)

[1500] This significantly improves the user experience by allowing users to receive high-quality personalized content based on their interests and preferences, and by using collected feedback to generate new content, the quality of the content provided can be continuously improved.

[1501] As a result, the present invention is able to accommodate the diversity of user input data and efficiently utilize generative AI models to provide high-quality personalized content.

[1502] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1503] Step 1:

[1504] The user inputs input data (e.g., the text "What are your recommendations for the latest movies?") into the device. The device receives this input data and sends it to the server.

[1505] Input: User input data (text, audio, images, video)

[1506] Processing: The device receives the input data

[1507] Output: Sends the received input data to the server

[1508] Step 2:

[1509] The server analyzes the received input data and extracts important keywords using natural language processing (NLP) techniques.

[1510] Input: The input data submitted (e.g., the text "What are your recommendations for the latest movies?")

[1511] Processing: Analyzes text using NLP techniques to extract keywords (e.g., "latest," "movies," "recommended")

[1512] Output: A list of extracted keywords

[1513] Step 3:

[1514] The server generates a prompt based on the extracted keywords, which is then fed directly into the generative AI model.

[1515] Input: A list of extracted keywords

[1516] Processing: Generate a prompt based on keywords (e.g., "The user has previously expressed an interest in psychological thrillers. Provide a list of current popular movies, including recommendations with a particular focus on psychological thrillers.")

[1517] Output: Generated prompt statement

[1518] Step 4:

[1519] The server inputs a prompt sentence into the generative AI model to generate content. The generative AI model generates content with the specified content based on the prompt sentence.

[1520] Input: Generated prompt statement

[1521] Processing: Input a prompt into the generative AI model to generate content (e.g., "Top 5 new movies").

[1522] Output: Generated content

[1523] Step 5:

[1524] The server personalizes the generated content based on the user's past behavior history, referencing the past behavior history and feedback information stored in the database to customize it for each user.

[1525] Input: Generated content, user history

[1526] Processing: Tailoring and personalizing content based on past behavior

[1527] Output: personalized content

[1528] Step 6:

[1529] The server sends the final personalized content to the user's device, which displays the content.

[1530] Input: Personalized content

[1531] Processing: Send the content to the user's device, and the device displays the content

[1532] Output: The personalized content that is displayed to the user

[1533] Step 7:

[1534] The user views the provided content and provides feedback (e.g., "Very helpful" or "I'd like more details"). The device receives this feedback and sends it to the server.

[1535] Input: User feedback

[1536] Processing: The device receives the feedback and sends it to the server.

[1537] Output: Feedback is sent to the server

[1538] Step 8:

[1539] The server stores the received feedback in a database and uses this feedback the next time content is generated.

[1540] Input: User feedback

[1541] Processing: Store the feedback in a database and use it the next time you generate content.

[1542] Output: Improved next content generation process

[1543] 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.

[1544] This invention combines a system that uses generative AI and artificial general intelligence (AGI) to process input data from users and generate and provide personalized content with an emotion engine that recognizes user emotions. This system aims to achieve even higher user satisfaction by providing content that takes into account the user's emotional state.

[1545] System configuration

[1546] The system mainly consists of the following components:

[1547] 1. User terminal: A device that receives user input data and sends it to the server. Examples include smartphones, tablets, and PCs.

[1548] 2. Emotion engine: Analyzes emotions from user input data (voice, text) and uses that information to generate content.

[1549] 3. Server: Analyzes input data, generates content using generative AI and AGI, and provides personalized content, taking into account emotional information from the emotion engine.

[1550] 4. Database: Stores user behavior history and feedback and uses it when generating content.

[1551] Program processing

[1552] (1) Receiving user input

[1553] The user inputs a content generation request into the device. For example, the user inputs the text "I want you to generate an article about travel."

[1554] (2) Data preprocessing

[1555] The terminal receives this input data, converts the data into an appropriate format, and then transmits it to the server.

[1556] (3) Data analysis and emotion recognition

[1557] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[1558] The emotion engine analyzes the user's emotions from text or voice data and provides the emotional state to the server. For example, emotions such as "curious" or "excited" are detected.

[1559] (4) Keyword extraction

[1560] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "travel," "guide," and "recommended spots."

[1561] (5) Content generation

[1562] The server launches a generative AI model based on the extracted keywords, emotional information from the emotion engine, and the user's past behavioral history. The generative AI model generates new content (e.g., a travel guide article) based on the analyzed information. If the emotional information is "curious," the article will be written in an adventurous tone.

[1563] (6) Personalization

[1564] The server then further personalizes the generated content to match the user's interests. If the user has previously shown interest in "beach resorts," the server will generate personalized content that includes that information.

[1565] (7) Provision of content

[1566] The server formats the final personalized content and sends it to the terminal.

[1567] (8) Display of content

[1568] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[1569] (9) Feedback collection

[1570] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[1571] (10) Processing Feedback

[1572] The device collects feedback from the user and sends it to the server.

[1573] The server stores the received feedback in a database, which is then used to generate content that is more tailored to the user's needs.

[1574] Specific examples

[1575] As a specific scenario, we will explain the behavior when a user wants articles about "summer travel plans."

[1576] 1. The user enters the text "I want an article generated about summer travel plans."

[1577] 2. The device sends this text to the server.

[1578] 3. The server analyzes the text and extracts keywords such as "summer," "travel plans," and "recommended spots."

[1579] 4. The emotion engine analyzes the user's emotions from the text and detects emotions such as "high expectations."

[1580] 5. The server uses generative AI to create articles about "recommended summer travel spots" and "how to plan an effective trip." The articles are written in a tone that takes into account emotional information and builds anticipation.

[1581] 6. The server adds specific information, such as "beach resort" or "mountainous area," based on the user's past behavioral history.

[1582] 7. The server formats the final article and sends it to the device.

[1583] 8. The device displays personalized content to the user.

[1584] 9. Users view the content and provide feedback such as "Very helpful" or "I'd like more information."

[1585] 10. The device sends the feedback to the server, which stores it in a database.

[1586] In this way, the system can efficiently provide high-quality personalized content that responds to the user's needs and emotional state.

[1587] The processing flow will be explained below.

[1588] Step 1:

[1589] The user uses the device to make a content generation request. For example, the user inputs text such as "I want an article about summer travel plans to be generated."

[1590] Step 2:

[1591] The terminal receives input data, converts the data into a communication format, and sends the converted data to the server.

[1592] Step 3:

[1593] The server analyzes the received data and identifies whether the input data is text, audio, image, or video. In this example, it verifies that it is text data.

[1594] Step 4:

[1595] The server starts an emotion engine and analyzes the user's emotions from the text data. For example, the emotion engine detects the emotion "high expectations."

[1596] Step 5:

[1597] The server uses a text analysis engine to analyze the content of the input text and extract key keywords, such as "summer," "travel plans," and "recommended spots."

[1598] Step 6:

[1599] The server then activates a generative AI model based on the extracted keywords, emotional information, and the user's past behavioral history. The generative AI model generates new content (e.g., a travel guide article) based on the analyzed information.

[1600] Step 7:

[1601] The server tailors the generated content based on the user's emotional state, for example, if it detects a high sense of anticipation, it will create an article with an adventurous and fun tone.

[1602] Step 8:

[1603] The server then further personalizes the generated content to match the user's interests. For example, if the user has previously shown interest in "beach resorts," the server will provide personalized content that includes that information.

[1604] Step 9:

[1605] The server formats the final personalized content and sends it to the device.

[1606] Step 10:

[1607] The device will then display the personalized content it receives, for example, an article in a format optimized for a smartphone screen.

[1608] Step 11:

[1609] Users view the provided content and enter their feedback, such as their impressions and suggestions for improvement. They send feedback such as "This article was very helpful" or "I would like more detailed information."

[1610] Step 12:

[1611] The device collects feedback from the user and sends it to the server.

[1612] Step 13:

[1613] The server stores the received feedback in a database, which is then used to generate content that is more relevant to the user's needs.

[1614] In this way, by performing specific actions at each step, the system is able to efficiently provide personalized content that matches the user's emotions and needs.

[1615] Example 2

[1616] 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."

[1617] Conventional content generation systems analyze user input data and provide content without taking the user's emotions into account, resulting in low user satisfaction. In particular, it is difficult to efficiently provide personalized content that accurately reflects the user's emotional information. Furthermore, there is a lack of a means to continuously utilize user feedback to improve the quality of the content.

[1618] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1619] In this invention, the server includes means for receiving input data from a user, means for converting the received input data into an appropriate format, means for analyzing the converted input data and identifying its format, means for acquiring user emotional information from the input data using an emotion analysis engine, means for extracting keywords based on the analyzed format and emotional information, means for generating content based on the extracted keywords and emotional information using a generative artificial intelligence model, means for personalizing the generated content based on the user's past behavior history, means for transmitting the personalized content to the user's terminal, means for receiving feedback from the user and storing the feedback in a database, and means for utilizing the stored feedback when generating content next time. This makes it possible to efficiently provide high-quality personalized content that reflects the user's emotional information and improve user satisfaction.

[1620] "User" means a person or entity that utilizes the System to enter content generation requests and receive personalized content.

[1621] "Input Data" means information provided by a User to a System for content generation, including in the form of text, audio, images, video, or other information.

[1622] "Device" means a device used by a User to enter Input Data and view Generated Content, including a smartphone, tablet, or PC.

[1623] "Server" means a computer system that analyzes input data and performs content generation and personalization.

[1624] "Format" refers to the type and structure of data, and identifies whether the input data is text, audio, image, or video.

[1625] An "emotion analysis engine" is software or algorithms used to analyze and extract user emotional information from input data.

[1626] "Keywords" refer to important words and phrases extracted by analyzing the content of input data, and form the basis for content generation.

[1627] A "generative artificial intelligence model" is a type of AI technology used to generate new content based on extracted keywords and emotional information.

[1628] "Content" refers to information such as articles, reports, guides, etc. that are generated based on user input data and sentiment information.

[1629] "Personalization" is the process of optimizing content for individual users based on their past behavior and interests.

[1630] "Feedback" refers to input data such as user evaluations, opinions, and requests for improvement regarding the content provided.

[1631] A "database" is an information storage system for storing feedback received from users and past behavioral history.

[1632] This invention relates to a system that processes input data from users using generative AI and an emotion analysis engine to generate and provide personalized content. The purpose of this invention is to achieve higher user satisfaction by taking into account the user's emotional state.

[1633] System configuration

[1634] The system consists of the following elements:

[1635] 1. User Device

[1636] A device that receives user input data and sends it to a server. Examples include smartphones, tablets, and PCs. It provides an interface for users to enter input data.

[1637] 2. Sentiment Analysis Engine

[1638] Software or algorithms that analyze emotions from user input data (text, voice) and reflect that information in content generation. For example, Microsoft Azure's Text Analytics for Sentiment Analysis is used.

[1639] 3. Server

[1640] A computer system that analyzes input data and generates and personalizes content based on information from generative AI models and sentiment analysis engines, and delivers it to users. It also uses text analysis engines (e.g., SpaCy, NLTK).

[1641] 4. Database

[1642] An information storage system that stores users' past behavioral history and feedback and utilizes it when generating content.

[1643] Program processing

[1644] Receiving user input

[1645] A user enters a request via text, for example, "I want an article about summer travel plans."

[1646] Data Preprocessing

[1647] The terminal receives the input data, converts it to text, and then formats it appropriately, such as JSON, before sending it to the server.

[1648] Data analysis and emotion recognition

[1649] The server parses the received JSON data and analyzes the strings using a text analysis engine (e.g., SpaCy or NLTK). At the same time, a sentiment analysis engine analyzes the user's sentiment from the text and extracts emotional information such as "high expectations."

[1650] Keyword extraction

[1651] The server uses a text analysis engine to extract key keywords such as "travel," "summer," "guide," and "recommended spots" from the input text.

[1652] Content Generation

[1653] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate content based on the extracted keywords and sentiment information. For example, it generates articles about "recommended spots for summer travel" and "effective travel planning." It also crafts prompts to be written in a tone that builds anticipation.

[1654] Personalization

[1655] The server then personalizes the content it generates based on the user's past behavior. For example, if a user has previously expressed an interest in "beach resorts," that information will be added to the article.

[1656] Content provision

[1657] The server formats the final personalized content into HTML format and sends it to the device.

[1658] View content

[1659] The device then displays the received content to the user, for example, displaying an article in a format optimized for a smartphone screen.

[1660] Feedback collection

[1661] Users view the displayed article and enter their feedback, such as their impressions or suggestions for improvement, into the on-screen form. For example, they can enter feedback such as "This article was very helpful" or "I would like more detailed information."

[1662] Processing Feedback

[1663] The device receives feedback from the user, converts it into JSON format, and sends it to the server. The server stores the received feedback in a database and uses it for future content generation.

[1664] Examples of specific examples and prompts

[1665] As a concrete example, consider the case where a user enters the text "I want an article generated about summer travel plans." By entering this text into an application and submitting it, the system will perform the following actions:

[1666] 1. The user enters the text "I want an article generated about summer travel plans."

[1667] 2. The device converts the input text into JSON format and sends it to the server.

[1668] 3. The server performs text analysis and sentiment analysis to obtain the sentiment information of "high expectations" and extract key keywords.

[1669] 4. The server uses the generative AI model to generate content such as "Recommended spots for summer travel" and "How to plan an effective trip." The prompt includes elements that build anticipation: "Generate an article about travel. I'd especially like to know about recommended spots related to summer. Please also reflect my past interest in beach resorts."

[1670] 5. The server creates personalized articles based on the user's past behavioral history and sends them to the device.

[1671] 6. The device displays the article in a format optimized for the smartphone screen.

[1672] This mechanism enables the system to efficiently provide high-quality personalized content that reflects the user's emotional information, thereby improving user satisfaction.

[1673] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1674] Step 1:

[1675] The user inputs a content generation request. For example, they input text such as "I want you to generate an article about summer travel plans." The text is obtained as input data.

[1676] Step 2:

[1677] The terminal receives this input data. The terminal converts the received text data into an appropriate format and formats it in JSON format. The converted data is then output.

[1678] Step 3:

[1679] The terminal sends formatted input data to the server, which then passes the data to the server in JSON format.

[1680] Step 4:

[1681] The server parses the received JSON data. The server uses a text analysis engine (e.g. SpaCy, NLTK) to break the text into tokens and parse it. The parsed data is obtained.

[1682] Step 5:

[1683] The server starts the emotion analysis engine and obtains the user's emotional information from the analyzed text data. The emotion analysis engine analyzes the text data and extracts emotional information such as "high expectations." The emotional information is output.

[1684] Step 6:

[1685] The server uses a text analysis engine to extract key keywords from the analysis results. For example, keywords such as "travel," "summer," "guide," and "recommended spots" are extracted. The extracted keywords are then output.

[1686] Step 7:

[1687] The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate content based on the extracted keywords and sentiment information. The generative AI model receives a specific prompt (e.g., "Please generate an article about travel. I'd especially like to know about recommended spots for summer. Please also reflect past interest in beach resorts.") as input and generates the content. The generated content is output.

[1688] Step 8:

[1689] The server personalizes the generated content based on the user's past behavioral history. For example, if the user's past behavior indicates an interest in "beach resorts," that information is added to the article. The personalized content is then output.

[1690] Step 9:

[1691] The server formats the final personalized content into a display format such as HTML and sends it to the device. The formatted content is output and sent to the device.

[1692] Step 10:

[1693] The device displays the personalized content received. The article is displayed in a format optimized for the smartphone screen. The content to be displayed is output.

[1694] Step 11:

[1695] Users can view the displayed article and enter feedback such as their impressions and requests for improvement. For example, they can enter feedback such as "This article was very helpful" or "I would like more detailed information." The entered feedback can then be obtained.

[1696] Step 12:

[1697] The device receives feedback from the user, converts it into JSON format, outputs the converted feedback in JSON format, and sends it to the server.

[1698] Step 13:

[1699] The server stores the received feedback in a database. The stored feedback is used the next time content is generated. The feedback data to be used in the next generation is output.

[1700] (Application example 2)

[1701] 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."

[1702] Conventional personalized content generation systems generate content solely based on past behavioral history and input data without considering the user's emotional state, often resulting in insufficient empathy between the user and the content. Furthermore, advertising content also fails to reflect the user's emotions, resulting in a lack of ability to attract the user's interest. The objective of this invention is to solve these problems and provide personalized content and advertisements that are tailored to the user's emotional state and have a higher level of engagement.

[1703] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving input data from a user, means for analyzing the received input data and extracting keywords, means for generating content using a generation artificial intelligence based on the extracted keywords, means for personalizing the generated content based on the user's past behavior history, means for transmitting the personalized content to the user's terminal, means for receiving feedback from the user and saving the feedback in a database, means for utilizing the feedback the next time content is generated, means for analyzing emotions from the user's input data using an emotion engine and reflecting the emotion information in content generation, means for analyzing the user's gaze and voice in real time to collect emotion information, and means for generating personalized advertising content based on the emotion information and displaying it to the user. This makes it possible to provide highly accurate personalized content and advertising content that takes the user's emotional state into consideration.

[1704] "Means for receiving input data from the user" refers to a function for receiving data such as text, audio, images, and videos input by the user through the terminal.

[1705] The "means for analyzing received input data and extracting keywords" is a function for automatically extracting important keywords by analyzing received data.

[1706] "Means for generating content using generative artificial intelligence based on extracted keywords" refers to a function for generating appropriate content using generative artificial intelligence (AI) based on extracted keywords.

[1707] "Means for personalizing content generated based on a user's past behavioral history" refers to a function for optimizing content generated for individual users based on the user's past behavioral data.

[1708] "Means for sending personalized content to a user's device" refers to a function for transferring personalized content to a device used by a user.

[1709] "Means for receiving feedback from users and storing the feedback in a database" is a function for receiving opinions and evaluations from users and storing them in a database.

[1710] The "means for utilizing feedback when generating content next time" is a function for utilizing saved feedback information when generating content next time to provide more appropriate content.

[1711] "Means for analyzing emotions from user input data using an emotion engine and reflecting that emotional information in content generation" refers to a function for analyzing emotions from user input data using an emotion engine and generating content based on that emotional information.

[1712] "Means for analyzing a user's gaze and voice in real time to collect emotional information" is a function for analyzing a user's gaze and voice in real time to collect emotional information.

[1713] "Means for generating personalized advertising content based on the emotional information and displaying it to the user" refers to a function for individually optimizing advertising content based on the collected emotional information and displaying it on the user's device.

[1714] This invention is a system that generates and provides personalized content and advertising content that takes user emotions into consideration. This system is mainly composed of a user terminal, a server, an emotion engine, a generative AI model, and a database.

[1715] System configuration

[1716] User Device

[1717] A user terminal is a device used to receive input data from a user, such as a smartphone, tablet, personal computer, or smart glasses.

[1718] server

[1719] The server is the central unit that analyzes the input data, incorporates the results of the emotion engine, and uses generative AI models to generate personalized content and advertisements. The server also:

[1720] Receives user input data and converts the data into the appropriate format.

[1721] The emotion engine analyzes voice and gaze data from the user to obtain emotional information.

[1722] Keywords are extracted using a text analysis engine and input into a generative AI model.

[1723] Generate and personalize content based on emotional data and past behavioral history.

[1724] The generated content is transmitted to the user terminal.

[1725] Specific hardware includes cloud servers (e.g., Amazon AWS, Google Cloud), and software includes emotion engines (e.g., Microsoft Azure Emotion API) and generative AI models (e.g., OpenAI's GPT-4).

[1726] Emotion Engine

[1727] The emotion engine is software that analyzes emotions from user input data (voice, text, gaze, etc.). The analyzed emotional information is reflected when generating content.

[1728] Generative AI Models

[1729] A generative AI model is an artificial intelligence that automatically generates new content based on extracted keywords and emotional information. In particular, OpenAI's GPT-4 performs this function.

[1730] Database

[1731] The database stores the user's past behavior history and feedback, which is referenced the next time content is generated, resulting in content that is optimized for the user.

[1732] Examples and prompts

[1733] Assuming a user is wearing smart glasses, if the user verbally inputs a request such as "Tell me about recommended summer travel spots," the system will operate as follows:

[1734] 1. The user device (smart glasses) receives voice input and sends the data to the server.

[1735] 2. The server uses an emotion engine to analyze emotions such as "excitement" and "anticipation" from the user's voice data.

[1736] 3. Next, the server uses a text analysis engine to extract keywords such as "summer" and "travel."

[1737] 4. Use the generative AI model to create a prompt like this:

[1738] "Generate a personalized advertisement for a summer vacation. The user is feeling excited and curious. They have shown interest in resort beaches and hiking trails in the past."

[1739] 5. The generative AI model (GPT-4) generates content based on the prompt above.

[1740] 6. The server sends the generated content to the user's smart glasses for display.

[1741] In this way, the present invention analyzes the user's emotional state in real time and provides highly accurate personalized content and advertising content based on the analysis.

[1742] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1743] Step 1:

[1744] The user makes a voice input through the smart glasses, "Tell me about recommended summer travel spots." This voice data is received by the terminal and sent to the server. The input is the user's voice data, and the output is the transmission of the voice data to the server.

[1745] Step 2:

[1746] The server uses an emotion engine to analyze the received voice data and extract the user's emotional information. Specifically, emotional states such as "excitement" and "expectation" are analyzed from the voice data. The input is the received voice data, and the output is the extracted emotional information.

[1747] Step 3:

[1748] The server uses a text analysis engine to extract keywords from the user's voice data. For example, keywords such as "summer," "travel," and "recommended spots" are extracted. The input is the user's voice data, and the output is the extracted keywords.

[1749] Step 4:

[1750] The server sends a prompt to the generative AI model based on the emotional information, extracted keywords, and the user's past behavioral history. The specific prompt is, "Generate a personalized advertisement for a summer vacation. The user is feeling excited and curious. They have shown interest in beach resorts and hiking trails in the past." The input is emotional information, keywords, and past behavioral history, and the output is the generated prompt.

[1751] Step 5:

[1752] A generative AI model (e.g., GPT-4) generates content based on a prompt and returns the generated results to the server. The input is the prompt, and the output is the generated content (e.g., an article about recommended summer travel spots).

[1753] Step 6:

[1754] The server further personalizes the generated content based on the user's individual information and sends it to the user's device. Specifically, if the user has previously shown interest in "beach resorts" or "mountainous areas," that information is included. The input is the generated content and the user's individual information, and the output is the transmission of personalized content to the device.

[1755] Step 7:

[1756] The device (smart glasses) displays personalized content to the user. The user watches the displayed content and provides feedback as needed. The input is the personalized content, and the output is the display of the content and the generation of feedback.

[1757] Step 8:

[1758] Feedback from the user is received by the device and sent to the server. The server stores this feedback in a database and uses it the next time content is generated. The input is the user's feedback, and the output is the storage of the feedback in the database.

[1759] 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.

[1760] 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.

[1761] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1762] 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.

[1763] 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.

[1764] 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.

[1765] 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).

[1766] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1767] 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."

[1768] 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.

[1769] 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).

[1770] 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.

[1771] 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.

[1772] 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.

[1773] 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.

[1774] 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.

[1775] 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.

[1776] 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.

[1777] 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.

[1778] 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.

[1779] 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.

[1780] The following is further disclosed regarding the above embodiment.

[1781] (Claim 1)

[1782] means for receiving input data from a user;

[1783] means for analyzing received input data and extracting keywords;

[1784] A means for generating content using artificial intelligence based on the extracted keywords;

[1785] A means of personalizing content generated based on a user's past behavior; and

[1786] a means for sending personalized content to a user's device;

[1787] a means for receiving feedback from users and storing the feedback in a database;

[1788] How to use the feedback to improve your next content creation,

[1789] A system including:

[1790] (Claim 2)

[1791] 2. The system according to claim 1, wherein the input data is any one of text, audio, image, and video.

[1792] (Claim 3)

[1793] 2. The system of claim 1, wherein the keywords to be analyzed are automatically extracted from the input data.

[1794] "Example 1"

[1795] (Claim 1)

[1796] means for receiving input data from a user;

[1797] means for converting received input data into an appropriate format;

[1798] means for transmitting the converted data to a server;

[1799] A means for analyzing the data received by the server and extracting keywords;

[1800] A means for generating content using a generative AI model based on the extracted keywords;

[1801] A means of personalizing content generated based on a user's past behavior; and

[1802] means for transmitting and displaying personalized content to a user's device;

[1803] a means for receiving feedback from users and storing the feedback in a database;

[1804] How to use the feedback to improve your next content creation,

[1805] A system including:

[1806] (Claim 2)

[1807] 2. The system according to claim 1, wherein the input data is any one of text, audio, image, and video.

[1808] (Claim 3)

[1809] 2. The system of claim 1, wherein the keywords to be analyzed are automatically extracted from the input data.

[1810] "Application Example 1"

[1811] (Claim 1)

[1812] means for receiving input data from a user;

[1813] means for analyzing received input data and extracting keywords;

[1814] A means for generating content using artificial intelligence based on the extracted keywords;

[1815] A means of personalizing content generated based on a user's past behavior; and

[1816] means for displaying the generated content on a display terminal;

[1817] a means for receiving feedback from users and storing the feedback in a database;

[1818] How to use the feedback to improve your next content creation,

[1819] a means for generating a prompt sentence and inputting the prompt sentence into the generative AI model;

[1820] A system including:

[1821] (Claim 2)

[1822] 2. The system according to claim 1, wherein the input data is any one of text, audio, image, and video.

[1823] (Claim 3)

[1824] 2. The system of claim 1, wherein the keywords to be analyzed are automatically extracted from the input data.

[1825] "Example 2: Combining Emotion Engines"

[1826] (Claim 1)

[1827] means for receiving input data from a user;

[1828] means for converting received input data into an appropriate format;

[1829] means for parsing the converted input data and identifying its format;

[1830] a means for obtaining user emotion information from input data using a sentiment analysis engine;

[1831] means for extracting keywords based on the analyzed format and sentiment information;

[1832] means for generating content based on the extracted keywords and sentiment information using a generative artificial intelligence model;

[1833] A means of personalizing content generated based on a user's past behavior; and

[1834] a means for sending personalized content to a user's device;

[1835] a means for receiving feedback from users and storing the feedback in a database;

[1836] A way to use saved feedback for the next content generation,

[1837] A system including:

[1838] (Claim 2)

[1839] 2. The system according to claim 1, wherein the input data is any one of text, audio, image, and video.

[1840] (Claim 3)

[1841] 10. The system of claim 1, wherein the keywords and sentiment information to be analyzed are automatically extracted from the input data.

[1842] "Application example 2 when combining emotion engines"

[1843] (Claim 1)

[1844] means for receiving input data from a user;

[1845] means for analyzing received input data and extracting keywords;

[1846] A means for generating content using artificial intelligence based on the extracted keywords;

[1847] A means of personalizing content generated based on a user's past behavior; and

[1848] a means for sending personalized content to a user's device;

[1849] a means for receiving feedback from users and storing the feedback in a database;

[1850] How to use the feedback to improve your next content creation,

[1851] A means for analyzing emotions from user input data using an emotion engine and reflecting the emotion information in content generation;

[1852] A means of analyzing the user's gaze and voice in real time to collect emotional information,

[1853] A means for generating personalized advertising content based on the emotional information and displaying it to the user;

[1854] A system including:

[1855] (Claim 2)

[1856] 2. The system according to claim 1, wherein the input data is any one of text, audio, image, and video.

[1857] (Claim 3)

[1858] 2. The system of claim 1, wherein the keywords to be analyzed are automatically extracted from the input data. [Explanation of symbols]

[1859] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving input data from a user; means for analyzing received input data and extracting keywords; A means for generating content using artificial intelligence based on the extracted keywords; A means of personalizing content generated based on a user's past behavior; and a means for sending personalized content to a user's device; a means for receiving feedback from users and storing the feedback in a database; How to use the feedback to improve your next content creation, A system including:

2. 2. The system according to claim 1, wherein the input data is any one of text, audio, image, and video.

3. 2. The system of claim 1, wherein the keywords to be analyzed are automatically extracted from the input data.

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