System

Abstract

A system is provided.SOLUTION: A system, comprising: means for initializing an artificial intelligence model; means for receiving user-entered content; means for evaluating the user-entered content using the initialized artificial intelligence model; means for generating commentary regarding the user-entered content; and means for displaying the generated evaluation and commentary on a user device.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] As the amount of content generated by AI increases, there is a problem of a decline in the creativity of content due to the mass production of similar creations. This can lead to consumers becoming bored with such AI-generated content and overlooking the value of human-created creations. Therefore, there is a need for a system that can differentiate AI-generated content from human-created content and increase the value of human-created content. [Means for solving the problem]

[0005] The present invention provides a system including means for initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for generating a commentary on the content input by the user, and means for displaying the generated commentary and commentary on a user device. This system highlights the uniqueness and value of a user's creation and effectively communicates that value to consumers. Furthermore, by using means for formatting and outputting the evaluation results, users can visually confirm the evaluation of their own creations. Furthermore, by using means for providing a commentary divided into at least one of features, meaning, and background, it becomes easier to deeply understand the appeal and background of a user's creation.

[0006] An "artificial intelligence model" is a machine learning algorithm that has been trained to perform a specific task using artificial intelligence techniques.

[0007] "Initializing" refers to the operation of performing the initial settings required to run a system or program.

[0008] "User-inputted content" refers to data such as text, images, and audio that a user provides to the system.

[0009] "Receiving" refers to the operation of taking in data from the outside.

[0010] "Rating" refers to the act of judging the value or quality of content based on specific criteria.

[0011] "Generating a description" refers to the operation of creating an explanation or annotation about the content.

[0012] A "user device" refers to a hardware device used by a user, such as a smartphone, tablet, or PC.

[0013] "Display" refers to the act of visually presenting data or information on a user interface.

[0014] "Formatting and outputting" refers to the act of preparing data in a particular format for display or storage.

[0015] A "feature" is a unique characteristic or distinguishing feature of a particular piece of content.

[0016] "Meaning" refers to the inherent intention, such as themes or messages contained in the content.

[0017] "Context" refers to the circumstances and historical and cultural context in which the content was created. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The system of this invention uses an artificial intelligence model to evaluate content (e.g., paintings, text, etc.) entered by a user, and generates the evaluation results and a commentary on the user's content. The following describes the operation overview and processing procedure of this system.

[0040] System configuration

[0041] 1. Server: Responsible for the central processing of the entire system, initializing the artificial intelligence model, receiving data, evaluating it, and generating explanations.

[0042] 2. Terminal: The device used by the user to input content and receive feedback from the server. Examples include smartphones, tablets, and PCs.

[0043] 3. User: An individual who uses the system to enter their own content and receive ratings and commentary.

[0044] System Operation Overview

[0045] Initialization

[0046] The server imports the necessary libraries and frameworks at system startup and initializes the artificial intelligence model (e.g., GPT-3 (registered trademark)).

[0047] Receiving content

[0048] The user inputs data of their creation (e.g., a picture or piece of writing) into the terminal, which then transmits this data to the server.

[0049] Content Rating

[0050] The server uses an AI model to evaluate the content entered by the user. In this evaluation process, the user sends the AI ​​model a command such as "Please rate this human creation." The model generates an evaluation result based on the command.

[0051] Generate explanations

[0052] The server generates a specific description associated with the user's content, detailing aspects of the content such as its characteristics, meaning, and context, and helping the user understand the value of their creation.

[0053] Providing Feedback

[0054] The server receives the generated ratings and comments, formats them, and sends them back to the terminal, which displays this information in a user interface.

[0055] Specific examples

[0056] 1. The user enters a description of the painting, "A beautiful painting depicting a serene landscape." into the system.

[0057] 2. The device sends the entered description to the server.

[0058] 3. The server uses an artificial intelligence model to evaluate the painting and conclude that "this painting is very realistic and has excellent color choices and composition."

[0059] 4. The server then generates a description such as, "This piece has a unique character and expresses tranquility and peace."

[0060] 5. The server returns the evaluation results and explanations to the device.

[0061] 6. The device will display on its user interface such things as "AI evaluation: This painting is very realistic, with excellent color selection and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," and "Background: Strong connection with nature."

[0062] By using this system, users can understand in detail how their creations are evaluated and what value they are assigned. This feedback process will greatly help users improve their creativity and differentiate their content from AI-generated content.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The server imports the necessary libraries and frameworks, specifically libraries for working with artificial intelligence models (e.g., the transformers library), and prepares the pipeline for text generation.

[0066] Step 2:

[0067] The server calls the initialize_ai_model function to initialize the artificial intelligence model, which includes loading a text generation model (e.g., GPT-3) and making it ready for use.

[0068] Step 3:

[0069] The user inputs content data related to his / her creation into the input screen of the terminal, such as a description of a painting or a sentence.

[0070] Step 4:

[0071] The device collects the input content data and sends it to the server, typically via an HTTP POST request.

[0072] Step 5:

[0073] The server processes the received content data and calls the evaluate_human_content function to evaluate it. At this time, it generates an instruction statement for the AI ​​model saying, "Please evaluate this human creation," and passes it to the model.

[0074] Step 6:

[0075] The server receives the evaluation results returned by the AI ​​model, extracts their contents, and provides the results in a generated text format.

[0076] Step 7:

[0077] The server uses the generate_insights function to generate a description of the content, which details aspects of the content, such as its characteristics, meaning, and context.

[0078] Step 8:

[0079] The server formats the assessment results and generated commentary into a user-friendly format, which includes combining the appropriate parts to create a single, comprehensive feedback.

[0080] Step 9:

[0081] The server then sends the formatted feedback back to the device, again typically as an HTTP response.

[0082] Step 10:

[0083] The device displays the received feedback on the user interface, displaying the evaluation results and explanations in an easy-to-read format so that the user can easily understand them.

[0084] Step 11:

[0085] Users can view and understand the ratings and comments on their creations, which allows them to confirm the value of their work and understand how it stands out from other content.

[0086] Example 1

[0087] 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."

[0088] In recent years, content evaluation and commentary generation using artificial intelligence (AI) have attracted attention, but most of these methods have not yet provided detailed and easy-to-understand feedback that meets user needs. Furthermore, evaluation results and commentaries can sometimes lack consistency and reliability, which means they do not adequately support users' creative activities.

[0089] 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.

[0090] In this invention, the server includes means for initializing an artificial intelligence model, means for receiving user-inputted content, means for evaluating the user-inputted content based on a prompt using the generative AI model, means for generating a commentary on the user-inputted content, and means for formatting and displaying the generated commentary and commentary on the user device, thereby enabling users to receive consistent, reliable, and detailed feedback and improving the quality of their creative activities.

[0091] An "artificial intelligence model" is an algorithm or architecture designed to use data to learn and accomplish a specific task.

[0092] "Content" refers to information entered by the user, including descriptions of paintings and text.

[0093] A "generative AI model" is a trained artificial intelligence model that generates content ratings and commentary through natural language processing.

[0094] A "prompt" is a sentence that gives instructions to a generative AI model to perform a specific task.

[0095] "Means of evaluation" refers to the techniques and processes used to analyze user-entered content using a generative AI model and evaluate its value and characteristics.

[0096] A "means for generating explanations" is a technology or process that generates explanations about the characteristics, meaning, and context of input content.

[0097] "Formatting and displaying means" refers to the techniques and processes that format the generated ratings and commentaries and display them on a user device in a format that is easy for the user to understand.

[0098] A "user device" is a device through which a user interacts with the system, including a smartphone, tablet, or PC.

[0099] The system of this invention evaluates content (e.g., paintings, text, etc.) entered by a user using a generative AI model, and generates the evaluation results and commentary on the content. The following describes in detail the operation overview and embodiments of this system.

[0100] System configuration

[0101] 1. Server:

[0102] Responsible for central processing of the entire system.

[0103] The software used includes libraries such as TENSORFLOW (registered trademark) and PyTorch, as well as pre-trained generative AI models (e.g., GPT-3).

[0104] It initializes, receives data, evaluates it, and generates a commentary.

[0105] 2. Terminal:

[0106] It is a device that users use to input content and receive feedback from the server.

[0107] Examples include smartphones, tablets, and personal computers.

[0108] It provides a user interface, transmits input content to the server, and displays feedback from the server.

[0109] 3. User:

[0110] These are individuals who use the system to enter their own content and receive ratings and commentary.

[0111] System Operation Overview

[0112] 1. Initialization

[0113] The server imports the necessary libraries (e.g., TensorFlow, PyTorch) at system startup and initializes the generative AI model (e.g., GPT-3).

[0114] 2. Receiving Content

[0115] The user inputs data of their creation (e.g., a picture or piece of writing) into the terminal, which then transmits this data to the server.

[0116] As a concrete example, a user enters "A beautiful painting depicting a serene landscape." into a web form and presses the submit button.

[0117] 3. Content Rating

[0118] The server sends prompts to the generative AI model to process the received content.

[0119] Example prompt: "Evaluate this human creation: A beautiful painting depicting a serene landscape."

[0120] The model generates evaluation results based on the instructions.

[0121] 4. Generating Explanations

[0122] The server generates a commentary based on the content rating.

[0123] An example of a specific prompt: "This piece has a unique character and conveys a sense of serenity and peace."

[0124] The generated commentary includes details about the content's characteristics, meaning, and background.

[0125] 5. Providing Feedback

[0126] The server receives the generated ratings and comments, formats them, and sends them back to the terminal, which displays this information in a user interface.

[0127] To give an example of how it actually works, the user's browser will display the following: "AI rating: This painting is highly realistic, with excellent color choices and composition," and "Description: This work has unique characteristics and conveys tranquility and peace."

[0128] By using this system, users can understand in detail how their creations are evaluated by the generative AI model and what value they are assigned. This process not only improves users' creative activities, but also increases user satisfaction by increasing the reliability of the evaluation results and explanations as they match.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1:

[0131] The server imports the necessary libraries (e.g., TensorFlow, PyTorch) at system startup and initializes the generative AI model (e.g., GPT-3).

[0132] Input: System startup signal

[0133] Output: Initialized libraries and models

[0134] Specific operation: Call import tensorflow as tf or model = GPT3(api_key="your_api_key") .

[0135] Step 2:

[0136] The user inputs data of their own creation (e.g., painting, writing) into the terminal.

[0137] Input: Content entered by the user (e.g., "A beautiful painting depicting a serene landscape.")

[0138] Output: Content entered into the device

[0139] What happens: The user enters text into a displayed web form.

[0140] Step 3:

[0141] The terminal transmits the input content to the server.

[0142] Input: Content entered into the device

[0143] Output: Content data sent to the server

[0144] Specific operation: Call requests.post('server_url', data={'content': user_input}).

[0145] Step 4:

[0146] The server sends prompts to the generative AI model to process the received content.

[0147] Input: Content data received from the device

[0148] Output: Prompts and evaluation results sent to the generative AI model

[0149] Specific behavior: Generates the prompt sentence "Please rate this human creation: A beautiful painting depicting a serene landscape." and executes response = model.evaluate("Please rate this human creation: " + user_input).

[0150] Step 5:

[0151] The server receives the evaluation results from the model.

[0152] Input: Evaluation results from a generative AI model

[0153] Output: Evaluation data

[0154] Specific operation: Obtain evaluation = response['evaluation'].

[0155] Step 6:

[0156] The server generates a commentary based on the content rating.

[0157] Input: Evaluation data

[0158] Output: Commentary data based on the evaluation results

[0159] Specific operation: To generate the explanation "This piece has unique characteristics and expresses tranquility and peace," run explanation = model.generate_explanation(evaluation).

[0160] Step 7:

[0161] The server formats the generated ratings and comments.

[0162] Input: Evaluation data and commentary data

[0163] Output: Formatted feedback data

[0164] Specific operation: Generate formatted_response = f"AI evaluation: {evaluation}\nExplanation: {explanation}".

[0165] Step 8:

[0166] The server sends the formatted data to the terminal.

[0167] Input: Formatted feedback data

[0168] Output: Feedback data sent back to the device

[0169] Specific operation: Call requests.post('client_url', data={'response': formatted_response}).

[0170] Step 9:

[0171] The terminal displays the received data on a user interface.

[0172] Input: Feedback data sent from the server

[0173] Output: Feedback displayed in the user interface

[0174] Specific behavior: The following is displayed in the user's browser: "AI evaluation: This painting is highly realistic, with excellent color selection and composition" and "Description: This work has unique characteristics and conveys tranquility and peace."

[0175] (Application example 1)

[0176] 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."

[0177] Modern electronic payment services are required to accurately evaluate the content of reviews posted by users for products and services and provide feedback in a format that is easy for users to understand. However, existing systems lack automated means for evaluating the quality of reviews posted by users, making it difficult to improve the quality and ensure the reliability of reviews posted by users. Furthermore, while providing users with detailed explanations of reviews can help other users with their purchasing decisions, there has been no effective method for doing so.

[0178] 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.

[0179] In this invention, the server includes means for initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for evaluating review content input by the user using the artificial intelligence model and providing a detailed commentary, and means for displaying the generated rating and commentary on a user device. This makes it possible to automatically evaluate the quality of review content posted by a user and provide detailed feedback to the user based on the evaluation.

[0180] A "means for initializing an artificial intelligence model" is a device or software function for preparing an artificial intelligence model to an operational state.

[0181] A "means for receiving user-inputted content" is a device or software function for capturing user-submitted data or information.

[0182] A "means for evaluating user-input content" is a device or software function that analyzes data or information provided by a user and determines its value or quality.

[0183] The "means for generating an explanation about content entered by a user" refers to a device or software function that generates a detailed explanation about the content and background based on the entered data or information.

[0184] "Review content" refers to descriptions such as evaluations and opinions given by users regarding products and services.

[0185] A "means for providing detailed commentary" is a device or software function that generates and provides detailed information on specific content or background based on the input review content.

[0186] "Means for displaying the generated evaluation and commentary on a user device" refers to a device or software function that displays the evaluation results and commentary information created by the artificial intelligence model on the screen of a device used by the user.

[0187] To realize this invention, the following system configuration is used: The system mainly consists of three main elements: a server, a terminal, and a user.

[0188] server

[0189] The server plays a central role in the entire system and performs the following functions:

[0190] 1. Initialization of AI model: The server has the function to initialize generative AI models such as GPT-3, which makes the AI ​​model ready for operation.

[0191] 2. Content Evaluation: The AI ​​model evaluates the content submitted by users and generates detailed commentary based on the review content entered by the users.

[0192] 3. Commentary generation: Based on the evaluation results, a detailed commentary about the content entered by the user is generated, including detailed information such as the features, meaning, and background of the review.

[0193] 4. Display Results: Convert the generated assessments and commentary into an appropriate format and send it back to the user device.

[0194] Terminal

[0195] The terminal is responsible for inputting content from the user and receiving feedback from the server. The main functions are as follows:

[0196] 1. Content Input: Provide an interface for users to input review content. This interface runs on devices such as smartphones, tablets, and PCs.

[0197] 2. Data transmission: Has the function of sending the input content to the server.

[0198] 3. Displaying feedback: The evaluation results and explanations received from the server are displayed on the user interface.

[0199] User

[0200] The user uses the system to input review content and receive feedback from the server. Specifically, the user performs the following operations:

[0201] 1. Write a review: Write a review for the product or service you purchased.

[0202] 2. Check the feedback: Check the evaluation results and explanations returned from the server and improve or deepen your understanding of your review.

[0203] Hardware and software used

[0204] 1. Hardware:

[0205] Server: Server equipment with high-performance computing capabilities

[0206] Devices: smartphones, tablets, computers, etc.

[0207] 2. Software:

[0208] AI model: GPT-3 by OpenAI (registered trademark)

[0209] API Access: Access GPT-3 using the OpenAI API

[0210] Interface: The application through which users enter reviews (e.g., smartphone app, web app)

[0211] Specific examples

[0212] Specific examples are shown below.

[0213] For example, a user might write a review stating, "This smartwatch is very intuitive and easy to use. It has a long battery life and constantly monitors your heart rate. I especially like the extensive fitness features." When that review is sent to the server, the server uses GPT-3 to evaluate it and generate a detailed commentary such as the following: "This smartwatch is very intuitive to use and easy for users to use. The long battery life allows for long-term use, and the continuous heart rate monitoring feature is useful for health management. In addition, the extensive fitness features make it especially appealing to health-conscious users."

[0214] Prompt Sentence Examples

[0215] Rate the most influential user reviews based on the following statement: This smartwatch is very intuitive and easy to use. It has great battery life and continuous heart rate monitoring. I especially like the extensive fitness features.

[0216] And provide a detailed commentary on the review.

[0217] This makes it possible to automatically evaluate the quality of reviews posted by users and provide detailed feedback on their content.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] The server imports the necessary libraries and frameworks at system startup and initializes a generative AI model such as GPT-3. In this step, the generative AI model is prepared for operation. Specifically, it obtains access to the GPT-3 model through the OpenAI API. The input is the configuration information and libraries required at system startup, and the output is the initialized generative AI model.

[0221] Step 2:

[0222] A user inputs review content for a product or service into a terminal. The terminal receives the user's input and sends the data to a server. In this step, the user's review content is collected as text data. Specifically, the review content entered into a text area via a user interface is saved in a database. The input is the review content entered by the user, and the output is the review content data sent to the server.

[0223] Step 3:

[0224] The server receives the review content submitted by the user and generates a prompt for rating. Here, a prompt for rating (e.g., "Please rate the influential reviews submitted by the user based on the following statement: [user's review] and provide a detailed explanation for the review") is created. The input is the review content submitted by the user, and the output is the generated prompt.

[0225] Step 4:

[0226] The server sends the generated prompt to the GPT-3 model, requesting it to generate a rating and commentary for the review content. The AI ​​model then generates text data based on the prompt and outputs a rating and commentary for the review content. Specifically, the prompt is sent to the generative AI model using the OpenAI API, and a rating and detailed commentary are obtained in response. The input is the prompt, and the output is the generated rating and commentary.

[0227] Step 5:

[0228] The server receives the generated ratings and comments and formats them for display on the user device. This step involves formatting and categorizing the text data. Specifically, it converts the ratings and comments into an easily understandable format suitable for the user interface. The input is the generated ratings and comments, and the output is the formatted ratings and comments data.

[0229] Step 6:

[0230] The terminal receives the formatted evaluation and commentary sent from the server and displays them on the user interface. Here, the evaluation and commentary are displayed on the screen in a format that is easy for the user to check. Specifically, the user interface visualizes the evaluation results and commentary using a component that can display text data. The input is the formatted evaluation and commentary data, and the output is the evaluation and commentary displayed on the screen of the user device.

[0231] Step 7:

[0232] The user views the displayed evaluation results and explanations and improves the content of their review based on them. In this step, the user can take action to improve the quality of their review by utilizing the provided feedback. Specifically, the user can refer to the evaluation results and explanations and use them as reference when posting their next review. The input is the displayed evaluation results and explanations, and the output is the user's review improvement.

[0233] The above processing steps realize a system that effectively evaluates users' review content and provides detailed feedback.

[0234] 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.

[0235] The system of this invention uses an artificial intelligence model to evaluate content entered by a user and generate the evaluation results and a commentary on the user's content. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and adjust the evaluation and commentary based on those emotions. The following is an overview of the system's configuration and operation.

[0236] System configuration

[0237] 1. Server: Responsible for the central processing of the entire system, initializing the AI ​​model, receiving and evaluating data, generating commentary, and running the emotion engine.

[0238] 2. Terminal: The device used by the user to input content and receive feedback from the server. Examples include smartphones, tablets, and PCs.

[0239] 3. User: An individual who uses the system to enter their own content and receive ratings and commentary.

[0240] 4. Emotion Engine: A component for recognizing and analyzing the user's emotional state, allowing it to provide feedback that corresponds to the user's emotions.

[0241] System Operation Overview

[0242] Initialization

[0243] At system startup, the server imports the necessary libraries and frameworks, initializes the AI ​​model (e.g., GPT-3), and initializes the emotion engine, making it ready for use.

[0244] Receiving content

[0245] The user inputs data about their emotional state into the device's input screen along with data about their creative work (e.g., a painting or piece of writing). The emotional state data can be text input or facial expression data. The device then transmits this data to the server.

[0246] Assessment of content and emotional state

[0247] The server processes the received content data and emotion data and performs the following two evaluations.

[0248] 1. The initialized artificial intelligence model is used to evaluate the content entered by the user.

[0249] 2. Using the emotion engine, analyze the user's emotional state and obtain the results.

[0250] Generate and adjust commentary

[0251] The server combines the content evaluation results with the sentiment analysis results from the sentiment engine to generate a commentary about the content using the generate_insights function. This commentary provides detailed explanations about aspects such as the content's features, meaning, and context, and is appropriately adjusted based on the user's emotional state.

[0252] Providing feedback

[0253] The server formats the evaluation results and generated explanations in a user-friendly format and sends them back to the terminal, which displays this information in a user interface for easy user understanding.

[0254] Specific examples

[0255] 1. The user inputs the description of the painting "A beautiful painting depicting a serene landscape." along with their current emotional state (e.g., relaxed) into the system.

[0256] 2. The device sends the entered description and emotion data to the server.

[0257] 3. The server uses an artificial intelligence model to rate the painting, saying, "This painting is very realistic, and its color choices and composition are excellent."

[0258] 4. The server uses the emotion engine to analyze the user's emotional state. As a result of the analysis, it determines that the user is relaxed.

[0259] 5. The server further generates a description such as "This piece has unique characteristics and expresses tranquility and peace," adjusting it according to the user's relaxed emotional state.

[0260] 6. The server returns the evaluation results and explanations to the device.

[0261] 7. The device will display on its user interface, "AI evaluation: This painting is very realistic, with excellent color choice and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," "Background: Strong connection with nature," and even provide emotional feedback such as, "Looking at this painting in a relaxed state of mind will make you feel even more at peace."

[0262] By using this system, users can understand in detail how their creations are evaluated and what value they have, and receive feedback that reflects their emotional state. By combining this with the emotion engine, more personalized feedback that is valuable to the user is provided.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] The server imports the necessary libraries and frameworks, specifically libraries for operating artificial intelligence models (e.g., the transformers library) and emotion engine libraries, and prepares the pipeline for text generation and emotion analysis.

[0266] Step 2:

[0267] The server calls the initialize_ai_model function to initialize the artificial intelligence model. This includes loading a text generation model (e.g., GPT-3) and making it available for use. It also initializes the emotion engine, preparing it to analyze the user's emotional state.

[0268] Step 3:

[0269] Users input content data related to their creations and their own emotional state data into the input screen of their device. Examples of content data include descriptions of paintings and text, and emotional state data is obtained through text input, facial expression analysis, and voice input.

[0270] Step 4:

[0271] The device collects the input content data and emotional state data and sends them to the server, typically via an HTTP POST request.

[0272] Step 5:

[0273] The server processes the received content data and calls the evaluate_human_content function to evaluate it. At this time, it generates an instruction statement for the AI ​​model saying, "Please evaluate this human creation," and passes it to the model.

[0274] Step 6:

[0275] The server receives the evaluation results returned by the AI ​​model, extracts their contents, and provides the results in a generated text format.

[0276] Step 7:

[0277] The server passes the received emotional state data to the emotion engine, which analyzes the user's emotions by determining the user's current emotional state (e.g., relaxed, excited, etc.) based on the text entered by the user and other emotional state data.

[0278] Step 8:

[0279] The server uses the generate_insights function to generate commentary about the content, which details aspects of the content, such as its characteristics, meaning, and context, and is adjusted based on the user's emotional state.

[0280] Step 9:

[0281] The server formats the assessment results and generated commentary into a user-friendly format, which includes combining the appropriate parts to create a single, comprehensive feedback.

[0282] Step 10:

[0283] The server then sends formatted feedback back to the device, typically as an HTTP response.

[0284] Step 11:

[0285] The device displays the received feedback on the user interface, displaying the evaluation results and explanations in an easy-to-read format so that the user can easily understand them.

[0286] Step 12:

[0287] Users can view and understand the ratings and comments on their creations, helping them understand the value of their work and how it differentiates from other content. Additionally, feedback based on the user's emotional state provides a more personalized experience.

[0288] Example 2

[0289] 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."

[0290] In today's information environment, users lack the means to quickly and accurately evaluate the value of their own creations and content. Furthermore, providing personalized feedback based on the user's emotional state is currently insufficient. A system that can solve these issues and provide users with more detailed and useful feedback is needed.

[0291] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for analyzing the emotional state of the user using an emotion analysis engine, means for generating a commentary based on the content evaluation result and the emotion analysis result, and means for displaying the generated evaluation and commentary on the user device. This allows the user to understand in detail how their creation is evaluated and what value it has, and to receive personalized feedback based on their emotional state.

[0292] An "artificial intelligence model" is a computational model trained to perform a specific task and used to evaluate user-entered content.

[0293] A "user" is an individual who uses the system to enter their own content and receive ratings and comments.

[0294] "Content" refers to information that a user inputs into the system, and may be in the form of text, images, audio, or the like.

[0295] "Means for receiving" refers to the process or function for receiving content data input by a user within the system.

[0296] The "means for evaluating" refers to the process and function of using an initialized artificial intelligence model to analyze and evaluate user-entered content.

[0297] An "emotion analysis engine" is a component for analyzing the emotional state from user input data, and includes algorithms and software for classifying and evaluating emotions.

[0298] "Means for generating commentary" refers to the process and function of creating detailed explanations and analyses of content based on the results of content evaluation and sentiment analysis.

[0299] "User Device" means a device used by a user to interact with the system, including a smartphone, tablet, or PC.

[0300] The "means for displaying" refers to a function for visually showing the generated evaluation or commentary to the user on the user device.

[0301] The system of this invention uses an artificial intelligence model to evaluate content entered by a user and provides the evaluation results and commentary. By combining it with an emotion analysis engine, it is possible to recognize the user's emotional state and provide feedback based on that emotion. The system configuration and operation are described in detail below.

[0302] System configuration

[0303] 1. Server

[0304] The server plays a central role in the entire system. It imports necessary libraries and frameworks (e.g., TensorFlow, PyTorch), initializes the artificial intelligence model (e.g., GPT-3), and initializes the sentiment analysis engine (e.g., Google® Cloud Natural Language API) for use.

[0305] 2. Terminal

[0306] A terminal is a device through which a user inputs content. Examples include smartphones, tablets, and PCs. The terminal communicates with a server, sends the data entered by the user to the server, and receives feedback from the server.

[0307] 3. Users

[0308] Users are individuals who use the system to enter content and receive ratings and comments. Users can enter content in a variety of forms, including digital art, written text, and audio recordings.

[0309] 4. Sentiment Analysis Engine

[0310] The emotion analysis engine is a component for analyzing the user's emotional state, thereby providing feedback based on the user's current emotional state.

[0311] Operational Overview

[0312] 1. Initialization

[0313] When the system starts up, the server initializes the artificial intelligence model and the emotion analysis engine at the same time, making them ready for use.

[0314] 2. Receiving Content

[0315] The user inputs a description of the painting, such as "A beautiful painting depicting a serene landscape," and their current emotional state (e.g., "Relaxed") into the device. The device then transmits this data to the server.

[0316] 3. Content Rating

[0317] The server uses the initialized artificial intelligence model to evaluate the received content data, for example, "This painting is very realistic and its color selection and composition are excellent."

[0318] 4. Emotional state analysis

[0319] The server uses an emotion analysis engine to analyze the user's emotional state, for example determining that the user is relaxed.

[0320] 5. Generating Explanations

[0321] The server generates a commentary based on the content evaluation and sentiment analysis results. Specifically, it generates a commentary such as, "This piece has unique characteristics and expresses tranquility and peace."

[0322] 6. Providing Feedback

[0323] The server formats the evaluation results and generated commentary in an easy-to-understand format and returns it to the user device, which displays this information in its user interface.

[0324] Specific examples

[0325] 1. The user enters the description of the painting "A beautiful painting depicting a serene landscape." along with their current emotional state "Relaxed" into the system.

[0326] 2. The device sends the entered description and emotion data to the server.

[0327] 3. The server uses an artificial intelligence model to rate the painting, saying, "This painting is very realistic, and its color choices and composition are excellent."

[0328] 4. The server uses an emotion analysis engine to analyze the user's emotional state and determine that the user is relaxed.

[0329] 5. The server generates a description such as "This piece has unique characteristics and expresses tranquility and peace," adjusting it according to the user's relaxed emotional state.

[0330] 6. The server returns the evaluation results and explanations to the terminal.

[0331] 7. The device will display on its user interface, "AI evaluation: This painting is very realistic, with excellent color choice and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," "Background: Strong connection with nature," and even provide emotional feedback such as, "Looking at this painting in a relaxed state of mind will make you feel even more at peace."

[0332] The system allows users to understand in detail how their creations are evaluated and what value they are assigned, and receive feedback based on their emotional state. Combined with a sentiment analysis engine, the system provides more personalized feedback.

[0333] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0334] Step 1: Initialization

[0335] When the system starts, the server imports the necessary libraries and frameworks (e.g., TensorFlow, PyTorch). It also initializes the artificial intelligence model (e.g., GPT-3) and the sentiment analysis engine (e.g., Google Cloud Natural Language API). In this step, the server processes the following data: the system configuration information is the input, and the initialized model and engine are the output.

[0336] Step 2: Enter your content

[0337] The user uses the device to input their own content (e.g., a description of a painting, "A beautiful painting depicting a serene landscape.") and their current emotional state (e.g., "Relaxed"). This input data is entered into the device via an interface such as a web form. The input consists of the user's text data and emotional data, and the device processes this as output and prepares it to be sent to the server.

[0338] Step 3: Sending data

[0339] The device sends the input content data and emotion data to the server. Specifically, the device wraps the data in a structured format such as JSON and sends it to the server using an HTTP POST request. The input is content and emotion data from the user, and the output is the data sent to the server.

[0340] Step 4: Evaluate your content

[0341] The server evaluates the content data received from the terminal using an initialized artificial intelligence model. In this process, the input text data is tokenized and passed to the model to generate an evaluation result. The received content data is the input, and the evaluation result text is obtained as the output.

[0342] Step 5: Analyze emotional state

[0343] The server uses a sentiment analysis engine to analyze the received sentiment data. In this analysis process, the input sentiment text is passed to the analysis engine, which outputs detailed data about the user's emotional state. The input is the received sentiment data, and the output is the sentiment analysis result data.

[0344] Step 6: Generate a description

[0345] The server generates a commentary based on the content's rating and sentiment analysis results. In this commentary generation process, the generate_insights function is used to generate commentary text that combines the input rating and sentiment analysis results. The inputs are the rating and sentiment analysis results, and the output is the generated commentary text.

[0346] Step 7: Format and submit your feedback

[0347] The server formats the generated evaluation results and explanations into a format that is easy for users to understand. This formatted feedback data is sent from the server to the terminal. The generated evaluation results and explanations are the input, and the formatted feedback data is the output.

[0348] Step 8: Viewing feedback

[0349] The terminal displays the feedback data received from the server on the user interface. Specifically, the terminal visually displays the evaluation results, explanations, and emotion-related feedback on the screen. The input is the formatted feedback data, and the output is the information displayed on the user interface.

[0350] (Application example 2)

[0351] 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."

[0352] There is a need for a system that can provide appropriate evaluations and commentaries for user-generated content, as well as recognize users' emotions and personalize the commentary based on those emotions. However, conventional systems do not provide sufficient feedback based on users' emotions, and are therefore unable to provide optimal information to individual users.

[0353] 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 initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for recognizing and analyzing the emotional state of the user, means for adjusting and generating commentary on the content based on the emotional state of the user, and means for displaying the generated evaluation and commentary on the user device. This makes it possible to provide highly accurate evaluations of user-generated content and provide personalized commentary according to the emotional state of the user.

[0354] An "artificial intelligence model" is a computer algorithm used to evaluate user-entered content.

[0355] A "user device" is a device that a user uses to input content and receive feedback from a server.

[0356] "Emotional state" is information that indicates the user's current psychological state.

[0357] "Means for evaluation" refers to a function that uses an initialized artificial intelligence model to analyze content entered by a user and determine its value and quality.

[0358] "Means for generating explanations" refers to a function that creates an explanation for content entered by a user, including its characteristics, meaning, background, etc.

[0359] The "means for adjusting and generating" refers to a function for appropriately modifying the generated commentary based on the user's emotional state and providing it to the user in the most optimal form.

[0360] "Means for displaying" refers to the ability to visually or audibly present the generated ratings and commentary on a user device.

[0361] This invention relates to a system that uses an AI model to evaluate content entered by a user and generates evaluation results and commentary. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and adjust the evaluation and commentary based on those emotions. This system is composed of a user device, a server, an AI model, and an emotion engine.

[0362] System Components

[0363] server

[0364] The server is the central point of the entire system and is responsible for:

[0365] Initialize the AI ​​model: Import the required libraries and frameworks and initialize the AI ​​model (e.g., GPT-3).

[0366] Content evaluation: Receives the content data entered by the user and evaluates it using the initialized artificial intelligence model.

[0367] Sentiment analysis: An emotion engine is used to analyze the emotional data provided by the user.

[0368] Commentary generation and adjustment: Based on the content evaluation results and sentiment analysis results, a commentary is generated and adjusted to correspond to the user's emotional state.

[0369] Provide Feedback: Format and send the generated assessment and commentary to the user device.

[0370] User Device

[0371] User devices are used by users to input their own content and receive feedback from the server. Examples include smart glasses, smartphones, tablets, and personal computers.

[0372] Processing Details

[0373] The user inputs content (e.g., text or images) into the user device. The user's emotional state (e.g., whether they are relaxed or nervous) can also be input as various data (e.g., facial expression data, voice data).

[0374] The server receives content and emotion data sent from the user device. The received content is evaluated using an artificial intelligence model (e.g., GPT-3). The evaluation includes the quality, composition, and meaning of the content. For example, a rating such as "This painting is very realistic, and its color selection and composition are excellent" is generated.

[0375] In parallel, the emotion engine analyzes the user's emotion data, and the analysis results include information such as "the user is relaxed."

[0376] The server generates a commentary based on the evaluation results and sentiment analysis results. This commentary provides a detailed explanation of the content's characteristics, meaning, and background, and is adjusted based on the user's emotional state. For example, the server might generate feedback such as, "This work will make you feel even more calm if you watch it in a relaxed state."

[0377] After all results are generated, the server formats them in a user-friendly format and sends them to the user device, which displays this information in a user interface, or in the case of smart glasses, a real-time evaluation and commentary on the display.

[0378] Specific examples

[0379] As a concrete example, consider a user wearing smart glasses visiting an art gallery. Use the following prompt:

[0380] Example prompt sentence:

[0381] When the user is feeling relaxed, rate this content, "A beautiful painting of a serene landscape," and generate a commentary.

[0382] The smart glasses' camera captures the painting, and the user's emotional state is determined to be "relaxed" based on their facial expression. This data is sent to a server, where an AI model evaluates the painting as "very beautiful, with excellent color selection and composition." Based on the emotion analysis results, a commentary is generated, such as "Looking at this painting in a relaxed state will make you feel even more at peace." These results are displayed on the smart glasses' display, allowing the user to receive real-time feedback.

[0383] This system allows users to understand in detail how their content is rated and what value it has, and to receive feedback that reflects their emotional state.

[0384] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0385] Step 1:

[0386] User enters content and emotion data

[0387] A user inputs their own content (e.g., text, images) using a device such as smart glasses or a smartphone. They also input their emotional state (e.g., relaxed, nervous) as facial expression data and voice data. The device then transmits this data to a server. The input is content data and emotional data, and the output is data sent to the server.

[0388] Step 2:

[0389] The server receives the data

[0390] The server receives content data and emotion data sent from the user device and converts the received data into an analyzable format. The input is the content data and emotion data sent from the user device, and the output is the data converted into an analyzable format.

[0391] Step 3:

[0392] Evaluating content using artificial intelligence models

[0393] The server passes the received content data to an artificial intelligence model (e.g., GPT-3) to evaluate the content. This includes analyzing the content's quality, composition, meaning, etc. For example, it generates a rating such as "This painting is very realistic, and its color selection and composition are excellent." The input is the content data converted into an analyzable format, and the output is the evaluation result.

[0394] Step 4:

[0395] Analyzing the user's emotional state using an emotion engine

[0396] The server uses an emotion engine to recognize and analyze the user's emotional state from facial expression data and voice data. The analysis results can provide information such as "the user is relaxed." The input is emotion data converted into an analyzable format, and the output is the emotion analysis results.

[0397] Step 5:

[0398] Generate and adjust commentary based on evaluation results and sentiment analysis results

[0399] The server generates a commentary based on the content evaluation results and emotion analysis results. The content of the commentary is adjusted according to the user's emotional state. For example, it generates a commentary such as "Watching it in a relaxed state will make you feel even more calm." The input is the evaluation results and emotion analysis results, and the output is a commentary optimized for the user.

[0400] Step 6:

[0401] Formatting the evaluation results and generated commentary

[0402] The server formats the evaluation results and generated commentary into a user-friendly format, for example, in text or audio format. The input is the user-optimized commentary, and the output is the formatted evaluation results and commentary.

[0403] Step 7:

[0404] Send the formatted results to the user device

[0405] The server sends the formatted evaluation results and commentary to the user device, which can then display the results on a display such as smart glasses or provide audio feedback. The input is the formatted evaluation results and commentary, and the output is transmission to the user device.

[0406] Step 8:

[0407] The user device displays the results

[0408] The user device displays the evaluation results and commentary sent from the server. In the case of smart glasses, the display shows real-time feedback. The input is the result sent from the server, and the output is the user receiving visual or auditory feedback.

[0409] Through this series of steps, users can gain a detailed understanding of how their content is rated and what value it has, as well as receive feedback based on their emotional state.

[0410] 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.

[0411] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0412] 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.

[0413] [Second embodiment]

[0414] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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."

[0426] The system of this invention uses an artificial intelligence model to evaluate content (e.g., paintings, text, etc.) entered by a user, and generates the evaluation results and a commentary on the user's content. The following describes the operation overview and processing procedure of this system.

[0427] System configuration

[0428] 1. Server: Responsible for the central processing of the entire system, initializing the artificial intelligence model, receiving data, evaluating it, and generating explanations.

[0429] 2. Terminal: The device used by the user to input content and receive feedback from the server. Examples include smartphones, tablets, and PCs.

[0430] 3. User: An individual who uses the system to enter their own content and receive ratings and commentary.

[0431] System Operation Overview

[0432] Initialization

[0433] The server imports the necessary libraries and frameworks at system startup and initializes the artificial intelligence model (e.g., GPT-3).

[0434] Receiving content

[0435] The user inputs data of their creation (e.g., a picture or piece of writing) into the terminal, which then transmits this data to the server.

[0436] Content Rating

[0437] The server uses an AI model to evaluate the content entered by the user. In this evaluation process, the user sends the AI ​​model a command such as "Please rate this human creation." The model generates an evaluation result based on the command.

[0438] Generate explanations

[0439] The server generates a specific description associated with the user's content, detailing aspects of the content such as its characteristics, meaning, and context, and helping the user understand the value of their creation.

[0440] Providing Feedback

[0441] The server receives the generated ratings and comments, formats them, and sends them back to the terminal, which displays this information in a user interface.

[0442] Specific examples

[0443] 1. The user enters a description of the painting, "A beautiful painting depicting a serene landscape." into the system.

[0444] 2. The device sends the entered description to the server.

[0445] 3. The server uses an artificial intelligence model to evaluate the painting and conclude that "this painting is very realistic and has excellent color choices and composition."

[0446] 4. The server then generates a description such as, "This piece has a unique character and expresses tranquility and peace."

[0447] 5. The server returns the evaluation results and explanations to the device.

[0448] 6. The device will display on its user interface such things as "AI evaluation: This painting is very realistic, with excellent color selection and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," and "Background: Strong connection with nature."

[0449] By using this system, users can understand in detail how their creations are evaluated and what value they are assigned. This feedback process will greatly help users improve their creativity and differentiate their content from AI-generated content.

[0450] The processing flow will be explained below.

[0451] Step 1:

[0452] The server imports the necessary libraries and frameworks, specifically libraries for working with artificial intelligence models (e.g., the transformers library), and prepares the pipeline for text generation.

[0453] Step 2:

[0454] The server calls the initialize_ai_model function to initialize the artificial intelligence model, which includes loading a text generation model (e.g., GPT-3) and making it ready for use.

[0455] Step 3:

[0456] The user inputs content data related to his / her creation into the input screen of the terminal, such as a description of a painting or a sentence.

[0457] Step 4:

[0458] The device collects the input content data and sends it to the server, typically via an HTTP POST request.

[0459] Step 5:

[0460] The server processes the received content data and calls the evaluate_human_content function to evaluate it. At this time, it generates an instruction statement for the AI ​​model saying, "Please evaluate this human creation," and passes it to the model.

[0461] Step 6:

[0462] The server receives the evaluation results returned by the AI ​​model, extracts their contents, and provides the results in a generated text format.

[0463] Step 7:

[0464] The server uses the generate_insights function to generate a description of the content, which details aspects of the content, such as its characteristics, meaning, and context.

[0465] Step 8:

[0466] The server formats the assessment results and generated commentary into a user-friendly format, which includes combining the appropriate parts to create a single, comprehensive feedback.

[0467] Step 9:

[0468] The server then sends the formatted feedback back to the device, again typically as an HTTP response.

[0469] Step 10:

[0470] The device displays the received feedback on the user interface, displaying the evaluation results and explanations in an easy-to-read format so that the user can easily understand them.

[0471] Step 11:

[0472] Users can view and understand the ratings and comments on their creations, which allows them to confirm the value of their work and understand how it stands out from other content.

[0473] Example 1

[0474] 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."

[0475] In recent years, content evaluation and commentary generation using artificial intelligence (AI) have attracted attention, but most of these methods have not yet provided detailed and easy-to-understand feedback that meets user needs. Furthermore, evaluation results and commentaries can sometimes lack consistency and reliability, which means they do not adequately support users' creative activities.

[0476] 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.

[0477] In this invention, the server includes means for initializing an artificial intelligence model, means for receiving user-inputted content, means for evaluating the user-inputted content based on a prompt using the generative AI model, means for generating a commentary on the user-inputted content, and means for formatting and displaying the generated commentary and commentary on the user device, thereby enabling users to receive consistent, reliable, and detailed feedback and improving the quality of their creative activities.

[0478] An "artificial intelligence model" is an algorithm or architecture designed to use data to learn and accomplish a specific task.

[0479] "Content" refers to information entered by the user, including descriptions of paintings and text.

[0480] A "generative AI model" is a trained artificial intelligence model that generates content ratings and commentary through natural language processing.

[0481] A "prompt" is a sentence that gives instructions to a generative AI model to perform a specific task.

[0482] "Means of evaluation" refers to the techniques and processes used to analyze user-entered content using a generative AI model and evaluate its value and characteristics.

[0483] A "means for generating explanations" is a technology or process that generates explanations about the characteristics, meaning, and context of input content.

[0484] "Formatting and displaying means" refers to the techniques and processes that format the generated ratings and commentaries and display them on a user device in a format that is easy for the user to understand.

[0485] A "user device" is a device through which a user interacts with the system, including a smartphone, tablet, or PC.

[0486] The system of this invention evaluates content (e.g., paintings, text, etc.) entered by a user using a generative AI model, and generates the evaluation results and commentary on the content. The following describes in detail the operation overview and embodiments of this system.

[0487] System configuration

[0488] 1. Server:

[0489] Responsible for central processing of the entire system.

[0490] The software used includes libraries such as TensorFlow and PyTorch, as well as pre-trained generative AI models (e.g., GPT-3).

[0491] It initializes, receives data, evaluates it, and generates a commentary.

[0492] 2. Terminal:

[0493] It is a device that users use to input content and receive feedback from the server.

[0494] Examples include smartphones, tablets, and personal computers.

[0495] It provides a user interface, transmits input content to the server, and displays feedback from the server.

[0496] 3. User:

[0497] These are individuals who use the system to enter their own content and receive ratings and commentary.

[0498] System Operation Overview

[0499] 1. Initialization

[0500] The server imports the necessary libraries (e.g., TensorFlow, PyTorch) at system startup and initializes the generative AI model (e.g., GPT-3).

[0501] 2. Receiving Content

[0502] The user inputs data of their creation (e.g., a picture or piece of writing) into the terminal, which then transmits this data to the server.

[0503] As a concrete example, a user enters "A beautiful painting depicting a serene landscape." into a web form and presses the submit button.

[0504] 3. Content Rating

[0505] The server sends prompts to the generative AI model to process the received content.

[0506] Example prompt: "Evaluate this human creation: A beautiful painting depicting a serene landscape."

[0507] The model generates evaluation results based on the instructions.

[0508] 4. Generating Explanations

[0509] The server generates a commentary based on the content rating.

[0510] An example of a specific prompt: "This piece has a unique character and conveys a sense of serenity and peace."

[0511] The generated commentary includes details about the content's characteristics, meaning, and background.

[0512] 5. Providing Feedback

[0513] The server receives the generated ratings and comments, formats them, and sends them back to the terminal, which displays this information in a user interface.

[0514] As an example of how it actually works, the user's browser will display the following: "AI evaluation: This painting is highly realistic, with excellent color choices and composition," and "Description: This work has unique characteristics and conveys tranquility and peace."

[0515] By using this system, users can understand in detail how their creations are evaluated by the generative AI model and what value they are assigned. This process not only improves users' creative activities, but also increases user satisfaction by increasing the reliability of the evaluation results and explanations as they match.

[0516] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0517] Step 1:

[0518] The server imports the necessary libraries (e.g., TensorFlow, PyTorch) at system startup and initializes the generative AI model (e.g., GPT-3).

[0519] Input: System startup signal

[0520] Output: Initialized libraries and models

[0521] Specific operation: Call import tensorflow as tf or model = GPT3(api_key="your_api_key") .

[0522] Step 2:

[0523] The user inputs data of their own creation (e.g., painting, writing) into the terminal.

[0524] Input: Content entered by the user (e.g., "A beautiful painting depicting a serene landscape.")

[0525] Output: Content entered into the device

[0526] What happens: The user enters text into a displayed web form.

[0527] Step 3:

[0528] The terminal transmits the input content to the server.

[0529] Input: Content entered into the device

[0530] Output: Content data sent to the server

[0531] Specific operation: Call requests.post('server_url', data={'content': user_input}).

[0532] Step 4:

[0533] The server sends prompts to the generative AI model to process the received content.

[0534] Input: Content data received from the device

[0535] Output: Prompts and evaluation results sent to the generative AI model

[0536] Specific behavior: Generates the prompt sentence "Please rate this human creation: A beautiful painting depicting a serene landscape." and executes response = model.evaluate("Please rate this human creation: " + user_input).

[0537] Step 5:

[0538] The server receives the evaluation results from the model.

[0539] Input: Evaluation results from a generative AI model

[0540] Output: Evaluation data

[0541] Specific operation: Obtain evaluation = response['evaluation'].

[0542] Step 6:

[0543] The server generates a commentary based on the content rating.

[0544] Input: Evaluation data

[0545] Output: Commentary data based on the evaluation results

[0546] Specific operation: To generate the explanation "This piece has unique characteristics and expresses tranquility and peace," run explanation = model.generate_explanation(evaluation).

[0547] Step 7:

[0548] The server formats the generated ratings and comments.

[0549] Input: Evaluation data and commentary data

[0550] Output: Formatted feedback data

[0551] Specific operation: Generate formatted_response = f"AI evaluation: {evaluation}\nExplanation: {explanation}".

[0552] Step 8:

[0553] The server sends the formatted data to the terminal.

[0554] Input: Formatted feedback data

[0555] Output: Feedback data sent back to the device

[0556] Specific operation: Call requests.post('client_url', data={'response': formatted_response}).

[0557] Step 9:

[0558] The terminal displays the received data on a user interface.

[0559] Input: Feedback data sent from the server

[0560] Output: Feedback displayed in the user interface

[0561] Specific behavior: The following is displayed in the user's browser: "AI evaluation: This painting is highly realistic, with excellent color selection and composition" and "Description: This work has unique characteristics and conveys tranquility and peace."

[0562] (Application example 1)

[0563] 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."

[0564] Modern electronic payment services are required to accurately evaluate the content of reviews posted by users for products and services and provide feedback in a format that is easy for users to understand. However, existing systems lack automated means for evaluating the quality of reviews posted by users, making it difficult to improve the quality and ensure the reliability of reviews posted by users. Furthermore, while providing users with detailed explanations of reviews can help other users with their purchasing decisions, there has been no effective method for doing so.

[0565] 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.

[0566] In this invention, the server includes means for initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for evaluating review content input by the user using the artificial intelligence model and providing a detailed commentary, and means for displaying the generated rating and commentary on a user device. This makes it possible to automatically evaluate the quality of review content posted by a user and provide detailed feedback to the user based on the evaluation.

[0567] A "means for initializing an artificial intelligence model" is a device or software function for preparing an artificial intelligence model to an operational state.

[0568] A "means for receiving user-inputted content" is a device or software function for capturing user-submitted data or information.

[0569] A "means for evaluating user-input content" is a device or software function that analyzes data or information provided by a user and determines its value or quality.

[0570] The "means for generating an explanation about content entered by a user" refers to a device or software function that generates a detailed explanation about the content and background based on the entered data or information.

[0571] "Review content" refers to descriptions such as evaluations and opinions given by users regarding products and services.

[0572] A "means for providing detailed commentary" is a device or software function that generates and provides detailed information on specific content or background based on the input review content.

[0573] "Means for displaying the generated evaluation and commentary on a user device" refers to a device or software function that displays the evaluation results and commentary information created by the artificial intelligence model on the screen of a device used by the user.

[0574] To realize this invention, the following system configuration is used: The system mainly consists of three main elements: a server, a terminal, and a user.

[0575] server

[0576] The server plays a central role in the entire system and performs the following functions:

[0577] 1. Initialization of AI model: The server has the function to initialize generative AI models such as GPT-3, which makes the AI ​​model ready for operation.

[0578] 2. Content Evaluation: The AI ​​model evaluates the content submitted by users and generates detailed commentary based on the review content entered by the users.

[0579] 3. Commentary generation: Based on the evaluation results, a detailed commentary about the content entered by the user is generated, including detailed information such as the features, meaning, and background of the review.

[0580] 4. Display Results: Convert the generated assessments and commentary into an appropriate format and send it back to the user device.

[0581] Terminal

[0582] The terminal is responsible for inputting content from the user and receiving feedback from the server. The main functions are as follows:

[0583] 1. Content Input: Provide an interface for users to input review content. This interface runs on devices such as smartphones, tablets, and PCs.

[0584] 2. Data transmission: Has the function of sending the input content to the server.

[0585] 3. Displaying feedback: The evaluation results and explanations received from the server are displayed on the user interface.

[0586] User

[0587] The user uses the system to input review content and receive feedback from the server. Specifically, the user performs the following operations:

[0588] 1. Write a review: Write a review for the product or service you purchased.

[0589] 2. Check the feedback: Check the evaluation results and explanations returned from the server and improve or deepen your understanding of your review.

[0590] Hardware and software used

[0591] 1. Hardware:

[0592] Server: Server equipment with high-performance computing capabilities

[0593] Devices: smartphones, tablets, computers, etc.

[0594] 2. Software:

[0595] AI model: OpenAI's GPT-3

[0596] API Access: Access GPT-3 using the OpenAI API

[0597] Interface: The application through which users enter reviews (e.g., smartphone app, web app)

[0598] Specific examples

[0599] Specific examples are shown below.

[0600] For example, a user might write a review stating, "This smartwatch is very intuitive and easy to use. It has a long battery life and constantly monitors your heart rate. I especially like the extensive fitness features." When that review is sent to the server, the server uses GPT-3 to evaluate it and generate a detailed commentary such as the following: "This smartwatch is very intuitive to use and easy for users to use. The long battery life allows for long-term use, and the continuous heart rate monitoring feature is useful for health management. In addition, the extensive fitness features make it especially appealing to health-conscious users."

[0601] Prompt Sentence Examples

[0602] Rate the most influential user reviews based on the following statement: This smartwatch is very intuitive and easy to use. It has great battery life and continuous heart rate monitoring. I especially like the extensive fitness features.

[0603] And provide a detailed commentary on the review.

[0604] This makes it possible to automatically evaluate the quality of reviews posted by users and provide detailed feedback on their content.

[0605] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0606] Step 1:

[0607] The server imports the necessary libraries and frameworks at system startup and initializes a generative AI model such as GPT-3. In this step, the generative AI model is prepared for operation. Specifically, it obtains access to the GPT-3 model through the OpenAI API. The input is the configuration information and libraries required at system startup, and the output is the initialized generative AI model.

[0608] Step 2:

[0609] A user inputs review content for a product or service into a terminal. The terminal receives the user's input and sends the data to a server. In this step, the user's review content is collected as text data. Specifically, the review content entered into a text area via a user interface is saved in a database. The input is the review content entered by the user, and the output is the review content data sent to the server.

[0610] Step 3:

[0611] The server receives the review content submitted by the user and generates a prompt for rating. Here, a prompt for rating (e.g., "Please rate the influential reviews submitted by the user based on the following statement: [user's review] and provide a detailed explanation for the review") is created. The input is the review content submitted by the user, and the output is the generated prompt.

[0612] Step 4:

[0613] The server sends the generated prompt to the GPT-3 model, requesting it to generate a rating and commentary for the review content. The AI ​​model then generates text data based on the prompt and outputs a rating and commentary for the review content. Specifically, the prompt is sent to the generative AI model using the OpenAI API, and a rating and detailed commentary are obtained in response. The input is the prompt, and the output is the generated rating and commentary.

[0614] Step 5:

[0615] The server receives the generated ratings and comments and formats them for display on the user device. This step involves formatting and categorizing the text data. Specifically, it converts the ratings and comments into an easily understandable format suitable for the user interface. The input is the generated ratings and comments, and the output is the formatted ratings and comments data.

[0616] Step 6:

[0617] The terminal receives the formatted evaluation and commentary sent from the server and displays them on the user interface. Here, the evaluation and commentary are displayed on the screen in a format that is easy for the user to check. Specifically, the user interface visualizes the evaluation results and commentary using a component that can display text data. The input is the formatted evaluation and commentary data, and the output is the evaluation and commentary displayed on the screen of the user device.

[0618] Step 7:

[0619] The user views the displayed evaluation results and explanations and improves the content of their review based on them. In this step, the user can take action to improve the quality of their review by utilizing the provided feedback. Specifically, the user can refer to the evaluation results and explanations and use them as reference when posting their next review. The input is the displayed evaluation results and explanations, and the output is the user's review improvement.

[0620] The above processing steps realize a system that effectively evaluates users' review content and provides detailed feedback.

[0621] 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.

[0622] The system of this invention uses an artificial intelligence model to evaluate content entered by a user and generate the evaluation results and a commentary on the user's content. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and adjust the evaluation and commentary based on those emotions. The following is an overview of the system's configuration and operation.

[0623] System configuration

[0624] 1. Server: Responsible for the central processing of the entire system, initializing the AI ​​model, receiving and evaluating data, generating commentary, and running the emotion engine.

[0625] 2. Terminal: The device used by the user to input content and receive feedback from the server. Examples include smartphones, tablets, and PCs.

[0626] 3. User: An individual who uses the system to enter their own content and receive ratings and commentary.

[0627] 4. Emotion Engine: A component for recognizing and analyzing the user's emotional state, allowing it to provide feedback that corresponds to the user's emotions.

[0628] System Operation Overview

[0629] Initialization

[0630] At system startup, the server imports the necessary libraries and frameworks, initializes the AI ​​model (e.g., GPT-3), and initializes the emotion engine, making it ready for use.

[0631] Receiving content

[0632] The user inputs data about their emotional state into the device's input screen along with data about their creative work (e.g., a painting or piece of writing). The emotional state data can be text input or facial expression data. The device then transmits this data to the server.

[0633] Assessment of content and emotional state

[0634] The server processes the received content data and emotion data and performs the following two evaluations.

[0635] 1. The initialized artificial intelligence model is used to evaluate the content entered by the user.

[0636] 2. Using the emotion engine, analyze the user's emotional state and obtain the results.

[0637] Generate and adjust commentary

[0638] The server combines the content evaluation results with the sentiment analysis results from the sentiment engine to generate a commentary about the content using the generate_insights function. This commentary provides detailed explanations about aspects such as the content's features, meaning, and context, and is appropriately adjusted based on the user's emotional state.

[0639] Providing Feedback

[0640] The server formats the evaluation results and generated explanations in a user-friendly format and sends them back to the terminal, which displays this information in a user interface for easy user understanding.

[0641] Specific examples

[0642] 1. The user inputs the description of the painting "A beautiful painting depicting a serene landscape." along with their current emotional state (e.g., relaxed) into the system.

[0643] 2. The device sends the entered description and emotion data to the server.

[0644] 3. The server uses an artificial intelligence model to rate the painting, saying, "This painting is very realistic, and its color choices and composition are excellent."

[0645] 4. The server uses the emotion engine to analyze the user's emotional state. As a result of the analysis, it determines that the user is relaxed.

[0646] 5. The server further generates a description such as "This piece has unique characteristics and expresses tranquility and peace," adjusting it according to the user's relaxed emotional state.

[0647] 6. The server returns the evaluation results and explanations to the device.

[0648] 7. The device will display on its user interface, "AI evaluation: This painting is very realistic, with excellent color choice and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," "Background: Strong connection with nature," and even provide emotional feedback such as, "Looking at this painting in a relaxed state of mind will make you feel even more at peace."

[0649] By using this system, users can understand in detail how their creations are evaluated and what value they have, and receive feedback that reflects their emotional state. By combining this with the emotion engine, more personalized feedback that is valuable to the user is provided.

[0650] The processing flow will be explained below.

[0651] Step 1:

[0652] The server imports the necessary libraries and frameworks, specifically libraries for operating artificial intelligence models (e.g., the transformers library) and emotion engine libraries, and prepares the pipeline for text generation and emotion analysis.

[0653] Step 2:

[0654] The server calls the initialize_ai_model function to initialize the artificial intelligence model. This includes loading a text generation model (e.g., GPT-3) and making it available for use. It also initializes the emotion engine, preparing it to analyze the user's emotional state.

[0655] Step 3:

[0656] Users input content data related to their creations and their own emotional state data into the input screen of their device. Examples of content data include descriptions of paintings and text, and emotional state data is obtained through text input, facial expression analysis, and voice input.

[0657] Step 4:

[0658] The device collects the input content data and emotional state data and sends them to the server, typically via an HTTP POST request.

[0659] Step 5:

[0660] The server processes the received content data and calls the evaluate_human_content function to evaluate it. At this time, it generates an instruction statement for the AI ​​model saying, "Please evaluate this human creation," and passes it to the model.

[0661] Step 6:

[0662] The server receives the evaluation results returned by the AI ​​model, extracts their contents, and provides the results in a generated text format.

[0663] Step 7:

[0664] The server passes the received emotional state data to the emotion engine, which analyzes the user's emotions by determining the user's current emotional state (e.g., relaxed, excited, etc.) based on the text entered by the user and other emotional state data.

[0665] Step 8:

[0666] The server uses the generate_insights function to generate commentary about the content, which details aspects of the content, such as its characteristics, meaning, and context, and is adjusted based on the user's emotional state.

[0667] Step 9:

[0668] The server formats the assessment results and generated commentary into a user-friendly format, which includes combining the appropriate parts to create a single, comprehensive feedback.

[0669] Step 10:

[0670] The server then sends formatted feedback back to the device, typically as an HTTP response.

[0671] Step 11:

[0672] The device displays the received feedback on the user interface, displaying the evaluation results and explanations in an easy-to-read format so that the user can easily understand them.

[0673] Step 12:

[0674] Users can view and understand the ratings and comments on their creations, helping them understand the value of their work and how it differentiates from other content. Additionally, feedback based on the user's emotional state provides a more personalized experience.

[0675] Example 2

[0676] 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."

[0677] In today's information environment, users lack the means to quickly and accurately evaluate the value of their own creations and content. Furthermore, providing personalized feedback based on the user's emotional state is currently insufficient. A system that can solve these issues and provide users with more detailed and useful feedback is needed.

[0678] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for analyzing the emotional state of the user using an emotion analysis engine, means for generating a commentary based on the content evaluation result and the emotion analysis result, and means for displaying the generated evaluation and commentary on the user device. This allows the user to understand in detail how their creation is evaluated and what value it has, and to receive personalized feedback based on their emotional state.

[0679] An "artificial intelligence model" is a computational model trained to perform a specific task and used to evaluate user-entered content.

[0680] A "user" is an individual who uses the system to enter their own content and receive ratings and comments.

[0681] "Content" refers to information that a user inputs into the system, and may be in the form of text, images, audio, or the like.

[0682] "Means for receiving" refers to the process or function for receiving content data input by a user within the system.

[0683] The "means for evaluating" refers to the process and function of using an initialized artificial intelligence model to analyze and evaluate user-entered content.

[0684] An "emotion analysis engine" is a component for analyzing the emotional state from user input data, and includes algorithms and software for classifying and evaluating emotions.

[0685] "Means for generating commentary" refers to the process and function of creating detailed explanations and analyses of content based on the results of content evaluation and sentiment analysis.

[0686] "User Device" means a device used by a user to interact with the system, including a smartphone, tablet, or PC.

[0687] The "means for displaying" refers to a function for visually showing the generated evaluation or commentary to the user on the user device.

[0688] The system of this invention uses an artificial intelligence model to evaluate content entered by a user and provides the evaluation results and commentary. By combining it with an emotion analysis engine, it is possible to recognize the user's emotional state and provide feedback based on that emotion. The system configuration and operation are described in detail below.

[0689] System configuration

[0690] 1. Server

[0691] The server plays a central role in the entire system. It imports necessary libraries and frameworks (e.g., TensorFlow, PyTorch), initializes the artificial intelligence model (e.g., GPT-3), and initializes the sentiment analysis engine (e.g., Google Cloud Natural Language API) for use.

[0692] 2. Terminal

[0693] A terminal is a device through which a user inputs content. Examples include smartphones, tablets, and PCs. The terminal communicates with a server, sends the data entered by the user to the server, and receives feedback from the server.

[0694] 3. Users

[0695] Users are individuals who use the system to enter content and receive ratings and comments. Users can enter content in a variety of forms, including digital art, written text, and audio recordings.

[0696] 4. Sentiment Analysis Engine

[0697] The emotion analysis engine is a component for analyzing the user's emotional state, thereby providing feedback based on the user's current emotional state.

[0698] Operational Overview

[0699] 1. Initialization

[0700] When the system starts up, the server initializes the artificial intelligence model and the emotion analysis engine at the same time, making them ready for use.

[0701] 2. Receiving Content

[0702] The user inputs a description of the painting, such as "A beautiful painting depicting a serene landscape," and their current emotional state (e.g., "Relaxed") into the device. The device then transmits this data to the server.

[0703] 3. Content Rating

[0704] The server uses the initialized artificial intelligence model to evaluate the received content data, for example, "This painting is very realistic and its color selection and composition are excellent."

[0705] 4. Emotional state analysis

[0706] The server uses an emotion analysis engine to analyze the user's emotional state, for example determining that the user is relaxed.

[0707] 5. Generating Explanations

[0708] The server generates a commentary based on the content evaluation and sentiment analysis results. Specifically, it generates a commentary such as, "This piece has unique characteristics and expresses tranquility and peace."

[0709] 6. Providing Feedback

[0710] The server formats the evaluation results and generated commentary in an easy-to-understand format and returns it to the user device, which displays this information in its user interface.

[0711] Specific examples

[0712] 1. The user enters the description of the painting "A beautiful painting depicting a serene landscape." along with their current emotional state "Relaxed" into the system.

[0713] 2. The device sends the entered description and emotion data to the server.

[0714] 3. The server uses an artificial intelligence model to rate the painting, saying, "This painting is very realistic, and its color choices and composition are excellent."

[0715] 4. The server uses an emotion analysis engine to analyze the user's emotional state and determine that the user is relaxed.

[0716] 5. The server generates a description such as "This piece has unique characteristics and expresses tranquility and peace," adjusting it according to the user's relaxed emotional state.

[0717] 6. The server returns the evaluation results and explanations to the terminal.

[0718] 7. The device will display on its user interface, "AI evaluation: This painting is very realistic, with excellent color choice and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," "Background: Strong connection with nature," and even provide emotional feedback such as, "Looking at this painting in a relaxed state of mind will make you feel even more at peace."

[0719] The system allows users to understand in detail how their creations are evaluated and what value they are assigned, and receive feedback based on their emotional state. Combined with a sentiment analysis engine, the system provides more personalized feedback.

[0720] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0721] Step 1: Initialization

[0722] When the system starts, the server imports the necessary libraries and frameworks (e.g., TensorFlow, PyTorch). It also initializes the artificial intelligence model (e.g., GPT-3) and the sentiment analysis engine (e.g., Google Cloud Natural Language API). In this step, the server processes the following data: the system configuration information is the input, and the initialized model and engine are the output.

[0723] Step 2: Enter your content

[0724] The user uses the device to input their own content (e.g., a description of a painting, "A beautiful painting depicting a serene landscape.") and their current emotional state (e.g., "Relaxed"). This input data is entered into the device via an interface such as a web form. The input consists of the user's text data and emotional data, and the device processes this as output and prepares it to be sent to the server.

[0725] Step 3: Sending data

[0726] The device sends the input content data and emotion data to the server. Specifically, the device wraps the data in a structured format such as JSON and sends it to the server using an HTTP POST request. The input is content and emotion data from the user, and the output is the data sent to the server.

[0727] Step 4: Evaluate your content

[0728] The server evaluates the content data received from the terminal using an initialized artificial intelligence model. In this process, the input text data is tokenized and passed to the model to generate an evaluation result. The received content data is the input, and the evaluation result text is obtained as the output.

[0729] Step 5: Analyze emotional state

[0730] The server uses a sentiment analysis engine to analyze the received sentiment data. In this analysis process, the input sentiment text is passed to the analysis engine, which outputs detailed data about the user's emotional state. The input is the received sentiment data, and the output is the sentiment analysis result data.

[0731] Step 6: Generate a description

[0732] The server generates a commentary based on the content's rating and sentiment analysis results. In this commentary generation process, the generate_insights function is used to generate commentary text that combines the input rating and sentiment analysis results. The inputs are the rating and sentiment analysis results, and the output is the generated commentary text.

[0733] Step 7: Format and submit your feedback

[0734] The server formats the generated evaluation results and explanations into a format that is easy for users to understand. This formatted feedback data is sent from the server to the terminal. The generated evaluation results and explanations are the input, and the formatted feedback data is the output.

[0735] Step 8: Viewing feedback

[0736] The terminal displays the feedback data received from the server on the user interface. Specifically, the terminal visually displays the evaluation results, explanations, and emotion-related feedback on the screen. The input is the formatted feedback data, and the output is the information displayed on the user interface.

[0737] (Application example 2)

[0738] 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."

[0739] There is a need for a system that can provide appropriate evaluations and commentaries for user-generated content, as well as recognize users' emotions and personalize the commentary based on those emotions. However, conventional systems do not provide sufficient feedback based on users' emotions, and are therefore unable to provide optimal information to individual users.

[0740] 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 initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for recognizing and analyzing the emotional state of the user, means for adjusting and generating commentary on the content based on the emotional state of the user, and means for displaying the generated evaluation and commentary on the user device. This makes it possible to provide highly accurate evaluations of user-generated content and provide personalized commentary according to the emotional state of the user.

[0741] An "artificial intelligence model" is a computer algorithm used to evaluate user-entered content.

[0742] A "user device" is a device that a user uses to input content and receive feedback from a server.

[0743] "Emotional state" is information that indicates the user's current psychological state.

[0744] "Means for evaluation" refers to a function that uses an initialized artificial intelligence model to analyze content entered by a user and determine its value and quality.

[0745] "Means for generating explanations" refers to a function that creates an explanation for content entered by a user, including its characteristics, meaning, background, etc.

[0746] The "means for adjusting and generating" refers to a function for appropriately modifying the generated commentary based on the user's emotional state and providing it to the user in the most optimal form.

[0747] "Means for displaying" refers to the ability to visually or audibly present the generated ratings and commentary on a user device.

[0748] This invention relates to a system that uses an AI model to evaluate content entered by a user and generates evaluation results and commentary. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and adjust the evaluation and commentary based on those emotions. This system is composed of a user device, a server, an AI model, and an emotion engine.

[0749] System Components

[0750] server

[0751] The server is the central point of the entire system and is responsible for:

[0752] Initialize the AI ​​model: Import the required libraries and frameworks and initialize the AI ​​model (e.g., GPT-3).

[0753] Content evaluation: Receives the content data entered by the user and evaluates it using the initialized artificial intelligence model.

[0754] Sentiment analysis: An emotion engine is used to analyze the emotional data provided by the user.

[0755] Commentary generation and adjustment: Based on the content evaluation results and sentiment analysis results, a commentary is generated and adjusted to correspond to the user's emotional state.

[0756] Provide Feedback: Format and send the generated assessment and commentary to the user device.

[0757] User Device

[0758] User devices are used by users to input their own content and receive feedback from the server. Examples include smart glasses, smartphones, tablets, and personal computers.

[0759] Processing Details

[0760] The user inputs content (e.g., text or images) into the user device. The user's emotional state (e.g., whether they are relaxed or nervous) can also be input as various data (e.g., facial expression data, voice data).

[0761] The server receives content and emotion data sent from the user device. The received content is evaluated using an artificial intelligence model (e.g., GPT-3). The evaluation includes the quality, composition, and meaning of the content. For example, a rating such as "This painting is very realistic, and its color selection and composition are excellent" is generated.

[0762] In parallel, the emotion engine analyzes the user's emotion data, and the analysis results include information such as "the user is relaxed."

[0763] The server generates a commentary based on the evaluation results and sentiment analysis results. This commentary provides a detailed explanation of the content's characteristics, meaning, and background, and is adjusted based on the user's emotional state. For example, the server might generate feedback such as, "This work will make you feel even more calm if you watch it in a relaxed state."

[0764] After all results are generated, the server formats them in a user-friendly format and sends them to the user device, which displays this information in a user interface, or in the case of smart glasses, a real-time evaluation and commentary on the display.

[0765] Specific examples

[0766] As a concrete example, consider a user wearing smart glasses visiting an art gallery. Use the following prompt:

[0767] Example prompt sentence:

[0768] When the user is feeling relaxed, rate this content, "A beautiful painting of a serene landscape," and generate a commentary.

[0769] The smart glasses' camera captures the painting, and the user's emotional state is determined to be "relaxed" based on their facial expression. This data is sent to a server, where an AI model evaluates the painting as "very beautiful, with excellent color selection and composition." Based on the emotion analysis results, a commentary is generated, such as "Looking at this painting in a relaxed state will make you feel even more at peace." These results are displayed on the smart glasses' display, allowing the user to receive real-time feedback.

[0770] This system allows users to understand in detail how their content is rated and what value it has, and to receive feedback that reflects their emotional state.

[0771] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0772] Step 1:

[0773] User enters content and emotion data

[0774] A user inputs their own content (e.g., text, images) using a device such as smart glasses or a smartphone. They also input their emotional state (e.g., relaxed, nervous) as facial expression data and voice data. The device then transmits this data to a server. The input is content data and emotional data, and the output is data sent to the server.

[0775] Step 2:

[0776] The server receives the data

[0777] The server receives content data and emotion data sent from the user device and converts the received data into an analyzable format. The input is the content data and emotion data sent from the user device, and the output is the data converted into an analyzable format.

[0778] Step 3:

[0779] Evaluating content using artificial intelligence models

[0780] The server passes the received content data to an artificial intelligence model (e.g., GPT-3) to evaluate the content. This includes analyzing the content's quality, composition, meaning, etc. For example, it generates a rating such as "This painting is very realistic, and its color selection and composition are excellent." The input is the content data converted into an analyzable format, and the output is the evaluation result.

[0781] Step 4:

[0782] Analyzing the user's emotional state using an emotion engine

[0783] The server uses an emotion engine to recognize and analyze the user's emotional state from facial expression data and voice data. The analysis results can provide information such as "the user is relaxed." The input is emotion data converted into an analyzable format, and the output is the emotion analysis results.

[0784] Step 5:

[0785] Generate and adjust commentary based on evaluation results and sentiment analysis results

[0786] The server generates a commentary based on the content evaluation results and emotion analysis results. The content of the commentary is adjusted according to the user's emotional state. For example, it generates a commentary such as "Watching it in a relaxed state will make you feel even more calm." The input is the evaluation results and emotion analysis results, and the output is a commentary optimized for the user.

[0787] Step 6:

[0788] Formatting the evaluation results and generated commentary

[0789] The server formats the evaluation results and generated commentary into a user-friendly format, for example, in text or audio format. The input is the user-optimized commentary, and the output is the formatted evaluation results and commentary.

[0790] Step 7:

[0791] Send the formatted results to the user device

[0792] The server sends the formatted evaluation results and commentary to the user device, which can then display the results on a display such as smart glasses or provide audio feedback. The input is the formatted evaluation results and commentary, and the output is transmission to the user device.

[0793] Step 8:

[0794] The user device displays the results

[0795] The user device displays the evaluation results and commentary sent from the server. In the case of smart glasses, the display shows real-time feedback. The input is the result sent from the server, and the output is the user receiving visual or auditory feedback.

[0796] Through this series of steps, users can gain a detailed understanding of how their content is rated and what value it has, as well as receive feedback based on their emotional state.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] [Third embodiment]

[0801] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0802] 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.

[0803] 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).

[0804] 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.

[0805] 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.

[0806] 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).

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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."

[0813] The system of this invention uses an artificial intelligence model to evaluate content (e.g., paintings, text, etc.) entered by a user, and generates the evaluation results and a commentary on the user's content. The following describes the operation overview and processing procedure of this system.

[0814] System configuration

[0815] 1. Server: Responsible for the central processing of the entire system, initializing the artificial intelligence model, receiving data, evaluating it, and generating explanations.

[0816] 2. Terminal: The device used by the user to input content and receive feedback from the server. Examples include smartphones, tablets, and PCs.

[0817] 3. User: An individual who uses the system to enter their own content and receive ratings and commentary.

[0818] System Operation Overview

[0819] Initialization

[0820] The server imports the necessary libraries and frameworks at system startup and initializes the artificial intelligence model (e.g., GPT-3).

[0821] Receiving content

[0822] The user inputs data of their creation (e.g., a picture or piece of writing) into the terminal, which then transmits this data to the server.

[0823] Content Rating

[0824] The server uses an AI model to evaluate the content entered by the user. In this evaluation process, the user sends the AI ​​model a command such as "Please rate this human creation." The model generates an evaluation result based on the command.

[0825] Generate explanations

[0826] The server generates a specific description associated with the user's content, detailing aspects of the content such as its characteristics, meaning, and context, and helping the user understand the value of their creation.

[0827] Providing Feedback

[0828] The server receives the generated ratings and comments, formats them, and sends them back to the terminal, which displays this information in a user interface.

[0829] Specific examples

[0830] 1. The user enters a description of the painting, "A beautiful painting depicting a serene landscape." into the system.

[0831] 2. The device sends the entered description to the server.

[0832] 3. The server uses an artificial intelligence model to evaluate the painting and conclude that "this painting is very realistic and has excellent color choices and composition."

[0833] 4. The server then generates a description such as, "This piece has a unique character and expresses tranquility and peace."

[0834] 5. The server returns the evaluation results and explanations to the device.

[0835] 6. The device will display on its user interface such things as "AI evaluation: This painting is very realistic, with excellent color selection and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," and "Background: Strong connection with nature."

[0836] By using this system, users can understand in detail how their creations are evaluated and what value they are assigned. This feedback process will greatly help users improve their creativity and differentiate their content from AI-generated content.

[0837] The processing flow will be explained below.

[0838] Step 1:

[0839] The server imports the necessary libraries and frameworks, specifically libraries for working with artificial intelligence models (e.g., the transformers library), and prepares the pipeline for text generation.

[0840] Step 2:

[0841] The server calls the initialize_ai_model function to initialize the artificial intelligence model, which includes loading a text generation model (e.g., GPT-3) and making it ready for use.

[0842] Step 3:

[0843] The user inputs content data related to his / her creation into the input screen of the terminal, such as a description of a painting or a sentence.

[0844] Step 4:

[0845] The device collects the input content data and sends it to the server, typically via an HTTP POST request.

[0846] Step 5:

[0847] The server processes the received content data and calls the evaluate_human_content function to evaluate it. At this time, it generates an instruction statement for the AI ​​model saying, "Please evaluate this human creation," and passes it to the model.

[0848] Step 6:

[0849] The server receives the evaluation results returned by the AI ​​model, extracts their contents, and provides the results in a generated text format.

[0850] Step 7:

[0851] The server uses the generate_insights function to generate a description of the content, which details aspects of the content, such as its characteristics, meaning, and context.

[0852] Step 8:

[0853] The server formats the assessment results and generated commentary into a user-friendly format, which includes combining the appropriate parts to create a single, comprehensive feedback.

[0854] Step 9:

[0855] The server then sends the formatted feedback back to the device, again typically as an HTTP response.

[0856] Step 10:

[0857] The device displays the received feedback on the user interface, displaying the evaluation results and explanations in an easy-to-read format so that the user can easily understand them.

[0858] Step 11:

[0859] Users can view and understand the ratings and comments on their creations, which allows them to confirm the value of their work and understand how it stands out from other content.

[0860] Example 1

[0861] 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."

[0862] In recent years, content evaluation and commentary generation using artificial intelligence (AI) have attracted attention, but most of these methods have not yet provided detailed and easy-to-understand feedback that meets user needs. Furthermore, evaluation results and commentaries can sometimes lack consistency and reliability, which means they do not adequately support users' creative activities.

[0863] 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.

[0864] In this invention, the server includes means for initializing an artificial intelligence model, means for receiving user-inputted content, means for evaluating the user-inputted content based on a prompt using the generative AI model, means for generating a commentary on the user-inputted content, and means for formatting and displaying the generated commentary and commentary on the user device, thereby enabling users to receive consistent, reliable, and detailed feedback and improving the quality of their creative activities.

[0865] An "artificial intelligence model" is an algorithm or architecture designed to use data to learn and accomplish a specific task.

[0866] "Content" refers to information entered by the user, including descriptions of paintings and text.

[0867] A "generative AI model" is a trained artificial intelligence model that generates content ratings and commentary through natural language processing.

[0868] A "prompt" is a sentence that gives instructions to a generative AI model to perform a specific task.

[0869] "Means of evaluation" refers to the techniques and processes used to analyze user-entered content using a generative AI model and evaluate its value and characteristics.

[0870] A "means for generating explanations" is a technology or process that generates explanations about the characteristics, meaning, and context of input content.

[0871] "Formatting and displaying means" refers to the techniques and processes that format the generated ratings and commentaries and display them on a user device in a format that is easy for the user to understand.

[0872] A "user device" is a device through which a user interacts with the system, including a smartphone, tablet, or PC.

[0873] The system of this invention evaluates content (e.g., paintings, text, etc.) entered by a user using a generative AI model, and generates the evaluation results and commentary on the content. The following describes in detail the operation overview and embodiments of this system.

[0874] System configuration

[0875] 1. Server:

[0876] Responsible for central processing of the entire system.

[0877] The software used includes libraries such as TensorFlow and PyTorch, as well as pre-trained generative AI models (e.g., GPT-3).

[0878] It initializes, receives data, evaluates it, and generates a commentary.

[0879] 2. Terminal:

[0880] It is a device that users use to input content and receive feedback from the server.

[0881] Examples include smartphones, tablets, and personal computers.

[0882] It provides a user interface, transmits input content to the server, and displays feedback from the server.

[0883] 3. User:

[0884] These are individuals who use the system to enter their own content and receive ratings and commentary.

[0885] System Operation Overview

[0886] 1. Initialization

[0887] The server imports the necessary libraries (e.g., TensorFlow, PyTorch) at system startup and initializes the generative AI model (e.g., GPT-3).

[0888] 2. Receiving Content

[0889] The user inputs data of their creation (e.g., a picture or piece of writing) into the terminal, which then transmits this data to the server.

[0890] As a concrete example, a user enters "A beautiful painting depicting a serene landscape." into a web form and presses the submit button.

[0891] 3. Content Rating

[0892] The server sends prompts to the generative AI model to process the received content.

[0893] Example prompt: "Evaluate this human creation: A beautiful painting depicting a serene landscape."

[0894] The model generates evaluation results based on the instructions.

[0895] 4. Generating Explanations

[0896] The server generates a commentary based on the content rating.

[0897] An example of a specific prompt: "This piece has a unique character and conveys a sense of serenity and peace."

[0898] The generated commentary includes details about the content's characteristics, meaning, and background.

[0899] 5. Providing Feedback

[0900] The server receives the generated ratings and comments, formats them, and sends them back to the terminal, which displays this information in a user interface.

[0901] To give an example of how it actually works, the user's browser will display the following: "AI rating: This painting is highly realistic, with excellent color choices and composition," and "Description: This work has unique characteristics and conveys tranquility and peace."

[0902] By using this system, users can understand in detail how their creations are evaluated by the generative AI model and what value they are assigned. This process not only improves users' creative activities, but also increases user satisfaction by increasing the reliability of the evaluation results and explanations as they match.

[0903] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0904] Step 1:

[0905] The server imports the necessary libraries (e.g., TensorFlow, PyTorch) at system startup and initializes the generative AI model (e.g., GPT-3).

[0906] Input: System startup signal

[0907] Output: Initialized libraries and models

[0908] Specific operation: Call import tensorflow as tf or model = GPT3(api_key="your_api_key") .

[0909] Step 2:

[0910] The user inputs data of their own creation (e.g., painting, writing) into the terminal.

[0911] Input: Content entered by the user (e.g., "A beautiful painting depicting a serene landscape.")

[0912] Output: Content entered into the device

[0913] What happens: The user enters text into a displayed web form.

[0914] Step 3:

[0915] The terminal transmits the input content to the server.

[0916] Input: Content entered into the device

[0917] Output: Content data sent to the server

[0918] Specific operation: Call requests.post('server_url', data={'content': user_input}).

[0919] Step 4:

[0920] The server sends prompts to the generative AI model to process the received content.

[0921] Input: Content data received from the device

[0922] Output: Prompts and evaluation results sent to the generative AI model

[0923] Specific behavior: Generates the prompt sentence "Please rate this human creation: A beautiful painting depicting a serene landscape." and executes response = model.evaluate("Please rate this human creation: " + user_input).

[0924] Step 5:

[0925] The server receives the evaluation results from the model.

[0926] Input: Evaluation results from a generative AI model

[0927] Output: Evaluation data

[0928] Specific operation: Obtain evaluation = response['evaluation'].

[0929] Step 6:

[0930] The server generates a commentary based on the content rating.

[0931] Input: Evaluation data

[0932] Output: Commentary data based on the evaluation results

[0933] Specific operation: To generate the explanation "This piece has unique characteristics and expresses tranquility and peace," run explanation = model.generate_explanation(evaluation).

[0934] Step 7:

[0935] The server formats the generated ratings and comments.

[0936] Input: Evaluation data and commentary data

[0937] Output: Formatted feedback data

[0938] Specific operation: Generate formatted_response = f"AI evaluation: {evaluation}\nExplanation: {explanation}".

[0939] Step 8:

[0940] The server sends the formatted data to the terminal.

[0941] Input: Formatted feedback data

[0942] Output: Feedback data sent back to the device

[0943] Specific operation: Call requests.post('client_url', data={'response': formatted_response}).

[0944] Step 9:

[0945] The terminal displays the received data on a user interface.

[0946] Input: Feedback data sent from the server

[0947] Output: Feedback displayed in the user interface

[0948] Specific behavior: The following is displayed in the user's browser: "AI evaluation: This painting is highly realistic, with excellent color selection and composition" and "Description: This work has unique characteristics and conveys tranquility and peace."

[0949] (Application example 1)

[0950] 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."

[0951] Modern electronic payment services are required to accurately evaluate the content of reviews posted by users for products and services and provide feedback in a format that is easy for users to understand. However, existing systems lack automated means for evaluating the quality of reviews posted by users, making it difficult to improve the quality and ensure the reliability of reviews posted by users. Furthermore, while providing users with detailed explanations of reviews can help other users with their purchasing decisions, there has been no effective method for doing so.

[0952] 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.

[0953] In this invention, the server includes means for initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for evaluating review content input by the user using the artificial intelligence model and providing a detailed commentary, and means for displaying the generated rating and commentary on a user device. This makes it possible to automatically evaluate the quality of review content posted by a user and provide detailed feedback to the user based on the evaluation.

[0954] A "means for initializing an artificial intelligence model" is a device or software function for preparing an artificial intelligence model to an operational state.

[0955] A "means for receiving user-inputted content" is a device or software function for capturing user-submitted data or information.

[0956] A "means for evaluating user-input content" is a device or software function that analyzes data or information provided by a user and determines its value or quality.

[0957] The "means for generating an explanation about content entered by a user" refers to a device or software function that generates a detailed explanation about the content and background based on the entered data or information.

[0958] "Review content" refers to descriptions such as evaluations and opinions given by users regarding products and services.

[0959] A "means for providing detailed commentary" is a device or software function that generates and provides detailed information on specific content or background based on the input review content.

[0960] "Means for displaying the generated evaluation and commentary on a user device" refers to a device or software function that displays the evaluation results and commentary information created by the artificial intelligence model on the screen of a device used by the user.

[0961] To realize this invention, the following system configuration is used: The system mainly consists of three main elements: a server, a terminal, and a user.

[0962] server

[0963] The server plays a central role in the entire system and performs the following functions:

[0964] 1. Initialization of AI model: The server has the function to initialize generative AI models such as GPT-3, which makes the AI ​​model ready for operation.

[0965] 2. Content Evaluation: The AI ​​model evaluates the content submitted by users and generates detailed commentary based on the review content entered by the users.

[0966] 3. Commentary generation: Based on the evaluation results, a detailed commentary about the content entered by the user is generated, including detailed information such as the features, meaning, and background of the review.

[0967] 4. Display Results: Convert the generated assessments and commentary into an appropriate format and send it back to the user device.

[0968] Terminal

[0969] The terminal is responsible for inputting content from the user and receiving feedback from the server. The main functions are as follows:

[0970] 1. Content Input: Provide an interface for users to input review content. This interface runs on devices such as smartphones, tablets, and PCs.

[0971] 2. Data transmission: Has the function of sending the input content to the server.

[0972] 3. Displaying feedback: The evaluation results and explanations received from the server are displayed on the user interface.

[0973] User

[0974] The user uses the system to input review content and receive feedback from the server. Specifically, the user performs the following operations:

[0975] 1. Write a review: Write a review for the product or service you purchased.

[0976] 2. Check the feedback: Check the evaluation results and explanations returned from the server and improve or deepen your understanding of your review.

[0977] Hardware and software used

[0978] 1. Hardware:

[0979] Server: Server equipment with high-performance computing capabilities

[0980] Devices: smartphones, tablets, computers, etc.

[0981] 2. Software:

[0982] AI model: OpenAI's GPT-3

[0983] API Access: Access GPT-3 using the OpenAI API

[0984] Interface: The application through which users enter reviews (e.g., smartphone app, web app)

[0985] Specific examples

[0986] Specific examples are shown below.

[0987] For example, a user might write a review stating, "This smartwatch is very intuitive and easy to use. It has a long battery life and constantly monitors your heart rate. I especially like the extensive fitness features." When that review is sent to the server, the server uses GPT-3 to evaluate it and generate a detailed commentary such as the following: "This smartwatch is very intuitive to use and easy for users to use. The long battery life allows for long-term use, and the continuous heart rate monitoring feature is useful for health management. In addition, the extensive fitness features make it especially appealing to health-conscious users."

[0988] Prompt Sentence Examples

[0989] Rate the most influential user reviews based on the following statement: This smartwatch is very intuitive and easy to use. It has great battery life and continuous heart rate monitoring. I especially like the extensive fitness features.

[0990] And provide a detailed commentary on the review.

[0991] This makes it possible to automatically evaluate the quality of reviews posted by users and provide detailed feedback on their content.

[0992] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0993] Step 1:

[0994] The server imports the necessary libraries and frameworks at system startup and initializes a generative AI model such as GPT-3. In this step, the generative AI model is prepared for operation. Specifically, it obtains access to the GPT-3 model through the OpenAI API. The input is the configuration information and libraries required at system startup, and the output is the initialized generative AI model.

[0995] Step 2:

[0996] A user inputs review content for a product or service into a terminal. The terminal receives the user's input and sends the data to a server. In this step, the user's review content is collected as text data. Specifically, the review content entered into a text area via a user interface is saved in a database. The input is the review content entered by the user, and the output is the review content data sent to the server.

[0997] Step 3:

[0998] The server receives the review content submitted by the user and generates a prompt for rating. Here, a prompt for rating (e.g., "Please rate the influential reviews submitted by the user based on the following statement: [user's review] and provide a detailed explanation for the review") is created. The input is the review content submitted by the user, and the output is the generated prompt.

[0999] Step 4:

[1000] The server sends the generated prompt to the GPT-3 model, requesting it to generate a rating and commentary for the review content. The AI ​​model then generates text data based on the prompt and outputs a rating and commentary for the review content. Specifically, the prompt is sent to the generative AI model using the OpenAI API, and a rating and detailed commentary are obtained in response. The input is the prompt, and the output is the generated rating and commentary.

[1001] Step 5:

[1002] The server receives the generated ratings and comments and formats them for display on the user device. This step involves formatting and categorizing the text data. Specifically, it converts the ratings and comments into an easily understandable format suitable for the user interface. The input is the generated ratings and comments, and the output is the formatted ratings and comments data.

[1003] Step 6:

[1004] The terminal receives the formatted evaluation and commentary sent from the server and displays them on the user interface. Here, the evaluation and commentary are displayed on the screen in a format that is easy for the user to check. Specifically, the user interface visualizes the evaluation results and commentary using a component that can display text data. The input is the formatted evaluation and commentary data, and the output is the evaluation and commentary displayed on the screen of the user device.

[1005] Step 7:

[1006] The user views the displayed evaluation results and explanations and improves the content of their review based on them. In this step, the user can take action to improve the quality of their review by utilizing the provided feedback. Specifically, the user can refer to the evaluation results and explanations and use them as reference when posting their next review. The input is the displayed evaluation results and explanations, and the output is the user's review improvement.

[1007] The above processing steps realize a system that effectively evaluates users' review content and provides detailed feedback.

[1008] 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.

[1009] The system of this invention uses an artificial intelligence model to evaluate content entered by a user and generate the evaluation results and a commentary on the user's content. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and adjust the evaluation and commentary based on those emotions. The following is an overview of the system's configuration and operation.

[1010] System configuration

[1011] 1. Server: Responsible for the central processing of the entire system, initializing the AI ​​model, receiving and evaluating data, generating commentary, and running the emotion engine.

[1012] 2. Terminal: The device used by the user to input content and receive feedback from the server. Examples include smartphones, tablets, and PCs.

[1013] 3. User: An individual who uses the system to enter their own content and receive ratings and commentary.

[1014] 4. Emotion Engine: A component for recognizing and analyzing the user's emotional state, allowing it to provide feedback that corresponds to the user's emotions.

[1015] System Operation Overview

[1016] Initialization

[1017] At system startup, the server imports the necessary libraries and frameworks, initializes the AI ​​model (e.g., GPT-3), and initializes the emotion engine, making it ready for use.

[1018] Receiving content

[1019] The user inputs data about their emotional state into the device's input screen along with data about their creative work (e.g., a painting or piece of writing). The emotional state data can be text input or facial expression data. The device then transmits this data to the server.

[1020] Assessment of content and emotional state

[1021] The server processes the received content data and emotion data and performs the following two evaluations.

[1022] 1. The initialized artificial intelligence model is used to evaluate the content entered by the user.

[1023] 2. Using the emotion engine, analyze the user's emotional state and obtain the results.

[1024] Generate and adjust commentary

[1025] The server combines the content evaluation results with the sentiment analysis results from the sentiment engine to generate a commentary about the content using the generate_insights function. This commentary provides detailed explanations about aspects such as the content's features, meaning, and context, and is appropriately adjusted based on the user's emotional state.

[1026] Providing Feedback

[1027] The server formats the evaluation results and generated explanations in a user-friendly format and sends them back to the terminal, which displays this information in a user interface for easy user understanding.

[1028] Specific examples

[1029] 1. The user inputs the description of the painting "A beautiful painting depicting a serene landscape." along with their current emotional state (e.g., relaxed) into the system.

[1030] 2. The device sends the entered description and emotion data to the server.

[1031] 3. The server uses an artificial intelligence model to rate the painting, saying, "This painting is very realistic, and its color choices and composition are excellent."

[1032] 4. The server uses the emotion engine to analyze the user's emotional state. As a result of the analysis, it determines that the user is relaxed.

[1033] 5. The server further generates a description such as "This piece has unique characteristics and expresses tranquility and peace," adjusting it according to the user's relaxed emotional state.

[1034] 6. The server returns the evaluation results and explanations to the device.

[1035] 7. The device will display on its user interface, "AI evaluation: This painting is very realistic, with excellent color choice and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," "Background: Strong connection with nature," and even provide emotional feedback such as, "Looking at this painting in a relaxed state of mind will make you feel even more at peace."

[1036] By using this system, users can understand in detail how their creations are evaluated and what value they have, and receive feedback that reflects their emotional state. By combining this with the emotion engine, more personalized feedback that is valuable to the user is provided.

[1037] The processing flow will be explained below.

[1038] Step 1:

[1039] The server imports the necessary libraries and frameworks, specifically libraries for operating artificial intelligence models (e.g., the transformers library) and emotion engine libraries, and prepares the pipeline for text generation and emotion analysis.

[1040] Step 2:

[1041] The server calls the initialize_ai_model function to initialize the artificial intelligence model. This includes loading a text generation model (e.g., GPT-3) and making it available for use. It also initializes the emotion engine, preparing it to analyze the user's emotional state.

[1042] Step 3:

[1043] Users input content data related to their creations and their own emotional state data into the input screen of their device. Examples of content data include descriptions of paintings and text, and emotional state data is obtained through text input, facial expression analysis, and voice input.

[1044] Step 4:

[1045] The device collects the input content data and emotional state data and sends them to the server, typically via an HTTP POST request.

[1046] Step 5:

[1047] The server processes the received content data and calls the evaluate_human_content function to evaluate it. At this time, it generates an instruction statement for the AI ​​model saying, "Please evaluate this human creation," and passes it to the model.

[1048] Step 6:

[1049] The server receives the evaluation results returned by the AI ​​model, extracts their contents, and provides the results in a generated text format.

[1050] Step 7:

[1051] The server passes the received emotional state data to the emotion engine, which analyzes the user's emotions by determining the user's current emotional state (e.g., relaxed, excited, etc.) based on the text entered by the user and other emotional state data.

[1052] Step 8:

[1053] The server uses the generate_insights function to generate commentary about the content, which details aspects of the content, such as its characteristics, meaning, and context, and is adjusted based on the user's emotional state.

[1054] Step 9:

[1055] The server formats the assessment results and generated commentary into a user-friendly format, which includes combining the appropriate parts to create a single, comprehensive feedback.

[1056] Step 10:

[1057] The server then sends formatted feedback back to the device, typically as an HTTP response.

[1058] Step 11:

[1059] The device displays the received feedback on the user interface, displaying the evaluation results and explanations in an easy-to-read format so that the user can easily understand them.

[1060] Step 12:

[1061] Users can view and understand the ratings and comments on their creations, helping them understand the value of their work and how it differentiates from other content. Additionally, feedback based on the user's emotional state provides a more personalized experience.

[1062] Example 2

[1063] 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."

[1064] In today's information environment, users lack the means to quickly and accurately evaluate the value of their own creations and content. Furthermore, providing personalized feedback based on the user's emotional state is currently insufficient. A system that can solve these issues and provide users with more detailed and useful feedback is needed.

[1065] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for analyzing the emotional state of the user using an emotion analysis engine, means for generating a commentary based on the content evaluation result and the emotion analysis result, and means for displaying the generated evaluation and commentary on the user device. This allows the user to understand in detail how their creation is evaluated and what value it has, and to receive personalized feedback based on their emotional state.

[1066] An "artificial intelligence model" is a computational model trained to perform a specific task and used to evaluate user-entered content.

[1067] A "user" is an individual who uses the system to enter their own content and receive ratings and comments.

[1068] "Content" refers to information that a user inputs into the system, and may be in the form of text, images, audio, or the like.

[1069] "Means for receiving" refers to the process or function for receiving content data input by a user within the system.

[1070] The "means for evaluating" refers to the process and function of using an initialized artificial intelligence model to analyze and evaluate user-entered content.

[1071] An "emotion analysis engine" is a component for analyzing the emotional state from user input data, and includes algorithms and software for classifying and evaluating emotions.

[1072] "Means for generating commentary" refers to the process and function of creating detailed explanations and analyses of content based on the results of content evaluation and sentiment analysis.

[1073] "User Device" means a device used by a user to interact with the system, including a smartphone, tablet, or PC.

[1074] The "means for displaying" refers to a function for visually showing the generated evaluation or commentary to the user on the user device.

[1075] The system of this invention uses an artificial intelligence model to evaluate content entered by a user and provides the evaluation results and commentary. By combining it with an emotion analysis engine, it is possible to recognize the user's emotional state and provide feedback based on that emotion. The system configuration and operation are described in detail below.

[1076] System configuration

[1077] 1. Server

[1078] The server plays a central role in the entire system. It imports necessary libraries and frameworks (e.g., TensorFlow, PyTorch), initializes the artificial intelligence model (e.g., GPT-3), and initializes the sentiment analysis engine (e.g., Google Cloud Natural Language API) for use.

[1079] 2. Terminal

[1080] A terminal is a device through which a user inputs content. Examples include smartphones, tablets, and PCs. The terminal communicates with a server, sends the data entered by the user to the server, and receives feedback from the server.

[1081] 3. Users

[1082] Users are individuals who use the system to enter content and receive ratings and comments. Users can enter content in a variety of forms, including digital art, written text, and audio recordings.

[1083] 4. Sentiment Analysis Engine

[1084] The emotion analysis engine is a component for analyzing the user's emotional state, thereby providing feedback based on the user's current emotional state.

[1085] Operational Overview

[1086] 1. Initialization

[1087] When the system starts up, the server initializes the artificial intelligence model and the emotion analysis engine at the same time, making them ready for use.

[1088] 2. Receiving Content

[1089] The user inputs a description of the painting, such as "A beautiful painting depicting a serene landscape," and their current emotional state (e.g., "Relaxed") into the device. The device then transmits this data to the server.

[1090] 3. Content Rating

[1091] The server uses the initialized artificial intelligence model to evaluate the received content data, for example, "This painting is very realistic and its color selection and composition are excellent."

[1092] 4. Emotional state analysis

[1093] The server uses an emotion analysis engine to analyze the user's emotional state, for example determining that the user is relaxed.

[1094] 5. Generating Explanations

[1095] The server generates a commentary based on the content evaluation and sentiment analysis results. Specifically, it generates a commentary such as, "This piece has unique characteristics and expresses tranquility and peace."

[1096] 6. Providing Feedback

[1097] The server formats the evaluation results and generated commentary in an easy-to-understand format and returns it to the user device, which displays this information in its user interface.

[1098] Specific examples

[1099] 1. The user enters the description of the painting "A beautiful painting depicting a serene landscape." along with their current emotional state "Relaxed" into the system.

[1100] 2. The device sends the entered description and emotion data to the server.

[1101] 3. The server uses an artificial intelligence model to rate the painting, saying, "This painting is very realistic, and its color choices and composition are excellent."

[1102] 4. The server uses an emotion analysis engine to analyze the user's emotional state and determine that the user is relaxed.

[1103] 5. The server generates a description such as "This piece has unique characteristics and expresses tranquility and peace," adjusting it according to the user's relaxed emotional state.

[1104] 6. The server returns the evaluation results and explanations to the terminal.

[1105] 7. The device will display on its user interface, "AI evaluation: This painting is very realistic, with excellent color choice and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," "Background: Strong connection with nature," and even provide emotional feedback such as, "Looking at this painting in a relaxed state of mind will make you feel even more at peace."

[1106] The system allows users to understand in detail how their creations are evaluated and what value they are assigned, and receive feedback based on their emotional state. Combined with a sentiment analysis engine, the system provides more personalized feedback.

[1107] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1108] Step 1: Initialization

[1109] When the system starts, the server imports the necessary libraries and frameworks (e.g., TensorFlow, PyTorch). It also initializes the artificial intelligence model (e.g., GPT-3) and the sentiment analysis engine (e.g., Google Cloud Natural Language API). In this step, the server processes the following data: the system configuration information is the input, and the initialized model and engine are the output.

[1110] Step 2: Enter your content

[1111] The user uses the device to input their own content (e.g., a description of a painting, "A beautiful painting depicting a serene landscape.") and their current emotional state (e.g., "Relaxed"). This input data is entered into the device via an interface such as a web form. The input consists of the user's text data and emotional data, and the device processes this as output and prepares it to be sent to the server.

[1112] Step 3: Sending data

[1113] The device sends the input content data and emotion data to the server. Specifically, the device wraps the data in a structured format such as JSON and sends it to the server using an HTTP POST request. The input is content and emotion data from the user, and the output is the data sent to the server.

[1114] Step 4: Evaluate your content

[1115] The server evaluates the content data received from the terminal using an initialized artificial intelligence model. In this process, the input text data is tokenized and passed to the model to generate an evaluation result. The received content data is the input, and the evaluation result text is obtained as the output.

[1116] Step 5: Analyze emotional state

[1117] The server uses a sentiment analysis engine to analyze the received sentiment data. In this analysis process, the input sentiment text is passed to the analysis engine, which outputs detailed data about the user's emotional state. The input is the received sentiment data, and the output is the sentiment analysis result data.

[1118] Step 6: Generate a description

[1119] The server generates a commentary based on the content's rating and sentiment analysis results. In this commentary generation process, the generate_insights function is used to generate commentary text that combines the input rating and sentiment analysis results. The inputs are the rating and sentiment analysis results, and the output is the generated commentary text.

[1120] Step 7: Format and submit your feedback

[1121] The server formats the generated evaluation results and explanations into a format that is easy for users to understand. This formatted feedback data is sent from the server to the terminal. The generated evaluation results and explanations are the input, and the formatted feedback data is the output.

[1122] Step 8: Viewing feedback

[1123] The terminal displays the feedback data received from the server on the user interface. Specifically, the terminal visually displays the evaluation results, explanations, and emotion-related feedback on the screen. The input is the formatted feedback data, and the output is the information displayed on the user interface.

[1124] (Application example 2)

[1125] 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."

[1126] There is a need for a system that can provide appropriate evaluations and commentaries for user-generated content, as well as recognize users' emotions and personalize the commentary based on those emotions. However, conventional systems do not provide sufficient feedback based on users' emotions, and are therefore unable to provide optimal information to individual users.

[1127] 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 initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for recognizing and analyzing the emotional state of the user, means for adjusting and generating commentary on the content based on the emotional state of the user, and means for displaying the generated evaluation and commentary on the user device. This makes it possible to provide highly accurate evaluations of user-generated content and provide personalized commentary according to the emotional state of the user.

[1128] An "artificial intelligence model" is a computer algorithm used to evaluate user-entered content.

[1129] A "user device" is a device that a user uses to input content and receive feedback from a server.

[1130] "Emotional state" is information that indicates the user's current psychological state.

[1131] "Means for evaluation" refers to a function that uses an initialized artificial intelligence model to analyze content entered by a user and determine its value and quality.

[1132] "Means for generating explanations" refers to a function that creates an explanation for content entered by a user, including its characteristics, meaning, background, etc.

[1133] The "means for adjusting and generating" refers to a function for appropriately modifying the generated commentary based on the user's emotional state and providing it to the user in the most optimal form.

[1134] "Means for displaying" refers to the ability to visually or audibly present the generated ratings and commentary on a user device.

[1135] This invention relates to a system that uses an AI model to evaluate content entered by a user and generates evaluation results and commentary. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and adjust the evaluation and commentary based on those emotions. This system is composed of a user device, a server, an AI model, and an emotion engine.

[1136] System Components

[1137] server

[1138] The server is the central point of the entire system and is responsible for:

[1139] Initialize the AI ​​model: Import the required libraries and frameworks and initialize the AI ​​model (e.g., GPT-3).

[1140] Content evaluation: Receives the content data entered by the user and evaluates it using the initialized artificial intelligence model.

[1141] Sentiment analysis: An emotion engine is used to analyze the emotional data provided by the user.

[1142] Commentary generation and adjustment: Based on the content evaluation results and sentiment analysis results, a commentary is generated and adjusted to correspond to the user's emotional state.

[1143] Provide Feedback: Format and send the generated assessment and commentary to the user device.

[1144] User Device

[1145] User devices are used by users to input their own content and receive feedback from the server. Examples include smart glasses, smartphones, tablets, and personal computers.

[1146] Processing Details

[1147] The user inputs content (e.g., text or images) into the user device. The user's emotional state (e.g., whether they are relaxed or nervous) can also be input as various data (e.g., facial expression data, voice data).

[1148] The server receives content and emotion data sent from the user device. The received content is evaluated using an artificial intelligence model (e.g., GPT-3). The evaluation includes the quality, composition, and meaning of the content. For example, a rating such as "This painting is very realistic, and its color selection and composition are excellent" is generated.

[1149] In parallel, the emotion engine analyzes the user's emotion data, and the analysis results include information such as "the user is relaxed."

[1150] The server generates a commentary based on the evaluation results and sentiment analysis results. This commentary provides a detailed explanation of the content's characteristics, meaning, and background, and is adjusted based on the user's emotional state. For example, the server might generate feedback such as, "This work will make you feel even more calm if you watch it in a relaxed state."

[1151] After all results are generated, the server formats them in a user-friendly format and sends them to the user device, which displays this information in a user interface, or in the case of smart glasses, a real-time evaluation and commentary on the display.

[1152] Specific examples

[1153] As a concrete example, consider a user wearing smart glasses visiting an art gallery. Use the following prompt:

[1154] Example prompt sentence:

[1155] When the user is feeling relaxed, rate this content, "A beautiful painting of a serene landscape," and generate a commentary.

[1156] The smart glasses' camera captures the painting, and the user's emotional state is determined to be "relaxed" based on their facial expression. This data is sent to a server, where an AI model evaluates the painting as "very beautiful, with excellent color selection and composition." Based on the emotion analysis results, a commentary is generated, such as "Looking at this painting in a relaxed state will make you feel even more at peace." These results are displayed on the smart glasses' display, allowing the user to receive real-time feedback.

[1157] This system allows users to understand in detail how their content is rated and what value it has, and to receive feedback that reflects their emotional state.

[1158] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1159] Step 1:

[1160] User enters content and emotion data

[1161] A user inputs their own content (e.g., text, images) using a device such as smart glasses or a smartphone. They also input their emotional state (e.g., relaxed, nervous) as facial expression data and voice data. The device then transmits this data to a server. The input is content data and emotional data, and the output is data sent to the server.

[1162] Step 2:

[1163] The server receives the data

[1164] The server receives content data and emotion data sent from the user device and converts the received data into an analyzable format. The input is the content data and emotion data sent from the user device, and the output is the data converted into an analyzable format.

[1165] Step 3:

[1166] Evaluating content using artificial intelligence models

[1167] The server passes the received content data to an artificial intelligence model (e.g., GPT-3) to evaluate the content. This includes analyzing the content's quality, composition, meaning, etc. For example, it generates a rating such as "This painting is very realistic, and its color selection and composition are excellent." The input is the content data converted into an analyzable format, and the output is the evaluation result.

[1168] Step 4:

[1169] Analyzing the user's emotional state using an emotion engine

[1170] The server uses an emotion engine to recognize and analyze the user's emotional state from facial expression data and voice data. The analysis results can provide information such as "the user is relaxed." The input is emotion data converted into an analyzable format, and the output is the emotion analysis results.

[1171] Step 5:

[1172] Generate and adjust commentary based on evaluation results and sentiment analysis results

[1173] The server generates a commentary based on the content evaluation results and emotion analysis results. The content of the commentary is adjusted according to the user's emotional state. For example, it generates a commentary such as "Watching it in a relaxed state will make you feel even more calm." The input is the evaluation results and emotion analysis results, and the output is a commentary optimized for the user.

[1174] Step 6:

[1175] Formatting the evaluation results and generated commentary

[1176] The server formats the evaluation results and generated commentary into a user-friendly format, for example, in text or audio format. The input is the user-optimized commentary, and the output is the formatted evaluation results and commentary.

[1177] Step 7:

[1178] Send the formatted results to the user device

[1179] The server sends the formatted evaluation results and commentary to the user device, which can then display the results on a display such as smart glasses or provide audio feedback. The input is the formatted evaluation results and commentary, and the output is transmission to the user device.

[1180] Step 8:

[1181] The user device displays the results

[1182] The user device displays the evaluation results and commentary sent from the server. In the case of smart glasses, the display shows real-time feedback. The input is the result sent from the server, and the output is the user receiving visual or auditory feedback.

[1183] Through this series of steps, users can gain a detailed understanding of how their content is rated and what value it has, as well as receive feedback based on their emotional state.

[1184] 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.

[1185] 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.

[1186] 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.

[1187] [Fourth embodiment]

[1188] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1189] 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.

[1190] 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).

[1191] 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.

[1192] 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.

[1193] 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).

[1194] 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.

[1195] 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.

[1196] 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.

[1197] 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.

[1198] 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.

[1199] 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.

[1200] 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."

[1201] The system of this invention uses an artificial intelligence model to evaluate content (e.g., paintings, text, etc.) entered by a user, and generates the evaluation results and a commentary on the user's content. The following describes the operation overview and processing procedure of this system.

[1202] System configuration

[1203] 1. Server: Responsible for the central processing of the entire system, initializing the artificial intelligence model, receiving data, evaluating it, and generating explanations.

[1204] 2. Terminal: The device used by the user to input content and receive feedback from the server. Examples include smartphones, tablets, and PCs.

[1205] 3. User: An individual who uses the system to enter their own content and receive ratings and commentary.

[1206] System Operation Overview

[1207] Initialization

[1208] The server imports the necessary libraries and frameworks at system startup and initializes the artificial intelligence model (e.g., GPT-3).

[1209] Receiving content

[1210] The user inputs data of their creation (e.g., a picture or piece of writing) into the terminal, which then transmits this data to the server.

[1211] Content Rating

[1212] The server uses an AI model to evaluate the content entered by the user. In this evaluation process, the user sends the AI ​​model a command such as "Please rate this human creation." The model generates an evaluation result based on the command.

[1213] Generate explanations

[1214] The server generates a specific description associated with the user's content, detailing aspects of the content such as its characteristics, meaning, and context, and helping the user understand the value of their creation.

[1215] Providing Feedback

[1216] The server receives the generated ratings and comments, formats them, and sends them back to the terminal, which displays this information in a user interface.

[1217] Specific examples

[1218] 1. The user enters a description of the painting, "A beautiful painting depicting a serene landscape." into the system.

[1219] 2. The device sends the entered description to the server.

[1220] 3. The server uses an artificial intelligence model to evaluate the painting and conclude that "this painting is very realistic and has excellent color choices and composition."

[1221] 4. The server then generates a description such as, "This piece has a unique character and expresses tranquility and peace."

[1222] 5. The server returns the evaluation results and explanations to the device.

[1223] 6. The device will display on its user interface such things as "AI evaluation: This painting is very realistic, with excellent color selection and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," and "Background: Strong connection with nature."

[1224] By using this system, users can understand in detail how their creations are evaluated and what value they are assigned. This feedback process will greatly help users improve their creativity and differentiate their content from AI-generated content.

[1225] The processing flow will be explained below.

[1226] Step 1:

[1227] The server imports the necessary libraries and frameworks, specifically libraries for working with artificial intelligence models (e.g., the transformers library), and prepares the pipeline for text generation.

[1228] Step 2:

[1229] The server calls the initialize_ai_model function to initialize the artificial intelligence model, which includes loading a text generation model (e.g., GPT-3) and making it ready for use.

[1230] Step 3:

[1231] The user inputs content data related to his / her creation into the input screen of the terminal, such as a description of a painting or a sentence.

[1232] Step 4:

[1233] The device collects the input content data and sends it to the server, typically via an HTTP POST request.

[1234] Step 5:

[1235] The server processes the received content data and calls the evaluate_human_content function to evaluate it. At this time, it generates an instruction statement for the AI ​​model saying, "Please evaluate this human creation," and passes it to the model.

[1236] Step 6:

[1237] The server receives the evaluation results returned by the AI ​​model, extracts their contents, and provides the results in a generated text format.

[1238] Step 7:

[1239] The server uses the generate_insights function to generate a description of the content, which details aspects of the content, such as its characteristics, meaning, and context.

[1240] Step 8:

[1241] The server formats the assessment results and generated commentary into a user-friendly format, which includes combining the appropriate parts to create a single, comprehensive feedback.

[1242] Step 9:

[1243] The server then sends the formatted feedback back to the device, again typically as an HTTP response.

[1244] Step 10:

[1245] The device displays the received feedback on the user interface, displaying the evaluation results and explanations in an easy-to-read format so that the user can easily understand them.

[1246] Step 11:

[1247] Users can view and understand the ratings and comments on their creations, which allows them to confirm the value of their work and understand how it stands out from other content.

[1248] Example 1

[1249] 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."

[1250] In recent years, content evaluation and commentary generation using artificial intelligence (AI) have attracted attention, but most of these methods have not yet provided detailed and easy-to-understand feedback that meets user needs. Furthermore, evaluation results and commentaries can sometimes lack consistency and reliability, which means they do not adequately support users' creative activities.

[1251] 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.

[1252] In this invention, the server includes means for initializing an artificial intelligence model, means for receiving user-inputted content, means for evaluating the user-inputted content based on a prompt using the generative AI model, means for generating a commentary on the user-inputted content, and means for formatting and displaying the generated commentary and commentary on the user device, thereby enabling users to receive consistent, reliable, and detailed feedback and improving the quality of their creative activities.

[1253] An "artificial intelligence model" is an algorithm or architecture designed to use data to learn and accomplish a specific task.

[1254] "Content" refers to information entered by the user, including descriptions of paintings and text.

[1255] A "generative AI model" is a trained artificial intelligence model that generates content ratings and commentary through natural language processing.

[1256] A "prompt" is a sentence that gives instructions to a generative AI model to perform a specific task.

[1257] "Means of evaluation" refers to the techniques and processes used to analyze user-entered content using a generative AI model and evaluate its value and characteristics.

[1258] A "means for generating explanations" is a technology or process that generates explanations about the characteristics, meaning, and context of input content.

[1259] "Formatting and displaying means" refers to the techniques and processes that format the generated ratings and commentaries and display them on a user device in a format that is easy for the user to understand.

[1260] A "user device" is a device through which a user interacts with the system, including a smartphone, tablet, or PC.

[1261] The system of this invention evaluates content (e.g., paintings, text, etc.) entered by a user using a generative AI model, and generates the evaluation results and commentary on the content. The following describes in detail the operation overview and embodiments of this system.

[1262] System configuration

[1263] 1. Server:

[1264] Responsible for central processing of the entire system.

[1265] The software used includes libraries such as TensorFlow and PyTorch, as well as pre-trained generative AI models (e.g., GPT-3).

[1266] It initializes, receives data, evaluates it, and generates a commentary.

[1267] 2. Terminal:

[1268] It is a device that users use to input content and receive feedback from the server.

[1269] Examples include smartphones, tablets, and personal computers.

[1270] It provides a user interface, transmits input content to the server, and displays feedback from the server.

[1271] 3. User:

[1272] These are individuals who use the system to enter their own content and receive ratings and commentary.

[1273] System Operation Overview

[1274] 1. Initialization

[1275] The server imports the necessary libraries (e.g., TensorFlow, PyTorch) at system startup and initializes the generative AI model (e.g., GPT-3).

[1276] 2. Receiving Content

[1277] The user inputs data of their creation (e.g., a picture or piece of writing) into the terminal, which then transmits this data to the server.

[1278] As a concrete example, a user enters "A beautiful painting depicting a serene landscape." into a web form and presses the submit button.

[1279] 3. Content Rating

[1280] The server sends prompts to the generative AI model to process the received content.

[1281] Example prompt: "Evaluate this human creation: A beautiful painting depicting a serene landscape."

[1282] The model generates evaluation results based on the instructions.

[1283] 4. Generating Explanations

[1284] The server generates a commentary based on the content rating.

[1285] An example of a specific prompt: "This piece has a unique character and conveys a sense of serenity and peace."

[1286] The generated commentary includes details about the content's characteristics, meaning, and background.

[1287] 5. Providing Feedback

[1288] The server receives the generated ratings and comments, formats them, and sends them back to the terminal, which displays this information in a user interface.

[1289] To give an example of how it actually works, the user's browser will display the following: "AI rating: This painting is highly realistic, with excellent color choices and composition," and "Description: This work has unique characteristics and conveys tranquility and peace."

[1290] By using this system, users can understand in detail how their creations are evaluated by the generative AI model and what value they are assigned. This process not only improves users' creative activities, but also increases user satisfaction by increasing the reliability of the evaluation results and explanations as they match.

[1291] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1292] Step 1:

[1293] The server imports the necessary libraries (e.g., TensorFlow, PyTorch) at system startup and initializes the generative AI model (e.g., GPT-3).

[1294] Input: System startup signal

[1295] Output: Initialized libraries and models

[1296] Specific operation: Call import tensorflow as tf or model = GPT3(api_key="your_api_key") .

[1297] Step 2:

[1298] The user inputs data of their own creation (e.g., painting, writing) into the terminal.

[1299] Input: Content entered by the user (e.g., "A beautiful painting depicting a serene landscape.")

[1300] Output: Content entered into the device

[1301] What happens: The user enters text into a displayed web form.

[1302] Step 3:

[1303] The terminal transmits the input content to the server.

[1304] Input: Content entered into the device

[1305] Output: Content data sent to the server

[1306] Specific operation: Call requests.post('server_url', data={'content': user_input}).

[1307] Step 4:

[1308] The server sends prompts to the generative AI model to process the received content.

[1309] Input: Content data received from the device

[1310] Output: Prompts and evaluation results sent to the generative AI model

[1311] Specific behavior: Generates the prompt sentence "Please rate this human creation: A beautiful painting depicting a serene landscape." and executes response = model.evaluate("Please rate this human creation: " + user_input).

[1312] Step 5:

[1313] The server receives the evaluation results from the model.

[1314] Input: Evaluation results from a generative AI model

[1315] Output: Evaluation data

[1316] Specific operation: Obtain evaluation = response['evaluation'].

[1317] Step 6:

[1318] The server generates a commentary based on the content rating.

[1319] Input: Evaluation data

[1320] Output: Commentary data based on the evaluation results

[1321] Specific operation: To generate the explanation "This piece has unique characteristics and expresses tranquility and peace," run explanation = model.generate_explanation(evaluation).

[1322] Step 7:

[1323] The server formats the generated ratings and comments.

[1324] Input: Evaluation data and commentary data

[1325] Output: Formatted feedback data

[1326] Specific operation: Generate formatted_response = f"AI evaluation: {evaluation}\nExplanation: {explanation}".

[1327] Step 8:

[1328] The server sends the formatted data to the terminal.

[1329] Input: Formatted feedback data

[1330] Output: Feedback data sent back to the device

[1331] Specific operation: Call requests.post('client_url', data={'response': formatted_response}).

[1332] Step 9:

[1333] The terminal displays the received data on a user interface.

[1334] Input: Feedback data sent from the server

[1335] Output: Feedback displayed in the user interface

[1336] Specific behavior: The following is displayed in the user's browser: "AI evaluation: This painting is highly realistic, with excellent color selection and composition" and "Description: This work has unique characteristics and conveys tranquility and peace."

[1337] (Application example 1)

[1338] 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."

[1339] Modern electronic payment services are required to accurately evaluate the content of reviews posted by users for products and services and provide feedback in a format that is easy for users to understand. However, existing systems lack automated means for evaluating the quality of reviews posted by users, making it difficult to improve the quality and ensure the reliability of reviews posted by users. Furthermore, while providing users with detailed explanations of reviews can help other users with their purchasing decisions, there has been no effective method for doing so.

[1340] 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.

[1341] In this invention, the server includes means for initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for evaluating review content input by the user using the artificial intelligence model and providing a detailed commentary, and means for displaying the generated rating and commentary on a user device. This makes it possible to automatically evaluate the quality of review content posted by a user and provide detailed feedback to the user based on the evaluation.

[1342] A "means for initializing an artificial intelligence model" is a device or software function for preparing an artificial intelligence model to an operational state.

[1343] A "means for receiving user-inputted content" is a device or software function for capturing user-submitted data or information.

[1344] A "means for evaluating user-input content" is a device or software function that analyzes data or information provided by a user and determines its value or quality.

[1345] The "means for generating an explanation about content entered by a user" refers to a device or software function that generates a detailed explanation about the content and background based on the entered data or information.

[1346] "Review content" refers to descriptions such as evaluations and opinions given by users regarding products and services.

[1347] A "means for providing detailed commentary" is a device or software function that generates and provides detailed information on specific content or background based on the input review content.

[1348] "Means for displaying the generated evaluation and commentary on a user device" refers to a device or software function that displays the evaluation results and commentary information created by the artificial intelligence model on the screen of a device used by the user.

[1349] To realize this invention, the following system configuration is used: The system mainly consists of three main elements: a server, a terminal, and a user.

[1350] server

[1351] The server plays a central role in the entire system and performs the following functions:

[1352] 1. Initialization of AI model: The server has the function to initialize generative AI models such as GPT-3, which makes the AI ​​model ready for operation.

[1353] 2. Content Evaluation: The AI ​​model evaluates the content submitted by users and generates detailed commentary based on the review content entered by the users.

[1354] 3. Commentary generation: Based on the evaluation results, a detailed commentary about the content entered by the user is generated, including detailed information such as the features, meaning, and background of the review.

[1355] 4. Display Results: Convert the generated assessments and commentary into an appropriate format and send it back to the user device.

[1356] Terminal

[1357] The terminal is responsible for inputting content from the user and receiving feedback from the server. The main functions are as follows:

[1358] 1. Content Input: Provide an interface for users to input review content. This interface runs on devices such as smartphones, tablets, and PCs.

[1359] 2. Data transmission: Has the function of sending the input content to the server.

[1360] 3. Displaying feedback: The evaluation results and explanations received from the server are displayed on the user interface.

[1361] User

[1362] The user uses the system to input review content and receive feedback from the server. Specifically, the user performs the following operations:

[1363] 1. Write a review: Write a review for the product or service you purchased.

[1364] 2. Check the feedback: Check the evaluation results and explanations returned from the server and improve or deepen your understanding of your review.

[1365] Hardware and software used

[1366] 1. Hardware:

[1367] Server: Server equipment with high-performance computing capabilities

[1368] Devices: smartphones, tablets, computers, etc.

[1369] 2. Software:

[1370] AI model: OpenAI's GPT-3

[1371] API Access: Access GPT-3 using the OpenAI API

[1372] Interface: The application through which users enter reviews (e.g., smartphone app, web app)

[1373] Specific examples

[1374] Specific examples are shown below.

[1375] For example, a user might write a review stating, "This smartwatch is very intuitive and easy to use. It has a long battery life and constantly monitors your heart rate. I especially like the extensive fitness features." When that review is sent to the server, the server uses GPT-3 to evaluate it and generate a detailed commentary such as the following: "This smartwatch is very intuitive to use and easy for users to use. The long battery life allows for long-term use, and the continuous heart rate monitoring feature is useful for health management. In addition, the extensive fitness features make it especially appealing to health-conscious users."

[1376] Prompt Sentence Examples

[1377] Rate the most influential user reviews based on the following statement: This smartwatch is very intuitive and easy to use. It has great battery life and continuous heart rate monitoring. I especially like the extensive fitness features.

[1378] And provide a detailed commentary on the review.

[1379] This makes it possible to automatically evaluate the quality of reviews posted by users and provide detailed feedback on their content.

[1380] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1381] Step 1:

[1382] The server imports the necessary libraries and frameworks at system startup and initializes a generative AI model such as GPT-3. In this step, the generative AI model is prepared for operation. Specifically, it obtains access to the GPT-3 model through the OpenAI API. The input is the configuration information and libraries required at system startup, and the output is the initialized generative AI model.

[1383] Step 2:

[1384] A user inputs review content for a product or service into a terminal. The terminal receives the user's input and sends the data to a server. In this step, the user's review content is collected as text data. Specifically, the review content entered into a text area via a user interface is saved in a database. The input is the review content entered by the user, and the output is the review content data sent to the server.

[1385] Step 3:

[1386] The server receives the review content submitted by the user and generates a prompt for rating. Here, a prompt for rating (e.g., "Please rate the influential reviews submitted by the user based on the following statement: [user's review] and provide a detailed explanation for the review") is created. The input is the review content submitted by the user, and the output is the generated prompt.

[1387] Step 4:

[1388] The server sends the generated prompt to the GPT-3 model, requesting it to generate a rating and commentary for the review content. The AI ​​model then generates text data based on the prompt and outputs a rating and commentary for the review content. Specifically, the prompt is sent to the generative AI model using the OpenAI API, and a rating and detailed commentary are obtained in response. The input is the prompt, and the output is the generated rating and commentary.

[1389] Step 5:

[1390] The server receives the generated ratings and comments and formats them for display on the user device. This step involves formatting and categorizing the text data. Specifically, it converts the ratings and comments into an easily understandable format suitable for the user interface. The input is the generated ratings and comments, and the output is the formatted ratings and comments data.

[1391] Step 6:

[1392] The terminal receives the formatted evaluation and commentary sent from the server and displays them on the user interface. Here, the evaluation and commentary are displayed on the screen in a format that is easy for the user to check. Specifically, the user interface visualizes the evaluation results and commentary using a component that can display text data. The input is the formatted evaluation and commentary data, and the output is the evaluation and commentary displayed on the screen of the user device.

[1393] Step 7:

[1394] The user views the displayed evaluation results and explanations and improves the content of their review based on them. In this step, the user can take action to improve the quality of their review by utilizing the provided feedback. Specifically, the user can refer to the evaluation results and explanations and use them as reference when posting their next review. The input is the displayed evaluation results and explanations, and the output is the user's review improvement.

[1395] The above processing steps realize a system that effectively evaluates users' review content and provides detailed feedback.

[1396] 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.

[1397] The system of this invention uses an artificial intelligence model to evaluate content entered by a user and generate the evaluation results and a commentary on the user's content. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and adjust the evaluation and commentary based on those emotions. The following is an overview of the system's configuration and operation.

[1398] System configuration

[1399] 1. Server: Responsible for the central processing of the entire system, initializing the AI ​​model, receiving and evaluating data, generating commentary, and running the emotion engine.

[1400] 2. Terminal: The device used by the user to input content and receive feedback from the server. Examples include smartphones, tablets, and PCs.

[1401] 3. User: An individual who uses the system to enter their own content and receive ratings and commentary.

[1402] 4. Emotion Engine: A component for recognizing and analyzing the user's emotional state, allowing it to provide feedback that corresponds to the user's emotions.

[1403] System Operation Overview

[1404] Initialization

[1405] At system startup, the server imports the necessary libraries and frameworks, initializes the AI ​​model (e.g., GPT-3), and initializes the emotion engine, making it ready for use.

[1406] Receiving content

[1407] The user inputs data about their emotional state into the device's input screen along with data about their creative work (e.g., a painting or piece of writing). The emotional state data can be text input or facial expression data. The device then transmits this data to the server.

[1408] Assessment of content and emotional state

[1409] The server processes the received content data and emotion data and performs the following two evaluations.

[1410] 1. The initialized artificial intelligence model is used to evaluate the content entered by the user.

[1411] 2. Using the emotion engine, analyze the user's emotional state and obtain the results.

[1412] Generate and adjust commentary

[1413] The server combines the content evaluation results with the sentiment analysis results from the sentiment engine to generate a commentary about the content using the generate_insights function. This commentary provides detailed explanations about aspects such as the content's features, meaning, and context, and is appropriately adjusted based on the user's emotional state.

[1414] Providing Feedback

[1415] The server formats the evaluation results and generated explanations in a user-friendly format and sends them back to the terminal, which displays this information in a user interface for easy user understanding.

[1416] Specific examples

[1417] 1. The user inputs the description of the painting "A beautiful painting depicting a serene landscape." along with their current emotional state (e.g., relaxed) into the system.

[1418] 2. The device sends the entered description and emotion data to the server.

[1419] 3. The server uses an artificial intelligence model to rate the painting, saying, "This painting is very realistic, and its color choices and composition are excellent."

[1420] 4. The server uses the emotion engine to analyze the user's emotional state. As a result of the analysis, it determines that the user is relaxed.

[1421] 5. The server further generates a description such as "This piece has unique characteristics and expresses tranquility and peace," adjusting it according to the user's relaxed emotional state.

[1422] 6. The server returns the evaluation results and explanations to the device.

[1423] 7. The device will display on its user interface, "AI evaluation: This painting is very realistic, with excellent color choice and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," "Background: Strong connection with nature," and even provide emotional feedback such as, "Looking at this painting in a relaxed state of mind will make you feel even more at peace."

[1424] By using this system, users can understand in detail how their creations are evaluated and what value they have, and receive feedback that reflects their emotional state. By combining this with the emotion engine, more personalized feedback that is valuable to the user is provided.

[1425] The processing flow will be explained below.

[1426] Step 1:

[1427] The server imports the necessary libraries and frameworks, specifically libraries for operating artificial intelligence models (e.g., the transformers library) and emotion engine libraries, and prepares the pipeline for text generation and emotion analysis.

[1428] Step 2:

[1429] The server calls the initialize_ai_model function to initialize the artificial intelligence model. This includes loading a text generation model (e.g., GPT-3) and making it available for use. It also initializes the emotion engine, preparing it to analyze the user's emotional state.

[1430] Step 3:

[1431] Users input content data related to their creations and their own emotional state data into the input screen of their device. Examples of content data include descriptions of paintings and text, and emotional state data is obtained through text input, facial expression analysis, and voice input.

[1432] Step 4:

[1433] The device collects the input content data and emotional state data and sends them to the server, typically via an HTTP POST request.

[1434] Step 5:

[1435] The server processes the received content data and calls the evaluate_human_content function to evaluate it. At this time, it generates an instruction statement for the AI ​​model saying, "Please evaluate this human creation," and passes it to the model.

[1436] Step 6:

[1437] The server receives the evaluation results returned by the AI ​​model, extracts their contents, and provides the results in a generated text format.

[1438] Step 7:

[1439] The server passes the received emotional state data to the emotion engine, which analyzes the user's emotions by determining the user's current emotional state (e.g., relaxed, excited, etc.) based on the text entered by the user and other emotional state data.

[1440] Step 8:

[1441] The server uses the generate_insights function to generate commentary about the content, which details aspects of the content, such as its characteristics, meaning, and context, and is adjusted based on the user's emotional state.

[1442] Step 9:

[1443] The server formats the assessment results and generated commentary into a user-friendly format, which includes combining the appropriate parts to create a single, comprehensive feedback.

[1444] Step 10:

[1445] The server then sends formatted feedback back to the device, typically as an HTTP response.

[1446] Step 11:

[1447] The device displays the received feedback on the user interface, displaying the evaluation results and explanations in an easy-to-read format so that the user can easily understand them.

[1448] Step 12:

[1449] Users can view and understand the ratings and comments on their creations, helping them understand the value of their work and how it differentiates from other content. Additionally, feedback based on the user's emotional state provides a more personalized experience.

[1450] Example 2

[1451] 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."

[1452] In today's information environment, users lack the means to quickly and accurately evaluate the value of their own creations and content. Furthermore, providing personalized feedback based on the user's emotional state is currently insufficient. A system that can solve these issues and provide users with more detailed and useful feedback is needed.

[1453] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for analyzing the emotional state of the user using an emotion analysis engine, means for generating a commentary based on the content evaluation result and the emotion analysis result, and means for displaying the generated evaluation and commentary on the user device. This allows the user to understand in detail how their creation is evaluated and what value it has, and to receive personalized feedback based on their emotional state.

[1454] An "artificial intelligence model" is a computational model trained to perform a specific task and used to evaluate user-entered content.

[1455] A "user" is an individual who uses the system to enter their own content and receive ratings and comments.

[1456] "Content" refers to information that a user inputs into the system, and may be in the form of text, images, audio, or the like.

[1457] "Means for receiving" refers to the process or function for receiving content data input by a user within the system.

[1458] The "means for evaluating" refers to the process and function of using an initialized artificial intelligence model to analyze and evaluate user-entered content.

[1459] An "emotion analysis engine" is a component for analyzing the emotional state from user input data, and includes algorithms and software for classifying and evaluating emotions.

[1460] "Means for generating commentary" refers to the process and function of creating detailed explanations and analyses of content based on the results of content evaluation and sentiment analysis.

[1461] "User Device" means a device used by a user to interact with the system, including a smartphone, tablet, or PC.

[1462] The "means for displaying" refers to a function for visually showing the generated evaluation or commentary to the user on the user device.

[1463] The system of this invention uses an artificial intelligence model to evaluate content entered by a user and provides the evaluation results and commentary. By combining it with an emotion analysis engine, it is possible to recognize the user's emotional state and provide feedback based on that emotion. The system configuration and operation are described in detail below.

[1464] System configuration

[1465] 1. Server

[1466] The server plays a central role in the entire system. It imports necessary libraries and frameworks (e.g., TensorFlow, PyTorch), initializes the artificial intelligence model (e.g., GPT-3), and initializes the sentiment analysis engine (e.g., Google Cloud Natural Language API) for use.

[1467] 2. Terminal

[1468] A terminal is a device through which a user inputs content. Examples include smartphones, tablets, and PCs. The terminal communicates with a server, sends the data entered by the user to the server, and receives feedback from the server.

[1469] 3. Users

[1470] Users are individuals who use the system to enter content and receive ratings and comments. Users can enter content in a variety of forms, including digital art, written text, and audio recordings.

[1471] 4. Sentiment Analysis Engine

[1472] The emotion analysis engine is a component for analyzing the user's emotional state, thereby providing feedback based on the user's current emotional state.

[1473] Operational Overview

[1474] 1. Initialization

[1475] When the system starts up, the server initializes the artificial intelligence model and the emotion analysis engine at the same time, making them ready for use.

[1476] 2. Receiving Content

[1477] The user inputs a description of the painting, such as "A beautiful painting depicting a serene landscape," and their current emotional state (e.g., "Relaxed") into the device. The device then transmits this data to the server.

[1478] 3. Content Rating

[1479] The server uses the initialized artificial intelligence model to evaluate the received content data, for example, "This painting is very realistic and its color selection and composition are excellent."

[1480] 4. Emotional state analysis

[1481] The server uses an emotion analysis engine to analyze the user's emotional state, for example determining that the user is relaxed.

[1482] 5. Generating Explanations

[1483] The server generates a commentary based on the content evaluation and sentiment analysis results. Specifically, it generates a commentary such as, "This piece has unique characteristics and expresses tranquility and peace."

[1484] 6. Providing Feedback

[1485] The server formats the evaluation results and generated commentary in an easy-to-understand format and returns it to the user device, which displays this information in its user interface.

[1486] Specific examples

[1487] 1. The user enters the description of the painting "A beautiful painting depicting a serene landscape." along with their current emotional state "Relaxed" into the system.

[1488] 2. The device sends the entered description and emotion data to the server.

[1489] 3. The server uses an artificial intelligence model to rate the painting, saying, "This painting is very realistic, and its color choices and composition are excellent."

[1490] 4. The server uses an emotion analysis engine to analyze the user's emotional state and determine that the user is relaxed.

[1491] 5. The server generates a description such as "This piece has unique characteristics and expresses tranquility and peace," adjusting it according to the user's relaxed emotional state.

[1492] 6. The server returns the evaluation results and explanations to the terminal.

[1493] 7. The device will display on its user interface, "AI evaluation: This painting is very realistic, with excellent color choice and composition," "Characteristics: Unique use of color," "Meaning: Expression of tranquility and peace," "Background: Strong connection with nature," and even provide emotional feedback such as, "Looking at this painting in a relaxed state of mind will make you feel even more at peace."

[1494] The system allows users to understand in detail how their creations are evaluated and what value they are assigned, and receive feedback based on their emotional state. Combined with a sentiment analysis engine, the system provides more personalized feedback.

[1495] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1496] Step 1: Initialization

[1497] When the system starts, the server imports the necessary libraries and frameworks (e.g., TensorFlow, PyTorch). It also initializes the artificial intelligence model (e.g., GPT-3) and the sentiment analysis engine (e.g., Google Cloud Natural Language API). In this step, the server processes the following data: the system configuration information is the input, and the initialized model and engine are the output.

[1498] Step 2: Enter your content

[1499] The user uses the device to input their own content (e.g., a description of a painting, "A beautiful painting depicting a serene landscape.") and their current emotional state (e.g., "Relaxed"). This input data is entered into the device via an interface such as a web form. The input consists of the user's text data and emotional data, and the device processes this as output and prepares it to be sent to the server.

[1500] Step 3: Sending data

[1501] The device sends the input content data and emotion data to the server. Specifically, the device wraps the data in a structured format such as JSON and sends it to the server using an HTTP POST request. The input is content and emotion data from the user, and the output is the data sent to the server.

[1502] Step 4: Evaluate your content

[1503] The server evaluates the content data received from the terminal using an initialized artificial intelligence model. In this process, the input text data is tokenized and passed to the model to generate an evaluation result. The received content data is the input, and the evaluation result text is obtained as the output.

[1504] Step 5: Analyze emotional state

[1505] The server uses a sentiment analysis engine to analyze the received sentiment data. In this analysis process, the input sentiment text is passed to the analysis engine, which outputs detailed data about the user's emotional state. The input is the received sentiment data, and the output is the sentiment analysis result data.

[1506] Step 6: Generate a description

[1507] The server generates a commentary based on the content's rating and sentiment analysis results. In this commentary generation process, the generate_insights function is used to generate commentary text that combines the input rating and sentiment analysis results. The inputs are the rating and sentiment analysis results, and the output is the generated commentary text.

[1508] Step 7: Format and submit your feedback

[1509] The server formats the generated evaluation results and explanations into a format that is easy for users to understand. This formatted feedback data is sent from the server to the terminal. The generated evaluation results and explanations are the input, and the formatted feedback data is the output.

[1510] Step 8: Viewing feedback

[1511] The terminal displays the feedback data received from the server on the user interface. Specifically, the terminal visually displays the evaluation results, explanations, and emotion-related feedback on the screen. The input is the formatted feedback data, and the output is the information displayed on the user interface.

[1512] (Application example 2)

[1513] 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."

[1514] There is a need for a system that can provide appropriate evaluations and commentaries for user-generated content, as well as recognize users' emotions and personalize the commentary based on those emotions. However, conventional systems do not provide sufficient feedback based on users' emotions, and are therefore unable to provide optimal information to individual users.

[1515] 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 initializing an artificial intelligence model, means for receiving content input by a user, means for evaluating the content input by the user using the initialized artificial intelligence model, means for recognizing and analyzing the emotional state of the user, means for adjusting and generating commentary on the content based on the emotional state of the user, and means for displaying the generated evaluation and commentary on the user device. This makes it possible to provide highly accurate evaluations of user-generated content and provide personalized commentary according to the emotional state of the user.

[1516] An "artificial intelligence model" is a computer algorithm used to evaluate user-entered content.

[1517] A "user device" is a device that a user uses to input content and receive feedback from a server.

[1518] "Emotional state" is information that indicates the user's current psychological state.

[1519] "Means for evaluation" refers to a function that uses an initialized artificial intelligence model to analyze content entered by a user and determine its value and quality.

[1520] "Means for generating explanations" refers to a function that creates an explanation for content entered by a user, including its characteristics, meaning, background, etc.

[1521] The "means for adjusting and generating" refers to a function for appropriately modifying the generated commentary based on the user's emotional state and providing it to the user in the most optimal form.

[1522] "Means for displaying" refers to the ability to visually or audibly present the generated ratings and commentary on a user device.

[1523] This invention relates to a system that uses an AI model to evaluate content entered by a user and generates evaluation results and commentary. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions and adjust the evaluation and commentary based on those emotions. This system is composed of a user device, a server, an AI model, and an emotion engine.

[1524] System Components

[1525] server

[1526] The server is the central point of the entire system and is responsible for:

[1527] Initialize the AI ​​model: Import the required libraries and frameworks and initialize the AI ​​model (e.g., GPT-3).

[1528] Content evaluation: Receives the content data entered by the user and evaluates it using the initialized artificial intelligence model.

[1529] Sentiment analysis: An emotion engine is used to analyze the emotional data provided by the user.

[1530] Commentary generation and adjustment: Based on the content evaluation results and sentiment analysis results, a commentary is generated and adjusted to correspond to the user's emotional state.

[1531] Provide Feedback: Format and send the generated assessment and commentary to the user device.

[1532] User Device

[1533] User devices are used by users to input their own content and receive feedback from the server. Examples include smart glasses, smartphones, tablets, and personal computers.

[1534] Processing Details

[1535] The user inputs content (e.g., text or images) into the user device. The user's emotional state (e.g., whether they are relaxed or nervous) can also be input as various data (e.g., facial expression data, voice data).

[1536] The server receives content and emotion data sent from the user device. The received content is evaluated using an artificial intelligence model (e.g., GPT-3). The evaluation includes the quality, composition, and meaning of the content. For example, a rating such as "This painting is very realistic, and its color selection and composition are excellent" is generated.

[1537] In parallel, the emotion engine analyzes the user's emotion data, and the analysis results include information such as "the user is relaxed."

[1538] The server generates a commentary based on the evaluation results and sentiment analysis results. This commentary provides a detailed explanation of the content's characteristics, meaning, and background, and is adjusted based on the user's emotional state. For example, the server might generate feedback such as, "This work will make you feel even more calm if you watch it in a relaxed state."

[1539] After all results are generated, the server formats them in a user-friendly format and sends them to the user device, which displays this information in a user interface, or in the case of smart glasses, a real-time evaluation and commentary on the display.

[1540] Specific examples

[1541] As a concrete example, consider a user wearing smart glasses visiting an art gallery. Use the following prompt:

[1542] Example prompt sentence:

[1543] When the user is feeling relaxed, rate this content, "A beautiful painting of a serene landscape," and generate a commentary.

[1544] The smart glasses' camera captures the painting, and the user's emotional state is determined to be "relaxed" based on their facial expression. This data is sent to a server, where an AI model evaluates the painting as "very beautiful, with excellent color selection and composition." Based on the emotion analysis results, a commentary is generated, such as "Looking at this painting in a relaxed state will make you feel even more at peace." These results are displayed on the smart glasses' display, allowing the user to receive real-time feedback.

[1545] This system allows users to understand in detail how their content is rated and what value it has, and to receive feedback that reflects their emotional state.

[1546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1547] Step 1:

[1548] User enters content and emotion data

[1549] A user inputs their own content (e.g., text, images) using a device such as smart glasses or a smartphone. They also input their emotional state (e.g., relaxed, nervous) as facial expression data and voice data. The device then transmits this data to a server. The input is content data and emotional data, and the output is data sent to the server.

[1550] Step 2:

[1551] The server receives the data

[1552] The server receives content data and emotion data sent from the user device and converts the received data into an analyzable format. The input is the content data and emotion data sent from the user device, and the output is the data converted into an analyzable format.

[1553] Step 3:

[1554] Evaluating content using artificial intelligence models

[1555] The server passes the received content data to an artificial intelligence model (e.g., GPT-3) to evaluate the content. This includes analyzing the content's quality, composition, meaning, etc. For example, it generates a rating such as "This painting is very realistic, and its color selection and composition are excellent." The input is the content data converted into an analyzable format, and the output is the evaluation result.

[1556] Step 4:

[1557] Analyzing the user's emotional state using an emotion engine

[1558] The server uses an emotion engine to recognize and analyze the user's emotional state from facial expression data and voice data. The analysis results can provide information such as "the user is relaxed." The input is emotion data converted into an analyzable format, and the output is the emotion analysis results.

[1559] Step 5:

[1560] Generate and adjust commentary based on evaluation results and sentiment analysis results

[1561] The server generates a commentary based on the content evaluation results and emotion analysis results. The content of the commentary is adjusted according to the user's emotional state. For example, it generates a commentary such as "Watching it in a relaxed state will make you feel even more calm." The input is the evaluation results and emotion analysis results, and the output is a commentary optimized for the user.

[1562] Step 6:

[1563] Formatting the evaluation results and generated commentary

[1564] The server formats the evaluation results and generated commentary into a user-friendly format, for example, in text or audio format. The input is the user-optimized commentary, and the output is the formatted evaluation results and commentary.

[1565] Step 7:

[1566] Send the formatted results to the user device

[1567] The server sends the formatted evaluation results and commentary to the user device, which can then display the results on a display such as smart glasses or provide audio feedback. The input is the formatted evaluation results and commentary, and the output is transmission to the user device.

[1568] Step 8:

[1569] The user device displays the results

[1570] The user device displays the evaluation results and commentary sent from the server. In the case of smart glasses, the display shows real-time feedback. The input is the result sent from the server, and the output is the user receiving visual or auditory feedback.

[1571] Through this series of steps, users can gain a detailed understanding of how their content is rated and what value it has, as well as receive feedback based on their emotional state.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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).

[1579] 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.

[1580] 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."

[1581] 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.

[1582] 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).

[1583] 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.

[1584] 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.

[1585] 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.

[1586] 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.

[1587] 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.

[1588] 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.

[1589] 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.

[1590] 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.

[1591] 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.

[1592] 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.

[1593] The following is further disclosed regarding the above embodiment.

[1594] (Claim 1)

[1595] a means for initializing the artificial intelligence model;

[1596] means for receiving user-entered content;

[1597] means for evaluating user-entered content using the initialized artificial intelligence model;

[1598] means for generating commentary on the user-entered content;

[1599] means for displaying the generated ratings and commentary on a user device;

[1600] A system including:

[1601] (Claim 2)

[1602] 10. The system of claim 1, further comprising means for formatting and outputting the evaluation results for the content input by the user.

[1603] (Claim 3)

[1604] 10. The system of claim 1, further comprising means for providing a commentary on the content input by the user, categorized into at least one of features, meaning, and context.

[1605] "Example 1"

[1606] (Claim 1)

[1607] a means for initializing the artificial intelligence model;

[1608] means for receiving user-entered content;

[1609] a means for evaluating user-entered content based on the prompt using a generative AI model;

[1610] means for generating commentary on the user-entered content;

[1611] means for formatting and displaying the generated ratings and commentary on a user device;

[1612] A system including:

[1613] (Claim 2)

[1614] 10. The system of claim 1, further comprising means for formatting and outputting the evaluation results for the content input by the user.

[1615] (Claim 3)

[1616] 10. The system of claim 1, further comprising means for providing a commentary on the content input by the user, categorized into at least one of features, meaning, and context.

[1617] "Application Example 1"

[1618] (Claim 1)

[1619] a means for initializing the artificial intelligence model;

[1620] means for receiving user-entered content;

[1621] means for evaluating user-entered content using the initialized artificial intelligence model;

[1622] means for generating commentary on the user-entered content;

[1623] A means for evaluating review content entered by a user using an artificial intelligence model and providing a detailed commentary;

[1624] means for displaying the generated ratings and commentary on a user device;

[1625] A system including:

[1626] (Claim 2)

[1627] 10. The system of claim 1, further comprising means for formatting and outputting the evaluation results for the content input by the user.

[1628] (Claim 3)

[1629] 10. The system of claim 1, further comprising means for providing a commentary on the content input by the user, categorized into at least one of features, meaning, and context.

[1630] "Example 2: Combining Emotion Engines"

[1631] (Claim 1)

[1632] a means for initializing the artificial intelligence model;

[1633] means for receiving user-entered content;

[1634] means for evaluating user-entered content using the initialized artificial intelligence model;

[1635] means for analyzing a user's emotional state using an emotion analysis engine;

[1636] A means for generating commentary based on the content evaluation results and sentiment analysis results;

[1637] means for displaying the generated ratings and commentary on a user device;

[1638] A system including:

[1639] (Claim 2)

[1640] 10. The system of claim 1, further comprising means for formatting and outputting the evaluation results for the content input by the user.

[1641] (Claim 3)

[1642] 10. The system of claim 1, further comprising means for providing a commentary on the content input by the user, categorized into at least one of features, meaning, and context.

[1643] "Application example 2 when combining emotion engines"

[1644] (Claim 1)

[1645] a means for initializing the artificial intelligence model;

[1646] means for receiving user-entered content;

[1647] means for evaluating user-entered content using the initialized artificial intelligence model;

[1648] means for recognizing and analyzing the emotional state of a user;

[1649] means for adjusting and generating commentary about the content based on the emotional state of the user;

[1650] means for displaying the generated ratings and commentary on a user device;

[1651] A system including:

[1652] (Claim 2)

[1653] 10. The system of claim 1, further comprising means for formatting and outputting the evaluation results for the content input by the user.

[1654] (Claim 3)

[1655] 10. The system of claim 1, further comprising means for providing a commentary on the content input by the user, categorized into at least one of features, meaning, and context. [Explanation of symbols]

[1656] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for initializing the artificial intelligence model; means for receiving user-entered content; means for evaluating user-entered content using the initialized artificial intelligence model; means for generating commentary on the user-entered content; means for displaying the generated ratings and commentary on a user device; A system including:

2. The system of claim 1 , further comprising means for formatting and outputting the evaluation results for the content input by the user.

3. The system according to claim 1 , further comprising means for providing a commentary on the content input by the user, the commentary being categorized into at least one of features, meaning, and background.

Images