System

Abstract

A system is provided.SOLUTION: A system includes means for receiving generated content, means for extracting a feature amount from the received content, means for evaluating human creativity on the basis of the extracted feature amount and generating a discrimination result, and means for notifying a user of the discrimination result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, the production of content using generative AI has been increasing, and determining whether that content relies in part on human creativity or is entirely AI-generated has become an important issue. In order to properly assess copyright in accordance with current laws and guidelines, a system is needed that can accurately determine the degree of generative AI involvement and the degree to which human creativity is reflected. However, conventional technology has difficulty in making this determination efficiently and accurately, making appropriate copyright protection difficult. [Means for solving the problem]

[0005] To solve this problem, the present invention provides a system that includes a means for receiving generated content, a means for extracting features from the received content, a means for evaluating human creativity based on the extracted features and generating a judgment result, and a means for notifying the user of the judgment result. This allows users to check in advance how the copyright of content created using generative AI will be evaluated, thereby reducing copyright-related risks.

[0006] "Generated content" is text, images, audio, or other forms of digital content generated using generative AI.

[0007] "Means for receiving" refers to the interface and technical means for receiving digital content from a user.

[0008] A "means for extracting features" is an algorithm or analytical tool for calculating and extracting specific patterns or characteristic values ​​from generated content.

[0009] The "evaluation" in "evaluate" refers to the process of determining whether the content reflects human creativity based on the extracted features.

[0010] The "means for generating a discrimination result" refers to an algorithm or processing means for making a final judgment based on the evaluated feature amount and generating the result.

[0011] The "means for notifying the user" refers to a communication method and interface for conveying the evaluation and determination results to the user.

[0012] The "means for converting into a predetermined format" is a data processing means for converting received content into a format that is easy to analyze.

[0013] "Text, images, and audio" refer to written, visual, and auditory data, respectively, which are types of content that are stored and processed in digital form. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention relates to a system that determines the copyright of content generated using generative AI. This system is broadly composed of three elements: a server, a terminal, and a user. The functions and roles of each step are explained in detail below.

[0036] Server-side processing

[0037] 1. Content reception and data analysis

[0038] The server first receives the content (text, images, audio, etc.) provided by the user terminal. This received content is saved as data to be analyzed. At this stage, the received data is converted into a predetermined format as needed.

[0039] 2. Feature Extraction

[0040] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, for text content, grammatical features and lexical patterns are analyzed, for image content, color tones and shape patterns are analyzed, and for audio content, pitch range and rhythm are analyzed.

[0041] 3. Human Creativity Assessment

[0042] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0043] 4. Sending the results of the classification

[0044] Finally, the server generates a judgment result based on the evaluation results and sends it to the user's device. The judgment result may include, for example, a judgment such as "Human creativity is recognized" or "Completely AI-generated."

[0045] Terminal side processing

[0046] 1. User interface provision

[0047] The terminal provides an interface for users to upload content, including a file selection field and a submit button.

[0048] 2. Uploading Content

[0049] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[0050] 3. Display of discrimination results

[0051] The device will then display the results of the assessment received from the server to the user. For example, it may display a specific result such as, "This poem is a testament to human creativity and is likely to be copyrightable." Based on this information, users can commercialize and publish their own content with peace of mind.

[0052] Specific examples

[0053] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[0054] 1. Terminal: The user uploads a text file of a poem.

[0055] 2. Server: Receives the poem text, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[0056] 3. Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[0057] 4. Terminal: Receives the result of the judgment and displays to the user, "This poem is a recognition of human creativity and is likely to be copyrightable."

[0058] This system allows users to assess the copyright risk of their own creative works, allowing them to commercialize and publish them with peace of mind.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The terminal provides the user with an interface for uploading content, through which the user selects his / her content file and clicks the upload button.

[0062] Step 2:

[0063] The terminal transmits the content file selected by the user to the server, using an HTTP request to transmit the file data.

[0064] Step 3:

[0065] The server converts the received content into a specific format, for example, if it is text, it reads the text file as string data.

[0066] Step 4:

[0067] The server applies a generative AI model to extract features from the received content. For text content, it analyzes grammar, vocabulary selection, sentence structure, etc., and extracts these features as numerical data.

[0068] Step 5:

[0069] The server inputs the extracted features into an evaluation algorithm to calculate the degree to which human creativity is reflected, for example, by comparing it with known AI-generated patterns and calculating an originality score.

[0070] Step 6:

[0071] The server generates a judgment result based on the evaluation results, which may include information such as "human creativity is recognized" or "completely AI-generated."

[0072] Step 7:

[0073] The server sends the generated classification results to the user's device, where it returns the analysis results in JSON format using an HTTP response.

[0074] Step 8:

[0075] The terminal displays the received results to the user, who can then check the results and recognize to what extent their content reflects human creativity.

[0076] Example 1

[0077] 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."

[0078] In recent years, the amount of content created by generative AI has skyrocketed, but it is difficult to determine the extent to which this content reflects human creativity. Furthermore, copyright certification is required for generated content, but there is no efficient and accurate method for this evaluation. Therefore, there is a need for a system that can accurately evaluate the originality and copyrightability of generative AI content.

[0079] 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.

[0080] In this invention, the server includes means for receiving the generated information, means for extracting features from the received information, means for evaluating human creativity based on the extracted features and generating a judgment result, means for notifying the user of the judgment result, and means for providing an interface for the user to send information. This makes it possible to efficiently evaluate the originality of the generated content and quickly and accurately determine copyright.

[0081] "Generated information" refers to data such as text, images, and audio created by a generative AI model or other information generation means.

[0082] "Features" refers to numerical data that indicate the characteristics of information, such as grammatical features, vocabulary patterns, color tones, shape patterns, ranges, and rhythms extracted from the generated information.

[0083] "Human creativity" refers to the originality and unique patterns of expression that humans display in information and works. The originality of works is evaluated based on this creativity.

[0084] The "discrimination result" refers to the result of evaluating the degree to which the information generated based on the extracted features reflects human creativity. Specifically, it includes a judgment on whether human creativity is recognized.

[0085] "User" means any person or entity that receives the evaluation of information generated using this system.

[0086] "Means for receiving" refers to a function for sending information generated by a user to a server. An example would be a function for receiving data via an HTTP request.

[0087] "Interface" refers to the operation screens and tools that allow users to send generated information to the system, including the file upload button and the send button.

[0088] "Predetermined format" refers to the data format required to properly parse the received information, e.g., text data is encoded in UTF-8, image data is converted to JPEG format, etc.

[0089] This invention relates to a system that determines the copyright of content generated using a generation AI. This system is broadly composed of three elements: a server, a terminal, and a user. Specific embodiments of each element are described below.

[0090] Server-side processing

[0091] The server first receives the content (text, images, audio, etc.) provided by the user terminal. The received content is saved as data to be analyzed, and at this stage the received data is converted to a specified format as needed (for example, a text file is encoded into UTF-8, an image file is converted into JPEG format).

[0092] The server then applies algorithms from a generative AI model (e.g., GPT-3 for text, Vision Transformer for images) to extract features from the received content: grammatical features and lexical patterns for text content, color and shape patterns for image content, and pitch range and rhythm for audio content.

[0093] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0094] Finally, the server generates a judgment result based on the evaluation results and sends it to the user's device. The judgment result may include a judgment such as "human creativity is recognized" or "completely AI-generated."

[0095] Terminal side processing

[0096] The terminal provides an interface for users to upload content. This interface includes a file selection field and a submit button. The user uses this interface to select their content and clicks the upload button. The terminal then sends the selected content file to the server.

[0097] The device then displays the results of the assessment received from the server to the user. For example, it may display a specific result such as, "This poem is a testament to human creativity and is likely to be copyrightable." Based on this information, users can commercialize and publish their own content with peace of mind.

[0098] Specific examples

[0099] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[0100] Terminal: The user uploads a text file of the poem.

[0101] Server: Receives the text of the poem, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[0102] Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[0103] Terminal: Receives the result of the judgment and displays to the user, "This poem recognizes human creativity and is likely to be copyrightable."

[0104] Prompt Sentence Examples

[0105] Describe a system that receives a user-provided text file of a poem, 'mypoem.txt', and analyzes its grammatical features and lexical patterns to determine whether the poem is original.

[0106] The system allows users to quickly assess how original their creations are and whether they are likely to be copyrightable.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1: Receiving content

[0109] Input: User-uploaded content (text, images, audio, etc.).

[0110] How it works: The device receives content entered by the user and sends it to the server. For example, if the user uploads "mypoem.txt", the device will send this file to the server via an HTTP request.

[0111] Output: You get the content transferred to the server.

[0112] Step 2: Data storage and format conversion

[0113] Input: The content received by the server.

[0114] How it works: The server stores the received content in temporary storage and converts it to the required format if necessary: ​​if it's a text file, it's encoded in UTF-8, and if it's an image file, it's converted to JPEG.

[0115] Output: The formatted data is obtained for analysis.

[0116] Step 3: Feature extraction

[0117] Input: The reformatted content.

[0118] How it works: The server applies an appropriate generative AI model to extract features such as grammatical features and lexical patterns in the case of text, color and shape patterns in the case of images, and pitch and rhythm in the case of audio. For example, it uses GPT-3 to convert the grammatical and lexical patterns of "mypoem.txt" into numerical data.

[0119] Output: Numerical data of extracted features is obtained.

[0120] Step 4: Creativity Assessment

[0121] Input: Numerical data of extracted features.

[0122] How it works: The server runs a proprietary evaluation algorithm and compares the extracted features with predefined benchmarks and existing AI-generated patterns. This evaluation determines the degree to which the feature reflects human creativity.

[0123] Output: Numerical data is obtained as the evaluation result of creativity.

[0124] Step 5: Generate classification results

[0125] Input: Numerical data of creativity assessment results.

[0126] How it works: The server makes a final decision based on the evaluation results, generating a decision such as "human creativity is recognized" or "completely AI-generated."

[0127] Output: The generated discrimination results are obtained.

[0128] Step 6: Send the results

[0129] Input: Generated discrimination results.

[0130] Operation: The server sends the result of the determination to the user terminal as an HTTP response.

[0131] Output: The discrimination result is sent to the user's terminal.

[0132] Step 7: Displaying the discrimination results

[0133] Input: The result of the determination received from the server.

[0134] Operation: The device displays the received judgment results to the user. For example, a result such as "This poem is recognized as a work of human creativity and is likely to be copyrightable" is displayed on the interface.

[0135] Output: The results of the discrimination are displayed to the user.

[0136] (Application example 1)

[0137] 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."

[0138] In recent years, the amount of content generated using generative AI has increased, making it important to accurately determine its copyright. With conventional systems, determining the copyright of content was extremely difficult and required a great deal of time and effort. Furthermore, creators lacked objective criteria for evaluating the originality of their own work, which led to risks when commercializing or releasing it. These issues made it difficult for creators to release their content with confidence.

[0139] 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.

[0140] In this invention, the server includes means for receiving generated content, means for extracting features from the received content, means for evaluating human creativity based on the extracted features and generating a judgment result, means for notifying the user of the judgment result, means for providing an interface for users to upload their own content, and means for transferring the uploaded content to the server. This allows creators to objectively evaluate the originality of their own content and quickly and accurately determine copyright. Furthermore, commercialization and publication can be carried out with peace of mind based on the evaluation results.

[0141] The "means for receiving generated content" is a function for transmitting content such as text, images, and audio provided by a user to a server.

[0142] "Means for extracting features from received content" refers to a function that identifies important features from the content received by the server and extracts them as data.

[0143] The "means for evaluating human creativity based on extracted features and generating a judgment result" is a function that analyzes the extracted features to evaluate the extent to which human creativity is reflected, and judges the originality of the content based on the evaluation result.

[0144] The "means for notifying the user of the discrimination results" is a function for notifying the user of the evaluation results and discrimination results generated by the server.

[0145] The "means for providing an interface for users to upload their own content" is a function for providing an interface for users to upload content to the server.

[0146] The "means for transferring uploaded content to the server" is a function for transmitting content uploaded by a user through an interface to the server.

[0147] The "means for converting content into a predetermined format" is a function for converting received content into a predetermined format so that it can be easily analyzed.

[0148] "Means for receiving and processing at least one of text, images, and audio as content" refers to the function of receiving content in a wide variety of formats (text, images, audio) and analyzing and processing it.

[0149] "Means for extracting features using a generative AI model" refers to a function that utilizes an AI model to extract features of content.

[0150] "Means of inputting prompt sentences to a generative AI model and using the results for evaluation" refers to a function that uses prompt sentences to an AI model to extract content features and utilizes the results for evaluation.

[0151] The present invention relates to a system for determining copyright of content generated using a generative AI model, and specific embodiments thereof will be described below.

[0152] System configuration

[0153] This system consists of three elements: a server, a terminal, and a user. The server plays a central role in analyzing content, extracting its features, evaluating human creativity, and generating a judgment result. The terminal provides an interface for users to upload content and has the auxiliary function of transferring uploaded content to the server.

[0154] Hardware used

[0155] Server: A high-performance data analysis server is required, and this server should have enough computing resources (CPU, GPU) to run the generative AI model.

[0156] Client terminal: A device operated by a user, such as a smartphone or PC, that requires an internet connection.

[0157] Software used

[0158] Flask: A Python web framework that facilitates communication between servers and clients.

[0159] OpenAI API: An API for extracting content features using generative AI models (e.g., OpenAI's GPT-3).

[0160] Program processing

[0161] Content Upload

[0162] A user uploads their own content using a terminal. The terminal receives the content, such as text, images, and audio, through a user interface with a file selection field and a send button. The terminal then transfers the uploaded content to the server.

[0163] Feature extraction

[0164] The server analyzes the received content and uses a generative AI model to extract key features: for example, for text content, it extracts grammatical features and lexical patterns, for image content, it analyzes color and shape patterns, and for audio content, it analyzes pitch and rhythm.

[0165] Human creativity evaluation and discrimination result generation

[0166] Based on the extracted features, the server applies a unique evaluation algorithm to determine the degree to which human creativity is reflected in the work, and generates a judgment result based on the evaluation results and notifies the user.

[0167] Specific examples

[0168] For example, if a user uploads a poem called "mypoem.txt," the following prompt sentence is input to the generative AI model to extract features:

[0169] Example prompt:

[0170] Please provide a list of key features of the following text:

[0171] Everything in this world is reflected in my heart

[0172] The whisper of the wind, the song of the birds, the shape of the clouds

[0173] Feeling the breath of life in silence

[0174] They resonate deeply with me

[0175] The server sends this prompt to the generative AI model, which then uses a proprietary algorithm to evaluate it based on the returned features. The evaluation result is displayed to the user, saying, "This poem recognizes human creativity and is likely to be copyrightable."

[0176] As described above, the present invention enables users to objectively evaluate the originality of their own content and quickly and accurately determine copyright. Furthermore, based on the evaluation results, users can commercialize and release their content with peace of mind.

[0177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0178] Step 1:

[0179] A user uploads content using a device. The user selects a content file, such as text, image, or audio, through the user interface on the device and clicks the send button. The input is the content file selected by the user, and the output is the file sent from the device to the server.

[0180] Step 2:

[0181] The terminal transfers the uploaded content to the server. The terminal receives the selected content file and transfers it to the server as a data packet. The input is the content file uploaded by the user, and the output is that the file is transferred to the server.

[0182] Step 3:

[0183] The server analyzes the received content and extracts features using a generative AI model (e.g., OpenAI's API). In this step, the content is sent to the generative AI model using a specific prompt. The input is the transferred content file and the prompt, and the output is the feature data extracted from the generative AI model.

[0184] Step 4:

[0185] The server analyzes the extracted features and applies a unique evaluation algorithm to evaluate human creativity. The evaluation algorithm quantifies the extracted features and generates an evaluation score based on these values. The input is the extracted feature data, and the output is the evaluation score.

[0186] Step 5:

[0187] The server generates a judgment result based on the evaluation score. In this step, the evaluation score is analyzed and converted into a concrete result such as "human creativity recognized" or "AI generated" based on a reference value. The input is the evaluation score, and the output is the judgment result.

[0188] Step 6:

[0189] The server notifies the user of the classification result. The server sends the generated classification result to the terminal, which displays it to the user. The input is the classification result, and the output is the evaluation result displayed on the terminal.

[0190] 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.

[0191] This invention combines a system that uses generative AI to determine copyright of content generated by the system with an emotion engine that recognizes user emotions. This system is composed of a server, a terminal, and a user. The cooperation of these elements enables advanced copyright determination that links the evaluation of generated content with user emotion data. The functions and roles of each step are explained in detail below.

[0192] Server-side processing

[0193] 1. Content reception and data analysis

[0194] The server first receives the content (text, images, audio, etc.) provided by the user terminal. This received content is saved as data to be analyzed. At this stage, the received data is converted into a predetermined format as needed.

[0195] 2. Feature Extraction

[0196] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, for text content, grammatical features and lexical patterns are analyzed, for image content, color tones and shape patterns are analyzed, and for audio content, pitch range and rhythm are analyzed.

[0197] 3. Human Creativity Assessment

[0198] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0199] 4. Collecting and analyzing emotional data

[0200] The server collects emotional data from the user's reactions and inputs and analyzes it using an emotion engine. The emotion engine adjusts the parameters of the generative AI model based on the user's emotional data. For example, if the user reacts with surprise or emotion, the engine changes the settings of the generative AI model based on that emotional data, promoting the generation of more original content.

[0201] 5. Sending the results of the classification

[0202] Finally, the server generates a judgment result based on the evaluation results and emotion data and sends it to the user's device. The judgment result may include, for example, "Human creativity is recognized" or "Completely AI-generated."

[0203] Terminal side processing

[0204] 1. User interface provision

[0205] The terminal provides an interface for users to upload content, including a file selection field and a submit button.

[0206] 2. Uploading Content

[0207] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[0208] 3. Entering Emotional Reactions

[0209] The device also provides an interface for receiving user input, including text input, facial recognition cameras, and voice input, for reacting to content and receiving emotional input.

[0210] 4. Display of discrimination results

[0211] The device receives the results from the server and displays them to the user, who can then check the results and receive detailed feedback based on the emotion data.

[0212] Specific examples

[0213] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[0214] 1. Terminal: The user uploads a text file of a poem.

[0215] 2. Server: Receives the poem text, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[0216] 3. Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[0217] 4. Server: Collects user emotion data, analyzes it using the emotion engine, and adjusts the parameters of the generative AI model.

[0218] 5. Terminal: Receives the result of the judgment and displays to the user, "This poem is a recognition of human creativity and is likely to be copyrightable."

[0219] This system allows users to assess the copyright risk of their own creative works, and by optimizing the parameters of the generative AI based on emotional data, it becomes possible to create more valuable content.

[0220] The processing flow will be explained below.

[0221] Step 1:

[0222] The terminal provides the user with an interface for uploading content, through which the user selects his / her content file and clicks the upload button.

[0223] Step 2:

[0224] The terminal transmits the content file selected by the user to the server, using an HTTP request to transmit the file data.

[0225] Step 3:

[0226] The server converts the received content into a specific format, for example, if it is text, it reads the text file as string data.

[0227] Step 4:

[0228] The server applies a generative AI model to extract features from the received content. For example, in the case of text content, it analyzes grammar, vocabulary selection, sentence structure, etc., and extracts these features as numerical data.

[0229] Step 5:

[0230] The server inputs the extracted features into an evaluation algorithm to calculate the degree to which human creativity is reflected, for example, by comparing it with known AI-generated patterns and calculating an originality score.

[0231] Step 6:

[0232] The server receives the user's emotional response data and analyzes it using an emotion engine, which extracts emotional data from the user's input and reactions and adjusts the parameters of the generative AI model based on this data.

[0233] Step 7:

[0234] The server generates a judgment result based on the evaluation results and emotion data. The judgment result may include information such as "Human creativity is recognized" or "Completely AI-generated."

[0235] Step 8:

[0236] The server sends the generated classification results to the user's device, where it returns the analysis results in JSON format using an HTTP response.

[0237] Step 9:

[0238] The device displays the received results to the user, who can then check the results and receive detailed feedback based on the emotion data.

[0239] Step 10:

[0240] The device provides interfaces such as text input, a facial recognition camera, and voice input to continuously collect user emotional data. This data is used for future judgments and to further optimize the generative AI model.

[0241] Example 2

[0242] 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."

[0243] In conventional content generation systems, it is difficult to determine the degree to which human creativity is reflected. Furthermore, there is no mechanism for incorporating user emotions as evaluation data, making it difficult to improve the quality of content generation. Furthermore, complex analysis is required to accommodate different types of content (text, images, audio, etc.), and establishing comprehensive evaluation criteria is also an important challenge.

[0244] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0245] In this invention, the server includes means for acquiring generated data, means for deriving feature information from the acquired data, means for evaluating human creativity based on the derived feature information and generating a judgment result, means for notifying the user of the judgment result, means for collecting emotional information from the user, and means for analyzing the collected emotional information and adjusting the parameters of the generative AI model. This enables comprehensive copyright determination that combines judgment of human creativity and user emotional data.

[0246] "Generated data" refers to content (text, images, audio, etc.) generated by users or AI.

[0247] "Means for obtaining" refers to a method or mechanism for receiving data generated from a user's terminal or other input device.

[0248] "Feature information" refers to analyzable elements such as grammar, vocabulary, color tone, and rhythm that are extracted from the generated data.

[0249] "Means for deriving" refers to methods or algorithms for extracting feature information from the generated data.

[0250] "Means of evaluation" refers to methods and algorithms for evaluating human creativity based on extracted feature information.

[0251] "Judgment result" refers to the judgment result regarding human creativity that is generated based on characteristic information and evaluation results.

[0252] "Means for notifying" refers to a method or mechanism for communicating the determination result to the user.

[0253] "Emotional information" refers to data that indicates the user's reaction or emotional state (text comments, facial expression data, voice, etc.).

[0254] "Means for collecting" refers to a method or mechanism for acquiring emotion information from a user.

[0255] "Means for analyzing" refers to a method or mechanism for analyzing collected emotional information and classifying or evaluating its content.

[0256] "Generative AI model" refers to an artificial intelligence algorithm or program that evaluates and generates generated content.

[0257] "Means for adjusting parameters" refers to methods or mechanisms for optimizing the quality or characteristics of generated content by modifying the settings or components of a generative AI model.

[0258] This invention relates to an evaluation method that takes into account user emotions in a content copyright determination system that uses a generative AI model. This system is composed of a server, a terminal, and a user, and by cooperating with each other, it is possible to perform advanced copyright determination.

[0259] Hardware and software used

[0260] The server is equipped with a high-performance processor and a large amount of memory. It also has a database (e.g., MySQL) and various analysis software (e.g., a natural language processing library using Python, or a machine learning library such as TensorFlow or PyTorch) installed. The server receives, analyzes, evaluates, and notifies users of various types of data.

[0261] The device provides an interface for users to upload content, either through a browser-based application or a dedicated mobile application, which includes a file selection button, a send button, and emotional response input fields (e.g., text, camera, voice input).

[0262] Users send the content they generate to the server via their devices, and contribute to the evaluation of the generative AI model by inputting their emotional responses into the interface.

[0263] Specific methods for data processing and calculation

[0264] 1. Content Reception: The server receives content (text, images, audio, etc.) sent from the user's device. This is done using an HTTP POST request, etc. The received data is converted into the appropriate format within the server.

[0265] 2. Feature extraction: The server applies generative AI models to extract feature information from the incoming content. For example, for text content, it uses natural language processing (NLP) techniques to analyze grammar and vocabulary patterns. For image content, it uses computer vision techniques to extract color and shape patterns. For audio content, it analyzes pitch and rhythm.

[0266] 3. Human Creativity Evaluation: The server evaluates the human creativity based on the extracted feature information. This evaluation is performed by comparing it with predefined benchmarks and existing AI-generated patterns. The evaluation results are stored in a database as numerical data.

[0267] 4. Emotional data collection and analysis: The server collects emotional information from the user's emotional reactions and inputs and analyzes it using an emotion engine. For example, if the user expresses emotion or surprise, the parameters of the generative AI model are adjusted based on that emotional data.

[0268] 5. Generation and notification of judgment results: The server combines the evaluation results and emotional data to generate a judgment result. This results in a judgment such as "Human creativity is recognized" or "It is highly likely that the result was completely generated by AI." The judgment result is sent to the device via HTTP response, WebSocket, etc.

[0269] Specific examples

[0270] For example, if a user submits a poem they wrote, "mypoem.txt," to the classification system, the specific steps are as follows:

[0271] 1. Device: The user uploads a poem they wrote, "mypoem.txt." The device sends this file to the server.

[0272] 2. Server: Receives the text file of the poem, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts and saves them as numerical data.

[0273] 3. Server: Evaluates originality based on the extracted features and determines whether the work "reflects human creativity."

[0274] 4. Server: Collects user emotional input data (e.g., comments of surprise or excitement) and analyzes it using the emotion engine. Based on this data, the parameters of the generative AI model are adjusted.

[0275] 5. Server and terminal: Makes the final decision and notifies the user that "this poem is recognized as a work of human creativity and is likely to be copyrightable."

[0276] Prompt Sentence Examples

[0277] Below are some example prompts to input to a generative AI model:

[0278] "Evaluate the poem I have created and determine how well it reflects human creativity."

[0279] "Tell me if this image is different from existing AI-generated patterns."

[0280] "Please determine whether the provided audio file is original and return the result."

[0281] This allows users to assess the copyright risk of their own content, optimize the parameters of the generation AI based on emotional data, and generate more valuable content.

[0282] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0283] Step 1: Upload your content

[0284] Input: User-generated content (e.g., mypoem.txt)

[0285] How it works: The user selects their own content using the device's user interface and clicks the upload button. The device then sends the selected file to the server using an HTTP POST request or similar.

[0286] Output: Content data sent to the server

[0287] Step 2: Receiving content and formatting

[0288] Input: Content data received by the server

[0289] How it works: The server converts the content it receives from the device into the appropriate format, for example, converting text data into UTF-8 encoding, or converting image data into a standard image format.

[0290] Output: Content data converted into a format that can be processed internally

[0291] Step 3: Feature extraction

[0292] Input: Format-converted content data

[0293] How it works: The server uses a generative AI model to extract features from content. For text content, it extracts grammatical features and lexical patterns. For image content, it extracts color and shape patterns. For audio content, it analyzes the pitch and rhythm.

[0294] Output: Extracted feature data

[0295] Step 4: Human Creativity Assessment

[0296] Input: Extracted feature data

[0297] How it works: The server applies a unique evaluation algorithm to the stored feature data to assess the degree to which human creativity is reflected in the result, by comparing it with predefined benchmarks and existing AI-generated patterns.

[0298] Output: Evaluation result data

[0299] Step 5: Collecting sentiment data

[0300] Input: User emotional responses and inputs

[0301] How it works: The device provides an interface that accepts user emotional input. Users input their emotional responses through text input, a facial recognition camera, or voice input. The device then sends this to a server.

[0302] Output: Emotion data sent to the server

[0303] Step 6: Analyze the sentiment data

[0304] Input: Emotion data sent to the server

[0305] How it works: The server uses an emotion engine to analyze emotion data and classify it into various emotion categories (e.g., surprise, emotion, sadness). Based on the analysis results, it adjusts the parameters of the generative AI model.

[0306] Output: Analysis result data and adjusted generative AI model parameters

[0307] Step 7: Generate classification results

[0308] Input: Evaluation result data and analysis result data

[0309] How it works: The server combines the evaluation result data and the emotion data to generate a final judgment result, such as "This poem is highly likely to be copyrightable because it demonstrates human creativity."

[0310] Output: Classification result data

[0311] Step 8: Notification of the results

[0312] Input: Classification result data

[0313] Operation: The server sends the generated discrimination result data to the terminal. This transmission uses a communication method such as HTTP response or WebSocket. The terminal displays the received discrimination result on the user interface.

[0314] Output: The result displayed to the user

[0315] (Application example 2)

[0316] 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."

[0317] In recent years, determining copyright for content created using generative AI has become an important issue. However, current technology makes it difficult to accurately evaluate the extent to which content generated by generative AI reflects human creativity. Furthermore, there is no system that evaluates content while reflecting users' emotional reactions, resulting in incomplete evaluation of content value. This makes it difficult to properly evaluate original content and generate content that reflects user satisfaction.

[0318] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0319] In this invention, the server includes means for receiving generated content, means for extracting features from the received content, means for evaluating human creativity based on the extracted features and generating a discrimination result, means for recognizing user emotions and analyzing the data, means for adjusting parameters of the generative AI model based on the analyzed emotion data, and means for notifying the user of the discrimination result, thereby enabling accurate copyright discrimination of generated content and evaluation based on the user's emotional response.

[0320] "Generated content" is digital media, such as text, images, and audio, created using a generative AI model.

[0321] The "receiving means" refers to the function of transmitting content data from the user terminal to the server and receiving it on the server side.

[0322] "Means for extracting features" refers to technology that identifies important information and patterns from received content and executes a process to quantify them.

[0323] The "means for evaluating human creativity" is a system for measuring the degree to which generated content reflects original human ideas and ingenuity based on the extracted features.

[0324] The "means for generating a judgment result" is a function that determines whether the content is created by a human or generated by AI based on the evaluation results and documents that conclusion.

[0325] "Means for recognizing user emotions" refers to technology that detects the emotions (joy, surprise, sadness, etc.) expressed by a user when viewing content from facial expressions, voice, etc.

[0326] "Means for analyzing emotion data" refers to the process of analyzing the recognized emotion data of a user and converting the results into an understandable format.

[0327] The "means for adjusting the parameters of the generative AI model" is a system that changes the operating conditions and settings of the generative AI model based on analyzed emotional data, promoting more appropriate content generation.

[0328] The "means for notifying the evaluation result" is a function for displaying or reporting the evaluation result of the generated content to the user.

[0329] This invention combines a system that uses generative AI to determine copyright of content generated by generative AI with an emotion engine that recognizes user emotions. This system is composed of a server, a terminal, and a user. By cooperating with each element, advanced copyright determination is possible by linking the evaluation of generated content with user emotion data.

[0330] Server-side processing

[0331] Content reception and data analysis

[0332] The server first receives the content (text, images, audio, etc.) provided by the user's device. This received content is then saved as data to be analyzed. The hardware used is a cloud service such as AWS, and the software is Python and TensorFlow. The received data is converted into a specified format as needed.

[0333] Feature extraction

[0334] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, grammatical features and lexical patterns are analyzed for text content, color tones and shape patterns for image content, and pitch range and rhythm for audio content.

[0335] Human creativity assessment

[0336] Based on the extracted features, a unique evaluation algorithm is applied to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0337] Collecting and analyzing emotional data

[0338] The server collects emotional data from the user's reactions and inputs and analyzes it using an emotion engine. The emotion engine adjusts the parameters of the generative AI model based on the user's emotional data. For example, if the user reacts with surprise or emotion, the engine changes the settings of the generative AI model based on that emotional data, promoting the generation of more original content.

[0339] Sending the results of the judgment

[0340] Finally, the server generates a judgment result based on the evaluation results and emotion data and sends it to the user's device. The judgment result may include, for example, "Human creativity is recognized" or "Completely AI-generated."

[0341] Terminal side processing

[0342] User interface provided

[0343] The terminal provides an interface for users to upload content, including a file selection field and a send button. To collect emotion data, a smartphone or PC equipped with a facial recognition camera and voice input function is used.

[0344] Uploading content

[0345] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[0346] Entering emotional responses

[0347] The device also provides an interface for receiving user reactions to content and emotional input, including text input, facial recognition camera, voice input, etc. For example, a facial recognition camera can be used to analyze the user's facial expressions and collect them as emotional data.

[0348] Display of discrimination results

[0349] The device receives the results from the server and displays them to the user, who can then check the results and receive detailed feedback based on the emotion data.

[0350] Specific examples

[0351] For example, if a user uploads a self-made video "sample_video.mp4," the following steps are executed: the user uploads the video, uses the emotion engine to collect emotional data while watching, and adjusts the parameters of the generative AI model based on that data. The server then generates a classification result and provides feedback to the user.

[0352] Example prompt sentence:

[0353] Upload the video "sample_video.mp4", analyze user sentiment, determine copyright status, and display the results.

[0354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0355] Step 1:

[0356] A user uploads a video.

[0357] Input: The user selects a video file from their device and clicks the upload button.

[0358] Output: The video file is sent from the user device to the server.

[0359] Specific operation: The upload interface will be displayed on the user's device screen, and once a video file is selected, the file will be sent to the server via the HTTPS protocol.

[0360] Step 2:

[0361] The server receives and stores the video file.

[0362] Input: The video file sent from the user's device.

[0363] Output: The video file will be saved in the specified folder on the server.

[0364] What it does: The server-side script receives the video file and saves it to local storage or cloud storage.

[0365] Step 3:

[0366] The server converts the video file into a predetermined format for analysis.

[0367] Input: Video files stored on the server.

[0368] Output: Video data converted into the specified format.

[0369] What it does: Use a media conversion tool such as FFmpeg to convert the video file into a format that can be analyzed.

[0370] Step 4:

[0371] The server extracts the features.

[0372] Input: Video data converted to a specified format.

[0373] Output: Video features (e.g., image information for each frame, audio waveform data, etc.).

[0374] Specific operation: Using libraries such as OpenCV and Librosa, frames are extracted from video and features are extracted from audio.

[0375] Step 5:

[0376] The server evaluates human creativity based on the extracted features and generates a discrimination result.

[0377] Input: Extracted video feature data.

[0378] Output: Evaluation and discrimination results of human creativity.

[0379] Specific operation: Using an evaluation algorithm, feature data is analyzed and, based on the results, a determination is made as to whether the voice is AI-generated or human-generated.

[0380] Step 6:

[0381] The server collects and analyzes the user's emotional data.

[0382] Input: Emotion data (facial expressions, voice, etc.) collected from the user device.

[0383] Output: Parsed emotion data (e.g. emotion type and intensity).

[0384] Specific operation: Collects facial expressions and voice data while the user is watching a video, and performs emotion analysis using the Emotion API.

[0385] Step 7:

[0386] The server adjusts the parameters of the generative AI model based on the analyzed emotional data.

[0387] Input: Parsed emotion data.

[0388] Output: The adjusted parameters of the generative AI model.

[0389] Specific operation: Emotional data is fed back to the generative AI model, and parameters are automatically adjusted.

[0390] Step 8:

[0391] The server notifies the user terminal of the determination result.

[0392] Input: Evaluation results and classification results based on emotion data.

[0393] Output: The final evaluation result displayed on the user's terminal.

[0394] Specific operation: The evaluation results are sent to the user's device and displayed through the interface.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] [Second embodiment]

[0399] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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).

[0405] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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."

[0411] This invention relates to a system that determines the copyright of content generated using generative AI. This system is broadly composed of three elements: a server, a terminal, and a user. The functions and roles of each step are explained in detail below.

[0412] Server-side processing

[0413] 1. Content reception and data analysis

[0414] The server first receives the content (text, images, audio, etc.) provided by the user terminal. This received content is saved as data to be analyzed. At this stage, the received data is converted into a predetermined format as needed.

[0415] 2. Feature Extraction

[0416] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, for text content, grammatical features and lexical patterns are analyzed, for image content, color tones and shape patterns are analyzed, and for audio content, pitch range and rhythm are analyzed.

[0417] 3. Human Creativity Assessment

[0418] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0419] 4. Sending the results of the classification

[0420] Finally, the server generates a judgment result based on the evaluation results and sends it to the user's device. The judgment result may include, for example, a judgment such as "Human creativity is recognized" or "Completely AI-generated."

[0421] Terminal side processing

[0422] 1. User interface provision

[0423] The terminal provides an interface for users to upload content, including a file selection field and a submit button.

[0424] 2. Uploading Content

[0425] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[0426] 3. Display of discrimination results

[0427] The device will then display the results of the assessment received from the server to the user. For example, it may display a specific result such as, "This poem is a testament to human creativity and is likely to be copyrightable." Based on this information, users can commercialize and publish their own content with peace of mind.

[0428] Specific examples

[0429] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[0430] 1. Terminal: The user uploads a text file of a poem.

[0431] 2. Server: Receives the poem text, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[0432] 3. Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[0433] 4. Terminal: Receives the result of the judgment and displays to the user, "This poem is a recognition of human creativity and is likely to be copyrightable."

[0434] This system allows users to assess the copyright risk of their own creative works, allowing them to commercialize and publish them with peace of mind.

[0435] The processing flow will be explained below.

[0436] Step 1:

[0437] The terminal provides the user with an interface for uploading content, through which the user selects his / her content file and clicks the upload button.

[0438] Step 2:

[0439] The terminal transmits the content file selected by the user to the server, using an HTTP request to transmit the file data.

[0440] Step 3:

[0441] The server converts the received content into a specific format, for example, if it is text, it reads the text file as string data.

[0442] Step 4:

[0443] The server applies a generative AI model to extract features from the received content. For text content, it analyzes grammar, vocabulary selection, sentence structure, etc., and extracts these features as numerical data.

[0444] Step 5:

[0445] The server inputs the extracted features into an evaluation algorithm to calculate the degree to which human creativity is reflected, for example, by comparing it with known AI-generated patterns and calculating an originality score.

[0446] Step 6:

[0447] The server generates a judgment result based on the evaluation results, which may include information such as "human creativity is recognized" or "completely AI-generated."

[0448] Step 7:

[0449] The server sends the generated classification results to the user's device, where it returns the analysis results in JSON format using an HTTP response.

[0450] Step 8:

[0451] The terminal displays the received results to the user, who can then check the results and recognize to what extent their content reflects human creativity.

[0452] Example 1

[0453] 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."

[0454] In recent years, the amount of content created by generative AI has skyrocketed, but it is difficult to determine the extent to which this content reflects human creativity. Furthermore, copyright certification is required for generated content, but there is no efficient and accurate method for this evaluation. Therefore, there is a need for a system that can accurately evaluate the originality and copyrightability of generative AI content.

[0455] 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.

[0456] In this invention, the server includes means for receiving the generated information, means for extracting features from the received information, means for evaluating human creativity based on the extracted features and generating a judgment result, means for notifying the user of the judgment result, and means for providing an interface for the user to send information. This makes it possible to efficiently evaluate the originality of the generated content and quickly and accurately determine copyright.

[0457] "Generated information" refers to data such as text, images, and audio created by a generative AI model or other information generation means.

[0458] "Features" refers to numerical data that indicate the characteristics of information, such as grammatical features, vocabulary patterns, color tones, shape patterns, ranges, and rhythms extracted from the generated information.

[0459] "Human creativity" refers to the originality and unique patterns of expression that humans display in information and works. The originality of works is evaluated based on this creativity.

[0460] The "discrimination result" refers to the result of evaluating the degree to which the information generated based on the extracted features reflects human creativity. Specifically, it includes a judgment on whether human creativity is recognized.

[0461] "User" means any person or entity that receives the evaluation of information generated using this system.

[0462] "Means for receiving" refers to a function for sending information generated by a user to a server. An example would be a function for receiving data via an HTTP request.

[0463] "Interface" refers to the operation screens and tools that allow users to send generated information to the system, including the file upload button and the send button.

[0464] "Predetermined format" refers to the data format required to properly parse the received information, e.g., text data is encoded in UTF-8, image data is converted to JPEG format, etc.

[0465] This invention relates to a system that determines the copyright of content generated using a generation AI. This system is broadly composed of three elements: a server, a terminal, and a user. Specific embodiments of each element are described below.

[0466] Server-side processing

[0467] The server first receives the content (text, images, audio, etc.) provided by the user terminal. The received content is saved as data to be analyzed, and at this stage the received data is converted to a specified format as needed (for example, a text file is encoded into UTF-8, an image file is converted into JPEG format).

[0468] The server then applies algorithms from a generative AI model (e.g., GPT-3 for text, Vision Transformer for images) to extract features from the received content: grammatical features and lexical patterns for text content, color and shape patterns for image content, and pitch range and rhythm for audio content.

[0469] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0470] Finally, the server generates a judgment result based on the evaluation results and sends it to the user's device. The judgment result may include a judgment such as "human creativity is recognized" or "completely AI-generated."

[0471] Terminal side processing

[0472] The terminal provides an interface for users to upload content. This interface includes a file selection field and a submit button. The user uses this interface to select their content and clicks the upload button. The terminal then sends the selected content file to the server.

[0473] The device then displays the results of the assessment received from the server to the user. For example, it may display a specific result such as, "This poem is a testament to human creativity and is likely to be copyrightable." Based on this information, users can commercialize and publish their own content with peace of mind.

[0474] Specific examples

[0475] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[0476] Terminal: The user uploads a text file of the poem.

[0477] Server: Receives the text of the poem, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[0478] Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[0479] Terminal: Receives the result of the judgment and displays to the user, "This poem recognizes human creativity and is likely to be copyrightable."

[0480] Prompt Sentence Examples

[0481] Describe a system that receives a user-provided text file of a poem, 'mypoem.txt', and analyzes its grammatical features and lexical patterns to determine whether the poem is original.

[0482] The system allows users to quickly assess how original their creations are and whether they are likely to be copyrightable.

[0483] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0484] Step 1: Receiving content

[0485] Input: User-uploaded content (text, images, audio, etc.).

[0486] How it works: The device receives content entered by the user and sends it to the server. For example, if the user uploads "mypoem.txt", the device will send this file to the server via an HTTP request.

[0487] Output: You get the content transferred to the server.

[0488] Step 2: Data storage and format conversion

[0489] Input: The content received by the server.

[0490] How it works: The server stores the received content in temporary storage and converts it to the required format if necessary: ​​if it's a text file, it's encoded in UTF-8, and if it's an image file, it's converted to JPEG.

[0491] Output: The formatted data is obtained for analysis.

[0492] Step 3: Feature extraction

[0493] Input: The reformatted content.

[0494] How it works: The server applies an appropriate generative AI model to extract features such as grammatical features and lexical patterns in the case of text, color and shape patterns in the case of images, and pitch and rhythm in the case of audio. For example, it uses GPT-3 to convert the grammatical and lexical patterns of "mypoem.txt" into numerical data.

[0495] Output: Numerical data of extracted features is obtained.

[0496] Step 4: Creativity Assessment

[0497] Input: Numerical data of extracted features.

[0498] How it works: The server runs a proprietary evaluation algorithm and compares the extracted features with predefined benchmarks and existing AI-generated patterns. This evaluation determines the degree to which the feature reflects human creativity.

[0499] Output: Numerical data is obtained as the evaluation result of creativity.

[0500] Step 5: Generate classification results

[0501] Input: Numerical data of creativity assessment results.

[0502] How it works: The server makes a final decision based on the evaluation results, generating a decision such as "human creativity is recognized" or "completely AI-generated."

[0503] Output: The generated discrimination results are obtained.

[0504] Step 6: Send the results

[0505] Input: Generated discrimination results.

[0506] Operation: The server sends the result of the determination to the user terminal as an HTTP response.

[0507] Output: The discrimination result is sent to the user's terminal.

[0508] Step 7: Displaying the discrimination results

[0509] Input: The result of the determination received from the server.

[0510] Operation: The device displays the received judgment results to the user. For example, a result such as "This poem is recognized as a work of human creativity and is likely to be copyrightable" is displayed on the interface.

[0511] Output: The results of the discrimination are displayed to the user.

[0512] (Application example 1)

[0513] 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."

[0514] In recent years, the amount of content generated using generative AI has increased, making it important to accurately determine its copyright. With conventional systems, determining the copyright of content was extremely difficult and required a great deal of time and effort. Furthermore, creators lacked objective criteria for evaluating the originality of their own work, which led to risks when commercializing or releasing it. These issues made it difficult for creators to release their content with confidence.

[0515] 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.

[0516] In this invention, the server includes means for receiving generated content, means for extracting features from the received content, means for evaluating human creativity based on the extracted features and generating a judgment result, means for notifying the user of the judgment result, means for providing an interface for users to upload their own content, and means for transferring the uploaded content to the server. This allows creators to objectively evaluate the originality of their own content and quickly and accurately determine copyright. Furthermore, commercialization and publication can be carried out with peace of mind based on the evaluation results.

[0517] The "means for receiving generated content" is a function for transmitting content such as text, images, and audio provided by a user to a server.

[0518] "Means for extracting features from received content" refers to a function that identifies important features from the content received by the server and extracts them as data.

[0519] The "means for evaluating human creativity based on extracted features and generating a judgment result" is a function that analyzes the extracted features to evaluate the extent to which human creativity is reflected, and judges the originality of the content based on the evaluation result.

[0520] The "means for notifying the user of the discrimination results" is a function for notifying the user of the evaluation results and discrimination results generated by the server.

[0521] The "means for providing an interface for users to upload their own content" is a function for providing an interface for users to upload content to the server.

[0522] The "means for transferring uploaded content to the server" is a function for transmitting content uploaded by a user through an interface to the server.

[0523] The "means for converting content into a predetermined format" is a function for converting received content into a predetermined format so that it can be easily analyzed.

[0524] "Means for receiving and processing at least one of text, images, and audio as content" refers to the function of receiving content in a wide variety of formats (text, images, audio) and analyzing and processing it.

[0525] "Means for extracting features using a generative AI model" refers to a function that utilizes an AI model to extract features of content.

[0526] "Means of inputting prompt sentences to a generative AI model and using the results for evaluation" refers to a function that uses prompt sentences to an AI model to extract content features and utilizes the results for evaluation.

[0527] The present invention relates to a system for determining copyright of content generated using a generative AI model, and specific embodiments thereof will be described below.

[0528] System configuration

[0529] This system consists of three elements: a server, a terminal, and a user. The server plays a central role in analyzing content, extracting its features, evaluating human creativity, and generating a judgment result. The terminal provides an interface for users to upload content and has the auxiliary function of transferring uploaded content to the server.

[0530] Hardware used

[0531] Server: A high-performance data analysis server is required, and this server should have enough computing resources (CPU, GPU) to run the generative AI model.

[0532] Client terminal: A device operated by a user, such as a smartphone or PC, that requires an internet connection.

[0533] Software used

[0534] Flask: A Python web framework that facilitates communication between servers and clients.

[0535] OpenAI API: An API for extracting content features using generative AI models (e.g., OpenAI's GPT-3).

[0536] Program processing

[0537] Content Upload

[0538] A user uploads their own content using a terminal. The terminal receives the content, such as text, images, and audio, through a user interface with a file selection field and a send button. The terminal then transfers the uploaded content to the server.

[0539] Feature extraction

[0540] The server analyzes the received content and uses a generative AI model to extract key features: for example, for text content, it extracts grammatical features and lexical patterns, for image content, it analyzes color and shape patterns, and for audio content, it analyzes pitch and rhythm.

[0541] Human creativity evaluation and discrimination result generation

[0542] Based on the extracted features, the server applies a unique evaluation algorithm to determine the degree to which human creativity is reflected in the work, and generates a judgment result based on the evaluation results and notifies the user.

[0543] Specific examples

[0544] For example, if a user uploads a poem called "mypoem.txt," the following prompt sentence is input to the generative AI model to extract features:

[0545] Example prompt:

[0546] Please provide a list of key features of the following text:

[0547] Everything in this world is reflected in my heart

[0548] The whisper of the wind, the song of the birds, the shape of the clouds

[0549] Feeling the breath of life in silence

[0550] They resonate deeply with me

[0551] The server sends this prompt to the generative AI model, which then uses a proprietary algorithm to evaluate it based on the returned features. The evaluation result is displayed to the user, saying, "This poem recognizes human creativity and is likely to be copyrightable."

[0552] As described above, the present invention enables users to objectively evaluate the originality of their own content and quickly and accurately determine copyright. Furthermore, based on the evaluation results, users can commercialize and release their content with peace of mind.

[0553] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0554] Step 1:

[0555] A user uploads content using a device. The user selects a content file, such as text, image, or audio, through the user interface on the device and clicks the send button. The input is the content file selected by the user, and the output is the file sent from the device to the server.

[0556] Step 2:

[0557] The terminal transfers the uploaded content to the server. The terminal receives the selected content file and transfers it to the server as a data packet. The input is the content file uploaded by the user, and the output is that the file is transferred to the server.

[0558] Step 3:

[0559] The server analyzes the received content and extracts features using a generative AI model (e.g., OpenAI's API). In this step, the content is sent to the generative AI model using a specific prompt. The input is the transferred content file and the prompt, and the output is the feature data extracted from the generative AI model.

[0560] Step 4:

[0561] The server analyzes the extracted features and applies a unique evaluation algorithm to evaluate human creativity. The evaluation algorithm quantifies the extracted features and generates an evaluation score based on these values. The input is the extracted feature data, and the output is the evaluation score.

[0562] Step 5:

[0563] The server generates a judgment result based on the evaluation score. In this step, the evaluation score is analyzed and converted into a concrete result such as "human creativity recognized" or "AI generated" based on a reference value. The input is the evaluation score, and the output is the judgment result.

[0564] Step 6:

[0565] The server notifies the user of the classification result. The server sends the generated classification result to the terminal, which displays it to the user. The input is the classification result, and the output is the evaluation result displayed on the terminal.

[0566] 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.

[0567] This invention combines a system that uses generative AI to determine copyright of content generated by the system with an emotion engine that recognizes user emotions. This system is composed of a server, a terminal, and a user. The cooperation of these elements enables advanced copyright determination that links the evaluation of generated content with user emotion data. The functions and roles of each step are explained in detail below.

[0568] Server-side processing

[0569] 1. Content reception and data analysis

[0570] The server first receives the content (text, images, audio, etc.) provided by the user terminal. This received content is saved as data to be analyzed. At this stage, the received data is converted into a predetermined format as needed.

[0571] 2. Feature Extraction

[0572] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, for text content, grammatical features and lexical patterns are analyzed, for image content, color tones and shape patterns are analyzed, and for audio content, pitch range and rhythm are analyzed.

[0573] 3. Human Creativity Assessment

[0574] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0575] 4. Collecting and analyzing emotional data

[0576] The server collects emotional data from the user's reactions and inputs and analyzes it using an emotion engine. The emotion engine adjusts the parameters of the generative AI model based on the user's emotional data. For example, if the user reacts with surprise or emotion, the engine changes the settings of the generative AI model based on that emotional data, promoting the generation of more original content.

[0577] 5. Sending the results of the classification

[0578] Finally, the server generates a judgment result based on the evaluation results and emotion data and sends it to the user's device. The judgment result may include, for example, "Human creativity is recognized" or "Completely AI-generated."

[0579] Terminal side processing

[0580] 1. User interface provision

[0581] The terminal provides an interface for users to upload content, including a file selection field and a submit button.

[0582] 2. Uploading Content

[0583] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[0584] 3. Entering Emotional Reactions

[0585] The device also provides an interface for receiving user input, including text input, facial recognition cameras, and voice input, for reacting to content and receiving emotional input.

[0586] 4. Display of discrimination results

[0587] The device receives the results from the server and displays them to the user, who can then check the results and receive detailed feedback based on the emotion data.

[0588] Specific examples

[0589] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[0590] 1. Terminal: The user uploads a text file of a poem.

[0591] 2. Server: Receives the poem text, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[0592] 3. Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[0593] 4. Server: Collects user emotion data, analyzes it using the emotion engine, and adjusts the parameters of the generative AI model.

[0594] 5. Terminal: Receives the result of the judgment and displays to the user, "This poem is a recognition of human creativity and is likely to be copyrightable."

[0595] This system allows users to assess the copyright risk of their own creative works, and by optimizing the parameters of the generative AI based on emotional data, it becomes possible to create more valuable content.

[0596] The processing flow will be explained below.

[0597] Step 1:

[0598] The terminal provides the user with an interface for uploading content, through which the user selects his / her content file and clicks the upload button.

[0599] Step 2:

[0600] The terminal transmits the content file selected by the user to the server, using an HTTP request to transmit the file data.

[0601] Step 3:

[0602] The server converts the received content into a specific format, for example, if it is text, it reads the text file as string data.

[0603] Step 4:

[0604] The server applies a generative AI model to extract features from the received content. For example, in the case of text content, it analyzes grammar, vocabulary selection, sentence structure, etc., and extracts these features as numerical data.

[0605] Step 5:

[0606] The server inputs the extracted features into an evaluation algorithm to calculate the degree to which human creativity is reflected, for example, by comparing it with known AI-generated patterns and calculating an originality score.

[0607] Step 6:

[0608] The server receives the user's emotional response data and analyzes it using an emotion engine, which extracts emotional data from the user's input and reactions and adjusts the parameters of the generative AI model based on this data.

[0609] Step 7:

[0610] The server generates a judgment result based on the evaluation results and emotion data. The judgment result may include information such as "Human creativity is recognized" or "Completely AI-generated."

[0611] Step 8:

[0612] The server sends the generated classification results to the user's device, where it returns the analysis results in JSON format using an HTTP response.

[0613] Step 9:

[0614] The device displays the received results to the user, who can then check the results and receive detailed feedback based on the emotion data.

[0615] Step 10:

[0616] The device provides interfaces such as text input, a facial recognition camera, and voice input to continuously collect user emotional data. This data is used for future judgments and to further optimize the generative AI model.

[0617] Example 2

[0618] 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."

[0619] In conventional content generation systems, it is difficult to determine the degree to which human creativity is reflected. Furthermore, there is no mechanism for incorporating user emotions as evaluation data, making it difficult to improve the quality of content generation. Furthermore, complex analysis is required to accommodate different types of content (text, images, audio, etc.), and establishing comprehensive evaluation criteria is also an important challenge.

[0620] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0621] In this invention, the server includes means for acquiring generated data, means for deriving feature information from the acquired data, means for evaluating human creativity based on the derived feature information and generating a judgment result, means for notifying the user of the judgment result, means for collecting emotional information from the user, and means for analyzing the collected emotional information and adjusting the parameters of the generative AI model. This enables comprehensive copyright determination that combines judgment of human creativity and user emotional data.

[0622] "Generated data" refers to content (text, images, audio, etc.) generated by users or AI.

[0623] "Means for obtaining" refers to a method or mechanism for receiving data generated from a user's terminal or other input device.

[0624] "Feature information" refers to analyzable elements such as grammar, vocabulary, color tone, and rhythm that are extracted from the generated data.

[0625] "Means for deriving" refers to methods or algorithms for extracting feature information from the generated data.

[0626] "Means of evaluation" refers to methods and algorithms for evaluating human creativity based on extracted feature information.

[0627] "Judgment result" refers to the judgment result regarding human creativity that is generated based on characteristic information and evaluation results.

[0628] "Means for notifying" refers to a method or mechanism for communicating the determination result to the user.

[0629] "Emotional information" refers to data that indicates the user's reaction or emotional state (text comments, facial expression data, voice, etc.).

[0630] "Means for collecting" refers to a method or mechanism for acquiring emotion information from a user.

[0631] "Means for analyzing" refers to a method or mechanism for analyzing collected emotional information and classifying or evaluating its content.

[0632] "Generative AI model" refers to an artificial intelligence algorithm or program that evaluates and generates generated content.

[0633] "Means for adjusting parameters" refers to methods or mechanisms for optimizing the quality or characteristics of generated content by modifying the settings or components of a generative AI model.

[0634] This invention relates to an evaluation method that takes into account user emotions in a content copyright determination system that uses a generative AI model. This system is composed of a server, a terminal, and a user, and by cooperating with each other, it is possible to perform advanced copyright determination.

[0635] Hardware and software used

[0636] The server is equipped with a high-performance processor and a large amount of memory. It also has a database (e.g., MySQL) and various analysis software (e.g., a natural language processing library using Python, or a machine learning library such as TensorFlow or PyTorch) installed. The server receives, analyzes, evaluates, and notifies users of various types of data.

[0637] The device provides an interface for users to upload content, either through a browser-based application or a dedicated mobile application, which includes a file selection button, a send button, and emotional response input fields (e.g., text, camera, voice input).

[0638] Users send the content they generate to the server via their devices, and contribute to the evaluation of the generative AI model by inputting their emotional responses into the interface.

[0639] Specific methods for data processing and calculation

[0640] 1. Content Reception: The server receives content (text, images, audio, etc.) sent from the user's device. This is done using an HTTP POST request, etc. The received data is converted into the appropriate format within the server.

[0641] 2. Feature extraction: The server applies generative AI models to extract feature information from the incoming content. For example, for text content, it uses natural language processing (NLP) techniques to analyze grammar and vocabulary patterns. For image content, it uses computer vision techniques to extract color and shape patterns. For audio content, it analyzes pitch and rhythm.

[0642] 3. Human Creativity Evaluation: The server evaluates the human creativity based on the extracted feature information. This evaluation is performed by comparing it with predefined benchmarks and existing AI-generated patterns. The evaluation results are stored in a database as numerical data.

[0643] 4. Emotional data collection and analysis: The server collects emotional information from the user's emotional reactions and inputs and analyzes it using an emotion engine. For example, if the user expresses emotion or surprise, the parameters of the generative AI model are adjusted based on that emotional data.

[0644] 5. Generation and notification of judgment results: The server combines the evaluation results and emotional data to generate a judgment result. This results in a judgment such as "Human creativity is recognized" or "It is highly likely that the result was completely generated by AI." The judgment result is sent to the device via HTTP response, WebSocket, etc.

[0645] Specific examples

[0646] For example, if a user submits a poem they wrote, "mypoem.txt," to the classification system, the specific steps are as follows:

[0647] 1. Device: The user uploads a poem they wrote, "mypoem.txt." The device sends this file to the server.

[0648] 2. Server: Receives the text file of the poem, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts and saves them as numerical data.

[0649] 3. Server: Evaluates originality based on the extracted features and determines whether the work "reflects human creativity."

[0650] 4. Server: Collects user emotional input data (e.g., comments of surprise or excitement) and analyzes it using the emotion engine. Based on this data, the parameters of the generative AI model are adjusted.

[0651] 5. Server and terminal: Makes the final decision and notifies the user that "this poem is recognized as a work of human creativity and is likely to be copyrightable."

[0652] Prompt Sentence Examples

[0653] Below are some example prompts to input to a generative AI model:

[0654] "Evaluate the poem I have created and determine how well it reflects human creativity."

[0655] "Tell me if this image is different from existing AI-generated patterns."

[0656] "Please determine whether the provided audio file is original and return the result."

[0657] This allows users to assess the copyright risk of their own content, optimize the parameters of the generation AI based on emotional data, and generate more valuable content.

[0658] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0659] Step 1: Upload your content

[0660] Input: User-generated content (e.g., mypoem.txt)

[0661] How it works: The user selects their own content using the device's user interface and clicks the upload button. The device then sends the selected file to the server using an HTTP POST request or similar.

[0662] Output: Content data sent to the server

[0663] Step 2: Receiving content and formatting

[0664] Input: Content data received by the server

[0665] How it works: The server converts the content it receives from the device into the appropriate format, for example, converting text data into UTF-8 encoding, or converting image data into a standard image format.

[0666] Output: Content data converted into a format that can be processed internally

[0667] Step 3: Feature extraction

[0668] Input: Format-converted content data

[0669] How it works: The server uses a generative AI model to extract features from content. For text content, it extracts grammatical features and lexical patterns. For image content, it extracts color and shape patterns. For audio content, it analyzes the pitch and rhythm.

[0670] Output: Extracted feature data

[0671] Step 4: Human Creativity Assessment

[0672] Input: Extracted feature data

[0673] How it works: The server applies a unique evaluation algorithm to the stored feature data to assess the degree to which human creativity is reflected in the result, by comparing it with predefined benchmarks and existing AI-generated patterns.

[0674] Output: Evaluation result data

[0675] Step 5: Collecting sentiment data

[0676] Input: User emotional responses and inputs

[0677] How it works: The device provides an interface that accepts user emotional input. Users input their emotional responses through text input, a facial recognition camera, or voice input. The device then sends this to a server.

[0678] Output: Emotion data sent to the server

[0679] Step 6: Analyze the sentiment data

[0680] Input: Emotion data sent to the server

[0681] How it works: The server uses an emotion engine to analyze emotion data and classify it into various emotion categories (e.g., surprise, emotion, sadness). Based on the analysis results, it adjusts the parameters of the generative AI model.

[0682] Output: Analysis result data and adjusted generative AI model parameters

[0683] Step 7: Generate classification results

[0684] Input: Evaluation result data and analysis result data

[0685] How it works: The server combines the evaluation result data and the emotion data to generate a final judgment result, such as "This poem is highly likely to be copyrightable because it demonstrates human creativity."

[0686] Output: Classification result data

[0687] Step 8: Notification of the results

[0688] Input: Classification result data

[0689] Operation: The server sends the generated discrimination result data to the terminal. This transmission uses a communication method such as HTTP response or WebSocket. The terminal displays the received discrimination result on the user interface.

[0690] Output: The result displayed to the user

[0691] (Application example 2)

[0692] 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."

[0693] In recent years, determining copyright for content created using generative AI has become an important issue. However, current technology makes it difficult to accurately evaluate the extent to which content generated by generative AI reflects human creativity. Furthermore, there is no system that evaluates content while reflecting users' emotional reactions, resulting in incomplete evaluation of content value. This makes it difficult to properly evaluate original content and generate content that reflects user satisfaction.

[0694] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0695] In this invention, the server includes means for receiving generated content, means for extracting features from the received content, means for evaluating human creativity based on the extracted features and generating a discrimination result, means for recognizing user emotions and analyzing the data, means for adjusting parameters of the generative AI model based on the analyzed emotion data, and means for notifying the user of the discrimination result, thereby enabling accurate copyright discrimination of generated content and evaluation based on the user's emotional response.

[0696] "Generated content" is digital media, such as text, images, and audio, created using a generative AI model.

[0697] The "receiving means" refers to the function of transmitting content data from the user terminal to the server and receiving it on the server side.

[0698] "Means for extracting features" refers to technology that identifies important information and patterns from received content and executes a process to quantify them.

[0699] The "means for evaluating human creativity" is a system for measuring the degree to which generated content reflects original human ideas and ingenuity based on the extracted features.

[0700] The "means for generating a judgment result" is a function that determines whether the content is created by a human or generated by AI based on the evaluation results and documents that conclusion.

[0701] "Means for recognizing user emotions" refers to technology that detects the emotions (joy, surprise, sadness, etc.) expressed by a user when viewing content from facial expressions, voice, etc.

[0702] "Means for analyzing emotion data" refers to the process of analyzing the recognized emotion data of a user and converting the results into an understandable format.

[0703] The "means for adjusting the parameters of the generative AI model" is a system that changes the operating conditions and settings of the generative AI model based on analyzed emotional data, promoting more appropriate content generation.

[0704] The "means for notifying the evaluation result" is a function for displaying or reporting the evaluation result of the generated content to the user.

[0705] This invention combines a system that uses generative AI to determine copyright of content generated by generative AI with an emotion engine that recognizes user emotions. This system is composed of a server, a terminal, and a user. By cooperating with each element, advanced copyright determination is possible by linking the evaluation of generated content with user emotion data.

[0706] Server-side processing

[0707] Content reception and data analysis

[0708] The server first receives the content (text, images, audio, etc.) provided by the user's device. This received content is then saved as data to be analyzed. The hardware used is a cloud service such as AWS, and the software is Python and TensorFlow. The received data is converted into a specified format as needed.

[0709] Feature extraction

[0710] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, grammatical features and lexical patterns are analyzed for text content, color tones and shape patterns for image content, and pitch range and rhythm for audio content.

[0711] Human creativity assessment

[0712] Based on the extracted features, a unique evaluation algorithm is applied to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0713] Collecting and analyzing emotional data

[0714] The server collects emotional data from the user's reactions and inputs and analyzes it using an emotion engine. The emotion engine adjusts the parameters of the generative AI model based on the user's emotional data. For example, if the user reacts with surprise or emotion, the engine changes the settings of the generative AI model based on that emotional data, promoting the generation of more original content.

[0715] Sending the results of the judgment

[0716] Finally, the server generates a judgment result based on the evaluation results and emotion data and sends it to the user's device. The judgment result may include, for example, "Human creativity is recognized" or "Completely AI-generated."

[0717] Terminal side processing

[0718] User interface provided

[0719] The terminal provides an interface for users to upload content, including a file selection field and a send button. To collect emotion data, a smartphone or PC equipped with a facial recognition camera and voice input function is used.

[0720] Uploading content

[0721] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[0722] Entering emotional responses

[0723] The device also provides an interface for receiving user reactions to content and emotional input, including text input, facial recognition camera, voice input, etc. For example, a facial recognition camera can be used to analyze the user's facial expressions and collect them as emotional data.

[0724] Display of discrimination results

[0725] The device receives the results from the server and displays them to the user, who can then check the results and receive detailed feedback based on the emotion data.

[0726] Specific examples

[0727] For example, if a user uploads a self-made video "sample_video.mp4," the following steps are executed: the user uploads the video, uses the emotion engine to collect emotional data while watching, and adjusts the parameters of the generative AI model based on that data. The server then generates a classification result and provides feedback to the user.

[0728] Example prompt sentence:

[0729] Upload the video "sample_video.mp4", analyze user sentiment, determine copyright status, and display the results.

[0730] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0731] Step 1:

[0732] A user uploads a video.

[0733] Input: The user selects a video file from their device and clicks the upload button.

[0734] Output: The video file is sent from the user device to the server.

[0735] Specific operation: The upload interface will be displayed on the user's device screen, and once a video file is selected, the file will be sent to the server via the HTTPS protocol.

[0736] Step 2:

[0737] The server receives and stores the video file.

[0738] Input: The video file sent from the user's device.

[0739] Output: The video file will be saved in the specified folder on the server.

[0740] What it does: The server-side script receives the video file and saves it to local storage or cloud storage.

[0741] Step 3:

[0742] The server converts the video file into a predetermined format for analysis.

[0743] Input: Video files stored on the server.

[0744] Output: Video data converted into the specified format.

[0745] What it does: Use a media conversion tool such as FFmpeg to convert the video file into a format that can be analyzed.

[0746] Step 4:

[0747] The server extracts the features.

[0748] Input: Video data converted to a specified format.

[0749] Output: Video features (e.g., image information for each frame, audio waveform data, etc.).

[0750] Specific operation: Using libraries such as OpenCV and Librosa, frames are extracted from video and features are extracted from audio.

[0751] Step 5:

[0752] The server evaluates human creativity based on the extracted features and generates a discrimination result.

[0753] Input: Extracted video feature data.

[0754] Output: Evaluation and discrimination results of human creativity.

[0755] Specific operation: Using an evaluation algorithm, feature data is analyzed and, based on the results, a determination is made as to whether the voice is AI-generated or human-generated.

[0756] Step 6:

[0757] The server collects and analyzes the user's emotional data.

[0758] Input: Emotion data (facial expressions, voice, etc.) collected from the user device.

[0759] Output: Parsed emotion data (e.g. emotion type and intensity).

[0760] Specific operation: Collects facial expressions and voice data while the user is watching a video, and performs emotion analysis using the Emotion API.

[0761] Step 7:

[0762] The server adjusts the parameters of the generative AI model based on the analyzed emotional data.

[0763] Input: Parsed emotion data.

[0764] Output: The adjusted parameters of the generative AI model.

[0765] Specific operation: Emotional data is fed back to the generative AI model, and parameters are automatically adjusted.

[0766] Step 8:

[0767] The server notifies the user terminal of the determination result.

[0768] Input: Evaluation results and classification results based on emotion data.

[0769] Output: The final evaluation result displayed on the user's terminal.

[0770] Specific operation: The evaluation results are sent to the user's device and displayed through the interface.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] [Third embodiment]

[0775] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0776] 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.

[0777] 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).

[0778] 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.

[0779] 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.

[0780] 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).

[0781] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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."

[0787] This invention relates to a system that determines the copyright of content generated using generative AI. This system is broadly composed of three elements: a server, a terminal, and a user. The functions and roles of each step are explained in detail below.

[0788] Server-side processing

[0789] 1. Content reception and data analysis

[0790] The server first receives the content (text, images, audio, etc.) provided by the user terminal. This received content is saved as data to be analyzed. At this stage, the received data is converted into a predetermined format as needed.

[0791] 2. Feature Extraction

[0792] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, for text content, grammatical features and lexical patterns are analyzed, for image content, color tones and shape patterns are analyzed, and for audio content, pitch range and rhythm are analyzed.

[0793] 3. Human Creativity Assessment

[0794] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0795] 4. Sending the results of the classification

[0796] Finally, the server generates a judgment result based on the evaluation results and sends it to the user's device. The judgment result may include, for example, a judgment such as "Human creativity is recognized" or "Completely AI-generated."

[0797] Terminal side processing

[0798] 1. User interface provision

[0799] The terminal provides an interface for users to upload content, including a file selection field and a submit button.

[0800] 2. Uploading Content

[0801] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[0802] 3. Display of discrimination results

[0803] The device will then display the results of the assessment received from the server to the user. For example, it may display a specific result such as, "This poem is a testament to human creativity and is likely to be copyrightable." Based on this information, users can commercialize and publish their own content with peace of mind.

[0804] Specific examples

[0805] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[0806] 1. Terminal: The user uploads a text file of a poem.

[0807] 2. Server: Receives the poem text, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[0808] 3. Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[0809] 4. Terminal: Receives the result of the judgment and displays to the user, "This poem is a recognition of human creativity and is likely to be copyrightable."

[0810] This system allows users to assess the copyright risk of their own creative works, allowing them to commercialize and publish them with peace of mind.

[0811] The processing flow will be explained below.

[0812] Step 1:

[0813] The terminal provides the user with an interface for uploading content, through which the user selects his / her content file and clicks the upload button.

[0814] Step 2:

[0815] The terminal transmits the content file selected by the user to the server, using an HTTP request to transmit the file data.

[0816] Step 3:

[0817] The server converts the received content into a specific format, for example, if it is text, it reads the text file as string data.

[0818] Step 4:

[0819] The server applies a generative AI model to extract features from the received content. For text content, it analyzes grammar, vocabulary selection, sentence structure, etc., and extracts these features as numerical data.

[0820] Step 5:

[0821] The server inputs the extracted features into an evaluation algorithm to calculate the degree to which human creativity is reflected, for example, by comparing it with known AI-generated patterns and calculating an originality score.

[0822] Step 6:

[0823] The server generates a judgment result based on the evaluation results, which may include information such as "human creativity is recognized" or "completely AI-generated."

[0824] Step 7:

[0825] The server sends the generated classification results to the user's device, where it returns the analysis results in JSON format using an HTTP response.

[0826] Step 8:

[0827] The terminal displays the received results to the user, who can then check the results and recognize to what extent their content reflects human creativity.

[0828] Example 1

[0829] 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."

[0830] In recent years, the amount of content created by generative AI has skyrocketed, but it is difficult to determine the extent to which this content reflects human creativity. Furthermore, copyright certification is required for generated content, but there is no efficient and accurate method for this evaluation. Therefore, there is a need for a system that can accurately evaluate the originality and copyrightability of generative AI content.

[0831] 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.

[0832] In this invention, the server includes means for receiving the generated information, means for extracting features from the received information, means for evaluating human creativity based on the extracted features and generating a judgment result, means for notifying the user of the judgment result, and means for providing an interface for the user to send information. This makes it possible to efficiently evaluate the originality of the generated content and quickly and accurately determine copyright.

[0833] "Generated information" refers to data such as text, images, and audio created by a generative AI model or other information generation means.

[0834] "Features" refers to numerical data that indicate the characteristics of information, such as grammatical features, vocabulary patterns, color tones, shape patterns, ranges, and rhythms extracted from the generated information.

[0835] "Human creativity" refers to the originality and unique patterns of expression that humans display in information and works. The originality of works is evaluated based on this creativity.

[0836] The "discrimination result" refers to the result of evaluating the degree to which the information generated based on the extracted features reflects human creativity. Specifically, it includes a judgment on whether human creativity is recognized.

[0837] "User" means any person or entity that receives the evaluation of information generated using this system.

[0838] "Means for receiving" refers to a function for sending information generated by a user to a server. An example would be a function for receiving data via an HTTP request.

[0839] "Interface" refers to the operation screens and tools that allow users to send generated information to the system, including the file upload button and the send button.

[0840] "Predetermined format" refers to the data format required to properly parse the received information, e.g., text data is encoded in UTF-8, image data is converted to JPEG format, etc.

[0841] This invention relates to a system that determines the copyright of content generated using a generation AI. This system is broadly composed of three elements: a server, a terminal, and a user. Specific embodiments of each element are described below.

[0842] Server-side processing

[0843] The server first receives the content (text, images, audio, etc.) provided by the user terminal. The received content is saved as data to be analyzed, and at this stage the received data is converted to a specified format as needed (for example, a text file is encoded into UTF-8, an image file is converted into JPEG format).

[0844] The server then applies algorithms from a generative AI model (e.g., GPT-3 for text, Vision Transformer for images) to extract features from the received content: grammatical features and lexical patterns for text content, color and shape patterns for image content, and pitch range and rhythm for audio content.

[0845] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0846] Finally, the server generates a judgment result based on the evaluation results and sends it to the user's device. The judgment result may include a judgment such as "human creativity is recognized" or "completely AI-generated."

[0847] Terminal side processing

[0848] The terminal provides an interface for users to upload content. This interface includes a file selection field and a submit button. The user uses this interface to select their content and clicks the upload button. The terminal then sends the selected content file to the server.

[0849] The device then displays the results of the assessment received from the server to the user. For example, it may display a specific result such as, "This poem is a testament to human creativity and is likely to be copyrightable." Based on this information, users can commercialize and publish their own content with peace of mind.

[0850] Specific examples

[0851] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[0852] Terminal: The user uploads a text file of the poem.

[0853] Server: Receives the text of the poem, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[0854] Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[0855] Terminal: Receives the result of the judgment and displays to the user, "This poem recognizes human creativity and is likely to be copyrightable."

[0856] Prompt Sentence Examples

[0857] Describe a system that receives a user-provided text file of a poem, 'mypoem.txt', and analyzes its grammatical features and lexical patterns to determine whether the poem is original.

[0858] The system allows users to quickly assess how original their creations are and whether they are likely to be copyrightable.

[0859] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0860] Step 1: Receiving content

[0861] Input: User-uploaded content (text, images, audio, etc.).

[0862] How it works: The device receives content entered by the user and sends it to the server. For example, if the user uploads "mypoem.txt", the device will send this file to the server via an HTTP request.

[0863] Output: You get the content transferred to the server.

[0864] Step 2: Data storage and format conversion

[0865] Input: The content received by the server.

[0866] How it works: The server stores the received content in temporary storage and converts it to the required format if necessary: ​​if it's a text file, it's encoded in UTF-8, and if it's an image file, it's converted to JPEG.

[0867] Output: The formatted data is obtained for analysis.

[0868] Step 3: Feature extraction

[0869] Input: The reformatted content.

[0870] How it works: The server applies an appropriate generative AI model to extract features such as grammatical features and lexical patterns in the case of text, color and shape patterns in the case of images, and pitch and rhythm in the case of audio. For example, it uses GPT-3 to convert the grammatical and lexical patterns of "mypoem.txt" into numerical data.

[0871] Output: Numerical data of extracted features is obtained.

[0872] Step 4: Creativity Assessment

[0873] Input: Numerical data of extracted features.

[0874] How it works: The server runs a proprietary evaluation algorithm and compares the extracted features with predefined benchmarks and existing AI-generated patterns. This evaluation determines the degree to which the feature reflects human creativity.

[0875] Output: Numerical data is obtained as the evaluation result of creativity.

[0876] Step 5: Generate classification results

[0877] Input: Numerical data of creativity assessment results.

[0878] How it works: The server makes a final decision based on the evaluation results, generating a decision such as "human creativity is recognized" or "completely AI-generated."

[0879] Output: The generated discrimination results are obtained.

[0880] Step 6: Send the results

[0881] Input: Generated discrimination results.

[0882] Operation: The server sends the result of the determination to the user terminal as an HTTP response.

[0883] Output: The discrimination result is sent to the user's terminal.

[0884] Step 7: Displaying the discrimination results

[0885] Input: The result of the determination received from the server.

[0886] Operation: The device displays the received judgment results to the user. For example, a result such as "This poem is recognized as a work of human creativity and is likely to be copyrightable" is displayed on the interface.

[0887] Output: The results of the discrimination are displayed to the user.

[0888] (Application example 1)

[0889] 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."

[0890] In recent years, the amount of content generated using generative AI has increased, making it important to accurately determine its copyright. With conventional systems, determining the copyright of content was extremely difficult and required a great deal of time and effort. Furthermore, creators lacked objective criteria for evaluating the originality of their own work, which led to risks when commercializing or releasing it. These issues made it difficult for creators to release their content with confidence.

[0891] 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.

[0892] In this invention, the server includes means for receiving generated content, means for extracting features from the received content, means for evaluating human creativity based on the extracted features and generating a judgment result, means for notifying the user of the judgment result, means for providing an interface for users to upload their own content, and means for transferring the uploaded content to the server. This allows creators to objectively evaluate the originality of their own content and quickly and accurately determine copyright. Furthermore, commercialization and publication can be carried out with peace of mind based on the evaluation results.

[0893] The "means for receiving generated content" is a function for transmitting content such as text, images, and audio provided by a user to a server.

[0894] "Means for extracting features from received content" refers to a function that identifies important features from the content received by the server and extracts them as data.

[0895] The "means for evaluating human creativity based on extracted features and generating a judgment result" is a function that analyzes the extracted features to evaluate the extent to which human creativity is reflected, and judges the originality of the content based on the evaluation result.

[0896] The "means for notifying the user of the discrimination results" is a function for notifying the user of the evaluation results and discrimination results generated by the server.

[0897] The "means for providing an interface for users to upload their own content" is a function for providing an interface for users to upload content to the server.

[0898] The "means for transferring uploaded content to the server" is a function for transmitting content uploaded by a user through an interface to the server.

[0899] The "means for converting content into a predetermined format" is a function for converting received content into a predetermined format so that it can be easily analyzed.

[0900] "Means for receiving and processing at least one of text, images, and audio as content" refers to the function of receiving content in a wide variety of formats (text, images, audio) and analyzing and processing it.

[0901] "Means for extracting features using a generative AI model" refers to a function that utilizes an AI model to extract features of content.

[0902] "Means of inputting prompt sentences to a generative AI model and using the results for evaluation" refers to a function that uses prompt sentences to an AI model to extract content features and utilizes the results for evaluation.

[0903] The present invention relates to a system for determining copyright of content generated using a generative AI model, and specific embodiments thereof will be described below.

[0904] System configuration

[0905] This system consists of three elements: a server, a terminal, and a user. The server plays a central role in analyzing content, extracting its features, evaluating human creativity, and generating a judgment result. The terminal provides an interface for users to upload content and has the auxiliary function of transferring uploaded content to the server.

[0906] Hardware used

[0907] Server: A high-performance data analysis server is required, and this server should have enough computing resources (CPU, GPU) to run the generative AI model.

[0908] Client terminal: A device operated by a user, such as a smartphone or PC, that requires an internet connection.

[0909] Software used

[0910] Flask: A Python web framework that facilitates communication between servers and clients.

[0911] OpenAI API: An API for extracting content features using generative AI models (e.g., OpenAI's GPT-3).

[0912] Program processing

[0913] Content Upload

[0914] A user uploads their own content using a terminal. The terminal receives the content, such as text, images, and audio, through a user interface with a file selection field and a send button. The terminal then transfers the uploaded content to the server.

[0915] Feature extraction

[0916] The server analyzes the received content and uses a generative AI model to extract key features: for example, for text content, it extracts grammatical features and lexical patterns, for image content, it analyzes color and shape patterns, and for audio content, it analyzes pitch and rhythm.

[0917] Human creativity evaluation and discrimination result generation

[0918] Based on the extracted features, the server applies a unique evaluation algorithm to determine the degree to which human creativity is reflected in the work, and generates a judgment result based on the evaluation results and notifies the user.

[0919] Specific examples

[0920] For example, if a user uploads a poem called "mypoem.txt," the following prompt sentence is input to the generative AI model to extract features:

[0921] Example prompt:

[0922] Please provide a list of key features of the following text:

[0923] Everything in this world is reflected in my heart

[0924] The whisper of the wind, the song of the birds, the shape of the clouds

[0925] Feeling the breath of life in silence

[0926] They resonate deeply with me

[0927] The server sends this prompt to the generative AI model, which then uses a proprietary algorithm to evaluate it based on the returned features. The evaluation result is displayed to the user, saying, "This poem recognizes human creativity and is likely to be copyrightable."

[0928] As described above, the present invention enables users to objectively evaluate the originality of their own content and quickly and accurately determine copyright. Furthermore, based on the evaluation results, users can commercialize and release their content with peace of mind.

[0929] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0930] Step 1:

[0931] A user uploads content using a device. The user selects a content file, such as text, image, or audio, through the user interface on the device and clicks the send button. The input is the content file selected by the user, and the output is the file sent from the device to the server.

[0932] Step 2:

[0933] The terminal transfers the uploaded content to the server. The terminal receives the selected content file and transfers it to the server as a data packet. The input is the content file uploaded by the user, and the output is that the file is transferred to the server.

[0934] Step 3:

[0935] The server analyzes the received content and extracts features using a generative AI model (e.g., OpenAI's API). In this step, the content is sent to the generative AI model using a specific prompt. The input is the transferred content file and the prompt, and the output is the feature data extracted from the generative AI model.

[0936] Step 4:

[0937] The server analyzes the extracted features and applies a unique evaluation algorithm to evaluate human creativity. The evaluation algorithm quantifies the extracted features and generates an evaluation score based on these values. The input is the extracted feature data, and the output is the evaluation score.

[0938] Step 5:

[0939] The server generates a judgment result based on the evaluation score. In this step, the evaluation score is analyzed and converted into a concrete result such as "human creativity recognized" or "AI generated" based on a reference value. The input is the evaluation score, and the output is the judgment result.

[0940] Step 6:

[0941] The server notifies the user of the classification result. The server sends the generated classification result to the terminal, which displays it to the user. The input is the classification result, and the output is the evaluation result displayed on the terminal.

[0942] 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.

[0943] This invention combines a system that uses generative AI to determine copyright of content generated by the system with an emotion engine that recognizes user emotions. This system is composed of a server, a terminal, and a user. The cooperation of these elements enables advanced copyright determination that links the evaluation of generated content with user emotion data. The functions and roles of each step are explained in detail below.

[0944] Server-side processing

[0945] 1. Content reception and data analysis

[0946] The server first receives the content (text, images, audio, etc.) provided by the user terminal. This received content is saved as data to be analyzed. At this stage, the received data is converted into a predetermined format as needed.

[0947] 2. Feature Extraction

[0948] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, for text content, grammatical features and lexical patterns are analyzed, for image content, color tones and shape patterns are analyzed, and for audio content, pitch range and rhythm are analyzed.

[0949] 3. Human Creativity Assessment

[0950] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[0951] 4. Collecting and analyzing emotional data

[0952] The server collects emotional data from the user's reactions and inputs and analyzes it using an emotion engine. The emotion engine adjusts the parameters of the generative AI model based on the user's emotional data. For example, if the user reacts with surprise or emotion, the engine changes the settings of the generative AI model based on that emotional data, promoting the generation of more original content.

[0953] 5. Sending the results of the classification

[0954] Finally, the server generates a judgment result based on the evaluation results and emotion data and sends it to the user's device. The judgment result may include, for example, "Human creativity is recognized" or "Completely AI-generated."

[0955] Terminal side processing

[0956] 1. User interface provision

[0957] The terminal provides an interface for users to upload content, including a file selection field and a submit button.

[0958] 2. Uploading Content

[0959] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[0960] 3. Entering Emotional Reactions

[0961] The device also provides an interface for receiving user input, including text input, facial recognition cameras, and voice input, for reacting to content and receiving emotional input.

[0962] 4. Display of discrimination results

[0963] The device receives the results from the server and displays them to the user, who can then check the results and receive detailed feedback based on the emotion data.

[0964] Specific examples

[0965] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[0966] 1. Terminal: The user uploads a text file of a poem.

[0967] 2. Server: Receives the poem text, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[0968] 3. Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[0969] 4. Server: Collects user emotion data, analyzes it using the emotion engine, and adjusts the parameters of the generative AI model.

[0970] 5. Terminal: Receives the result of the judgment and displays to the user, "This poem is a recognition of human creativity and is likely to be copyrightable."

[0971] This system allows users to assess the copyright risk of their own creative works, and by optimizing the parameters of the generative AI based on emotional data, it becomes possible to create more valuable content.

[0972] The processing flow will be explained below.

[0973] Step 1:

[0974] The terminal provides the user with an interface for uploading content, through which the user selects his / her content file and clicks the upload button.

[0975] Step 2:

[0976] The terminal transmits the content file selected by the user to the server, using an HTTP request to transmit the file data.

[0977] Step 3:

[0978] The server converts the received content into a specific format, for example, if it is text, it reads the text file as string data.

[0979] Step 4:

[0980] The server applies a generative AI model to extract features from the received content. For example, in the case of text content, it analyzes grammar, vocabulary selection, sentence structure, etc., and extracts these features as numerical data.

[0981] Step 5:

[0982] The server inputs the extracted features into an evaluation algorithm to calculate the degree to which human creativity is reflected, for example, by comparing it with known AI-generated patterns and calculating an originality score.

[0983] Step 6:

[0984] The server receives the user's emotional response data and analyzes it using an emotion engine, which extracts emotional data from the user's input and reactions and adjusts the parameters of the generative AI model based on this data.

[0985] Step 7:

[0986] The server generates a judgment result based on the evaluation results and emotion data. The judgment result may include information such as "Human creativity is recognized" or "Completely AI-generated."

[0987] Step 8:

[0988] The server sends the generated classification results to the user's device, where it returns the analysis results in JSON format using an HTTP response.

[0989] Step 9:

[0990] The device displays the received results to the user, who can then check the results and receive detailed feedback based on the emotion data.

[0991] Step 10:

[0992] The device provides interfaces such as text input, a facial recognition camera, and voice input to continuously collect user emotional data. This data is used for future judgments and to further optimize the generative AI model.

[0993] Example 2

[0994] 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."

[0995] In conventional content generation systems, it is difficult to determine the degree to which human creativity is reflected. Furthermore, there is no mechanism for incorporating user emotions as evaluation data, making it difficult to improve the quality of content generation. Furthermore, complex analysis is required to accommodate different types of content (text, images, audio, etc.), and establishing comprehensive evaluation criteria is also an important challenge.

[0996] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0997] In this invention, the server includes means for acquiring generated data, means for deriving feature information from the acquired data, means for evaluating human creativity based on the derived feature information and generating a judgment result, means for notifying the user of the judgment result, means for collecting emotional information from the user, and means for analyzing the collected emotional information and adjusting the parameters of the generative AI model. This enables comprehensive copyright determination that combines judgment of human creativity and user emotional data.

[0998] "Generated data" refers to content (text, images, audio, etc.) generated by users or AI.

[0999] "Means for obtaining" refers to a method or mechanism for receiving data generated from a user's terminal or other input device.

[1000] "Feature information" refers to analyzable elements such as grammar, vocabulary, color tone, and rhythm that are extracted from the generated data.

[1001] "Means for deriving" refers to methods or algorithms for extracting feature information from the generated data.

[1002] "Means of evaluation" refers to methods and algorithms for evaluating human creativity based on extracted feature information.

[1003] "Judgment result" refers to the judgment result regarding human creativity that is generated based on characteristic information and evaluation results.

[1004] "Means for notifying" refers to a method or mechanism for communicating the determination result to the user.

[1005] "Emotional information" refers to data that indicates the user's reaction or emotional state (text comments, facial expression data, voice, etc.).

[1006] "Means for collecting" refers to a method or mechanism for acquiring emotion information from a user.

[1007] "Means for analyzing" refers to a method or mechanism for analyzing collected emotional information and classifying or evaluating its content.

[1008] "Generative AI model" refers to an artificial intelligence algorithm or program that evaluates and generates generated content.

[1009] "Means for adjusting parameters" refers to methods or mechanisms for optimizing the quality or characteristics of generated content by modifying the settings or components of a generative AI model.

[1010] This invention relates to an evaluation method that takes into account user emotions in a content copyright determination system that uses a generative AI model. This system is composed of a server, a terminal, and a user, and by cooperating with each other, it is possible to perform advanced copyright determination.

[1011] Hardware and software used

[1012] The server is equipped with a high-performance processor and a large amount of memory. It also has a database (e.g., MySQL) and various analysis software (e.g., a natural language processing library using Python, or a machine learning library such as TensorFlow or PyTorch) installed. The server receives, analyzes, evaluates, and notifies users of various types of data.

[1013] The device provides an interface for users to upload content, either through a browser-based application or a dedicated mobile application, which includes a file selection button, a send button, and emotional response input fields (e.g., text, camera, voice input).

[1014] Users send the content they generate to the server via their devices, and contribute to the evaluation of the generative AI model by inputting their emotional responses into the interface.

[1015] Specific methods for data processing and calculation

[1016] 1. Content Reception: The server receives content (text, images, audio, etc.) sent from the user's device. This is done using an HTTP POST request, etc. The received data is converted into the appropriate format within the server.

[1017] 2. Feature extraction: The server applies generative AI models to extract feature information from the incoming content. For example, for text content, it uses natural language processing (NLP) techniques to analyze grammar and vocabulary patterns. For image content, it uses computer vision techniques to extract color and shape patterns. For audio content, it analyzes pitch and rhythm.

[1018] 3. Human Creativity Evaluation: The server evaluates the human creativity based on the extracted feature information. This evaluation is performed by comparing it with predefined benchmarks and existing AI-generated patterns. The evaluation results are stored in a database as numerical data.

[1019] 4. Emotional data collection and analysis: The server collects emotional information from the user's emotional reactions and inputs and analyzes it using an emotion engine. For example, if the user expresses emotion or surprise, the parameters of the generative AI model are adjusted based on that emotional data.

[1020] 5. Generation and notification of judgment results: The server combines the evaluation results and emotional data to generate a judgment result. This results in a judgment such as "Human creativity is recognized" or "It is highly likely that the result was completely generated by AI." The judgment result is sent to the device via HTTP response, WebSocket, etc.

[1021] Specific examples

[1022] For example, if a user submits a poem they wrote, "mypoem.txt," to the classification system, the specific steps are as follows:

[1023] 1. Device: The user uploads a poem they wrote, "mypoem.txt." The device sends this file to the server.

[1024] 2. Server: Receives the text file of the poem, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts and saves them as numerical data.

[1025] 3. Server: Evaluates originality based on the extracted features and determines whether the work "reflects human creativity."

[1026] 4. Server: Collects user emotional input data (e.g., comments of surprise or excitement) and analyzes it using the emotion engine. Based on this data, the parameters of the generative AI model are adjusted.

[1027] 5. Server and terminal: Makes the final decision and notifies the user that "this poem is recognized as a work of human creativity and is likely to be copyrightable."

[1028] Prompt Sentence Examples

[1029] Below are some example prompts to input to a generative AI model:

[1030] "Evaluate the poem I have created and determine how well it reflects human creativity."

[1031] "Tell me if this image is different from existing AI-generated patterns."

[1032] "Please determine whether the provided audio file is original and return the result."

[1033] This allows users to assess the copyright risk of their own content, optimize the parameters of the generation AI based on emotional data, and generate more valuable content.

[1034] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1035] Step 1: Upload your content

[1036] Input: User-generated content (e.g., mypoem.txt)

[1037] How it works: The user selects their own content using the device's user interface and clicks the upload button. The device then sends the selected file to the server using an HTTP POST request or similar.

[1038] Output: Content data sent to the server

[1039] Step 2: Receiving content and formatting

[1040] Input: Content data received by the server

[1041] How it works: The server converts the content it receives from the device into the appropriate format, for example, converting text data into UTF-8 encoding, or converting image data into a standard image format.

[1042] Output: Content data converted into a format that can be processed internally

[1043] Step 3: Feature extraction

[1044] Input: Format-converted content data

[1045] How it works: The server uses a generative AI model to extract features from content. For text content, it extracts grammatical features and lexical patterns. For image content, it extracts color and shape patterns. For audio content, it analyzes the pitch and rhythm.

[1046] Output: Extracted feature data

[1047] Step 4: Human Creativity Assessment

[1048] Input: Extracted feature data

[1049] How it works: The server applies a unique evaluation algorithm to the stored feature data to assess the degree to which human creativity is reflected in the result, by comparing it with predefined benchmarks and existing AI-generated patterns.

[1050] Output: Evaluation result data

[1051] Step 5: Collecting sentiment data

[1052] Input: User emotional responses and inputs

[1053] How it works: The device provides an interface that accepts user emotional input. Users input their emotional responses through text input, a facial recognition camera, or voice input. The device then sends this to a server.

[1054] Output: Emotion data sent to the server

[1055] Step 6: Analyze the sentiment data

[1056] Input: Emotion data sent to the server

[1057] How it works: The server uses an emotion engine to analyze emotion data and classify it into various emotion categories (e.g., surprise, emotion, sadness). Based on the analysis results, it adjusts the parameters of the generative AI model.

[1058] Output: Analysis result data and adjusted generative AI model parameters

[1059] Step 7: Generate classification results

[1060] Input: Evaluation result data and analysis result data

[1061] How it works: The server combines the evaluation result data and the emotion data to generate a final judgment result, such as "This poem is highly likely to be copyrightable because it demonstrates human creativity."

[1062] Output: Classification result data

[1063] Step 8: Notification of the results

[1064] Input: Classification result data

[1065] Operation: The server sends the generated discrimination result data to the terminal. This transmission uses a communication method such as HTTP response or WebSocket. The terminal displays the received discrimination result on the user interface.

[1066] Output: The result displayed to the user

[1067] (Application example 2)

[1068] 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."

[1069] In recent years, determining copyright for content created using generative AI has become an important issue. However, current technology makes it difficult to accurately evaluate the extent to which content generated by generative AI reflects human creativity. Furthermore, there is no system that evaluates content while reflecting users' emotional reactions, resulting in incomplete evaluation of content value. This makes it difficult to properly evaluate original content and generate content that reflects user satisfaction.

[1070] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1071] In this invention, the server includes means for receiving generated content, means for extracting features from the received content, means for evaluating human creativity based on the extracted features and generating a discrimination result, means for recognizing user emotions and analyzing the data, means for adjusting parameters of the generative AI model based on the analyzed emotion data, and means for notifying the user of the discrimination result, thereby enabling accurate copyright discrimination of generated content and evaluation based on the user's emotional response.

[1072] "Generated content" is digital media, such as text, images, and audio, created using a generative AI model.

[1073] The "receiving means" refers to the function of transmitting content data from the user terminal to the server and receiving it on the server side.

[1074] "Means for extracting features" refers to technology that identifies important information and patterns from received content and executes a process to quantify them.

[1075] The "means for evaluating human creativity" is a system for measuring the degree to which generated content reflects original human ideas and ingenuity based on the extracted features.

[1076] The "means for generating a judgment result" is a function that determines whether the content is created by a human or generated by AI based on the evaluation results and documents that conclusion.

[1077] "Means for recognizing user emotions" refers to technology that detects the emotions (joy, surprise, sadness, etc.) expressed by a user when viewing content from facial expressions, voice, etc.

[1078] "Means for analyzing emotion data" refers to the process of analyzing the recognized emotion data of a user and converting the results into an understandable format.

[1079] The "means for adjusting the parameters of the generative AI model" is a system that changes the operating conditions and settings of the generative AI model based on analyzed emotional data, promoting more appropriate content generation.

[1080] The "means for notifying the evaluation result" is a function for displaying or reporting the evaluation result of the generated content to the user.

[1081] This invention combines a system that uses generative AI to determine copyright of content generated by generative AI with an emotion engine that recognizes user emotions. This system is composed of a server, a terminal, and a user. By cooperating with each element, advanced copyright determination is possible by linking the evaluation of generated content with user emotion data.

[1082] Server-side processing

[1083] Content reception and data analysis

[1084] The server first receives the content (text, images, audio, etc.) provided by the user's device. This received content is then saved as data to be analyzed. The hardware used is a cloud service such as AWS, and the software is Python and TensorFlow. The received data is converted into a specified format as needed.

[1085] Feature extraction

[1086] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, grammatical features and lexical patterns are analyzed for text content, color tones and shape patterns for image content, and pitch range and rhythm for audio content.

[1087] Human creativity assessment

[1088] Based on the extracted features, a unique evaluation algorithm is applied to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[1089] Collecting and analyzing emotional data

[1090] The server collects emotional data from the user's reactions and inputs and analyzes it using an emotion engine. The emotion engine adjusts the parameters of the generative AI model based on the user's emotional data. For example, if the user reacts with surprise or emotion, the engine changes the settings of the generative AI model based on that emotional data, promoting the generation of more original content.

[1091] Sending the results of the judgment

[1092] Finally, the server generates a judgment result based on the evaluation results and emotion data and sends it to the user's device. The judgment result may include, for example, "Human creativity is recognized" or "Completely AI-generated."

[1093] Terminal side processing

[1094] User interface provided

[1095] The terminal provides an interface for users to upload content, including a file selection field and a send button. To collect emotion data, a smartphone or PC equipped with a facial recognition camera and voice input function is used.

[1096] Uploading content

[1097] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[1098] Entering emotional responses

[1099] The device also provides an interface for receiving user reactions to content and emotional input, including text input, facial recognition camera, voice input, etc. For example, a facial recognition camera can be used to analyze the user's facial expressions and collect them as emotional data.

[1100] Display of discrimination results

[1101] The device receives the results from the server and displays them to the user, who can then check the results and receive detailed feedback based on the emotion data.

[1102] Specific examples

[1103] For example, if a user uploads a self-made video "sample_video.mp4," the following steps are executed: the user uploads the video, uses the emotion engine to collect emotional data while watching, and adjusts the parameters of the generative AI model based on that data. The server then generates a classification result and provides feedback to the user.

[1104] Example prompt sentence:

[1105] Upload the video "sample_video.mp4", analyze user sentiment, determine copyright status, and display the results.

[1106] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1107] Step 1:

[1108] A user uploads a video.

[1109] Input: The user selects a video file from their device and clicks the upload button.

[1110] Output: The video file is sent from the user device to the server.

[1111] Specific operation: The upload interface will be displayed on the user's device screen, and once a video file is selected, the file will be sent to the server via the HTTPS protocol.

[1112] Step 2:

[1113] The server receives and stores the video file.

[1114] Input: The video file sent from the user's device.

[1115] Output: The video file will be saved in the specified folder on the server.

[1116] What it does: The server-side script receives the video file and saves it to local storage or cloud storage.

[1117] Step 3:

[1118] The server converts the video file into a predetermined format for analysis.

[1119] Input: Video files stored on the server.

[1120] Output: Video data converted into the specified format.

[1121] What it does: Use a media conversion tool such as FFmpeg to convert the video file into a format that can be analyzed.

[1122] Step 4:

[1123] The server extracts the features.

[1124] Input: Video data converted to a specified format.

[1125] Output: Video features (e.g., image information for each frame, audio waveform data, etc.).

[1126] Specific operation: Using libraries such as OpenCV and Librosa, frames are extracted from video and features are extracted from audio.

[1127] Step 5:

[1128] The server evaluates human creativity based on the extracted features and generates a discrimination result.

[1129] Input: Extracted video feature data.

[1130] Output: Evaluation and discrimination results of human creativity.

[1131] Specific operation: Using an evaluation algorithm, feature data is analyzed and, based on the results, a determination is made as to whether the voice is AI-generated or human-generated.

[1132] Step 6:

[1133] The server collects and analyzes the user's emotional data.

[1134] Input: Emotion data (facial expressions, voice, etc.) collected from the user device.

[1135] Output: Parsed emotion data (e.g. emotion type and intensity).

[1136] Specific operation: Collects facial expressions and voice data while the user is watching a video, and performs emotion analysis using the Emotion API.

[1137] Step 7:

[1138] The server adjusts the parameters of the generative AI model based on the analyzed emotional data.

[1139] Input: Parsed emotion data.

[1140] Output: The adjusted parameters of the generative AI model.

[1141] Specific operation: Emotional data is fed back to the generative AI model, and parameters are automatically adjusted.

[1142] Step 8:

[1143] The server notifies the user terminal of the determination result.

[1144] Input: Evaluation results and classification results based on emotion data.

[1145] Output: The final evaluation result displayed on the user's terminal.

[1146] Specific operation: The evaluation results are sent to the user's device and displayed through the interface.

[1147] 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.

[1148] 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.

[1149] 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.

[1150] [Fourth embodiment]

[1151] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1152] 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.

[1153] 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).

[1154] 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.

[1155] 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.

[1156] 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).

[1157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1158] 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.

[1159] 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.

[1160] 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.

[1161] 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.

[1162] 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.

[1163] 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."

[1164] This invention relates to a system that determines the copyright of content generated using generative AI. This system is broadly composed of three elements: a server, a terminal, and a user. The functions and roles of each step are explained in detail below.

[1165] Server-side processing

[1166] 1. Content reception and data analysis

[1167] The server first receives the content (text, images, audio, etc.) provided by the user terminal. This received content is saved as data to be analyzed. At this stage, the received data is converted into a predetermined format as needed.

[1168] 2. Feature Extraction

[1169] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, for text content, grammatical features and lexical patterns are analyzed, for image content, color tones and shape patterns are analyzed, and for audio content, pitch range and rhythm are analyzed.

[1170] 3. Human Creativity Assessment

[1171] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[1172] 4. Sending the results of the classification

[1173] Finally, the server generates a judgment result based on the evaluation results and sends it to the user's device. The judgment result may include, for example, a judgment such as "Human creativity is recognized" or "Completely AI-generated."

[1174] Terminal side processing

[1175] 1. User interface provision

[1176] The terminal provides an interface for users to upload content, including a file selection field and a submit button.

[1177] 2. Uploading Content

[1178] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[1179] 3. Display of discrimination results

[1180] The device will then display the results of the assessment received from the server to the user. For example, it may display a specific result such as, "This poem is a testament to human creativity and is likely to be copyrightable." Based on this information, users can commercialize and publish their own content with peace of mind.

[1181] Specific examples

[1182] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[1183] 1. Terminal: The user uploads a text file of a poem.

[1184] 2. Server: Receives the poem text, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[1185] 3. Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[1186] 4. Terminal: Receives the result of the judgment and displays to the user, "This poem is a recognition of human creativity and is likely to be copyrightable."

[1187] This system allows users to assess the copyright risk of their own creative works, allowing them to commercialize and publish them with peace of mind.

[1188] The processing flow will be explained below.

[1189] Step 1:

[1190] The terminal provides the user with an interface for uploading content, through which the user selects his / her content file and clicks the upload button.

[1191] Step 2:

[1192] The terminal transmits the content file selected by the user to the server, using an HTTP request to transmit the file data.

[1193] Step 3:

[1194] The server converts the received content into a specific format, for example, if it is text, it reads the text file as string data.

[1195] Step 4:

[1196] The server applies a generative AI model to extract features from the received content. For text content, it analyzes grammar, vocabulary selection, sentence structure, etc., and extracts these features as numerical data.

[1197] Step 5:

[1198] The server inputs the extracted features into an evaluation algorithm to calculate the degree to which human creativity is reflected, for example, by comparing it with known AI-generated patterns and calculating an originality score.

[1199] Step 6:

[1200] The server generates a judgment result based on the evaluation results, which may include information such as "human creativity is recognized" or "completely AI-generated."

[1201] Step 7:

[1202] The server sends the generated classification results to the user's device, where it returns the analysis results in JSON format using an HTTP response.

[1203] Step 8:

[1204] The terminal displays the received results to the user, who can then check the results and recognize to what extent their content reflects human creativity.

[1205] Example 1

[1206] 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."

[1207] In recent years, the amount of content created by generative AI has skyrocketed, but it is difficult to determine the extent to which this content reflects human creativity. Furthermore, copyright certification is required for generated content, but there is no efficient and accurate method for this evaluation. Therefore, there is a need for a system that can accurately evaluate the originality and copyrightability of generative AI content.

[1208] 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.

[1209] In this invention, the server includes means for receiving the generated information, means for extracting features from the received information, means for evaluating human creativity based on the extracted features and generating a judgment result, means for notifying the user of the judgment result, and means for providing an interface for the user to send information. This makes it possible to efficiently evaluate the originality of the generated content and quickly and accurately determine copyright.

[1210] "Generated information" refers to data such as text, images, and audio created by a generative AI model or other information generation means.

[1211] "Features" refers to numerical data that indicate the characteristics of information, such as grammatical features, vocabulary patterns, color tones, shape patterns, ranges, and rhythms extracted from the generated information.

[1212] "Human creativity" refers to the originality and unique patterns of expression that humans display in information and works. The originality of works is evaluated based on this creativity.

[1213] The "discrimination result" refers to the result of evaluating the degree to which the information generated based on the extracted features reflects human creativity. Specifically, it includes a judgment on whether human creativity is recognized.

[1214] "User" means any person or entity that receives the evaluation of information generated using this system.

[1215] "Means for receiving" refers to a function for sending information generated by a user to a server. An example would be a function for receiving data via an HTTP request.

[1216] "Interface" refers to the operation screens and tools that allow users to send generated information to the system, including the file upload button and the send button.

[1217] "Predetermined format" refers to the data format required to properly parse the received information, e.g., text data is encoded in UTF-8, image data is converted to JPEG format, etc.

[1218] This invention relates to a system that determines the copyright of content generated using a generation AI. This system is broadly composed of three elements: a server, a terminal, and a user. Specific embodiments of each element are described below.

[1219] Server-side processing

[1220] The server first receives the content (text, images, audio, etc.) provided by the user terminal. The received content is saved as data to be analyzed, and at this stage the received data is converted to a specified format as needed (for example, a text file is encoded into UTF-8, an image file is converted into JPEG format).

[1221] The server then applies algorithms from a generative AI model (e.g., GPT-3 for text, Vision Transformer for images) to extract features from the received content: grammatical features and lexical patterns for text content, color and shape patterns for image content, and pitch range and rhythm for audio content.

[1222] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[1223] Finally, the server generates a judgment result based on the evaluation results and sends it to the user's device. The judgment result may include a judgment such as "human creativity is recognized" or "completely AI-generated."

[1224] Terminal side processing

[1225] The terminal provides an interface for users to upload content. This interface includes a file selection field and a submit button. The user uses this interface to select their content and clicks the upload button. The terminal then sends the selected content file to the server.

[1226] The device then displays the results of the assessment received from the server to the user. For example, it may display a specific result such as, "This poem is a testament to human creativity and is likely to be copyrightable." Based on this information, users can commercialize and publish their own content with peace of mind.

[1227] Specific examples

[1228] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[1229] Terminal: The user uploads a text file of the poem.

[1230] Server: Receives the text of the poem, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[1231] Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[1232] Terminal: Receives the result of the judgment and displays to the user, "This poem recognizes human creativity and is likely to be copyrightable."

[1233] Prompt Sentence Examples

[1234] Describe a system that receives a user-provided text file of a poem, 'mypoem.txt', and analyzes its grammatical features and lexical patterns to determine whether the poem is original.

[1235] The system allows users to quickly assess how original their creations are and whether they are likely to be copyrightable.

[1236] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1237] Step 1: Receiving content

[1238] Input: User-uploaded content (text, images, audio, etc.).

[1239] How it works: The device receives content entered by the user and sends it to the server. For example, if the user uploads "mypoem.txt", the device will send this file to the server via an HTTP request.

[1240] Output: You get the content transferred to the server.

[1241] Step 2: Data storage and format conversion

[1242] Input: The content received by the server.

[1243] How it works: The server stores the received content in temporary storage and converts it to the required format if necessary: ​​if it's a text file, it's encoded in UTF-8, and if it's an image file, it's converted to JPEG.

[1244] Output: The formatted data is obtained for analysis.

[1245] Step 3: Feature extraction

[1246] Input: The reformatted content.

[1247] How it works: The server applies an appropriate generative AI model to extract features such as grammatical features and lexical patterns in the case of text, color and shape patterns in the case of images, and pitch and rhythm in the case of audio. For example, it uses GPT-3 to convert the grammatical and lexical patterns of "mypoem.txt" into numerical data.

[1248] Output: Numerical data of extracted features is obtained.

[1249] Step 4: Creativity Assessment

[1250] Input: Numerical data of extracted features.

[1251] How it works: The server runs a proprietary evaluation algorithm and compares the extracted features with predefined benchmarks and existing AI-generated patterns. This evaluation determines the degree to which the feature reflects human creativity.

[1252] Output: Numerical data is obtained as the evaluation result of creativity.

[1253] Step 5: Generate classification results

[1254] Input: Numerical data of creativity assessment results.

[1255] How it works: The server makes a final decision based on the evaluation results, generating a decision such as "human creativity is recognized" or "completely AI-generated."

[1256] Output: The generated discrimination results are obtained.

[1257] Step 6: Send the results

[1258] Input: Generated discrimination results.

[1259] Operation: The server sends the result of the determination to the user terminal as an HTTP response.

[1260] Output: The discrimination result is sent to the user's terminal.

[1261] Step 7: Displaying the discrimination results

[1262] Input: The result of the determination received from the server.

[1263] Operation: The device displays the received judgment results to the user. For example, a result such as "This poem is recognized as a work of human creativity and is likely to be copyrightable" is displayed on the interface.

[1264] Output: The results of the discrimination are displayed to the user.

[1265] (Application example 1)

[1266] 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."

[1267] In recent years, the amount of content generated using generative AI has increased, making it important to accurately determine its copyright. With conventional systems, determining the copyright of content was extremely difficult and required a great deal of time and effort. Furthermore, creators lacked objective criteria for evaluating the originality of their own work, which led to risks when commercializing or releasing it. These issues made it difficult for creators to release their content with confidence.

[1268] 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.

[1269] In this invention, the server includes means for receiving generated content, means for extracting features from the received content, means for evaluating human creativity based on the extracted features and generating a judgment result, means for notifying the user of the judgment result, means for providing an interface for users to upload their own content, and means for transferring the uploaded content to the server. This allows creators to objectively evaluate the originality of their own content and quickly and accurately determine copyright. Furthermore, commercialization and publication can be carried out with peace of mind based on the evaluation results.

[1270] The "means for receiving generated content" is a function for transmitting content such as text, images, and audio provided by a user to a server.

[1271] "Means for extracting features from received content" refers to a function that identifies important features from the content received by the server and extracts them as data.

[1272] The "means for evaluating human creativity based on extracted features and generating a judgment result" is a function that analyzes the extracted features to evaluate the extent to which human creativity is reflected, and judges the originality of the content based on the evaluation result.

[1273] The "means for notifying the user of the discrimination results" is a function for notifying the user of the evaluation results and discrimination results generated by the server.

[1274] The "means for providing an interface for users to upload their own content" is a function for providing an interface for users to upload content to the server.

[1275] The "means for transferring uploaded content to the server" is a function for transmitting content uploaded by a user through an interface to the server.

[1276] The "means for converting content into a predetermined format" is a function for converting received content into a predetermined format so that it can be easily analyzed.

[1277] "Means for receiving and processing at least one of text, images, and audio as content" refers to the function of receiving content in a wide variety of formats (text, images, audio) and analyzing and processing it.

[1278] "Means for extracting features using a generative AI model" refers to a function that utilizes an AI model to extract features of content.

[1279] "Means of inputting prompt sentences to a generative AI model and using the results for evaluation" refers to a function that uses prompt sentences to an AI model to extract content features and utilizes the results for evaluation.

[1280] The present invention relates to a system for determining copyright of content generated using a generative AI model, and specific embodiments thereof will be described below.

[1281] System configuration

[1282] This system consists of three elements: a server, a terminal, and a user. The server plays a central role in analyzing content, extracting its features, evaluating human creativity, and generating a judgment result. The terminal provides an interface for users to upload content and has the auxiliary function of transferring uploaded content to the server.

[1283] Hardware used

[1284] Server: A high-performance data analysis server is required, and this server should have enough computing resources (CPU, GPU) to run the generative AI model.

[1285] Client terminal: A device operated by a user, such as a smartphone or PC, that requires an internet connection.

[1286] Software used

[1287] Flask: A Python web framework that facilitates communication between servers and clients.

[1288] OpenAI API: An API for extracting content features using generative AI models (e.g., OpenAI's GPT-3).

[1289] Program processing

[1290] Content Upload

[1291] A user uploads their own content using a terminal. The terminal receives the content, such as text, images, and audio, through a user interface with a file selection field and a send button. The terminal then transfers the uploaded content to the server.

[1292] Feature extraction

[1293] The server analyzes the received content and uses a generative AI model to extract key features: for example, for text content, it extracts grammatical features and lexical patterns, for image content, it analyzes color and shape patterns, and for audio content, it analyzes pitch and rhythm.

[1294] Human creativity evaluation and discrimination result generation

[1295] Based on the extracted features, the server applies a unique evaluation algorithm to determine the degree to which human creativity is reflected in the work, and generates a judgment result based on the evaluation results and notifies the user.

[1296] Specific examples

[1297] For example, if a user uploads a poem called "mypoem.txt," the following prompt sentence is input to the generative AI model to extract features:

[1298] Example prompt:

[1299] Please provide a list of key features of the following text:

[1300] Everything in this world is reflected in my heart

[1301] The whisper of the wind, the song of the birds, the shape of the clouds

[1302] Feeling the breath of life in silence

[1303] They resonate deeply with me

[1304] The server sends this prompt to the generative AI model, which then uses a proprietary algorithm to evaluate it based on the returned features. The evaluation result is displayed to the user, saying, "This poem recognizes human creativity and is likely to be copyrightable."

[1305] As described above, the present invention enables users to objectively evaluate the originality of their own content and quickly and accurately determine copyright. Furthermore, based on the evaluation results, users can commercialize and release their content with peace of mind.

[1306] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1307] Step 1:

[1308] A user uploads content using a device. The user selects a content file, such as text, image, or audio, through the user interface on the device and clicks the send button. The input is the content file selected by the user, and the output is the file sent from the device to the server.

[1309] Step 2:

[1310] The terminal transfers the uploaded content to the server. The terminal receives the selected content file and transfers it to the server as a data packet. The input is the content file uploaded by the user, and the output is that the file is transferred to the server.

[1311] Step 3:

[1312] The server analyzes the received content and extracts features using a generative AI model (e.g., OpenAI's API). In this step, the content is sent to the generative AI model using a specific prompt. The input is the transferred content file and the prompt, and the output is the feature data extracted from the generative AI model.

[1313] Step 4:

[1314] The server analyzes the extracted features and applies a unique evaluation algorithm to evaluate human creativity. The evaluation algorithm quantifies the extracted features and generates an evaluation score based on these values. The input is the extracted feature data, and the output is the evaluation score.

[1315] Step 5:

[1316] The server generates a judgment result based on the evaluation score. In this step, the evaluation score is analyzed and converted into a concrete result such as "human creativity recognized" or "AI generated" based on a reference value. The input is the evaluation score, and the output is the judgment result.

[1317] Step 6:

[1318] The server notifies the user of the classification result. The server sends the generated classification result to the terminal, which displays it to the user. The input is the classification result, and the output is the evaluation result displayed on the terminal.

[1319] 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.

[1320] This invention combines a system that uses generative AI to determine copyright of content generated by the system with an emotion engine that recognizes user emotions. This system is composed of a server, a terminal, and a user. The cooperation of these elements enables advanced copyright determination that links the evaluation of generated content with user emotion data. The functions and roles of each step are explained in detail below.

[1321] Server-side processing

[1322] 1. Content reception and data analysis

[1323] The server first receives the content (text, images, audio, etc.) provided by the user terminal. This received content is saved as data to be analyzed. At this stage, the received data is converted into a predetermined format as needed.

[1324] 2. Feature Extraction

[1325] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, for text content, grammatical features and lexical patterns are analyzed, for image content, color tones and shape patterns are analyzed, and for audio content, pitch range and rhythm are analyzed.

[1326] 3. Human Creativity Assessment

[1327] The server applies a proprietary evaluation algorithm based on the extracted features to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[1328] 4. Collecting and analyzing emotional data

[1329] The server collects emotional data from the user's reactions and inputs and analyzes it using an emotion engine. The emotion engine adjusts the parameters of the generative AI model based on the user's emotional data. For example, if the user reacts with surprise or emotion, the engine changes the settings of the generative AI model based on that emotional data, promoting the generation of more original content.

[1330] 5. Sending the results of the classification

[1331] Finally, the server generates a judgment result based on the evaluation results and emotion data and sends it to the user's device. The judgment result may include, for example, "Human creativity is recognized" or "Completely AI-generated."

[1332] Terminal side processing

[1333] 1. User interface provision

[1334] The terminal provides an interface for users to upload content, including a file selection field and a submit button.

[1335] 2. Uploading Content

[1336] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[1337] 3. Entering Emotional Reactions

[1338] The device also provides an interface for receiving user input, including text input, facial recognition cameras, and voice input, for reacting to content and receiving emotional input.

[1339] 4. Display of discrimination results

[1340] The device receives the results from the server and displays them to the user, who can then check the results and receive detailed feedback based on the emotion data.

[1341] Specific examples

[1342] For example, if a user submits a poem he or she wrote, "mypoem.txt," to the classification system, the following steps are performed:

[1343] 1. Terminal: The user uploads a text file of a poem.

[1344] 2. Server: Receives the poem text, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts these features as numerical data.

[1345] 3. Server: As a result of the feature analysis, it is recognized that the poem is original and different from known AI-generated patterns, and it is determined that it reflects human creativity.

[1346] 4. Server: Collects user emotion data, analyzes it using the emotion engine, and adjusts the parameters of the generative AI model.

[1347] 5. Terminal: Receives the result of the judgment and displays to the user, "This poem is a recognition of human creativity and is likely to be copyrightable."

[1348] This system allows users to assess the copyright risk of their own creative works, and by optimizing the parameters of the generative AI based on emotional data, it becomes possible to create more valuable content.

[1349] The processing flow will be explained below.

[1350] Step 1:

[1351] The terminal provides the user with an interface for uploading content, through which the user selects his / her content file and clicks the upload button.

[1352] Step 2:

[1353] The terminal transmits the content file selected by the user to the server, using an HTTP request to transmit the file data.

[1354] Step 3:

[1355] The server converts the received content into a specific format, for example, if it is text, it reads the text file as string data.

[1356] Step 4:

[1357] The server applies a generative AI model to extract features from the received content. For example, in the case of text content, it analyzes grammar, vocabulary selection, sentence structure, etc., and extracts these features as numerical data.

[1358] Step 5:

[1359] The server inputs the extracted features into an evaluation algorithm to calculate the degree to which human creativity is reflected, for example, by comparing it with known AI-generated patterns and calculating an originality score.

[1360] Step 6:

[1361] The server receives the user's emotional response data and analyzes it using an emotion engine, which extracts emotional data from the user's input and reactions and adjusts the parameters of the generative AI model based on this data.

[1362] Step 7:

[1363] The server generates a judgment result based on the evaluation results and emotion data. The judgment result may include information such as "Human creativity is recognized" or "Completely AI-generated."

[1364] Step 8:

[1365] The server sends the generated classification results to the user's device, where it returns the analysis results in JSON format using an HTTP response.

[1366] Step 9:

[1367] The device displays the received results to the user, who can then check the results and receive detailed feedback based on the emotion data.

[1368] Step 10:

[1369] The device provides interfaces such as text input, a facial recognition camera, and voice input to continuously collect user emotional data. This data is used for future judgments and to further optimize the generative AI model.

[1370] Example 2

[1371] 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."

[1372] In conventional content generation systems, it is difficult to determine the degree to which human creativity is reflected. Furthermore, there is no mechanism for incorporating user emotions as evaluation data, making it difficult to improve the quality of content generation. Furthermore, complex analysis is required to accommodate different types of content (text, images, audio, etc.), and establishing comprehensive evaluation criteria is also an important challenge.

[1373] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1374] In this invention, the server includes means for acquiring generated data, means for deriving feature information from the acquired data, means for evaluating human creativity based on the derived feature information and generating a judgment result, means for notifying the user of the judgment result, means for collecting emotional information from the user, and means for analyzing the collected emotional information and adjusting the parameters of the generative AI model. This enables comprehensive copyright determination that combines judgment of human creativity and user emotional data.

[1375] "Generated data" refers to content (text, images, audio, etc.) generated by users or AI.

[1376] "Means for obtaining" refers to a method or mechanism for receiving data generated from a user's terminal or other input device.

[1377] "Feature information" refers to analyzable elements such as grammar, vocabulary, color tone, and rhythm that are extracted from the generated data.

[1378] "Means for deriving" refers to methods or algorithms for extracting feature information from the generated data.

[1379] "Means of evaluation" refers to methods and algorithms for evaluating human creativity based on extracted feature information.

[1380] "Judgment result" refers to the judgment result regarding human creativity that is generated based on characteristic information and evaluation results.

[1381] "Means for notifying" refers to a method or mechanism for communicating the determination result to the user.

[1382] "Emotional information" refers to data that indicates the user's reaction or emotional state (text comments, facial expression data, voice, etc.).

[1383] "Means for collecting" refers to a method or mechanism for acquiring emotion information from a user.

[1384] "Means for analyzing" refers to a method or mechanism for analyzing collected emotional information and classifying or evaluating its content.

[1385] "Generative AI model" refers to an artificial intelligence algorithm or program that evaluates and generates generated content.

[1386] "Means for adjusting parameters" refers to methods or mechanisms for optimizing the quality or characteristics of generated content by modifying the settings or components of a generative AI model.

[1387] This invention relates to an evaluation method that takes into account user emotions in a content copyright determination system that uses a generative AI model. This system is composed of a server, a terminal, and a user, and by cooperating with each other, it is possible to perform advanced copyright determination.

[1388] Hardware and software used

[1389] The server is equipped with a high-performance processor and a large amount of memory. It also has a database (e.g., MySQL) and various analysis software (e.g., a natural language processing library using Python, or a machine learning library such as TensorFlow or PyTorch) installed. The server receives, analyzes, evaluates, and notifies users of various types of data.

[1390] The device provides an interface for users to upload content, either through a browser-based application or a dedicated mobile application, which includes a file selection button, a send button, and emotional response input fields (e.g., text, camera, voice input).

[1391] Users send the content they generate to the server via their devices, and contribute to the evaluation of the generative AI model by inputting their emotional responses into the interface.

[1392] Specific methods for data processing and calculation

[1393] 1. Content Reception: The server receives content (text, images, audio, etc.) sent from the user's device. This is done using an HTTP POST request, etc. The received data is converted into the appropriate format within the server.

[1394] 2. Feature extraction: The server applies generative AI models to extract feature information from the incoming content. For example, for text content, it uses natural language processing (NLP) techniques to analyze grammar and vocabulary patterns. For image content, it uses computer vision techniques to extract color and shape patterns. For audio content, it analyzes pitch and rhythm.

[1395] 3. Human Creativity Evaluation: The server evaluates the human creativity based on the extracted feature information. This evaluation is performed by comparing it with predefined benchmarks and existing AI-generated patterns. The evaluation results are stored in a database as numerical data.

[1396] 4. Emotional data collection and analysis: The server collects emotional information from the user's emotional reactions and inputs and analyzes it using an emotion engine. For example, if the user expresses emotion or surprise, the parameters of the generative AI model are adjusted based on that emotional data.

[1397] 5. Generation and notification of judgment results: The server combines the evaluation results and emotional data to generate a judgment result. This results in a judgment such as "Human creativity is recognized" or "It is highly likely that the result was completely generated by AI." The judgment result is sent to the device via HTTP response, WebSocket, etc.

[1398] Specific examples

[1399] For example, if a user submits a poem they wrote, "mypoem.txt," to the classification system, the specific steps are as follows:

[1400] 1. Device: The user uploads a poem they wrote, "mypoem.txt." The device sends this file to the server.

[1401] 2. Server: Receives the text file of the poem, analyzes its features such as grammar, vocabulary choice, and sentence structure, and extracts and saves them as numerical data.

[1402] 3. Server: Evaluates originality based on the extracted features and determines whether the work "reflects human creativity."

[1403] 4. Server: Collects user emotional input data (e.g., comments of surprise or excitement) and analyzes it using the emotion engine. Based on this data, the parameters of the generative AI model are adjusted.

[1404] 5. Server and terminal: Makes the final decision and notifies the user that "this poem is recognized as a work of human creativity and is likely to be copyrightable."

[1405] Prompt Sentence Examples

[1406] Below are some example prompts to input to a generative AI model:

[1407] "Evaluate the poem I have created and determine how well it reflects human creativity."

[1408] "Tell me if this image is different from existing AI-generated patterns."

[1409] "Please determine whether the provided audio file is original and return the result."

[1410] This allows users to assess the copyright risk of their own content, optimize the parameters of the generation AI based on emotional data, and generate more valuable content.

[1411] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1412] Step 1: Upload your content

[1413] Input: User-generated content (e.g., mypoem.txt)

[1414] How it works: The user selects their own content using the device's user interface and clicks the upload button. The device then sends the selected file to the server using an HTTP POST request or similar.

[1415] Output: Content data sent to the server

[1416] Step 2: Receiving content and formatting

[1417] Input: Content data received by the server

[1418] How it works: The server converts the content it receives from the device into the appropriate format, for example, converting text data into UTF-8 encoding, or converting image data into a standard image format.

[1419] Output: Content data converted into a format that can be processed internally

[1420] Step 3: Feature extraction

[1421] Input: Format-converted content data

[1422] How it works: The server uses a generative AI model to extract features from content. For text content, it extracts grammatical features and lexical patterns. For image content, it extracts color and shape patterns. For audio content, it analyzes the pitch and rhythm.

[1423] Output: Extracted feature data

[1424] Step 4: Human Creativity Assessment

[1425] Input: Extracted feature data

[1426] How it works: The server applies a unique evaluation algorithm to the stored feature data to assess the degree to which human creativity is reflected in the result, by comparing it with predefined benchmarks and existing AI-generated patterns.

[1427] Output: Evaluation result data

[1428] Step 5: Collecting sentiment data

[1429] Input: User emotional responses and inputs

[1430] How it works: The device provides an interface that accepts user emotional input. Users input their emotional responses through text input, a facial recognition camera, or voice input. The device then sends this to a server.

[1431] Output: Emotion data sent to the server

[1432] Step 6: Analyze the sentiment data

[1433] Input: Emotion data sent to the server

[1434] How it works: The server uses an emotion engine to analyze emotion data and classify it into various emotion categories (e.g., surprise, emotion, sadness). Based on the analysis results, it adjusts the parameters of the generative AI model.

[1435] Output: Analysis result data and adjusted generative AI model parameters

[1436] Step 7: Generate classification results

[1437] Input: Evaluation result data and analysis result data

[1438] How it works: The server combines the evaluation result data and the emotion data to generate a final judgment result, such as "This poem is highly likely to be copyrightable because it demonstrates human creativity."

[1439] Output: Classification result data

[1440] Step 8: Notification of the results

[1441] Input: Classification result data

[1442] Operation: The server sends the generated discrimination result data to the terminal. This transmission uses a communication method such as HTTP response or WebSocket. The terminal displays the received discrimination result on the user interface.

[1443] Output: The result displayed to the user

[1444] (Application example 2)

[1445] 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."

[1446] In recent years, determining copyright for content created using generative AI has become an important issue. However, current technology makes it difficult to accurately evaluate the extent to which content generated by generative AI reflects human creativity. Furthermore, there is no system that evaluates content while reflecting users' emotional reactions, resulting in incomplete evaluation of content value. This makes it difficult to properly evaluate original content and generate content that reflects user satisfaction.

[1447] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1448] In this invention, the server includes means for receiving generated content, means for extracting features from the received content, means for evaluating human creativity based on the extracted features and generating a discrimination result, means for recognizing user emotions and analyzing the data, means for adjusting parameters of the generative AI model based on the analyzed emotion data, and means for notifying the user of the discrimination result, thereby enabling accurate copyright discrimination of generated content and evaluation based on the user's emotional response.

[1449] "Generated content" is digital media, such as text, images, and audio, created using a generative AI model.

[1450] The "receiving means" refers to the function of transmitting content data from the user terminal to the server and receiving it on the server side.

[1451] "Means for extracting features" refers to technology that identifies important information and patterns from received content and executes a process to quantify them.

[1452] The "means for evaluating human creativity" is a system for measuring the degree to which generated content reflects original human ideas and ingenuity based on the extracted features.

[1453] The "means for generating a judgment result" is a function that determines whether the content is created by a human or generated by AI based on the evaluation results and documents that conclusion.

[1454] "Means for recognizing user emotions" refers to technology that detects the emotions (joy, surprise, sadness, etc.) expressed by a user when viewing content from facial expressions, voice, etc.

[1455] "Means for analyzing emotion data" refers to the process of analyzing the recognized emotion data of a user and converting the results into an understandable format.

[1456] The "means for adjusting the parameters of the generative AI model" is a system that changes the operating conditions and settings of the generative AI model based on analyzed emotional data, promoting more appropriate content generation.

[1457] The "means for notifying the evaluation result" is a function for displaying or reporting the evaluation result of the generated content to the user.

[1458] This invention combines a system that uses generative AI to determine copyright of content generated by generative AI with an emotion engine that recognizes user emotions. This system is composed of a server, a terminal, and a user. By cooperating with each element, advanced copyright determination is possible by linking the evaluation of generated content with user emotion data.

[1459] Server-side processing

[1460] Content reception and data analysis

[1461] The server first receives the content (text, images, audio, etc.) provided by the user's device. This received content is then saved as data to be analyzed. The hardware used is a cloud service such as AWS, and the software is Python and TensorFlow. The received data is converted into a specified format as needed.

[1462] Feature extraction

[1463] The server applies a generative AI model to extract features from the received content. Different algorithms are used for this feature extraction depending on the type of content. For example, grammatical features and lexical patterns are analyzed for text content, color tones and shape patterns for image content, and pitch range and rhythm for audio content.

[1464] Human creativity assessment

[1465] Based on the extracted features, a unique evaluation algorithm is applied to determine the degree to which human creativity is reflected. This evaluation is performed by comparing with predefined benchmarks and existing AI-generated patterns. The evaluation results are saved as numerical data and used as criteria for selecting new content.

[1466] Collecting and analyzing emotional data

[1467] The server collects emotional data from the user's reactions and inputs and analyzes it using an emotion engine. The emotion engine adjusts the parameters of the generative AI model based on the user's emotional data. For example, if the user reacts with surprise or emotion, the engine changes the settings of the generative AI model based on that emotional data, promoting the generation of more original content.

[1468] Sending the results of the judgment

[1469] Finally, the server generates a judgment result based on the evaluation results and emotion data and sends it to the user's device. The judgment result may include, for example, "Human creativity is recognized" or "Completely AI-generated."

[1470] Terminal side processing

[1471] User interface provided

[1472] The terminal provides an interface for users to upload content, including a file selection field and a send button. To collect emotion data, a smartphone or PC equipped with a facial recognition camera and voice input function is used.

[1473] Uploading content

[1474] The user uses this interface to select their content and clicks the upload button, and the terminal sends the selected content file to the server.

[1475] Entering emotional responses

[1476] The device also provides an interface for receiving user reactions to content and emotional input, including text input, facial recognition camera, voice input, etc. For example, a facial recognition camera can be used to analyze the user's facial expressions and collect them as emotional data.

[1477] Display of discrimination results

[1478] The device receives the results from the server and displays them to the user, who can then check the results and receive detailed feedback based on the emotion data.

[1479] Specific examples

[1480] For example, if a user uploads a self-made video "sample_video.mp4," the following steps are executed: the user uploads the video, uses the emotion engine to collect emotional data while watching, and adjusts the parameters of the generative AI model based on that data. The server then generates a classification result and provides feedback to the user.

[1481] Example prompt sentence:

[1482] Upload the video "sample_video.mp4", analyze user sentiment, determine copyright status, and display the results.

[1483] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1484] Step 1:

[1485] A user uploads a video.

[1486] Input: The user selects a video file from their device and clicks the upload button.

[1487] Output: The video file is sent from the user device to the server.

[1488] Specific operation: The upload interface will be displayed on the user's device screen, and once a video file is selected, the file will be sent to the server via the HTTPS protocol.

[1489] Step 2:

[1490] The server receives and stores the video file.

[1491] Input: The video file sent from the user's device.

[1492] Output: The video file will be saved in the specified folder on the server.

[1493] What it does: The server-side script receives the video file and saves it to local storage or cloud storage.

[1494] Step 3:

[1495] The server converts the video file into a predetermined format for analysis.

[1496] Input: Video files stored on the server.

[1497] Output: Video data converted into the specified format.

[1498] What it does: Use a media conversion tool such as FFmpeg to convert the video file into a format that can be analyzed.

[1499] Step 4:

[1500] The server extracts the features.

[1501] Input: Video data converted to a specified format.

[1502] Output: Video features (e.g., image information for each frame, audio waveform data, etc.).

[1503] Specific operation: Using libraries such as OpenCV and Librosa, frames are extracted from video and features are extracted from audio.

[1504] Step 5:

[1505] The server evaluates human creativity based on the extracted features and generates a discrimination result.

[1506] Input: Extracted video feature data.

[1507] Output: Evaluation and discrimination results of human creativity.

[1508] Specific operation: Using an evaluation algorithm, feature data is analyzed and, based on the results, a determination is made as to whether the voice is AI-generated or human-generated.

[1509] Step 6:

[1510] The server collects and analyzes the user's emotional data.

[1511] Input: Emotion data (facial expressions, voice, etc.) collected from the user device.

[1512] Output: Parsed emotion data (e.g. emotion type and intensity).

[1513] Specific operation: Collects facial expressions and voice data while the user is watching a video, and performs emotion analysis using the Emotion API.

[1514] Step 7:

[1515] The server adjusts the parameters of the generative AI model based on the analyzed emotional data.

[1516] Input: Parsed emotion data.

[1517] Output: The adjusted parameters of the generative AI model.

[1518] Specific operation: Emotional data is fed back to the generative AI model, and parameters are automatically adjusted.

[1519] Step 8:

[1520] The server notifies the user terminal of the determination result.

[1521] Input: Evaluation results and classification results based on emotion data.

[1522] Output: The final evaluation result displayed on the user's terminal.

[1523] Specific operation: The evaluation results are sent to the user's device and displayed through the interface.

[1524] 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.

[1525] 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.

[1526] 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.

[1527] 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.

[1528] 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.

[1529] 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.

[1530] 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).

[1531] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1532] 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."

[1533] 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.

[1534] 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).

[1535] 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.

[1536] 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.

[1537] 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.

[1538] 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.

[1539] 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.

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] The following is further disclosed regarding the above embodiment.

[1546] (Claim 1)

[1547] means for receiving the generated content;

[1548] means for extracting features from received content;

[1549] a means for evaluating human creativity based on the extracted features and generating a discrimination result;

[1550] means for notifying a user of the determination result;

[1551] A system including:

[1552] (Claim 2)

[1553] 10. The system of claim 1, further comprising: means for converting the content to a predetermined format.

[1554] (Claim 3)

[1555] 10. The system of claim 1, further comprising means for receiving and processing at least one of text, images, and audio as content.

[1556] "Example 1"

[1557] (Claim 1)

[1558] means for receiving the generated information;

[1559] means for extracting features from the received information;

[1560] a means for evaluating human creativity based on the extracted features and generating a discrimination result;

[1561] a means for notifying a user of the determination result;

[1562] means for providing an interface for a user to submit information;

[1563] A system including:

[1564] (Claim 2)

[1565] 10. The system of claim 1, further comprising: means for converting the information into a predetermined format.

[1566] (Claim 3)

[1567] 10. The system of claim 1, further comprising means for receiving and processing at least one of text, images, and audio as information.

[1568] "Application Example 1"

[1569] (Claim 1)

[1570] means for receiving the generated content;

[1571] means for extracting features from received content;

[1572] a means for evaluating human creativity based on the extracted features and generating a discrimination result;

[1573] means for notifying a user of the determination result;

[1574] means for providing an interface for users to upload their content;

[1575] means for transferring the uploaded content to a server;

[1576] A system including:

[1577] (Claim 2)

[1578] 10. The system of claim 1, further comprising: means for converting the content to a predetermined format.

[1579] (Claim 3)

[1580] 10. The system of claim 1, further comprising means for receiving and processing at least one of text, images, and audio as content.

[1581] (Claim 4)

[1582] 10. The system of claim 1, further comprising means for extracting features using a generative AI model.

[1583] (Claim 5)

[1584] 10. The system of claim 1, further comprising means for inputting prompt statements to the generative AI model and using the results for evaluation.

[1585] "Example 2: Combining Emotion Engines"

[1586] (Claim 1)

[1587] a means for obtaining the generated data;

[1588] means for deriving feature information from the acquired data;

[1589] a means for evaluating human creativity based on the derived feature information and generating a judgment result;

[1590] a means for notifying the user of the determination result;

[1591] A means for collecting emotional information from a user;

[1592] A means for analyzing the collected emotional information and adjusting the parameters of the generative AI model;

[1593] A system including:

[1594] (Claim 2)

[1595] 10. The system of claim 1, further comprising: means for converting the data into a predetermined format.

[1596] (Claim 3)

[1597] 10. The system according to claim 1, further comprising means for acquiring and processing at least one of text, image, and sound as the processing target.

[1598] "Application example 2 when combining emotion engines"

[1599] (Claim 1)

[1600] means for receiving the generated content;

[1601] means for extracting features from received content;

[1602] a means for evaluating human creativity based on the extracted features and generating a discrimination result;

[1603] means for recognizing a user's emotions and analyzing the data;

[1604] A means for adjusting parameters of the generative AI model based on the analyzed emotion data; and

[1605] means for notifying a user of the determination result;

[1606] A system including:

[1607] (Claim 2)

[1608] 10. The system of claim 1, further comprising: means for converting the content to a predetermined format.

[1609] (Claim 3)

[1610] 10. The system of claim 1, further comprising means for receiving and processing at least one of text, images, and audio as content. [Explanation of symbols]

[1611] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving the generated content; means for extracting features from received content; a means for evaluating human creativity based on the extracted features and generating a discrimination result; means for notifying a user of the determination result; A system including:

2. The system of claim 1 further comprising means for converting the content into a predetermined format.

3. The system of claim 1 , further comprising means for receiving and processing at least one of text, images, and audio as content.

Images