system

Abstract

Provide a system. 【Solution means】 Means for acquiring data on laws, regulations and ethical standards and performing learning by a generation model, Means for receiving a content generation request from a user, Means for generating initial content based on the received request, Means for evaluating the generated content based on laws and ethical standards and checking for compliance, Means for detecting and correcting inappropriate content, Means for collecting feedback on the generated content and improving the generation model, Means for providing the corrected content to the user, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern information society, when enterprises and individuals create content, there is a risk of violating laws, regulations, and ethical standards. Especially when creating a large amount of content quickly, a great deal of time and labor are required for manual verification work. Also, it is difficult to avoid the risk of inappropriate expressions and copyright infringement, which may damage the credibility of the brand. Furthermore, there is a problem that feedback cannot be effectively utilized by conventional methods and it is difficult to improve the generation process.

Means for Solving the Problems

[0005] This system provides a means of acquiring data on laws, regulations, and ethical standards and training a generative model with it. This means receives content generation requests from users and generates initial content using a generative AI model. The generated content is immediately evaluated against laws and ethical standards and checked for compliance. Furthermore, if inappropriate content is detected, the system automatically corrects it and collects feedback on the generated content to improve the model. This process allows for the rapid delivery of corrected content to users, efficiently resolving legal risks and ethical challenges.

[0006] "Laws" are rules and standards established by a nation or region, intended to maintain social order and ensure fair trade.

[0007] "Rules" are guidelines for behavior and procedures established by a specific group or organization, and their purpose is to ensure the smooth operation of the organization.

[0008] "Ethical standards" are criteria that represent moral judgments and values ​​shared by a society or culture, and they serve as guidelines when people act or make decisions.

[0009] "Data" refers to a collection of information obtained from facts and observations, which is used for analysis and decision-making.

[0010] A "generative model" refers to an algorithm or system that automatically creates new data or content based on input information.

[0011] "Content" refers to informational materials such as information, text, images, and videos provided in digital or physical form.

[0012] "Conformity" refers to the degree to which something meets specific standards or conditions, indicating the degree of agreement with the required standards.

[0013] "Inappropriate content" refers to information or expressions that are inconsistent with laws, regulations, or ethical standards and whose publication or use may be problematic.

[0014] "Feedback" refers to evaluations or opinions on the outcome of a particular action or process, and is information used for improvement or adjustment.

[0015] "Correction" refers to actions involving changes or adjustments to fix problems or non-conformities that have been found. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that uses a generation AI model to check the legal and ethical compliance of content and eliminate inappropriate content. The system is configured based on the division of roles between the server, terminal, and user.

[0038] First, the server collects data on necessary laws, regulations, and ethical standards from external sources. Based on this data, it trains a generative AI model on these laws and ethical standards. This ensures that the model understands the latest legal and ethical requirements.

[0039] Next, the device receives a content generation request from the user. This request may include the purpose and topic of the content to be generated. For example, a request might be made to generate advertising copy about a new product.

[0040] Upon receiving a request, the server generates initial content using a generative AI model. This content is then appropriately adjusted in style and content based on the user's specifications.

[0041] The generated content is evaluated on the server in accordance with legal and ethical standards. The evaluation process accurately determines whether there is potential copyright infringement or excessive exaggeration, and checks whether it contains discriminatory or inappropriate language.

[0042] Content deemed non-compliant is automatically corrected by the server. The correction process aims to replace problematic expressions with appropriate ones while preserving the intent of the original content.

[0043] The revised content is then resent to the device and provided to the user. The user can review this final content and make further adjustments as needed.

[0044] As a concrete example, suppose a user requests "advertising copy highlighting the environmentally friendly features of a new product." When the request is received through the device, the server generates content compliant with relevant laws and regulations based on a generative AI model. For example, a statement such as "This product fully complies with environmental standards" is generated and checked for legal appropriateness. If there are no problems, this copy is provided to the user.

[0045] Thus, the present invention implements a series of processes for efficiently generating and providing legally and ethically sound content to users.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server collects up-to-date information on laws, regulations, and ethical standards, and uses this information to train its generative AI model. This ensures the model is always up-to-date and can use this information to evaluate content.

[0049] Step 2:

[0050] The device receives a content generation request from the user. The user enters the details of the content they want to generate and sends the request through the device.

[0051] Step 3:

[0052] The server uses a generative AI model to generate an initial version of the content based on the request received from the terminal. During this process, the content's structure and style are adjusted according to the request.

[0053] Step 4:

[0054] The server evaluates the generated content against legal and ethical standards. Specifically, it checks for copyright issues, exaggerations, and discriminatory or inappropriate language.

[0055] Step 5:

[0056] The server automatically corrects any sections deemed inappropriate. The corrections aim to replace the original intent with safe and appropriate language.

[0057] Step 6:

[0058] The server sends the corrected content to the device. The device then presents the user with the content that has been verified for safety.

[0059] Step 7:

[0060] Users can review the final content and manually make additional corrections as needed. They can also request a re-evaluation if there are any unclear points.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In modern society, legal and ethical issues surrounding content creation pose significant challenges for individuals and businesses. This challenge, particularly in online content distribution, carries the risk of violating laws and social norms, necessitating a reliable checking mechanism. Current technology makes such checks difficult to perform efficiently and automatically, highlighting the need for a means to quickly adjust generated content to meet legal and ethical standards.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for collecting information on laws, regulations, and ethical standards from an external database and learning from it using a generative model; means for receiving content generation requests from user terminals; and means for generating initial content using a generative AI model based on the received requests. This makes it possible to efficiently meet legal and ethical standards regarding content generation and to quickly provide reliable content.

[0066] "Information regarding laws, regulations, and ethical standards" is a general term for information that includes legal and ethical requirements and guidelines, and is the data necessary to assess the suitability of content based on these.

[0067] A "generative model" is a mathematical model that uses artificial intelligence to generate content, enabling it to learn specific tasks using machine learning algorithms.

[0068] A "user terminal" is an electronic device used by a user to input content generation requests and receive the corrected content.

[0069] "Initial content" refers to the basic text data that the generative AI model initially creates based on user requests, and which is then subject to evaluation and modification.

[0070] A "generative AI model" is a component of artificial intelligence that uses natural language processing technology to analyze text data and generate and modify content.

[0071] "Assessing compliance" refers to the process of determining whether the generated content conforms to legal and ethical standards.

[0072] An "algorithm" is a set of computational procedures designed to solve a specific problem, possessing the function of efficiently detecting and correcting inappropriate content.

[0073] A "user interface" is a software component that provides input and output means for a user to interact with a system.

[0074] "Intellectual property infringement" refers to the act of using another person's copyright, trademark, or other rights without permission, and checking for such infringement is a crucial function of the system.

[0075] "Textual data" refers to information in text format that is analyzed by generative models, and serves as material for generating and evaluating content.

[0076] This invention is a system for efficiently generating content that complies with legal and ethical standards, utilizing a generative AI model. The main components of the system are a server, a terminal, and a user.

[0077] The server collects information on laws, regulations, and ethical standards from external databases. Specifically, it uses API interfaces and web scraping tools. The collected data is used to train a generative AI model. This generative AI model is based on natural language processing technology and is prepared to assess the legal and ethical compliance of content based on the collected information.

[0078] On the other hand, the terminal has an interface in which the user enters a request for content generation. For example, the user might enter a prompt message such as, "Generate advertising copy for a new environmentally friendly product and verify its legal compliance." The terminal then forwards this request to the server.

[0079] After the server generates initial content using a generation AI model, that content is evaluated on the server according to legal and ethical standards. The evaluation process thoroughly checks whether the text infringes on intellectual property rights or contains exaggerations that comply with legal and ethical standards. The server also has a function to automatically correct inappropriate content.

[0080] The revised content is provided to the user via their device. The user can review this content and manually make further adjustments as needed. In this way, the invention provides users with legally and ethically appropriate content and enables the dissemination of information as intended.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server collects information on laws, regulations, and ethical standards from an external database. Specific software tools used include API access and web scraping techniques. The input for this step is the URL or API endpoint of the external database, and the output is text data related to legal and ethical standards. Based on this data, the server prepares to train subsequent generative AI models.

[0084] Step 2:

[0085] The server trains a generative AI model using collected baseline data. Text data from legal documents and ethical guidelines are used as input. The output is an AI model capable of evaluating the legal and ethical compliance of content. Natural language processing techniques are used for data processing to improve the accuracy of the AI ​​model.

[0086] Step 3:

[0087] The terminal receives a content generation request entered by the user. This request may include a prompt such as, "Generate advertising copy for a new environmentally friendly product and verify its legal compliance." The input is the prompt from the user, and the output is the request to the server. The terminal receives this input via the UI and sends it to the server.

[0088] Step 4:

[0089] The server generates initial content using a generative AI model based on requests received from the user. The input is a prompt from the terminal, and the output is the generated initial content. The server constructs the content in an appropriate style and content according to the topic specified by the user.

[0090] Step 5:

[0091] The server evaluates the generated content based on legal and ethical standards. The input is the initial content, and the output is the result of the compliance evaluation. Specifically, this step involves scanning the generated text for keywords and phrases and checking for inappropriate elements.

[0092] Step 6:

[0093] The server automatically corrects any non-conforming content it detects. The input is the non-conforming portion detected during the evaluation process, and the output is the corrected content. Specifically, it uses an AI algorithm to replace problematic expressions with appropriate ones.

[0094] Step 7:

[0095] The terminal receives the corrected content and provides it to the user. The input is the corrected content from the server, and the output is the final content presented to the user. The terminal displays the content to the user through the UI, allowing the user to review and adjust the content.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] The present invention aims to enable the rapid and efficient provision of content that meets user requirements while ensuring legal and ethical compliance in content generation using a generative AI model. Furthermore, it aims to realize a system that saves users time and effort by providing an automatic content correction function.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; means for generating initial content based on received requests; means for detecting and correcting inappropriate content; means for collecting feedback on the generated content and improving the generative model; means for users to input prompts and review and adjust the generated content via an interface; and means for providing content to the user's device in real time. This makes it possible to efficiently generate and provide legally and ethically sound content to users.

[0101] "Data relating to laws, regulations, and ethical standards" refers to a collection of data that includes information based on laws, regulations, guidelines, and social and moral norms.

[0102] A "generative model" is an algorithm or AI system that generates content based on a given dataset, and is particularly capable of processing natural language.

[0103] A "user content generation request" is a request from a user to the generation system to create content based on a specific theme or condition.

[0104] "Initial content" refers to the raw text or other digital content initially generated by the generative model in response to a user request.

[0105] "Means of checking compliance" refers to a process or system for evaluating whether the generated content conforms to laws, regulations, and ethical standards.

[0106] "Means for detecting and correcting inappropriate content" refers to a function that automatically identifies legally or ethically problematic elements within generated content and adjusts them in a correctable manner.

[0107] "Means of collecting feedback and improving generative models" refers to the process of collecting and analyzing information based on user feedback and usage data to improve the performance and accuracy of generative AI models.

[0108] "Means of inputting prompts and reviewing and adjusting generated content via an interface" refers to an input device for the user to give instructions to the system and a user interface that allows the user to view and edit the content generated based on those instructions.

[0109] "Means of providing content in real time" refers to technologies or processes for instantly transmitting and presenting generated content to user devices without delay.

[0110] The system of the present invention consists of a server, a terminal, and a user. The server acquires data on laws, regulations, and ethical standards from external sources and uses this data to train a generative AI model. The generative AI model utilizes natural language processing technology and has the ability to generate content based on specified standards.

[0111] The user enters a content generation request via their device. This request includes the purpose and topic of the content to be generated as a prompt. For example, they might enter, "A catchy slogan emphasizing the benefits of eco-friendly materials."

[0112] Next, the device sends this prompt to the server, which generates initial content based on it. During this process, the generated content is checked against legal and ethical standards, and any inappropriate content is automatically corrected. The corrected content is then returned to the user's device in real time.

[0113] Users can review the generated content using the provided interface and make further adjustments as needed. The server collects user feedback to help improve the generating AI model.

[0114] As a concrete example, consider a company creating an advertisement for a product using new recycled materials. The marketing person wants the message to be "environmentally friendly and promotes sustainability," and enters a prompt based on that. This prompt allows the system to automatically generate and provide legally compliant advertising copy.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user enters a content generation request using a terminal. This input is a prompt message, which contains the purpose and topic of the content to be generated. Based on the user's input, the terminal prepares to send this prompt message to the server.

[0118] Step 2:

[0119] The terminal sends a prompt message to the server. When the server receives the prompt message, it parses it and formats it as input data for the generated AI model. This prompt message is transformed into a format that the AI ​​model can easily understand, based on specific keywords and context.

[0120] Step 3:

[0121] The server generates initial content using a generative AI model. Receiving prompt text as input data, the AI ​​model generates relevant text based on natural language processing techniques. Based on its learned data, the generative AI model produces logical and consistent content that meets the requirements.

[0122] Step 4:

[0123] The server evaluates the generated content. This evaluation includes compliance checks based on laws, regulations, and ethical standards. Specifically, it verifies that the generated text does not contain any legal issues, exaggerations, or ethically problematic expressions. If necessary, it corrects inappropriate parts and automatically replaces them with appropriate expressions.

[0124] Step 5:

[0125] The server sends the corrected content to the device in real time. The user reviews the generated content using the interface provided on the device. During this process, the user can further adjust the content or send feedback to the server.

[0126] Step 6:

[0127] User feedback is collected on the server and used to improve the generative AI model. The server analyzes the feedback and uses it as training data for the generative AI model, thereby improving the model's accuracy and reliability. This feedback loop allows for higher quality output in subsequent content generation.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] This invention is a system that generates legally and ethically compliant content that takes user emotions into consideration by incorporating an emotion engine in addition to a generation AI model. This system operates based on the division of roles between the server, terminal, and user.

[0130] First, the server collects data necessary for laws, regulations, ethical standards, and emotion recognition, and uses this to train the generative AI model and emotion engine. This preparation allows the system to reflect not only legal and ethical standards but also the user's emotions when generating content.

[0131] Next, the device receives a content generation request from the user. This request may include information about the type of content the user wants to generate and the emotional nuances they desire. For example, the user might want to generate text in an inspirational tone.

[0132] After receiving a request, the server uses an emotion engine to analyze the user's emotional state. This analysis is based on the user's input information, the style of the content they intended to view, or direct feedback.

[0133] The server then generates initial content using a generative AI model. During this process, the analyzed user sentiment information is reflected in the content's style and tone. Simultaneously, a compliance assessment is conducted to ensure the content adheres to legal and ethical standards. If inappropriate elements are detected, they are automatically corrected.

[0134] The corrected content is sent to the device via the server. The device then provides this content to the user, who can view it and provide further feedback as needed.

[0135] As a concrete example, suppose a user wants to create a script for a product introduction video that evokes positive emotions. The device receives this request and sends it to the server. The server analyzes the user's emotions through an emotion engine, grasps the "positive" nuances, and then generates content using a generative AI model that satisfies legal and ethical standards. For example, a phrase like "This product brings brightness and energy to everyday life" is generated and checked for appropriateness.

[0136] Thus, the present invention is a system that integrates emotional, legal, and ethical considerations in content creation, enabling the provision of meaningful and safe content for users.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The server collects data necessary for laws, regulations, ethical standards, and sentiment recognition through the internet and specialized databases, and uses this data to train its generative AI model and sentiment engine. This data collection and training ensures the system is up-to-date and possesses the capabilities for sentiment analysis.

[0140] Step 2:

[0141] When the device receives a content generation request from a user, it also obtains information about the user's desired emotional nuances and tone. Users can select specific emotions and styles and submit their requests.

[0142] Step 3:

[0143] After receiving a request, the server uses an emotion engine to analyze the user's emotional state based on the user's input information. For example, the emotion engine identifies emotional states such as joy, sadness, and surprise from keywords and phrases.

[0144] Step 4:

[0145] The server considers the analyzed user's emotional state and generates initial content through a generative AI model. During this process, emotional information is reflected in the content's style and tone, adjusting it to evoke the desired emotion.

[0146] Step 5:

[0147] The server evaluates the generated content based on legal and ethical standards and checks for compliance. Specifically, it verifies whether the content infringes on copyright, contains exaggerations, or discriminatory language, and automatically corrects any non-compliant parts.

[0148] Step 6:

[0149] The server sends the corrected content to the device. The device then provides the user with content that is safe and reflects their desired emotions.

[0150] Step 7:

[0151] Users can review the provided content and provide additional feedback or request revisions as needed. If further adjustments are required, the same process is repeated to generate more appropriate content.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] In today's world, content generation systems are required to comply with legal and ethical standards while accurately reflecting user emotions. However, conventional systems struggle to consider these factors comprehensively, sometimes resulting in the creation of inappropriate content or content that does not align with user emotions. These challenges need to be addressed.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; and means for analyzing emotions based on the received requests and generating initial content. This makes it possible to generate content that reflects the user's emotions while complying with laws and ethics.

[0157] "Laws, regulations, and ethical standards" refer to a set of principles and requirements established to maintain social order and to restrict or guide individual behavior.

[0158] A "generative model" is a set of algorithms that can learn patterns based on data and generate new data.

[0159] "Means of receiving content generation requests from users" refers to an interface or process for obtaining information about the type and style of content desired by the user.

[0160] "Means of analyzing emotions and generating initial content" refers to the process of identifying a user's emotions and generating initial text or media content based on those emotions.

[0161] "Means of checking compliance" refers to an evaluation process that determines whether the generated content complies with laws and ethical standards.

[0162] "Means of correction" refers to the process of changing or correcting content that contains inappropriate elements as necessary.

[0163] "Means of providing to users" refers to the processes and systems used to present and make available the completed content to users.

[0164] "Methods for collecting feedback and improving generative models" refers to the process of gathering opinions and evaluations from users and using them to improve the performance and accuracy of generative models.

[0165] As a form for carrying out the invention, this system is configured as follows.

[0166] The server acquires data on laws, regulations, and ethical standards, and uses this data to train its generative AI model and emotion engine. Data is collected from the internet and internal databases, and the model is updated with the latest knowledge reflecting trends in law and ethics. Machine learning algorithms are used to train the model, helping to improve its emotion recognition capabilities.

[0167] The terminal is responsible for receiving content generation requests from users. Users input the type of content and desired emotional nuances, and then submit the request. The terminal responds to the user's request by sending this information to the server.

[0168] For example, a user might send a request such as, "I want to generate a product description in a positive tone." The device receives this request and forwards it to the server.

[0169] The server uses an emotion engine to analyze the sentiment of incoming requests and identify the emotional nuances the user is seeking. Based on this acquired sentiment information, a generative AI model then generates initial content. This content is evaluated not only emotionally but also against legal and ethical standards. If inappropriate elements are found, the server automatically corrects them. The corrected content is finally delivered to the user through their device.

[0170] This system integrates a generative AI model with an emotion engine to generate safe and appropriate content that responds to the user's emotions.

[0171] An example of a prompt message input to the generative AI model would be, "The user needs an inspirational and positive product description script." Based on this, content such as "This product brings brightness and energy to everyday life" would be generated.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The server retrieves data on laws, regulations, and ethical standards from the internet and internal databases. This data is used to train generative AI models and emotion engines. Inputs include legal information and ethical standards data, and the output is an updated model. This process uses machine learning algorithms to process the data and improve the model's accuracy.

[0175] Step 2:

[0176] The terminal sends content generation requests received from the user to the server. The user uses the terminal's interface to input the type of content and emotional nuances they wish to generate. The input is the user's request, and the output is the request sent to the server. Information regarding the specificity and tone of the content is explicitly stated here.

[0177] Step 3:

[0178] The server uses an emotion engine to analyze the user's emotional state based on the received request. The input is the user's request, and the output is the analyzed emotional information. This analysis identifies a content style that matches the emotions the user desires. Natural language processing techniques are used to analyze the emotional elements within the request.

[0179] Step 4:

[0180] The server generates initial content using a generative AI model based on emotional information. The input is emotional information and user requests, and the output is the initial text content. The model applies pre-trained data to generate text that aligns with the user's intent. The generated content reflects the tone and style desired by the user.

[0181] Step 5:

[0182] The server checks the generated content against legal and ethical standards for compliance. The input is the generated content, and the output is the content information that is compliant or requires correction. If inappropriate elements are detected, the system automatically attempts to correct them. A pre-registered set of rules is used for these corrections.

[0183] Step 6:

[0184] The server sends the corrected content to the terminal, which then provides it to the user. The user can view this content and provide feedback as needed. The input is the corrected content, and the output is what is provided to the user. User feedback is used to improve the generative model for future generations.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] Traditional content generation systems have been unable to accurately reflect users' emotional nuances, making it difficult to address individual needs. Furthermore, the generated content sometimes failed to meet legal and ethical standards, posing a challenge in providing safe and meaningful content to users.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; and means for analyzing emotional nuances from users. This makes it possible to generate content that reflects user emotions while satisfying legal and ethical standards.

[0190] "Data on laws, regulations, and ethical standards" refers to a collection of information that includes laws, social norms, and moral standards related to content creation.

[0191] A "generative model" is a system of algorithms designed to create new content based on input information.

[0192] A "user content generation request" is an instruction or request that specifically outlines the conditions and specifications of the content that the user wishes to generate.

[0193] "Initial content" refers to the version of the content initially created by the generative model, before any feedback or revisions.

[0194] "Emotional nuance" refers to elements that indicate specific emotions or atmospheres that a user wants to convey in their content.

[0195] "Evaluating based on legal and ethical standards" means determining whether the generated content conforms to laws and social norms.

[0196] "Collecting feedback" is the process of gathering opinions and suggestions from users regarding their experience and areas for improvement.

[0197] "Revised content" refers to content that has been modified from its original form to fully comply with laws and ethical standards.

[0198] To implement this invention, the server first acquires data on laws, regulations, and ethical standards and trains a generative AI model. The server also receives content generation requests from users. These requests include information such as the type of content to be generated and the desired emotional nuances. The terminal receives this request and sends it to the server.

[0199] The server uses an emotion engine to analyze the user's emotional nuances. Based on this analysis, a generative AI model is used to create initial content. The generated content is evaluated against legal and ethical standards, and any inappropriate elements are automatically corrected. The corrected content is then delivered to the user via their device.

[0200] This system uses a software framework based on programming languages ​​such as Python to ensure that content generation meets legal and ethical standards. It utilizes hardware such as smartphones and servers to enable the generation and distribution of content that reflects the emotions desired by the user. A specific use case would be an advertising agency wanting to create a positive and sustainable "new eco-bag advertisement" for a spring campaign.

[0201] For example, the following prompt may be provided by the user:

[0202] User input: I want to create an advertisement for our new spring eco-bag with a positive and sustainable tone.

[0203] The system can analyze this prompt and generate advertising content that meets the user's desired emotions as well as legal and ethical standards. This ensures that the advertising content effectively conveys the intended emotions and messages within legal limits.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The server collects and retrieves data on laws, regulations, and ethical standards. This data is used as training data to train a generative AI model. Through this process, the generative AI model comes to understand legal and ethical standards. The input is data on laws, regulations, and ethical standards, and the output is the trained generative AI model.

[0207] Step 2:

[0208] The user uses a terminal to input a content generation request. This request includes the type of content to be generated and the desired emotional nuances. The terminal receives this request and sends it directly to the server. The input is the user's content generation request, and the output is the request data sent to the server.

[0209] Step 3:

[0210] The server applies an emotion engine to analyze the user's emotional nuances based on the received content generation request. The analyzed emotional information supports the functioning of the generation AI model and serves as foundational data for determining the style and tone of the content. The input is the user's emotional nuance information, and the output is the analyzed emotional data.

[0211] Step 4:

[0212] The server creates initial content using a generative AI model based on the analyzed sentiment information and request data. In this process, the input is the analyzed sentiment data and content generation request, and the output is the generated initial content.

[0213] Step 5:

[0214] The server evaluates the generated initial content based on legal and ethical standards. It checks for inappropriate elements and automatically corrects them if necessary. The input is the initial content, and the output is the corrected content.

[0215] Step 6:

[0216] The corrected content is provided to the user via the device. The user receives this content and reviews it. The input is the corrected content, and the output is the content transmitted to the user.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention is a system that uses a generation AI model to check the legal and ethical compliance of content and eliminate inappropriate content. The system is configured based on the division of roles between the server, terminal, and user.

[0234] First, the server collects data on necessary laws, regulations, and ethical standards from external sources. Based on this data, it trains a generative AI model on these laws and ethical standards. This ensures that the model understands the latest legal and ethical requirements.

[0235] Next, the device receives a content generation request from the user. This request may include the purpose and topic of the content to be generated. For example, a request might be made to generate advertising copy about a new product.

[0236] Upon receiving a request, the server generates initial content using a generative AI model. This content is then appropriately adjusted in style and content based on the user's specifications.

[0237] The generated content is evaluated on the server in accordance with legal and ethical standards. The evaluation process accurately determines whether there is potential copyright infringement or excessive exaggeration, and checks whether it contains discriminatory or inappropriate language.

[0238] Content deemed non-compliant is automatically corrected by the server. The correction process aims to replace problematic expressions with appropriate ones while preserving the intent of the original content.

[0239] The revised content is then resent to the device and provided to the user. The user can review this final content and make further adjustments as needed.

[0240] As a concrete example, suppose a user requests "advertising copy highlighting the environmentally friendly features of a new product." When the request is received through the device, the server generates content compliant with relevant laws and regulations based on a generative AI model. For example, a statement such as "This product fully complies with environmental standards" is generated and checked for legal appropriateness. If there are no problems, this copy is provided to the user.

[0241] Thus, the present invention implements a series of processes for efficiently generating and providing legally and ethically sound content to users.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The server collects up-to-date information on laws, regulations, and ethical standards, and uses this information to train its generative AI model. This ensures the model is always up-to-date and can use this information to evaluate content.

[0245] Step 2:

[0246] The device receives a content generation request from the user. The user enters the details of the content they want to generate and sends the request through the device.

[0247] Step 3:

[0248] The server uses a generative AI model to generate an initial version of the content based on the request received from the terminal. During this process, the content's structure and style are adjusted according to the request.

[0249] Step 4:

[0250] The server evaluates the generated content against legal and ethical standards. Specifically, it checks for copyright issues, exaggerations, and discriminatory or inappropriate language.

[0251] Step 5:

[0252] The server automatically corrects any sections deemed inappropriate. The corrections aim to replace the original intent with safe and appropriate language.

[0253] Step 6:

[0254] The server sends the corrected content to the device. The device then presents the user with the content that has been verified for safety.

[0255] Step 7:

[0256] Users can review the final content and manually make additional corrections as needed. They can also request a re-evaluation if there are any unclear points.

[0257] (Example 1)

[0258] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0259] In modern society, legal and ethical issues surrounding content creation pose significant challenges for individuals and businesses. This challenge, particularly in online content distribution, carries the risk of violating laws and social norms, necessitating a reliable checking mechanism. Current technology makes such checks difficult to perform efficiently and automatically, highlighting the need for a means to quickly adjust generated content to meet legal and ethical standards.

[0260] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0261] In this invention, the server includes means for collecting information on laws, regulations, and ethical standards from an external database and learning from it using a generative model; means for receiving content generation requests from user terminals; and means for generating initial content using a generative AI model based on the received requests. This makes it possible to efficiently meet legal and ethical standards regarding content generation and to quickly provide reliable content.

[0262] "Information regarding laws, regulations, and ethical standards" is a general term for information that includes legal and ethical requirements and guidelines, and is the data necessary to assess the suitability of content based on these.

[0263] A "generative model" is a mathematical model that uses artificial intelligence to generate content, enabling it to learn specific tasks using machine learning algorithms.

[0264] A "user terminal" is an electronic device used by a user to input content generation requests and receive the corrected content.

[0265] "Initial content" refers to the basic text data that the generative AI model initially creates based on user requests, and which is then subject to evaluation and modification.

[0266] A "generative AI model" is a component of artificial intelligence that uses natural language processing technology to analyze text data and generate and modify content.

[0267] "Assessing compliance" refers to the process of determining whether the generated content conforms to legal and ethical standards.

[0268] An "algorithm" is a set of computational procedures designed to solve a specific problem, possessing the function of efficiently detecting and correcting inappropriate content.

[0269] A "user interface" is a software component that provides input and output means for a user to interact with a system.

[0270] "Intellectual property infringement" refers to the act of using another person's copyright, trademark, or other rights without permission, and checking for such infringement is a crucial function of the system.

[0271] "Textual data" refers to information in text format that is analyzed by generative models, and serves as material for generating and evaluating content.

[0272] This invention is a system for efficiently generating content that complies with legal and ethical standards, utilizing a generative AI model. The main components of the system are a server, a terminal, and a user.

[0273] The server collects information on laws, regulations, and ethical standards from external databases. Specifically, it uses API interfaces and web scraping tools. The collected data is used to train a generative AI model. This generative AI model is based on natural language processing technology and is prepared to assess the legal and ethical compliance of content based on the collected information.

[0274] On the other hand, the terminal has an interface in which the user enters a request for content generation. For example, the user might enter a prompt message such as, "Generate advertising copy for a new environmentally friendly product and verify its legal compliance." The terminal then forwards this request to the server.

[0275] After the server generates initial content using a generation AI model, that content is evaluated on the server according to legal and ethical standards. The evaluation process thoroughly checks whether the text infringes on intellectual property rights or contains exaggerations that comply with legal and ethical standards. The server also has a function to automatically correct inappropriate content.

[0276] The revised content is provided to the user via their device. The user can review this content and manually make further adjustments as needed. In this way, the invention provides users with legally and ethically appropriate content and enables the dissemination of information as intended.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The server collects information on laws, regulations, and ethical standards from external databases. As specific software tools, API access and web scraping technologies are used. The input for this step is the URL of the external database or the API endpoint, and the output is text data regarding legal and ethical standards. Based on this data, the server prepares for subsequent training of the generative AI model.

[0280] Step 2:

[0281] The server trains a generative AI model using the collected reference data. As input, text data from legal documents and ethical guidelines is used. The output is an AI model equipped with the ability to evaluate the legal and ethical compliance of content. Natural language processing techniques are used for data processing to improve the accuracy of the AI model.

[0282] Step 3:

[0283] The terminal receives the content generation request input by the user. This request includes, for example, a prompt such as "Generate an advertisement copy for an environmentally friendly new product and check its legal compliance." The input is the prompt text from the user, and the output is a request to the server. The terminal receives this input via the UI and sends it to the server.

[0284] Step 4:

[0285] The server uses the generative AI model based on the request received from the user to generate initial content. The input is the prompt text from the terminal, and the output is the generated initial content. The server constructs the content in an appropriate style and content according to the topic specified by the user.

[0286] Step 5:

[0287] The server evaluates the generated content based on legal and ethical standards. The input is the initial content, and the output is the result of the compliance evaluation. Specifically, this step involves scanning the generated text for keywords and phrases and checking for inappropriate elements.

[0288] Step 6:

[0289] The server automatically corrects any non-conforming content it detects. The input is the non-conforming portion detected during the evaluation process, and the output is the corrected content. Specifically, it uses an AI algorithm to replace problematic expressions with appropriate ones.

[0290] Step 7:

[0291] The terminal receives the corrected content and provides it to the user. The input is the corrected content from the server, and the output is the final content presented to the user. The terminal displays the content to the user through the UI, allowing the user to review and adjust the content.

[0292] (Application Example 1)

[0293] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0294] The present invention aims to enable the rapid and efficient provision of content that meets user requirements while ensuring legal and ethical compliance in content generation using a generative AI model. Furthermore, it aims to realize a system that saves users time and effort by providing an automatic content correction function.

[0295] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0296] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; means for generating initial content based on received requests; means for detecting and correcting inappropriate content; means for collecting feedback on the generated content and improving the generative model; means for users to input prompts and review and adjust the generated content via an interface; and means for providing content to the user's device in real time. This makes it possible to efficiently generate and provide legally and ethically sound content to users.

[0297] "Data relating to laws, regulations, and ethical standards" refers to a collection of data that includes information based on laws, regulations, guidelines, and social and moral norms.

[0298] A "generative model" is an algorithm or AI system that generates content based on a given dataset, and is particularly capable of processing natural language.

[0299] A "user content generation request" is a request from a user to the generation system to create content based on a specific theme or condition.

[0300] "Initial content" refers to the raw text or other digital content initially generated by the generative model in response to a user request.

[0301] "Means of checking compliance" refers to a process or system for evaluating whether the generated content conforms to laws, regulations, and ethical standards.

[0302] "Means for detecting and correcting inappropriate content" refers to a function that automatically identifies legally or ethically problematic elements within generated content and adjusts them in a correctable manner.

[0303] The means for "collecting feedback and improving the generation model" is a process of collecting and analyzing information to improve the performance and accuracy of the generative AI model based on user opinions and usage records.

[0304] The means for "inputting a prompt and being able to confirm and adjust the generated content via an interface" is an input device for the user to give instructions to the system and a user interface for displaying and editing the content generated based on it.

[0305] The means for "providing content in real time" is a technology or process for transmitting and presenting the generated content to the user device immediately without delay.

[0306] The system of the present invention is composed of each part of a server, a terminal, and a user. The server obtains data on laws, regulations, and ethical standards from the outside and makes the generative AI model learn it. The generative AI model utilizes natural language processing technology and has the ability to generate content based on the specified criteria of the model.

[0307] The user inputs a content generation request via the terminal. This request includes the purpose and topic of the content to be generated as a prompt sentence. For example, input "a catchphrase highlighting the advantages of eco-friendly materials".

[0308] Next, the terminal sends this prompt to the server, and the server generates initial content based on it. At this time, the generated content is checked for laws and ethical standards, and inappropriate content is automatically corrected. The corrected content is returned to the user's terminal in real time.

[0309] The user can check the generated content using the provided interface. It is also possible to make further adjustments if necessary. The server collects feedback from the user and uses it to improve the generative AI model.

[0310] As a concrete example, consider a company creating an advertisement for a product using new recycled materials. The marketing person wants the message to be "environmentally friendly and promotes sustainability," and enters a prompt based on that. This prompt allows the system to automatically generate and provide legally compliant advertising copy.

[0311] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0312] Step 1:

[0313] The user enters a content generation request using a terminal. This input is a prompt message, which contains the purpose and topic of the content to be generated. Based on the user's input, the terminal prepares to send this prompt message to the server.

[0314] Step 2:

[0315] The terminal sends a prompt message to the server. When the server receives the prompt message, it parses it and formats it as input data for the generated AI model. This prompt message is transformed into a format that the AI ​​model can easily understand, based on specific keywords and context.

[0316] Step 3:

[0317] The server generates initial content using a generative AI model. Receiving prompt text as input data, the AI ​​model generates relevant text based on natural language processing techniques. Based on its learned data, the generative AI model produces logical and consistent content that meets the requirements.

[0318] Step 4:

[0319] The server evaluates the generated content. This evaluation includes compliance checks based on laws, regulations, and ethical standards. Specifically, it verifies that the generated text does not contain any legal issues, exaggerations, or ethically problematic expressions. If necessary, it corrects inappropriate parts and automatically replaces them with appropriate expressions.

[0320] Step 5:

[0321] The server sends the corrected content to the device in real time. The user reviews the generated content using the interface provided on the device. During this process, the user can further adjust the content or send feedback to the server.

[0322] Step 6:

[0323] User feedback is collected on the server and used to improve the generative AI model. The server analyzes the feedback and uses it as training data for the generative AI model, thereby improving the model's accuracy and reliability. This feedback loop allows for higher quality output in subsequent content generation.

[0324] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0325] This invention is a system that generates legally and ethically compliant content that takes user emotions into consideration by incorporating an emotion engine in addition to a generation AI model. This system operates based on the division of roles between the server, terminal, and user.

[0326] First, the server collects data necessary for laws, regulations, ethical standards, and emotion recognition, and uses this to train the generative AI model and emotion engine. This preparation allows the system to reflect not only legal and ethical standards but also the user's emotions when generating content.

[0327] Next, the device receives a content generation request from the user. This request may include information about the type of content the user wants to generate and the emotional nuances they desire. For example, the user might want to generate text in an inspirational tone.

[0328] After receiving a request, the server uses an emotion engine to analyze the user's emotional state. This analysis is based on the user's input information, the style of the content they intended to view, or direct feedback.

[0329] The server then generates initial content using a generative AI model. During this process, the analyzed user sentiment information is reflected in the content's style and tone. Simultaneously, a compliance assessment is conducted to ensure the content adheres to legal and ethical standards. If inappropriate elements are detected, they are automatically corrected.

[0330] The corrected content is sent to the device via the server. The device then provides this content to the user, who can view it and provide further feedback as needed.

[0331] As a concrete example, suppose a user wants to create a script for a product introduction video that evokes positive emotions. The device receives this request and sends it to the server. The server analyzes the user's emotions through an emotion engine, grasps the "positive" nuances, and then generates content using a generative AI model that satisfies legal and ethical standards. For example, a phrase like "This product brings brightness and energy to everyday life" is generated and checked for appropriateness.

[0332] Thus, the present invention is a system that integrates emotional, legal, and ethical considerations in content creation, enabling the provision of meaningful and safe content for users.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] The server collects data necessary for laws, regulations, ethical standards, and sentiment recognition through the internet and specialized databases, and uses this data to train its generative AI model and sentiment engine. This data collection and training ensures the system is up-to-date and possesses the capabilities for sentiment analysis.

[0336] Step 2:

[0337] When the device receives a content generation request from a user, it also obtains information about the user's desired emotional nuances and tone. Users can select specific emotions and styles and submit their requests.

[0338] Step 3:

[0339] After receiving a request, the server uses an emotion engine to analyze the user's emotional state based on the user's input information. For example, the emotion engine identifies emotional states such as joy, sadness, and surprise from keywords and phrases.

[0340] Step 4:

[0341] The server considers the analyzed user's emotional state and generates initial content through a generative AI model. During this process, emotional information is reflected in the content's style and tone, adjusting it to evoke the desired emotion.

[0342] Step 5:

[0343] The server evaluates the generated content based on legal and ethical standards and checks for compliance. Specifically, it verifies whether the content infringes on copyright, contains exaggerations, or discriminatory language, and automatically corrects any non-compliant parts.

[0344] Step 6:

[0345] The server sends the corrected content to the device. The device then provides the user with content that is safe and reflects their desired emotions.

[0346] Step 7:

[0347] Users can review the provided content and provide additional feedback or request revisions as needed. If further adjustments are required, the same process is repeated to generate more appropriate content.

[0348] (Example 2)

[0349] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0350] In today's world, content generation systems are required to comply with legal and ethical standards while accurately reflecting user emotions. However, conventional systems struggle to consider these factors comprehensively, sometimes resulting in the creation of inappropriate content or content that does not align with user emotions. These challenges need to be addressed.

[0351] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0352] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; and means for analyzing emotions based on the received requests and generating initial content. This makes it possible to generate content that reflects the user's emotions while complying with laws and ethics.

[0353] "Laws, regulations, and ethical standards" refer to a set of principles and requirements established to maintain social order and to restrict or guide individual behavior.

[0354] A "generative model" is a set of algorithms that can learn patterns based on data and generate new data.

[0355] "Means of receiving content generation requests from users" refers to an interface or process for obtaining information about the type and style of content desired by the user.

[0356] "Means of analyzing emotions and generating initial content" refers to the process of identifying a user's emotions and generating initial text or media content based on those emotions.

[0357] "Means of checking compliance" refers to an evaluation process that determines whether the generated content complies with laws and ethical standards.

[0358] "Means of correction" refers to the process of changing or correcting content that contains inappropriate elements as necessary.

[0359] "Means of providing to users" refers to the processes and systems used to present and make available the completed content to users.

[0360] "Methods for collecting feedback and improving generative models" refers to the process of gathering opinions and evaluations from users and using them to improve the performance and accuracy of generative models.

[0361] As a form for carrying out the invention, this system is configured as follows.

[0362] The server acquires data on laws, regulations, and ethical standards, and uses this data to train its generative AI model and emotion engine. Data is collected from the internet and internal databases, and the model is updated with the latest knowledge reflecting trends in law and ethics. Machine learning algorithms are used to train the model, helping to improve its emotion recognition capabilities.

[0363] The terminal is responsible for receiving content generation requests from users. Users input the type of content and desired emotional nuances, and then submit the request. The terminal responds to the user's request by sending this information to the server.

[0364] For example, a user might send a request such as, "I want to generate a product description in a positive tone." The device receives this request and forwards it to the server.

[0365] The server uses an emotion engine to analyze the sentiment of incoming requests and identify the emotional nuances the user is seeking. Based on this acquired sentiment information, a generative AI model then generates initial content. This content is evaluated not only emotionally but also against legal and ethical standards. If inappropriate elements are found, the server automatically corrects them. The corrected content is finally delivered to the user through their device.

[0366] This system integrates a generative AI model with an emotion engine to generate safe and appropriate content that responds to the user's emotions.

[0367] An example of a prompt message input to the generative AI model would be, "The user needs an inspirational and positive product description script." Based on this, content such as "This product brings brightness and energy to everyday life" would be generated.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The server retrieves data on laws, regulations, and ethical standards from the internet and internal databases. This data is used to train generative AI models and emotion engines. Inputs include legal information and ethical standards data, and the output is an updated model. This process uses machine learning algorithms to process the data and improve the model's accuracy.

[0371] Step 2:

[0372] The terminal sends content generation requests received from the user to the server. The user uses the terminal's interface to input the type of content and emotional nuances they wish to generate. The input is the user's request, and the output is the request sent to the server. Information regarding the specificity and tone of the content is explicitly stated here.

[0373] Step 3:

[0374] The server uses an emotion engine to analyze the user's emotional state based on the received request. The input is the user's request, and the output is the analyzed emotional information. This analysis identifies a content style that matches the emotions the user desires. Natural language processing techniques are used to analyze the emotional elements within the request.

[0375] Step 4:

[0376] The server generates initial content using a generative AI model based on emotional information. The input is emotional information and user requests, and the output is the initial text content. The model applies pre-trained data to generate text that aligns with the user's intent. The generated content reflects the tone and style desired by the user.

[0377] Step 5:

[0378] The server checks the generated content against legal and ethical standards for compliance. The input is the generated content, and the output is the content information that is compliant or requires correction. If inappropriate elements are detected, the system automatically attempts to correct them. A pre-registered set of rules is used for these corrections.

[0379] Step 6:

[0380] The server sends the corrected content to the terminal, which then provides it to the user. The user can view this content and provide feedback as needed. The input is the corrected content, and the output is what is provided to the user. User feedback is used to improve the generative model for future generations.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] Traditional content generation systems have been unable to accurately reflect users' emotional nuances, making it difficult to address individual needs. Furthermore, the generated content sometimes failed to meet legal and ethical standards, posing a challenge in providing safe and meaningful content to users.

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

[0385] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; and means for analyzing emotional nuances from users. This makes it possible to generate content that reflects user emotions while satisfying legal and ethical standards.

[0386] "Data on laws, regulations, and ethical standards" refers to a collection of information that includes laws, social norms, and moral standards related to content creation.

[0387] A "generative model" is a system of algorithms designed to create new content based on input information.

[0388] A "user content generation request" is an instruction or request that specifically outlines the conditions and specifications of the content that the user wishes to generate.

[0389] "Initial content" refers to the version of the content initially created by the generative model, before any feedback or revisions.

[0390] "Emotional nuance" refers to elements that indicate specific emotions or atmospheres that a user wants to convey in their content.

[0391] "Evaluating based on legal and ethical standards" means determining whether the generated content conforms to laws and social norms.

[0392] "Collecting feedback" is the process of gathering opinions and suggestions from users regarding their experience and areas for improvement.

[0393] "Revised content" refers to content that has been modified from its original form to fully comply with laws and ethical standards.

[0394] To implement this invention, the server first acquires data on laws, regulations, and ethical standards and trains a generative AI model. The server also receives content generation requests from users. These requests include information such as the type of content to be generated and the desired emotional nuances. The terminal receives this request and sends it to the server.

[0395] The server uses an emotion engine to analyze the user's emotional nuances. Based on this analysis, a generative AI model is used to create initial content. The generated content is evaluated against legal and ethical standards, and any inappropriate elements are automatically corrected. The corrected content is then delivered to the user via their device.

[0396] This system uses a software framework based on programming languages ​​such as Python to ensure that content generation meets legal and ethical standards. It utilizes hardware such as smartphones and servers to enable the generation and distribution of content that reflects the emotions desired by the user. A specific use case would be an advertising agency wanting to create a positive and sustainable "new eco-bag advertisement" for a spring campaign.

[0397] For example, the following prompt may be provided by the user:

[0398] User input: I want to create an advertisement for our new spring eco-bag with a positive and sustainable tone.

[0399] The system can analyze this prompt and generate advertising content that meets the user's desired emotions as well as legal and ethical standards. This ensures that the advertising content effectively conveys the intended emotions and messages within legal limits.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The server collects and retrieves data on laws, regulations, and ethical standards. This data is used as training data to train a generative AI model. Through this process, the generative AI model comes to understand legal and ethical standards. The input is data on laws, regulations, and ethical standards, and the output is the trained generative AI model.

[0403] Step 2:

[0404] The user uses a terminal to input a content generation request. This request includes the type of content to be generated and the desired emotional nuances. The terminal receives this request and sends it directly to the server. The input is the user's content generation request, and the output is the request data sent to the server.

[0405] Step 3:

[0406] The server applies an emotion engine to analyze the user's emotional nuances based on the received content generation request. The analyzed emotional information supports the functioning of the generation AI model and serves as foundational data for determining the style and tone of the content. The input is the user's emotional nuance information, and the output is the analyzed emotional data.

[0407] Step 4:

[0408] The server creates initial content using a generative AI model based on the analyzed sentiment information and request data. In this process, the input is the analyzed sentiment data and content generation request, and the output is the generated initial content.

[0409] Step 5:

[0410] The server evaluates the generated initial content based on legal and ethical standards. It checks for inappropriate elements and automatically corrects them if necessary. The input is the initial content, and the output is the corrected content.

[0411] Step 6:

[0412] The corrected content is provided to the user via the device. The user receives this content and reviews it. The input is the corrected content, and the output is the content transmitted to the user.

[0413] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0414] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0415] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0419] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0420] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0421] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0422] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0423] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0424] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0425] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0426] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0427] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0428] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0429] This invention is a system that uses a generation AI model to check the legal and ethical compliance of content and eliminate inappropriate content. The system is configured based on the division of roles between the server, terminal, and user.

[0430] First, the server collects data on necessary laws, regulations, and ethical standards from external sources. Based on this data, it trains a generative AI model on these laws and ethical standards. This ensures that the model understands the latest legal and ethical requirements.

[0431] Next, the device receives a content generation request from the user. This request may include the purpose and topic of the content to be generated. For example, a request might be made to generate advertising copy about a new product.

[0432] Upon receiving a request, the server generates initial content using a generative AI model. This content is then appropriately adjusted in style and content based on the user's specifications.

[0433] The generated content is evaluated on the server in accordance with legal and ethical standards. The evaluation process accurately determines whether there is potential copyright infringement or excessive exaggeration, and checks whether it contains discriminatory or inappropriate language.

[0434] Content deemed non-compliant is automatically corrected by the server. The correction process aims to replace problematic expressions with appropriate ones while preserving the intent of the original content.

[0435] The revised content is then resent to the device and provided to the user. The user can review this final content and make further adjustments as needed.

[0436] As a concrete example, suppose a user requests "advertising copy highlighting the environmentally friendly features of a new product." When the request is received through the device, the server generates content compliant with relevant laws and regulations based on a generative AI model. For example, a statement such as "This product fully complies with environmental standards" is generated and checked for legal appropriateness. If there are no problems, this copy is provided to the user.

[0437] Thus, the present invention implements a series of processes for efficiently generating and providing legally and ethically sound content to users.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The server collects up-to-date information on laws, regulations, and ethical standards, and uses this information to train its generative AI model. This ensures the model is always up-to-date and can use this information to evaluate content.

[0441] Step 2:

[0442] The device receives a content generation request from the user. The user enters the details of the content they want to generate and sends the request through the device.

[0443] Step 3:

[0444] The server uses a generative AI model to generate an initial version of the content based on the request received from the terminal. During this process, the content's structure and style are adjusted according to the request.

[0445] Step 4:

[0446] The server evaluates the generated content against legal and ethical standards. Specifically, it checks for copyright issues, exaggerations, and discriminatory or inappropriate language.

[0447] Step 5:

[0448] The server automatically corrects any sections deemed inappropriate. The corrections aim to replace the original intent with safe and appropriate language.

[0449] Step 6:

[0450] The server sends the corrected content to the device. The device then presents the user with the content that has been verified for safety.

[0451] Step 7:

[0452] Users can review the final content and manually make additional corrections as needed. They can also request a re-evaluation if there are any unclear points.

[0453] (Example 1)

[0454] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0455] In modern society, legal and ethical issues surrounding content creation pose significant challenges for individuals and businesses. This challenge, particularly in online content distribution, carries the risk of violating laws and social norms, necessitating a reliable checking mechanism. Current technology makes such checks difficult to perform efficiently and automatically, highlighting the need for a means to quickly adjust generated content to meet legal and ethical standards.

[0456] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0457] In this invention, the server includes means for collecting information on laws, regulations, and ethical standards from an external database and learning from it using a generative model; means for receiving content generation requests from user terminals; and means for generating initial content using a generative AI model based on the received requests. This makes it possible to efficiently meet legal and ethical standards regarding content generation and to quickly provide reliable content.

[0458] "Information regarding laws, regulations, and ethical standards" is a general term for information that includes legal and ethical requirements and guidelines, and is the data necessary to assess the suitability of content based on these.

[0459] A "generative model" is a mathematical model that uses artificial intelligence to generate content, enabling it to learn specific tasks using machine learning algorithms.

[0460] A "user terminal" is an electronic device used by a user to input content generation requests and receive the corrected content.

[0461] "Initial content" refers to the basic text data that the generative AI model initially creates based on user requests, and which is then subject to evaluation and modification.

[0462] A "generative AI model" is a component of artificial intelligence that uses natural language processing technology to analyze text data and generate and modify content.

[0463] "Assessing compliance" refers to the process of determining whether the generated content conforms to legal and ethical standards.

[0464] An "algorithm" is a set of computational procedures designed to solve a specific problem, possessing the function of efficiently detecting and correcting inappropriate content.

[0465] A "user interface" is a software component that provides input and output means for a user to interact with a system.

[0466] "Intellectual property infringement" refers to the act of using another person's copyright, trademark, or other rights without permission, and checking for such infringement is a crucial function of the system.

[0467] "Textual data" refers to information in text format that is analyzed by generative models, and serves as material for generating and evaluating content.

[0468] This invention is a system for efficiently generating content that complies with legal and ethical standards, utilizing a generative AI model. The main components of the system are a server, a terminal, and a user.

[0469] The server collects information on laws, regulations, and ethical standards from external databases. Specifically, it uses API interfaces and web scraping tools. The collected data is used to train a generative AI model. This generative AI model is based on natural language processing technology and is prepared to assess the legal and ethical compliance of content based on the collected information.

[0470] On the other hand, the terminal has an interface in which the user enters a request for content generation. For example, the user might enter a prompt message such as, "Generate advertising copy for a new environmentally friendly product and verify its legal compliance." The terminal then forwards this request to the server.

[0471] After the server generates initial content using a generation AI model, that content is evaluated on the server according to legal and ethical standards. The evaluation process thoroughly checks whether the text infringes on intellectual property rights or contains exaggerations that comply with legal and ethical standards. The server also has a function to automatically correct inappropriate content.

[0472] The revised content is provided to the user via their device. The user can review this content and manually make further adjustments as needed. In this way, the invention provides users with legally and ethically appropriate content and enables the dissemination of information as intended.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The server collects information on laws, regulations, and ethical standards from an external database. Specific software tools used include API access and web scraping techniques. The input for this step is the URL or API endpoint of the external database, and the output is text data related to legal and ethical standards. Based on this data, the server prepares to train subsequent generative AI models.

[0476] Step 2:

[0477] The server trains a generative AI model using collected baseline data. Text data from legal documents and ethical guidelines are used as input. The output is an AI model capable of evaluating the legal and ethical compliance of content. Natural language processing techniques are used for data processing to improve the accuracy of the AI ​​model.

[0478] Step 3:

[0479] The terminal receives a content generation request entered by the user. This request may include a prompt such as, "Generate advertising copy for a new environmentally friendly product and verify its legal compliance." The input is the prompt from the user, and the output is the request to the server. The terminal receives this input via the UI and sends it to the server.

[0480] Step 4:

[0481] The server generates initial content using a generative AI model based on requests received from the user. The input is a prompt from the terminal, and the output is the generated initial content. The server constructs the content in an appropriate style and content according to the topic specified by the user.

[0482] Step 5:

[0483] The server evaluates the generated content based on legal and ethical standards. The input is the initial content, and the output is the result of the compliance evaluation. Specifically, this step involves scanning the generated text for keywords and phrases and checking for inappropriate elements.

[0484] Step 6:

[0485] The server automatically corrects any non-conforming content it detects. The input is the non-conforming portion detected during the evaluation process, and the output is the corrected content. Specifically, it uses an AI algorithm to replace problematic expressions with appropriate ones.

[0486] Step 7:

[0487] The terminal receives the corrected content and provides it to the user. The input is the corrected content from the server, and the output is the final content presented to the user. The terminal displays the content to the user through the UI, allowing the user to review and adjust the content.

[0488] (Application Example 1)

[0489] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0490] The present invention aims to enable the rapid and efficient provision of content that meets user requirements while ensuring legal and ethical compliance in content generation using a generative AI model. Furthermore, it aims to realize a system that saves users time and effort by providing an automatic content correction function.

[0491] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0492] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; means for generating initial content based on received requests; means for detecting and correcting inappropriate content; means for collecting feedback on the generated content and improving the generative model; means for users to input prompts and review and adjust the generated content via an interface; and means for providing content to the user's device in real time. This makes it possible to efficiently generate and provide legally and ethically sound content to users.

[0493] "Data relating to laws, regulations, and ethical standards" refers to a collection of data that includes information based on laws, regulations, guidelines, and social and moral norms.

[0494] A "generative model" is an algorithm or AI system that generates content based on a given dataset, and is particularly capable of processing natural language.

[0495] A "user content generation request" is a request from a user to the generation system to create content based on a specific theme or condition.

[0496] "Initial content" refers to the raw text or other digital content initially generated by the generative model in response to a user request.

[0497] "Means of checking compliance" refers to a process or system for evaluating whether the generated content conforms to laws, regulations, and ethical standards.

[0498] "Means for detecting and correcting inappropriate content" refers to a function that automatically identifies legally or ethically problematic elements within generated content and adjusts them in a correctable manner.

[0499] "Means of collecting feedback and improving generative models" refers to the process of collecting and analyzing information based on user feedback and usage data to improve the performance and accuracy of generative AI models.

[0500] "Means of inputting prompts and reviewing and adjusting generated content via an interface" refers to an input device for the user to give instructions to the system and a user interface that allows the user to view and edit the content generated based on those instructions.

[0501] "Means of providing content in real time" refers to technologies or processes for instantly transmitting and presenting generated content to user devices without delay.

[0502] The system of the present invention consists of a server, a terminal, and a user. The server acquires data on laws, regulations, and ethical standards from external sources and uses this data to train a generative AI model. The generative AI model utilizes natural language processing technology and has the ability to generate content based on specified standards.

[0503] The user enters a content generation request via their device. This request includes the purpose and topic of the content to be generated as a prompt. For example, they might enter, "A catchy slogan emphasizing the benefits of eco-friendly materials."

[0504] Next, the device sends this prompt to the server, which generates initial content based on it. During this process, the generated content is checked against legal and ethical standards, and any inappropriate content is automatically corrected. The corrected content is then returned to the user's device in real time.

[0505] Users can review the generated content using the provided interface and make further adjustments as needed. The server collects user feedback to help improve the generating AI model.

[0506] As a concrete example, consider a company creating an advertisement for a product using new recycled materials. The marketing person wants the message to be "environmentally friendly and promotes sustainability," and enters a prompt based on that. This prompt allows the system to automatically generate and provide legally compliant advertising copy.

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The user enters a content generation request using a terminal. This input is a prompt message, which contains the purpose and topic of the content to be generated. Based on the user's input, the terminal prepares to send this prompt message to the server.

[0510] Step 2:

[0511] The terminal sends a prompt message to the server. When the server receives the prompt message, it parses it and formats it as input data for the generated AI model. This prompt message is transformed into a format that the AI ​​model can easily understand, based on specific keywords and context.

[0512] Step 3:

[0513] The server generates initial content using a generative AI model. Receiving prompt text as input data, the AI ​​model generates relevant text based on natural language processing techniques. Based on its learned data, the generative AI model produces logical and consistent content that meets the requirements.

[0514] Step 4:

[0515] The server evaluates the generated content. This evaluation includes compliance checks based on laws, regulations, and ethical standards. Specifically, it verifies that the generated text does not contain any legal issues, exaggerations, or ethically problematic expressions. If necessary, it corrects inappropriate parts and automatically replaces them with appropriate expressions.

[0516] Step 5:

[0517] The server sends the corrected content to the device in real time. The user reviews the generated content using the interface provided on the device. During this process, the user can further adjust the content or send feedback to the server.

[0518] Step 6:

[0519] User feedback is collected on the server and used to improve the generative AI model. The server analyzes the feedback and uses it as training data for the generative AI model, thereby improving the model's accuracy and reliability. This feedback loop allows for higher quality output in subsequent content generation.

[0520] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0521] This invention is a system that generates legally and ethically compliant content that takes user emotions into consideration by incorporating an emotion engine in addition to a generation AI model. This system operates based on the division of roles between the server, terminal, and user.

[0522] First, the server collects data necessary for laws, regulations, ethical standards, and emotion recognition, and uses this to train the generative AI model and emotion engine. This preparation allows the system to reflect not only legal and ethical standards but also the user's emotions when generating content.

[0523] Next, the device receives a content generation request from the user. This request may include information about the type of content the user wants to generate and the emotional nuances they desire. For example, the user might want to generate text in an inspirational tone.

[0524] After receiving a request, the server uses an emotion engine to analyze the user's emotional state. This analysis is based on the user's input information, the style of the content they intended to view, or direct feedback.

[0525] The server then generates initial content using a generative AI model. During this process, the analyzed user sentiment information is reflected in the content's style and tone. Simultaneously, a compliance assessment is conducted to ensure the content adheres to legal and ethical standards. If inappropriate elements are detected, they are automatically corrected.

[0526] The corrected content is sent to the device via the server. The device then provides this content to the user, who can view it and provide further feedback as needed.

[0527] As a concrete example, suppose a user wants to create a script for a product introduction video that evokes positive emotions. The device receives this request and sends it to the server. The server analyzes the user's emotions through an emotion engine, grasps the "positive" nuances, and then generates content using a generative AI model that satisfies legal and ethical standards. For example, a phrase like "This product brings brightness and energy to everyday life" is generated and checked for appropriateness.

[0528] Thus, the present invention is a system that integrates emotional, legal, and ethical considerations in content creation, enabling the provision of meaningful and safe content for users.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] The server collects data necessary for laws, regulations, ethical standards, and sentiment recognition through the internet and specialized databases, and uses this data to train its generative AI model and sentiment engine. This data collection and training ensures the system is up-to-date and possesses the capabilities for sentiment analysis.

[0532] Step 2:

[0533] When the device receives a content generation request from a user, it also obtains information about the user's desired emotional nuances and tone. Users can select specific emotions and styles and submit their requests.

[0534] Step 3:

[0535] After receiving a request, the server uses an emotion engine to analyze the user's emotional state based on the user's input information. For example, the emotion engine identifies emotional states such as joy, sadness, and surprise from keywords and phrases.

[0536] Step 4:

[0537] The server considers the analyzed user's emotional state and generates initial content through a generative AI model. During this process, emotional information is reflected in the content's style and tone, adjusting it to evoke the desired emotion.

[0538] Step 5:

[0539] The server evaluates the generated content based on legal and ethical standards and checks for compliance. Specifically, it verifies whether the content infringes on copyright, contains exaggerations, or discriminatory language, and automatically corrects any non-compliant parts.

[0540] Step 6:

[0541] The server sends the corrected content to the device. The device then provides the user with content that is safe and reflects their desired emotions.

[0542] Step 7:

[0543] Users can review the provided content and provide additional feedback or request revisions as needed. If further adjustments are required, the same process is repeated to generate more appropriate content.

[0544] (Example 2)

[0545] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0546] In today's world, content generation systems are required to comply with legal and ethical standards while accurately reflecting user emotions. However, conventional systems struggle to consider these factors comprehensively, sometimes resulting in the creation of inappropriate content or content that does not align with user emotions. These challenges need to be addressed.

[0547] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0548] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; and means for analyzing emotions based on the received requests and generating initial content. This makes it possible to generate content that reflects the user's emotions while complying with laws and ethics.

[0549] "Laws, regulations, and ethical standards" refer to a set of principles and requirements established to maintain social order and to restrict or guide individual behavior.

[0550] A "generative model" is a set of algorithms that can learn patterns based on data and generate new data.

[0551] "Means of receiving content generation requests from users" refers to an interface or process for obtaining information about the type and style of content desired by the user.

[0552] "Means of analyzing emotions and generating initial content" refers to the process of identifying a user's emotions and generating initial text or media content based on those emotions.

[0553] "Means of checking compliance" refers to an evaluation process that determines whether the generated content complies with laws and ethical standards.

[0554] "Means of correction" refers to the process of changing or correcting content that contains inappropriate elements as necessary.

[0555] "Means of providing to users" refers to the processes and systems used to present and make available the completed content to users.

[0556] "Methods for collecting feedback and improving generative models" refers to the process of gathering opinions and evaluations from users and using them to improve the performance and accuracy of generative models.

[0557] As a form for carrying out the invention, this system is configured as follows.

[0558] The server acquires data on laws, regulations, and ethical standards, and uses this data to train its generative AI model and emotion engine. Data is collected from the internet and internal databases, and the model is updated with the latest knowledge reflecting trends in law and ethics. Machine learning algorithms are used to train the model, helping to improve its emotion recognition capabilities.

[0559] The terminal is responsible for receiving content generation requests from users. Users input the type of content and desired emotional nuances, and then submit the request. The terminal responds to the user's request by sending this information to the server.

[0560] For example, a user might send a request such as, "I want to generate a product description in a positive tone." The device receives this request and forwards it to the server.

[0561] The server uses an emotion engine to analyze the sentiment of incoming requests and identify the emotional nuances the user is seeking. Based on this acquired sentiment information, a generative AI model then generates initial content. This content is evaluated not only emotionally but also against legal and ethical standards. If inappropriate elements are found, the server automatically corrects them. The corrected content is finally delivered to the user through their device.

[0562] This system integrates a generative AI model with an emotion engine to generate safe and appropriate content that responds to the user's emotions.

[0563] An example of a prompt message input to the generative AI model would be, "The user needs an inspirational and positive product description script." Based on this, content such as "This product brings brightness and energy to everyday life" would be generated.

[0564] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0565] Step 1:

[0566] The server retrieves data on laws, regulations, and ethical standards from the internet and internal databases. This data is used to train generative AI models and emotion engines. Inputs include legal information and ethical standards data, and the output is an updated model. This process uses machine learning algorithms to process the data and improve the model's accuracy.

[0567] Step 2:

[0568] The terminal sends content generation requests received from the user to the server. The user uses the terminal's interface to input the type of content and emotional nuances they wish to generate. The input is the user's request, and the output is the request sent to the server. Information regarding the specificity and tone of the content is explicitly stated here.

[0569] Step 3:

[0570] The server uses an emotion engine to analyze the user's emotional state based on the received request. The input is the user's request, and the output is the analyzed emotional information. This analysis identifies a content style that matches the emotions the user desires. Natural language processing techniques are used to analyze the emotional elements within the request.

[0571] Step 4:

[0572] The server generates initial content using a generative AI model based on emotional information. The input is emotional information and user requests, and the output is the initial text content. The model applies pre-trained data to generate text that aligns with the user's intent. The generated content reflects the tone and style desired by the user.

[0573] Step 5:

[0574] The server checks the generated content against legal and ethical standards for compliance. The input is the generated content, and the output is the content information that is compliant or requires correction. If inappropriate elements are detected, the system automatically attempts to correct them. A pre-registered set of rules is used for these corrections.

[0575] Step 6:

[0576] The server sends the corrected content to the terminal, which then provides it to the user. The user can view this content and provide feedback as needed. The input is the corrected content, and the output is what is provided to the user. User feedback is used to improve the generative model for future generations.

[0577] (Application Example 2)

[0578] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0579] Traditional content generation systems have been unable to accurately reflect users' emotional nuances, making it difficult to address individual needs. Furthermore, the generated content sometimes failed to meet legal and ethical standards, posing a challenge in providing safe and meaningful content to users.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0581] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; and means for analyzing emotional nuances from users. This makes it possible to generate content that reflects user emotions while satisfying legal and ethical standards.

[0582] "Data on laws, regulations, and ethical standards" refers to a collection of information that includes laws, social norms, and moral standards related to content creation.

[0583] A "generative model" is a system of algorithms designed to create new content based on input information.

[0584] A "user content generation request" is an instruction or request that specifically outlines the conditions and specifications of the content that the user wishes to generate.

[0585] "Initial content" refers to the version of the content initially created by the generative model, before any feedback or revisions.

[0586] "Emotional nuance" refers to elements that indicate specific emotions or atmospheres that a user wants to convey in their content.

[0587] "Evaluating based on legal and ethical standards" means determining whether the generated content conforms to laws and social norms.

[0588] "Collecting feedback" is the process of gathering opinions and suggestions from users regarding their experience and areas for improvement.

[0589] "Revised content" refers to content that has been modified from its original form to fully comply with laws and ethical standards.

[0590] To implement this invention, the server first acquires data on laws, regulations, and ethical standards and trains a generative AI model. The server also receives content generation requests from users. These requests include information such as the type of content to be generated and the desired emotional nuances. The terminal receives this request and sends it to the server.

[0591] The server uses an emotion engine to analyze the user's emotional nuances. Based on this analysis, a generative AI model is used to create initial content. The generated content is evaluated against legal and ethical standards, and any inappropriate elements are automatically corrected. The corrected content is then delivered to the user via their device.

[0592] This system uses a software framework based on programming languages ​​such as Python to ensure that content generation meets legal and ethical standards. It utilizes hardware such as smartphones and servers to enable the generation and distribution of content that reflects the emotions desired by the user. A specific use case would be an advertising agency wanting to create a positive and sustainable "new eco-bag advertisement" for a spring campaign.

[0593] For example, the following prompt may be provided by the user:

[0594] User input: I want to create an advertisement for our new spring eco-bag with a positive and sustainable tone.

[0595] The system can analyze this prompt and generate advertising content that meets the user's desired emotions as well as legal and ethical standards. This ensures that the advertising content effectively conveys the intended emotions and messages within legal limits.

[0596] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0597] Step 1:

[0598] The server collects and retrieves data on laws, regulations, and ethical standards. This data is used as training data to train a generative AI model. Through this process, the generative AI model comes to understand legal and ethical standards. The input is data on laws, regulations, and ethical standards, and the output is the trained generative AI model.

[0599] Step 2:

[0600] The user uses a terminal to input a content generation request. This request includes the type of content to be generated and the desired emotional nuances. The terminal receives this request and sends it directly to the server. The input is the user's content generation request, and the output is the request data sent to the server.

[0601] Step 3:

[0602] The server applies an emotion engine to analyze the user's emotional nuances based on the received content generation request. The analyzed emotional information supports the functioning of the generation AI model and serves as foundational data for determining the style and tone of the content. The input is the user's emotional nuance information, and the output is the analyzed emotional data.

[0603] Step 4:

[0604] The server creates initial content using a generative AI model based on the analyzed sentiment information and request data. In this process, the input is the analyzed sentiment data and content generation request, and the output is the generated initial content.

[0605] Step 5:

[0606] The server evaluates the generated initial content based on legal and ethical standards. It checks for inappropriate elements and automatically corrects them if necessary. The input is the initial content, and the output is the corrected content.

[0607] Step 6:

[0608] The corrected content is provided to the user via the device. The user receives this content and reviews it. The input is the corrected content, and the output is the content transmitted to the user.

[0609] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0610] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0611] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0612] [Fourth Embodiment]

[0613] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0614] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0615] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0616] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0617] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0618] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0619] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0620] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0621] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0622] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0623] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0624] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0625] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0626] This invention is a system that uses a generation AI model to check the legal and ethical compliance of content and eliminate inappropriate content. The system is configured based on the division of roles between the server, terminal, and user.

[0627] First, the server collects data on necessary laws, regulations, and ethical standards from external sources. Based on this data, it trains a generative AI model on these laws and ethical standards. This ensures that the model understands the latest legal and ethical requirements.

[0628] Next, the device receives a content generation request from the user. This request may include the purpose and topic of the content to be generated. For example, a request might be made to generate advertising copy about a new product.

[0629] Upon receiving a request, the server generates initial content using a generative AI model. This content is then appropriately adjusted in style and content based on the user's specifications.

[0630] The generated content is evaluated on the server in accordance with legal and ethical standards. The evaluation process accurately determines whether there is potential copyright infringement or excessive exaggeration, and checks whether it contains discriminatory or inappropriate language.

[0631] Content deemed non-compliant is automatically corrected by the server. The correction process aims to replace problematic expressions with appropriate ones while preserving the intent of the original content.

[0632] The revised content is then resent to the device and provided to the user. The user can review this final content and make further adjustments as needed.

[0633] As a concrete example, suppose a user requests "advertising copy highlighting the environmentally friendly features of a new product." When the request is received through the device, the server generates content compliant with relevant laws and regulations based on a generative AI model. For example, a statement such as "This product fully complies with environmental standards" is generated and checked for legal appropriateness. If there are no problems, this copy is provided to the user.

[0634] Thus, the present invention implements a series of processes for efficiently generating and providing legally and ethically sound content to users.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The server collects up-to-date information on laws, regulations, and ethical standards, and uses this information to train its generative AI model. This ensures the model is always up-to-date and can use this information to evaluate content.

[0638] Step 2:

[0639] The device receives a content generation request from the user. The user enters the details of the content they want to generate and sends the request through the device.

[0640] Step 3:

[0641] The server uses a generative AI model to generate an initial version of the content based on the request received from the terminal. During this process, the content's structure and style are adjusted according to the request.

[0642] Step 4:

[0643] The server evaluates the generated content against legal and ethical standards. Specifically, it checks for copyright issues, exaggerations, and discriminatory or inappropriate language.

[0644] Step 5:

[0645] The server automatically corrects any sections deemed inappropriate. The corrections aim to replace the original intent with safe and appropriate language.

[0646] Step 6:

[0647] The server sends the corrected content to the device. The device then presents the user with the content that has been verified for safety.

[0648] Step 7:

[0649] Users can review the final content and manually make additional corrections as needed. They can also request a re-evaluation if there are any unclear points.

[0650] (Example 1)

[0651] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0652] In modern society, legal and ethical issues surrounding content creation pose significant challenges for individuals and businesses. This challenge, particularly in online content distribution, carries the risk of violating laws and social norms, necessitating a reliable checking mechanism. Current technology makes such checks difficult to perform efficiently and automatically, highlighting the need for a means to quickly adjust generated content to meet legal and ethical standards.

[0653] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0654] In this invention, the server includes means for collecting information on laws, regulations, and ethical standards from an external database and learning from it using a generative model; means for receiving content generation requests from user terminals; and means for generating initial content using a generative AI model based on the received requests. This makes it possible to efficiently meet legal and ethical standards regarding content generation and to quickly provide reliable content.

[0655] "Information regarding laws, regulations, and ethical standards" is a general term for information that includes legal and ethical requirements and guidelines, and is the data necessary to assess the suitability of content based on these.

[0656] A "generative model" is a mathematical model that uses artificial intelligence to generate content, enabling it to learn specific tasks using machine learning algorithms.

[0657] A "user terminal" is an electronic device used by a user to input content generation requests and receive the corrected content.

[0658] "Initial content" refers to the basic text data that the generative AI model initially creates based on user requests, and which is then subject to subsequent evaluation and modification.

[0659] A "generative AI model" is a component of artificial intelligence that uses natural language processing technology to analyze text data and generate and modify content.

[0660] "Assessing compliance" refers to the process of determining whether the generated content conforms to legal and ethical standards.

[0661] An "algorithm" is a set of computational procedures designed to solve a specific problem, possessing the function of efficiently detecting and correcting inappropriate content.

[0662] A "user interface" is a software component that provides input and output means for a user to interact with a system.

[0663] "Intellectual property infringement" refers to the act of using another person's copyright, trademark, or other rights without permission, and checking for such infringement is a crucial function of the system.

[0664] "Textual data" refers to information in text format that is analyzed by generative models, and serves as material for generating and evaluating content.

[0665] This invention is a system for efficiently generating content that complies with legal and ethical standards, utilizing a generative AI model. The main components of the system are a server, a terminal, and a user.

[0666] The server collects information on laws, regulations, and ethical standards from external databases. Specifically, it uses API interfaces and web scraping tools. The collected data is used to train a generative AI model. This generative AI model is based on natural language processing technology and is prepared to assess the legal and ethical compliance of content based on the collected information.

[0667] On the other hand, the terminal has an interface in which the user enters a request for content generation. For example, the user might enter a prompt message such as, "Generate advertising copy for a new environmentally friendly product and verify its legal compliance." The terminal then forwards this request to the server.

[0668] After the server generates initial content using a generation AI model, that content is evaluated on the server according to legal and ethical standards. The evaluation process thoroughly checks whether the text infringes on intellectual property rights or contains exaggerations that comply with legal and ethical standards. The server also has a function to automatically correct inappropriate content.

[0669] The revised content is provided to the user via their device. The user can review this content and manually make further adjustments as needed. In this way, the invention provides users with legally and ethically appropriate content and enables the dissemination of information as intended.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] The server collects information on laws, regulations, and ethical standards from an external database. Specific software tools used include API access and web scraping techniques. The input for this step is the URL or API endpoint of the external database, and the output is text data related to legal and ethical standards. Based on this data, the server prepares to train subsequent generative AI models.

[0673] Step 2:

[0674] The server trains a generative AI model using collected baseline data. Text data from legal documents and ethical guidelines are used as input. The output is an AI model capable of evaluating the legal and ethical compliance of content. Natural language processing techniques are used for data processing to improve the accuracy of the AI ​​model.

[0675] Step 3:

[0676] The terminal receives a content generation request entered by the user. This request may include a prompt such as, "Generate advertising copy for a new environmentally friendly product and verify its legal compliance." The input is the prompt from the user, and the output is the request to the server. The terminal receives this input via the UI and sends it to the server.

[0677] Step 4:

[0678] The server generates initial content using a generative AI model based on requests received from the user. The input is a prompt from the terminal, and the output is the generated initial content. The server constructs the content in an appropriate style and content according to the topic specified by the user.

[0679] Step 5:

[0680] The server evaluates the generated content based on legal and ethical standards. The input is the initial content, and the output is the result of the compliance evaluation. Specifically, this step involves scanning the generated text for keywords and phrases and checking for inappropriate elements.

[0681] Step 6:

[0682] The server automatically corrects any non-conforming content it detects. The input is the non-conforming portion detected during the evaluation process, and the output is the corrected content. Specifically, it uses an AI algorithm to replace problematic expressions with appropriate ones.

[0683] Step 7:

[0684] The terminal receives the corrected content and provides it to the user. The input is the corrected content from the server, and the output is the final content presented to the user. The terminal displays the content to the user through the UI, allowing the user to review and adjust the content.

[0685] (Application Example 1)

[0686] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] The present invention aims to enable the rapid and efficient provision of content that meets user requirements while ensuring legal and ethical compliance in content generation using a generative AI model. Furthermore, it aims to realize a system that saves users time and effort by providing an automatic content correction function.

[0688] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0689] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; means for generating initial content based on received requests; means for detecting and correcting inappropriate content; means for collecting feedback on the generated content and improving the generative model; means for users to input prompts and review and adjust the generated content via an interface; and means for providing content to the user's device in real time. This makes it possible to efficiently generate and provide legally and ethically sound content to users.

[0690] "Data relating to laws, regulations, and ethical standards" refers to a collection of data that includes information based on laws, regulations, guidelines, and social and moral norms.

[0691] A "generative model" is an algorithm or AI system that generates content based on a given dataset, and is particularly capable of processing natural language.

[0692] A "user content generation request" is a request from a user to the generation system to create content based on a specific theme or condition.

[0693] "Initial content" refers to the raw text or other digital content initially generated by the generative model in response to a user request.

[0694] "Means of checking compliance" refers to a process or system for evaluating whether the generated content conforms to laws, regulations, and ethical standards.

[0695] "Means for detecting and correcting inappropriate content" refers to a function that automatically identifies legally or ethically problematic elements within generated content and adjusts them in a correctable manner.

[0696] "Means of collecting feedback and improving generative models" refers to the process of collecting and analyzing information based on user feedback and usage data to improve the performance and accuracy of generative AI models.

[0697] "Means of inputting prompts and reviewing and adjusting generated content via an interface" refers to an input device for the user to give instructions to the system and a user interface that allows the user to view and edit the content generated based on those instructions.

[0698] "Means of providing content in real time" refers to technologies or processes for instantly transmitting and presenting generated content to user devices without delay.

[0699] The system of the present invention consists of a server, a terminal, and a user. The server acquires data on laws, regulations, and ethical standards from external sources and uses this data to train a generative AI model. The generative AI model utilizes natural language processing technology and has the ability to generate content based on specified standards.

[0700] The user enters a content generation request via their device. This request includes the purpose and topic of the content to be generated as a prompt. For example, they might enter, "A catchy slogan emphasizing the benefits of eco-friendly materials."

[0701] Next, the device sends this prompt to the server, which generates initial content based on it. During this process, the generated content is checked against legal and ethical standards, and any inappropriate content is automatically corrected. The corrected content is then returned to the user's device in real time.

[0702] Users can review the generated content using the provided interface and make further adjustments as needed. The server collects user feedback to help improve the generating AI model.

[0703] As a concrete example, consider a company creating an advertisement for a product using new recycled materials. The marketing person wants the message to be "environmentally friendly and promotes sustainability," and enters a prompt based on that. This prompt allows the system to automatically generate and provide legally compliant advertising copy.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The user enters a content generation request using a terminal. This input is a prompt message, which contains the purpose and topic of the content to be generated. Based on the user's input, the terminal prepares to send this prompt message to the server.

[0707] Step 2:

[0708] The terminal sends a prompt message to the server. When the server receives the prompt message, it parses it and formats it as input data for the generated AI model. This prompt message is transformed into a format that the AI ​​model can easily understand, based on specific keywords and context.

[0709] Step 3:

[0710] The server generates initial content using a generative AI model. Receiving prompt text as input data, the AI ​​model generates relevant text based on natural language processing techniques. Based on its learned data, the generative AI model produces logical and consistent content that meets the requirements.

[0711] Step 4:

[0712] The server evaluates the generated content. This evaluation includes compliance checks based on laws, regulations, and ethical standards. Specifically, it verifies that the generated text does not contain any legal issues, exaggerations, or ethically problematic expressions. If necessary, it corrects inappropriate parts and automatically replaces them with appropriate expressions.

[0713] Step 5:

[0714] The server sends the corrected content to the device in real time. The user reviews the generated content using the interface provided on the device. During this process, the user can further adjust the content or send feedback to the server.

[0715] Step 6:

[0716] User feedback is collected on the server and used to improve the generative AI model. The server analyzes the feedback and uses it as training data for the generative AI model, thereby improving the model's accuracy and reliability. This feedback loop allows for higher quality output in subsequent content generation.

[0717] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0718] This invention is a system that generates legally and ethically compliant content that takes user emotions into consideration by incorporating an emotion engine in addition to a generation AI model. This system operates based on the division of roles between the server, terminal, and user.

[0719] First, the server collects data necessary for laws, regulations, ethical standards, and emotion recognition, and uses this to train the generative AI model and emotion engine. This preparation allows the system to reflect not only legal and ethical standards but also the user's emotions when generating content.

[0720] Next, the device receives a content generation request from the user. This request may include information about the type of content the user wants to generate and the emotional nuances they desire. For example, the user might want to generate text in an inspirational tone.

[0721] After receiving a request, the server uses an emotion engine to analyze the user's emotional state. This analysis is based on the user's input information, the style of the content they intended to view, or direct feedback.

[0722] The server then generates initial content using a generative AI model. During this process, the analyzed user sentiment information is reflected in the content's style and tone. Simultaneously, a compliance assessment is conducted to ensure the content adheres to legal and ethical standards. If inappropriate elements are detected, they are automatically corrected.

[0723] The corrected content is sent to the device via the server. The device then provides this content to the user, who can view it and provide further feedback as needed.

[0724] As a concrete example, suppose a user wants to create a script for a product introduction video that evokes positive emotions. The device receives this request and sends it to the server. The server analyzes the user's emotions through an emotion engine, grasps the "positive" nuances, and then generates content using a generative AI model that satisfies legal and ethical standards. For example, a phrase like "This product brings brightness and energy to everyday life" is generated and checked for appropriateness.

[0725] Thus, the present invention is a system that integrates emotional, legal, and ethical considerations in content creation, enabling the provision of meaningful and safe content for users.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The server collects data necessary for laws, regulations, ethical standards, and sentiment recognition through the internet and specialized databases, and uses this data to train its generative AI model and sentiment engine. This data collection and training ensures the system is up-to-date and possesses the capabilities for sentiment analysis.

[0729] Step 2:

[0730] When the device receives a content generation request from a user, it also obtains information about the user's desired emotional nuances and tone. Users can select specific emotions and styles and submit their requests.

[0731] Step 3:

[0732] After receiving a request, the server uses an emotion engine to analyze the user's emotional state based on the user's input information. For example, the emotion engine identifies emotional states such as joy, sadness, and surprise from keywords and phrases.

[0733] Step 4:

[0734] The server considers the analyzed user's emotional state and generates initial content through a generative AI model. During this process, emotional information is reflected in the content's style and tone, adjusting it to evoke the desired emotion.

[0735] Step 5:

[0736] The server evaluates the generated content based on legal and ethical standards and checks for compliance. Specifically, it verifies whether the content infringes on copyright, contains exaggerations, or discriminatory language, and automatically corrects any non-compliant parts.

[0737] Step 6:

[0738] The server sends the corrected content to the device. The device then provides the user with content that is safe and reflects their desired emotions.

[0739] Step 7:

[0740] Users can review the provided content and provide additional feedback or request revisions as needed. If further adjustments are required, the same process is repeated to generate more appropriate content.

[0741] (Example 2)

[0742] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] In today's world, content generation systems are required to comply with legal and ethical standards while accurately reflecting user emotions. However, conventional systems struggle to consider these factors comprehensively, sometimes resulting in the creation of inappropriate content or content that does not align with user emotions. These challenges need to be addressed.

[0744] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0745] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; and means for analyzing emotions based on the received requests and generating initial content. This makes it possible to generate content that reflects the user's emotions while complying with laws and ethics.

[0746] "Laws, regulations, and ethical standards" refer to a set of principles and requirements established to maintain social order and to restrict or guide individual behavior.

[0747] A "generative model" is a set of algorithms that can learn patterns based on data and generate new data.

[0748] "Means of receiving content generation requests from users" refers to an interface or process for obtaining information about the type and style of content desired by the user.

[0749] "Means of analyzing emotions and generating initial content" refers to the process of identifying a user's emotions and generating initial text or media content based on those emotions.

[0750] "Means of checking compliance" refers to an evaluation process that determines whether the generated content complies with laws and ethical standards.

[0751] "Means of correction" refers to the process of changing or correcting content that contains inappropriate elements as necessary.

[0752] "Means of providing to users" refers to the processes and systems used to present and make available the completed content to users.

[0753] "Methods for collecting feedback and improving generative models" refers to the process of gathering opinions and evaluations from users and using them to improve the performance and accuracy of generative models.

[0754] As a form for carrying out the invention, this system is configured as follows.

[0755] The server acquires data on laws, regulations, and ethical standards, and uses this data to train its generative AI model and emotion engine. Data is collected from the internet and internal databases, and the model is updated with the latest knowledge reflecting trends in law and ethics. Machine learning algorithms are used to train the model, helping to improve its emotion recognition capabilities.

[0756] The terminal is responsible for receiving content generation requests from users. Users input the type of content and desired emotional nuances, and then submit the request. The terminal responds to the user's request by sending this information to the server.

[0757] For example, a user might send a request such as, "I want to generate a product description in a positive tone." The device receives this request and forwards it to the server.

[0758] The server uses an emotion engine to analyze the sentiment of incoming requests and identify the emotional nuances the user is seeking. Based on this acquired sentiment information, a generative AI model then generates initial content. This content is evaluated not only emotionally but also against legal and ethical standards. If inappropriate elements are found, the server automatically corrects them. The corrected content is finally delivered to the user through their device.

[0759] This system integrates a generative AI model with an emotion engine to generate safe and appropriate content that responds to the user's emotions.

[0760] An example of a prompt message input to the generative AI model would be, "The user needs an inspirational and positive product description script." Based on this, content such as "This product brings brightness and energy to everyday life" would be generated.

[0761] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0762] Step 1:

[0763] The server retrieves data on laws, regulations, and ethical standards from the internet and internal databases. This data is used to train generative AI models and emotion engines. Inputs include legal information and ethical standards data, and the output is an updated model. This process uses machine learning algorithms to process the data and improve the model's accuracy.

[0764] Step 2:

[0765] The terminal sends content generation requests received from the user to the server. The user uses the terminal's interface to input the type of content and emotional nuances they wish to generate. The input is the user's request, and the output is the request sent to the server. Information regarding the specificity and tone of the content is explicitly stated here.

[0766] Step 3:

[0767] The server uses an emotion engine to analyze the user's emotional state based on the received request. The input is the user's request, and the output is the analyzed emotional information. This analysis identifies a content style that matches the emotions the user desires. Natural language processing techniques are used to analyze the emotional elements within the request.

[0768] Step 4:

[0769] The server generates initial content using a generative AI model based on emotional information. The input is emotional information and user requests, and the output is the initial text content. The model applies pre-trained data to generate text that aligns with the user's intent. The generated content reflects the tone and style desired by the user.

[0770] Step 5:

[0771] The server checks the generated content against legal and ethical standards for compliance. The input is the generated content, and the output is the content information that is compliant or requires correction. If inappropriate elements are detected, the system automatically attempts to correct them. A pre-registered set of rules is used for these corrections.

[0772] Step 6:

[0773] The server sends the corrected content to the terminal, which then provides it to the user. The user can view this content and provide feedback as needed. The input is the corrected content, and the output is what is provided to the user. User feedback is used to improve the generative model for future generations.

[0774] (Application Example 2)

[0775] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] Traditional content generation systems have been unable to accurately reflect users' emotional nuances, making it difficult to address individual needs. Furthermore, the generated content sometimes failed to meet legal and ethical standards, posing a challenge in providing safe and meaningful content to users.

[0777] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0778] In this invention, the server includes means for acquiring data on laws, regulations, and ethical standards and learning from a generative model; means for receiving content generation requests from users; and means for analyzing emotional nuances from users. This makes it possible to generate content that reflects user emotions while satisfying legal and ethical standards.

[0779] "Data on laws, regulations, and ethical standards" refers to a collection of information that includes laws, social norms, and moral standards related to content creation.

[0780] A "generative model" is a system of algorithms designed to create new content based on input information.

[0781] A "user content generation request" is an instruction or request that specifically outlines the conditions and specifications of the content that the user wishes to generate.

[0782] "Initial content" refers to the version of the content initially created by the generative model, before any feedback or revisions.

[0783] "Emotional nuance" refers to elements that indicate specific emotions or atmospheres that a user wants to convey in their content.

[0784] "Evaluating based on legal and ethical standards" means determining whether the generated content conforms to laws and social norms.

[0785] "Collecting feedback" is the process of gathering opinions and suggestions from users regarding their experience and areas for improvement.

[0786] "Revised content" refers to content that has been modified from its original form to fully comply with laws and ethical standards.

[0787] To implement this invention, the server first acquires data on laws, regulations, and ethical standards and trains a generative AI model. The server also receives content generation requests from users. These requests include information such as the type of content to be generated and the desired emotional nuances. The terminal receives this request and sends it to the server.

[0788] The server uses an emotion engine to analyze the user's emotional nuances. Based on this analysis, a generative AI model is used to create initial content. The generated content is evaluated against legal and ethical standards, and any inappropriate elements are automatically corrected. The corrected content is then delivered to the user via their device.

[0789] This system uses a software framework based on programming languages ​​such as Python to ensure that content generation meets legal and ethical standards. It utilizes hardware such as smartphones and servers to enable the generation and distribution of content that reflects the emotions desired by the user. A specific use case would be an advertising agency wanting to create a positive and sustainable "new eco-bag advertisement" for a spring campaign.

[0790] For example, the following prompt may be provided by the user:

[0791] User input: I want to create an advertisement for our new spring eco-bag with a positive and sustainable tone.

[0792] The system can analyze this prompt and generate advertising content that meets the user's desired emotions as well as legal and ethical standards. This ensures that the advertising content effectively conveys the intended emotions and messages within legal limits.

[0793] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0794] Step 1:

[0795] The server collects and retrieves data on laws, regulations, and ethical standards. This data is used as training data to train a generative AI model. Through this process, the generative AI model comes to understand legal and ethical standards. The input is data on laws, regulations, and ethical standards, and the output is the trained generative AI model.

[0796] Step 2:

[0797] The user uses a terminal to input a content generation request. This request includes the type of content to be generated and the desired emotional nuances. The terminal receives this request and sends it directly to the server. The input is the user's content generation request, and the output is the request data sent to the server.

[0798] Step 3:

[0799] The server applies an emotion engine to analyze the user's emotional nuances based on the received content generation request. The analyzed emotional information supports the functioning of the generation AI model and serves as foundational data for determining the style and tone of the content. The input is the user's emotional nuance information, and the output is the analyzed emotional data.

[0800] Step 4:

[0801] The server creates initial content using a generative AI model based on the analyzed sentiment information and request data. In this process, the input is the analyzed sentiment data and content generation request, and the output is the generated initial content.

[0802] Step 5:

[0803] The server evaluates the generated initial content based on legal and ethical standards. It checks for inappropriate elements and automatically corrects them if necessary. The input is the initial content, and the output is the corrected content.

[0804] Step 6:

[0805] The corrected content is provided to the user via the device. The user receives this content and reviews it. The input is the corrected content, and the output is the content transmitted to the user.

[0806] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0807] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0808] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0809] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0810] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0811] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0812] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0813] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0814] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0815] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0816] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0817] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0818] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0819] 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.

[0820] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0821] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0822] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0823] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0824] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0825] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0826] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] A means of acquiring data on laws, regulations, and ethical standards and learning from it using a generative model,

[0830] A means for receiving content generation requests from users,

[0831] A means for generating initial content based on a received request,

[0832] A means of evaluating the generated content based on legal and ethical standards and checking its compliance,

[0833] A means to detect and correct inappropriate content,

[0834] A means of collecting feedback on generated content and improving the generation model,

[0835] Means of providing users with corrected content,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1 for specifically checking for copyright infringement and exaggeration in generated content.

[0839] (Claim 3)

[0840] The system according to claim 1, wherein a generative model analyzes text data based on natural language processing techniques.

[0841] "Example 1"

[0842] (Claim 1)

[0843] A means of collecting information on laws, regulations, and ethical standards from external databases and learning from them using a generative model,

[0844] A means for receiving content generation requests from user terminals,

[0845] A means of generating initial content using an AI model based on a received request,

[0846] A means of analyzing the generated content based on legal and ethical standards and evaluating its compliance,

[0847] A means of automatically detecting inappropriate content and correcting it using an algorithm,

[0848] A means of collecting evaluation information on generated content and improving the performance of the generation model,

[0849] A means of providing corrected content through a user interface,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1 for detailed inspection of generated content for intellectual property infringement and exaggeration.

[0853] (Claim 3)

[0854] The system according to claim 1, wherein a generative model analyzes text data using natural language processing techniques.

[0855] "Application Example 1"

[0856] (Claim 1)

[0857] A means of acquiring data on laws, regulations, and ethical standards and learning from it using a generative model,

[0858] A means for receiving content generation requests from users,

[0859] A means for generating initial content based on a received request,

[0860] A means of evaluating the generated content based on legal and ethical standards and checking its compliance,

[0861] A means to detect and correct inappropriate content,

[0862] A means of collecting feedback on generated content and improving the generation model,

[0863] Means of providing users with corrected content,

[0864] A means by which the user can enter prompts and review and adjust the generated content via an interface,

[0865] A means of providing content to user devices in real time,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1 for specifically checking for copyright infringement and exaggeration in generated content.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein a generative model analyzes text data based on natural language processing techniques.

[0871] "Example 2 of combining an emotion engine"

[0872] (Claim 1)

[0873] A means of acquiring data on laws, regulations, and ethical standards and learning from it using a generative model,

[0874] A means for receiving content generation requests from users,

[0875] A means of analyzing emotions based on received requests and generating initial content,

[0876] A means of evaluating the generated content based on legal and ethical standards and checking its compliance,

[0877] A means to detect and correct inappropriate content,

[0878] Means of providing users with corrected content,

[0879] A means of collecting user feedback and improving the generative model,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, which identifies an emotional state from user input information using an emotion analysis engine.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein a generative model analyzes text data based on natural language processing techniques and sentiment information.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] A means of acquiring data on laws, regulations, and ethical standards and learning from it using a generative model,

[0888] A means for receiving content generation requests from users,

[0889] A means for generating initial content based on a received request,

[0890] A means of analyzing emotional nuances from users,

[0891] A means of adjusting the style and tone of generated content based on analyzed emotional information,

[0892] A means of evaluating the generated content based on legal and ethical standards and checking its compliance,

[0893] A means to detect and correct inappropriate content,

[0894] A means of collecting feedback on generated content and improving the generation model,

[0895] Means of providing users with corrected content,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1 for specifically checking for copyright infringement and exaggeration in generated content.

[0899] (Claim 3)

[0900] The system according to claim 1, wherein a generative model analyzes text data based on natural language processing techniques. [Explanation of Symbols]

[0901] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring data on laws, regulations, and ethical standards and learning from it using a generative model, A means for receiving content generation requests from users, A means for generating initial content based on a received request, A means of evaluating the generated content based on legal and ethical standards and checking its compliance, A means to detect and correct inappropriate content, A means of collecting feedback on generated content and improving the generation model, Means of providing users with corrected content, A system that includes this.

2. The system according to claim 1 for specifically checking for copyright infringement and exaggeration in generated content.

3. The system according to claim 1, wherein the generative model analyzes text data based on natural language processing techniques.

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