system

Abstract

The system according to this embodiment aims to simultaneously perform general checks and verification of proprietary regulations on creatives created by the generation AI. [Solution] The system according to the embodiment comprises a generation unit, a checking unit, a regulation confirmation unit, and a feedback unit. The generation unit generates creative content based on input data from the user. The checking unit checks the generated creative content from a general perspective. The regulation confirmation unit confirms the regulations set by the user. The feedback unit provides feedback on the problems identified by the checking unit and the regulation confirmation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 the prior art, there was a problem that it was difficult to simultaneously perform a general check and a confirmation of unique regulations on a creative created by generative AI.

[0005] The system according to the embodiment aims to simultaneously perform a general check and a confirmation of unique regulations on a creative created by generative AI.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a checking unit, a regulation confirmation unit, and a feedback unit. The generation unit generates creative content based on user input data. The checking unit checks the generated creative content from a general perspective. The regulation confirmation unit verifies the regulations set by the user. The feedback unit provides feedback on the problems identified by the checking unit and the regulation confirmation unit. [Effects of the Invention]

[0007] The system according to this embodiment can simultaneously perform general checks and verification of proprietary regulations on creatives created by the generation AI. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single 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), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] 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.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The creative check system according to an embodiment of the present invention is a system that, in addition to performing general creative checks on creatives (text, images, banners, pages, etc.) created by a generative AI, also takes into account regulations set independently by the user (company, department, service, etc.) and provides feedback on any problems. This system solves the problem that it takes time to check the output after content has been created using a generative AI. It also solves the problem that even though constraints are set when generating content with a generative AI, they are not accurately reflected because the constraints are numerous and diverse. For example, the generative AI creates the creative. Next, a general creative check is performed. For example, proofreading (typo check, grammar check, kanji check, etc.), legal checks (from the perspective of the Premiums and Representations Act, etc.), checks for the risk of backlash on social media, and checks from a marketing perspective (character limit, removal of unwanted expressions, etc.) are performed. Furthermore, the system takes into account regulations and tone and manner set independently by the user. For example, departmental or service-specific constraints can be freely added, and roles can be set for each perspective to be checked, such as legal, public relations, marketing, and personal data protection checks, and these can be executed as individual prompts. Finally, any problems are fed back. For example, if an image of a person is generated, the system can check and correct issues such as the fact that humans have two arms, five fingers on each hand, and two feet. This system reduces the time spent on creative checks and proofreading, and efficiently improves the quality of content. In this way, the creative check system can improve the quality of creatives created by the generation AI.

[0029] The creative check system according to the embodiment comprises a generation unit, a checking unit, a regulation confirmation unit, and a feedback unit. The generation unit generates creative content based on user input data. The generation unit generates creative content using, for example, a generation AI. The generation unit generates text using, for example, a text generation AI (e.g., LLM). The generation unit can also generate images using an image generation AI. The generation unit can generate high-quality images using, for example, a GAN (Generative Opposite Network). The generation unit can generate natural-sounding text using, for example, a Transformer model. The checking unit checks the generated creative content from a general perspective. The checking unit performs, for example, text proofreading. The checking unit can perform, for example, grammar checks and spell checks. The checking unit performs, for example, checks from a legal perspective. The checking unit can perform, for example, copyright checks and trademark checks. The checking unit performs, for example, social media risk checks. The checking unit can perform, for example, an assessment of online backlash risk and privacy checks. The checking unit performs, for example, checks from a marketing perspective. The checking unit can, for example, verify the suitability of the target audience and the consistency of the brand message. The regulation checking unit verifies the regulations set by the user. The regulation checking unit verifies the creative based on the constraints set by the user. The regulation checking unit can, for example, verify restrictions on the use of colors and fonts. The regulation checking unit can, for example, verify brand guidelines and legal restrictions. The feedback unit provides feedback on the problems identified by the checking unit and the regulation checking unit. The feedback unit, for example, provides feedback to the user on the identified problems and encourages corrections. The feedback unit can, for example, point out grammatical errors and design inconsistencies. The feedback unit can, for example, provide correction priorities and specific instructions. As a result, the creative checking system according to the embodiment can improve the quality of creatives created by the generative AI.

[0030] The generation unit generates creative content based on user input data. For example, the generation unit uses generative AI to generate creative content. Specifically, the generation unit uses text generation AI (e.g., LLM) to generate text. LLM has learned from a large amount of text data and can generate natural-sounding text based on user prompts. For example, if a user requests the creation of advertising copy, LLM generates text that takes into account the target audience and product features. The generation unit can also generate images using image generation AI. Image generation AI, for example, uses GAN (Generative Opposite Network) to generate high-quality images. GAN consists of two networks: a generative network and a discriminative network. The generative network generates a new image, and the discriminative network determines whether the image is real or fake. By repeating this process, highly realistic images can be generated. Furthermore, the generation unit can generate natural-sounding text using a Transformer model. Because the Transformer model uses a self-attention mechanism to generate contextually balanced text, it can generate natural-sounding text even in long or complex contexts. This allows the generation unit to generate diverse creative content with high quality and respond flexibly to user needs.

[0031] The checking department reviews the generated creative from a general perspective. Specifically, the checking department performs proofreading. Proofreading includes grammar checks and spell checks. Grammar checks verify that the generated sentences are grammatically correct, and spell checks verify that words are spelled correctly. This ensures that the generated sentences are easy to read and do not cause misunderstandings. The checking department also performs checks from a legal perspective. Legal checks include copyright and trademark checks. Copyright checks verify that the generated creative does not infringe on the copyrights of others, and trademark checks verify that the generated creative does not use the trademarks of others without permission. Furthermore, the checking department also performs social media risk checks. Social media risk checks include assessing the risk of online backlash and checking privacy. Online backlash assessments evaluate whether the generated creative has the potential to cause online backlash, and privacy checks verify that the generated creative does not handle personal information inappropriately. Finally, the checking department also performs checks from a marketing perspective. Marketing checks include verifying the suitability of the target audience and the consistency of the brand message. In the target audience suitability section, the system verifies whether the generated creative is appropriate for the target audience, and in the brand message consistency section, it verifies whether the generated creative matches the brand message. This allows the checking unit to evaluate the quality of the generated creative from multiple angles and identify problems early on.

[0032] The Regulation Verification Unit verifies the regulations set by the user. Specifically, the Regulation Verification Unit verifies the creative based on the constraints set by the user. For example, it can verify restrictions on the use of colors and fonts. For color usage restrictions, it can set constraints to use only specific colors in accordance with brand guidelines, and for font usage restrictions, it can set constraints to use only specific fonts that match the brand image. Furthermore, the Regulation Verification Unit can verify brand guidelines and legal restrictions. In verifying brand guidelines, it checks whether the generated creative matches the brand's visual identity, and in verifying legal restrictions, it checks whether the generated creative is legally compliant. This allows the Regulation Verification Unit to ensure the quality of the creative according to the user's requirements and manage brand image and legal risks. In addition, the Regulation Verification Unit can flexibly update the regulations set by the user. For example, if new brand guidelines are formulated or legal restrictions change, the Regulation Verification Unit can respond quickly and verify the creative based on the latest regulations. This allows the Regulation Verification Unit to always provide high-quality creative based on the latest information and support flexible responses to user requirements.

[0033] The Feedback Department provides feedback on issues identified by the Check Department and the Regulation Verification Department. Specifically, the Feedback Department provides feedback to the user on the identified issues and encourages corrections. For example, it can point out grammatical errors or design inconsistencies. When pointing out grammatical errors, it specifically indicates grammatical errors in the generated text and suggests how to correct them. When pointing out design inconsistencies, it indicates areas where the generated creative design does not conform to the brand guidelines and suggests how to correct them. Furthermore, the Feedback Department can provide correction priorities and specific instructions. Correction priorities instruct users to correct issues in order of importance, and specific instructions explain in detail which parts should be corrected and how. This allows the Feedback Department to enable users to efficiently correct creatives and improve quality. In addition, the Feedback Department can collect user feedback and use it to improve the system. For example, it can record how users responded to correction instructions and use this information to improve future feedback. The Feedback Department can also re-check after the user has completed the corrections to confirm that the issues have been resolved. This allows the Feedback Department to continuously improve the quality of creatives and increase user satisfaction.

[0034] The generation unit can generate creative content using generative AI. For example, the generation unit can generate text using text generation AI (e.g., LLM). The generation unit can also generate images using image generation AI. For example, the generation unit can generate high-quality images using GAN (Generative Opposite Network). For example, the generation unit can generate natural-sounding text using a Transformer model. As a result, the accuracy of creative content generation is improved by using generative AI. Generative AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generative AI. For example, the generation unit inputs user data into the generative AI, and the generative AI generates the creative content.

[0035] The checking unit can perform checks from various perspectives, including proofreading, legal considerations, social media risk assessment, and marketing considerations. For example, the checking unit can proofread the text. For example, the checking unit can perform grammar checks and spell checks. For example, the checking unit can perform checks from a legal perspective. For example, the checking unit can verify copyright and trademarks. For example, the checking unit can check social media risk. For example, the checking unit can assess the risk of online backlash and verify privacy. For example, the checking unit can perform checks from a marketing perspective. For example, the checking unit can verify the suitability of the target audience and the consistency of the brand message. This improves quality by checking the creative from multiple perspectives. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit inputs the generated creative into AI, which then performs proofreading and legal checks.

[0036] The regulation verification unit can verify the creative based on the constraints set by the user. For example, the regulation verification unit can verify restrictions on the use of colors and fonts. For example, the regulation verification unit can verify brand guidelines and legal restrictions. This improves compliance by verifying the creative based on the user's unique constraints. Some or all of the above processes in the regulation verification unit may be performed using AI or not. For example, the regulation verification unit inputs the constraints set by the user into the AI, and the AI ​​verifies the creative.

[0037] The feedback unit can provide feedback to the user about identified problems and encourage corrections. For example, the feedback unit can point out grammatical errors or design inconsistencies. For example, the feedback unit can provide priority for corrections and specific instructions. This encourages creative revisions and improves quality by providing feedback on problems. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit inputs identified problems into the AI, and the AI ​​generates the feedback content.

[0038] The generation unit can analyze the user's past creative history during generation and select the optimal generation method. For example, the generation unit can analyze the style of creatives the user has created in the past and generate new creatives in a similar style. For example, the generation unit can use the user's preferred colors and fonts to create new creatives using a generation AI. For example, the generation unit can incorporate elements of creatives that have received high ratings from the user in the past to generate new creatives. This allows for the selection of a more appropriate generation method by analyzing the past creative history. Generation AIs include, for example, text generation AIs (e.g., LLM), image generation AIs, or multimodal generation AIs. Some or all of the above processes in the generation unit are performed using generation AIs. For example, the generation unit inputs the user's past creative history into the generation AI, which then selects the optimal generation method.

[0039] The generation unit can customize the generated content based on the user's current projects and areas of interest during the generation process. For example, the generation unit can use a generation AI to create creative content that matches the theme of the project the user is currently working on. For example, the generation unit can use a generation AI to generate creative content by incorporating information related to the user's areas of interest. For example, the generation unit can use a generation AI to generate highly relevant creative content based on keywords specified by the user. This allows for the provision of more relevant creative content by customizing the generated content based on the user's current projects and areas of interest. Generation AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generation AI. For example, the generation unit inputs data about the user's current projects and areas of interest into the generation AI, and the generation AI customizes the generated content.

[0040] The generation unit can generate highly relevant creative content by considering the user's geographical location during the generation process. For example, if the user is in a specific region, the generation AI will create creative content by incorporating information relevant to that region. For example, if the user is traveling, the generation AI will generate creative content based on tourist information of the travel destination. For example, if the user is attending a specific event, the generation AI will create creative content related to that event. This allows for the provision of more relevant creative content by considering geographical location information. The generation AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, and the generation AI generates highly relevant creative content.

[0041] The generation unit can analyze a user's social media activity and generate relevant creative content during the generation process. For example, the generation unit can use its generation AI to create relevant creative content based on a user's recent posts. For example, the generation unit can analyze the trends of accounts a user follows and generate creative content that aligns with those trends. For example, the generation unit can use its generation AI to create creative content based on topics in online communities a user participates in. This allows for the provision of more relevant creative content by analyzing social media activity. The generation AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the processes described above in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's social media activity data into the generation AI, which then generates relevant creative content.

[0042] The checking unit can improve the accuracy of its checks by considering the interrelationships of creative elements during the checking process. For example, the checking unit checks the relationship between text and images to ensure consistency. For example, the checking unit checks whether the banner and page designs match. For example, the checking unit checks whether the overall tone and manner of the creative elements are consistent. This improves the accuracy of the checks by considering the interrelationships of the creative elements. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit can input data on the interrelationships of creative elements into the AI, which can then improve the accuracy of the checks.

[0043] The checking unit can perform checks while considering the attribute information of the creative submitter. For example, if the submitter is a new employee, the checking unit will focus on basic check items. If the submitter is an experienced employee, the checking unit will focus on detailed check items. If the submitter belongs to a specific department, the checking unit will perform checks tailored to the characteristics of that department. This allows for more appropriate checks by considering the submitter's attribute information. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit inputs the submitter's attribute information into the AI, and the AI ​​adjusts the check criteria.

[0044] The checking unit can perform checks while considering the geographical distribution of the creative work. For example, if the creative work is aimed at a specific region, the checking unit will perform checks based on the culture and regulations of that region. For example, if the creative work is aimed at a multinational audience, the checking unit will perform checks based on the regulations of each country. For example, if the creative work is aimed at a specific city, the checking unit will perform checks based on the characteristics of that city. This allows for more appropriate checks by considering geographical distribution. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit inputs geographical distribution data of the creative work into the AI, and the AI ​​adjusts the check criteria.

[0045] The checking unit can improve the accuracy of its checks by referring to relevant literature related to the creative work. For example, the checking unit may refer to laws and regulations related to the creative work when performing its checks. For example, the checking unit may refer to marketing guidelines related to the creative work when performing its checks. For example, the checking unit may refer to past cases related to the creative work when performing its checks. This improves the accuracy of the checks by referring to relevant literature. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit may input data on relevant literature related to the creative work into the AI, and the AI ​​may improve the accuracy of the checks.

[0046] The regulation verification unit can predict current regulations by referring to past regulation data during regulation verification. For example, the regulation verification unit predicts the trend of current regulations based on past regulation data. For example, the regulation verification unit verifies current regulations by referring to past regulation change history. For example, the regulation verification unit verifies current regulations based on past regulation violation cases. This allows for prediction of current regulation trends by referring to past regulation data, enabling more appropriate verification. Some or all of the above processes in the regulation verification unit may be performed using AI, or not. For example, the regulation verification unit inputs past regulation data into AI, and the AI ​​predicts current regulations.

[0047] The regulation verification unit can apply different verification methods to each creative category during regulation verification. For example, in the case of text-based creatives, the regulation verification unit focuses on proofreading and legal checks. For example, in the case of image-based creatives, the regulation verification unit focuses on checking the image content and copyright. For example, in the case of banner-based creatives, the regulation verification unit focuses on checking the design and marketing aspects. By applying different verification methods to each creative category, more appropriate verification can be performed. Some or all of the above processes in the regulation verification unit may be performed using AI or not. For example, the regulation verification unit inputs creative category data into the AI, and the AI ​​applies different verification methods.

[0048] The regulation verification unit can analyze changes in regulations based on the creative submission date when verifying regulations. For example, if the submission date falls around the time of a specific legal amendment, the regulation verification unit will verify the regulations based on that amendment. For example, if the submission date falls around the time of a specific event, the regulation verification unit will verify the regulations related to that event. For example, if the submission date falls within a specific season, the regulation verification unit will verify the regulations related to that season. This allows for more appropriate verification by analyzing changes in regulations based on the submission date. Some or all of the above-described processes in the regulation verification unit may be performed using AI or not. For example, the regulation verification unit inputs the creative submission date data into the AI, and the AI ​​analyzes the changes in regulations.

[0049] The regulation verification unit can analyze regulations by referring to relevant market data for creative works during regulation verification. For example, the regulation verification unit can verify regulations by referring to the regulations of the market targeted by the creative work. For example, the regulation verification unit can verify regulations by referring to the trends of the market targeted by the creative work. For example, the regulation verification unit can verify regulations by referring to the examples of competitors in the market targeted by the creative work. This improves the accuracy of regulation analysis by referring to relevant market data. Some or all of the above processing in the regulation verification unit may be performed using AI or not. For example, the regulation verification unit inputs relevant market data for creative works into AI, and the AI ​​analyzes the regulations.

[0050] The feedback unit can analyze the user's past feedback history to select the optimal feedback method when providing feedback. For example, the feedback unit can provide feedback in a similar format based on the user's preferred feedback format in the past. For example, the feedback unit can analyze the content of feedback the user has received in the past and focus on providing feedback on areas for improvement. For example, the feedback unit can provide feedback at an appropriate time based on the frequency of feedback the user has received in the past. This allows for the selection of a more appropriate feedback method by analyzing past feedback history. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's past feedback history into AI, and the AI ​​can select the optimal feedback method.

[0051] The feedback unit can customize the feedback content based on the user's current project status. For example, the feedback unit can provide appropriate feedback according to the progress of the project the user is currently working on. For example, the feedback unit can provide specific solutions to challenges the user is facing. For example, the feedback unit can evaluate the degree of achievement and provide feedback based on the goals set by the user. This allows for more appropriate feedback by customizing the feedback content based on the current project status. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's current project status data into the AI, and the AI ​​can customize the feedback content.

[0052] The feedback unit can select the optimal feedback method by considering the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit will provide feedback tailored to the characteristics of that region. For example, if the user is traveling, the feedback unit will provide feedback tailored to the situation at the travel destination. For example, if the user is participating in a specific event, the feedback unit will provide feedback related to that event. This allows for more appropriate feedback by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal feedback method.

[0053] The feedback unit can analyze a user's social media activity and suggest methods for providing feedback. For example, the feedback unit can provide relevant feedback based on the user's recent posts. For example, the feedback unit can analyze the trends of accounts the user follows and provide feedback tailored to those trends. For example, the feedback unit can provide relevant feedback based on topics in online communities the user participates in. By analyzing social media activity, it can suggest more appropriate methods for providing feedback. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's social media activity data into an AI, which then suggests methods for providing feedback.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The generation unit can analyze the user's past creative history during generation and select the optimal generation method. For example, the generation unit can analyze the style of creatives the user has created in the past and generate new creatives in a similar style. For example, the generation unit can use the user's preferred colors and fonts to create new creatives using a generation AI. For example, the generation unit can incorporate elements of creatives that have received high ratings from the user in the past to generate new creatives. This allows for the selection of a more appropriate generation method by analyzing the past creative history. Generation AIs include, for example, text generation AIs (e.g., LLM), image generation AIs, or multimodal generation AIs. Some or all of the above processes in the generation unit are performed using generation AIs. For example, the generation unit inputs the user's past creative history into the generation AI, which then selects the optimal generation method.

[0056] The generation unit can customize the generated content based on the user's current projects and areas of interest during the generation process. For example, the generation unit can use a generation AI to create creative content that matches the theme of the project the user is currently working on. For example, the generation unit can use a generation AI to generate creative content by incorporating information related to the user's areas of interest. For example, the generation unit can use a generation AI to generate highly relevant creative content based on keywords specified by the user. This allows for the provision of more relevant creative content by customizing the generated content based on the user's current projects and areas of interest. Generation AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generation AI. For example, the generation unit inputs data about the user's current projects and areas of interest into the generation AI, and the generation AI customizes the generated content.

[0057] The generation unit can generate highly relevant creative content by considering the user's geographical location during the generation process. For example, if the user is in a specific region, the generation AI will create creative content by incorporating information relevant to that region. For example, if the user is traveling, the generation AI will generate creative content based on tourist information of the travel destination. For example, if the user is attending a specific event, the generation AI will create creative content related to that event. This allows for the provision of more relevant creative content by considering geographical location information. The generation AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, and the generation AI generates highly relevant creative content.

[0058] The checking unit can improve the accuracy of its checks by considering the interrelationships of creative elements during the checking process. For example, the checking unit checks the relationship between text and images to ensure consistency. For example, the checking unit checks whether the banner and page designs match. For example, the checking unit checks whether the overall tone and manner of the creative elements are consistent. This improves the accuracy of the checks by considering the interrelationships of the creative elements. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit can input data on the interrelationships of creative elements into the AI, which can then improve the accuracy of the checks.

[0059] The checking unit can perform checks while considering the attribute information of the creative submitter. For example, if the submitter is a new employee, the checking unit will focus on basic check items. If the submitter is an experienced employee, the checking unit will focus on detailed check items. If the submitter belongs to a specific department, the checking unit will perform checks tailored to the characteristics of that department. This allows for more appropriate checks by considering the submitter's attribute information. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit inputs the submitter's attribute information into the AI, and the AI ​​adjusts the check criteria.

[0060] The checking unit can perform checks while considering the geographical distribution of the creative work. For example, if the creative work is aimed at a specific region, the checking unit will perform checks based on the culture and regulations of that region. For example, if the creative work is aimed at a multinational audience, the checking unit will perform checks based on the regulations of each country. For example, if the creative work is aimed at a specific city, the checking unit will perform checks based on the characteristics of that city. This allows for more appropriate checks by considering geographical distribution. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit inputs geographical distribution data of the creative work into the AI, and the AI ​​adjusts the check criteria.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The generation unit generates creative content based on user input data. The generation unit generates creative content using, for example, a generative AI. The generation unit generates text using, for example, a text generation AI (e.g., LLM). The generation unit can also generate images using an image generation AI. The generation unit can generate high-quality images using, for example, a GAN (Generative Opposite Network). The generation unit can generate natural-sounding text using, for example, a Transformer model. Step 2: The checking team checks the generated creative from a general perspective. The checking team performs proofreading, for example. The checking team can perform grammar checks and spell checks. The checking team performs checks from a legal perspective, for example. The checking team can perform copyright and trademark checks. The checking team performs social media risk checks, for example. The checking team can assess the risk of online backlash and verify privacy. The checking team performs checks from a marketing perspective, for example. The checking team can verify the suitability for the target audience and the consistency of the brand message. Step 3: The Regulation Verification Unit verifies the regulations set by the user. The Regulation Verification Unit verifies the creative based on the constraints set by the user, for example. The Regulation Verification Unit can verify restrictions on the use of colors and fonts, for example. The Regulation Verification Unit can verify brand guidelines and legal restrictions, for example. Step 4: The feedback unit provides feedback on the issues identified by the checking unit and the regulation verification unit. The feedback unit, for example, provides feedback to the user on the identified issues and encourages them to make corrections. The feedback unit may, for example, point out grammatical errors or design inconsistencies. The feedback unit may, for example, provide correction priorities and specific instructions.

[0063] (Example of form 2) The creative check system according to an embodiment of the present invention is a system that, in addition to performing general creative checks on creatives (text, images, banners, pages, etc.) created by a generative AI, also takes into account regulations set independently by the user (company, department, service, etc.) and provides feedback on any problems. This system solves the problem that it takes time to check the output after content has been created using a generative AI. It also solves the problem that even though constraints are set when generating content with a generative AI, they are not accurately reflected because the constraints are numerous and diverse. For example, the generative AI creates the creative. Next, a general creative check is performed. For example, proofreading (typo check, grammar check, kanji check, etc.), legal checks (from the perspective of the Premiums and Representations Act, etc.), checks for the risk of backlash on social media, and checks from a marketing perspective (character limit, removal of unwanted expressions, etc.) are performed. Furthermore, the system takes into account regulations and tone and manner set independently by the user. For example, departmental or service-specific constraints can be freely added, and roles can be set for each perspective to be checked, such as legal, public relations, marketing, and personal data protection checks, and these can be executed as individual prompts. Finally, any problems are fed back. For example, if an image of a person is generated, the system can check and correct issues such as the fact that humans have two arms, five fingers on each hand, and two feet. This system reduces the time spent on creative checks and proofreading, and efficiently improves the quality of content. In this way, the creative check system can improve the quality of creatives created by the generation AI.

[0064] The creative check system according to the embodiment comprises a generation unit, a checking unit, a regulation confirmation unit, and a feedback unit. The generation unit generates creative content based on user input data. The generation unit generates creative content using, for example, a generation AI. The generation unit generates text using, for example, a text generation AI (e.g., LLM). The generation unit can also generate images using an image generation AI. The generation unit can generate high-quality images using, for example, a GAN (Generative Opposite Network). The generation unit can generate natural-sounding text using, for example, a Transformer model. The checking unit checks the generated creative content from a general perspective. The checking unit performs, for example, text proofreading. The checking unit can perform, for example, grammar checks and spell checks. The checking unit performs, for example, checks from a legal perspective. The checking unit can perform, for example, copyright checks and trademark checks. The checking unit performs, for example, social media risk checks. The checking unit can perform, for example, an assessment of online backlash risk and privacy checks. The checking unit performs, for example, checks from a marketing perspective. The checking unit can, for example, verify the suitability of the target audience and the consistency of the brand message. The regulation checking unit verifies the regulations set by the user. The regulation checking unit verifies the creative based on the constraints set by the user. The regulation checking unit can, for example, verify restrictions on the use of colors and fonts. The regulation checking unit can, for example, verify brand guidelines and legal restrictions. The feedback unit provides feedback on the problems identified by the checking unit and the regulation checking unit. The feedback unit, for example, provides feedback to the user on the identified problems and encourages corrections. The feedback unit can, for example, point out grammatical errors and design inconsistencies. The feedback unit can, for example, provide correction priorities and specific instructions. As a result, the creative checking system according to the embodiment can improve the quality of creatives created by the generative AI.

[0065] The generation unit generates creative content based on user input data. For example, the generation unit uses generative AI to generate creative content. Specifically, the generation unit uses text generation AI (e.g., LLM) to generate text. LLM has learned from a large amount of text data and can generate natural-sounding text based on user prompts. For example, if a user requests the creation of advertising copy, LLM generates text that takes into account the target audience and product features. The generation unit can also generate images using image generation AI. Image generation AI, for example, uses GAN (Generative Opposite Network) to generate high-quality images. GAN consists of two networks: a generative network and a discriminative network. The generative network generates a new image, and the discriminative network determines whether the image is real or fake. By repeating this process, highly realistic images can be generated. Furthermore, the generation unit can generate natural-sounding text using a Transformer model. Because the Transformer model uses a self-attention mechanism to generate contextually balanced text, it can generate natural-sounding text even in long or complex contexts. This allows the generation unit to generate diverse creative content with high quality and respond flexibly to user needs.

[0066] The checking department reviews the generated creative from a general perspective. Specifically, the checking department performs proofreading. Proofreading includes grammar checks and spell checks. Grammar checks verify that the generated sentences are grammatically correct, and spell checks verify that words are spelled correctly. This ensures that the generated sentences are easy to read and do not cause misunderstandings. The checking department also performs checks from a legal perspective. Legal checks include copyright and trademark checks. Copyright checks verify that the generated creative does not infringe on the copyrights of others, and trademark checks verify that the generated creative does not use the trademarks of others without permission. Furthermore, the checking department also performs social media risk checks. Social media risk checks include assessing the risk of online backlash and checking privacy. Online backlash assessments evaluate whether the generated creative has the potential to cause online backlash, and privacy checks verify that the generated creative does not handle personal information inappropriately. Finally, the checking department also performs checks from a marketing perspective. Marketing checks include verifying the suitability of the target audience and the consistency of the brand message. In the target audience suitability section, the system verifies whether the generated creative is appropriate for the target audience, and in the brand message consistency section, it verifies whether the generated creative matches the brand message. This allows the checking unit to evaluate the quality of the generated creative from multiple angles and identify problems early on.

[0067] The Regulation Verification Unit verifies the regulations set by the user. Specifically, the Regulation Verification Unit verifies the creative based on the constraints set by the user. For example, it can verify restrictions on the use of colors and fonts. For color usage restrictions, it can set constraints to use only specific colors in accordance with brand guidelines, and for font usage restrictions, it can set constraints to use only specific fonts that match the brand image. Furthermore, the Regulation Verification Unit can verify brand guidelines and legal restrictions. In verifying brand guidelines, it checks whether the generated creative matches the brand's visual identity, and in verifying legal restrictions, it checks whether the generated creative is legally compliant. This allows the Regulation Verification Unit to ensure the quality of the creative according to the user's requirements and manage brand image and legal risks. In addition, the Regulation Verification Unit can flexibly update the regulations set by the user. For example, if new brand guidelines are formulated or legal restrictions change, the Regulation Verification Unit can respond quickly and verify the creative based on the latest regulations. This allows the Regulation Verification Unit to always provide high-quality creative based on the latest information and support flexible responses to user requirements.

[0068] The Feedback Department provides feedback on issues identified by the Check Department and the Regulation Verification Department. Specifically, the Feedback Department provides feedback to the user on the identified issues and encourages corrections. For example, it can point out grammatical errors or design inconsistencies. When pointing out grammatical errors, it specifically indicates grammatical errors in the generated text and suggests how to correct them. When pointing out design inconsistencies, it indicates areas where the generated creative design does not conform to the brand guidelines and suggests how to correct them. Furthermore, the Feedback Department can provide correction priorities and specific instructions. Correction priorities instruct users to correct issues in order of importance, and specific instructions explain in detail which parts should be corrected and how. This allows the Feedback Department to enable users to efficiently correct creatives and improve quality. In addition, the Feedback Department can collect user feedback and use it to improve the system. For example, it can record how users responded to correction instructions and use this information to improve future feedback. The Feedback Department can also re-check after the user has completed the corrections to confirm that the issues have been resolved. This allows the Feedback Department to continuously improve the quality of creatives and increase user satisfaction.

[0069] The generation unit can generate creative content using generative AI. For example, the generation unit can generate text using text generation AI (e.g., LLM). The generation unit can also generate images using image generation AI. For example, the generation unit can generate high-quality images using GAN (Generative Opposite Network). For example, the generation unit can generate natural-sounding text using a Transformer model. As a result, the accuracy of creative content generation is improved by using generative AI. Generative AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generative AI. For example, the generation unit inputs user data into the generative AI, and the generative AI generates the creative content.

[0070] The checking unit can perform checks from various perspectives, including proofreading, legal considerations, social media risk assessment, and marketing considerations. For example, the checking unit can proofread the text. For example, the checking unit can perform grammar checks and spell checks. For example, the checking unit can perform checks from a legal perspective. For example, the checking unit can verify copyright and trademarks. For example, the checking unit can check social media risk. For example, the checking unit can assess the risk of online backlash and verify privacy. For example, the checking unit can perform checks from a marketing perspective. For example, the checking unit can verify the suitability of the target audience and the consistency of the brand message. This improves quality by checking the creative from multiple perspectives. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit inputs the generated creative into AI, which then performs proofreading and legal checks.

[0071] The regulation verification unit can verify the creative based on the constraints set by the user. For example, the regulation verification unit can verify restrictions on the use of colors and fonts. For example, the regulation verification unit can verify brand guidelines and legal restrictions. This improves compliance by verifying the creative based on the user's unique constraints. Some or all of the above processes in the regulation verification unit may be performed using AI or not. For example, the regulation verification unit inputs the constraints set by the user into the AI, and the AI ​​verifies the creative.

[0072] The feedback unit can provide feedback to the user about identified problems and encourage corrections. For example, the feedback unit can point out grammatical errors or design inconsistencies. For example, the feedback unit can provide priority for corrections and specific instructions. This encourages creative revisions and improves quality by providing feedback on problems. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit inputs identified problems into the AI, and the AI ​​generates the feedback content.

[0073] The generation unit can estimate the user's emotions and adjust the tone of the creative content it generates based on those emotions. For example, if the user is relaxed, the generation AI will generate text and images with a soft tone. If the user is excited, the generation AI will generate creative content with an energetic tone. If the user is sad, the generation AI will generate creative content with a comforting tone. This allows for the provision of more appropriate creative content by generating content with a tone that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the tone of the creative content.

[0074] The generation unit can analyze the user's past creative history during generation and select the optimal generation method. For example, the generation unit can analyze the style of creatives the user has created in the past and generate new creatives in a similar style. For example, the generation unit can use the user's preferred colors and fonts to create new creatives using a generation AI. For example, the generation unit can incorporate elements of creatives that have received high ratings from the user in the past to generate new creatives. This allows for the selection of a more appropriate generation method by analyzing the past creative history. Generation AIs include, for example, text generation AIs (e.g., LLM), image generation AIs, or multimodal generation AIs. Some or all of the above processes in the generation unit are performed using generation AIs. For example, the generation unit inputs the user's past creative history into the generation AI, which then selects the optimal generation method.

[0075] The generation unit can customize the generated content based on the user's current projects and areas of interest during the generation process. For example, the generation unit can use a generation AI to create creative content that matches the theme of the project the user is currently working on. For example, the generation unit can use a generation AI to generate creative content by incorporating information related to the user's areas of interest. For example, the generation unit can use a generation AI to generate highly relevant creative content based on keywords specified by the user. This allows for the provision of more relevant creative content by customizing the generated content based on the user's current projects and areas of interest. Generation AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generation AI. For example, the generation unit inputs data about the user's current projects and areas of interest into the generation AI, and the generation AI customizes the generated content.

[0076] The generation unit can estimate the user's emotions and determine the priority of the creative content to generate based on the estimated emotions. For example, if the user is stressed, the generation AI will prioritize generating relaxing creative content. If the user is excited, the generation AI will prioritize generating energetic creative content. If the user is calm, the generation AI will prioritize generating creative content with a calm tone. This allows for the provision of more appropriate creative content by prioritizing creative content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI determines the priority of the creative content.

[0077] The generation unit can generate highly relevant creative content by considering the user's geographical location during the generation process. For example, if the user is in a specific region, the generation AI will create creative content by incorporating information relevant to that region. For example, if the user is traveling, the generation AI will generate creative content based on tourist information of the travel destination. For example, if the user is attending a specific event, the generation AI will create creative content related to that event. This allows for the provision of more relevant creative content by considering geographical location information. The generation AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, and the generation AI generates highly relevant creative content.

[0078] The generation unit can analyze a user's social media activity and generate relevant creative content during the generation process. For example, the generation unit can use its generation AI to create relevant creative content based on a user's recent posts. For example, the generation unit can analyze the trends of accounts a user follows and generate creative content that aligns with those trends. For example, the generation unit can use its generation AI to create creative content based on topics in online communities a user participates in. This allows for the provision of more relevant creative content by analyzing social media activity. The generation AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the processes described above in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's social media activity data into the generation AI, which then generates relevant creative content.

[0079] The checking unit can estimate the user's emotions and adjust the checking criteria based on the estimated emotions. For example, if the user is nervous, the checking unit will check the creative using strict criteria. If the user is relaxed, the checking unit will check the creative using flexible criteria. If the user is in a hurry, the checking unit will set criteria for quick checking. This allows for more appropriate checking by adjusting the checking criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit inputs user emotion data into the AI, and the AI ​​adjusts the checking criteria.

[0080] The checking unit can improve the accuracy of its checks by considering the interrelationships of creative elements during the checking process. For example, the checking unit checks the relationship between text and images to ensure consistency. For example, the checking unit checks whether the banner and page designs match. For example, the checking unit checks whether the overall tone and manner of the creative elements are consistent. This improves the accuracy of the checks by considering the interrelationships of the creative elements. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit can input data on the interrelationships of creative elements into the AI, which can then improve the accuracy of the checks.

[0081] The checking unit can perform checks while considering the attribute information of the creative submitter. For example, if the submitter is a new employee, the checking unit will focus on basic check items. If the submitter is an experienced employee, the checking unit will focus on detailed check items. If the submitter belongs to a specific department, the checking unit will perform checks tailored to the characteristics of that department. This allows for more appropriate checks by considering the submitter's attribute information. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit inputs the submitter's attribute information into the AI, and the AI ​​adjusts the check criteria.

[0082] The checking unit can estimate the user's emotions and adjust the order in which the check results are displayed based on the estimated emotions. For example, if the user is nervous, the checking unit will display important check results first. If the user is relaxed, the checking unit will display detailed check results sequentially. If the user is in a hurry, the checking unit will prioritize displaying the most important check results. This allows for more appropriate feedback by adjusting the order in which the check results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit inputs user emotion data into the AI, and the AI ​​adjusts the order in which the check results are displayed.

[0083] The checking unit can perform checks while considering the geographical distribution of the creative work. For example, if the creative work is aimed at a specific region, the checking unit will perform checks based on the culture and regulations of that region. For example, if the creative work is aimed at a multinational audience, the checking unit will perform checks based on the regulations of each country. For example, if the creative work is aimed at a specific city, the checking unit will perform checks based on the characteristics of that city. This allows for more appropriate checks by considering geographical distribution. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit inputs geographical distribution data of the creative work into the AI, and the AI ​​adjusts the check criteria.

[0084] The checking unit can improve the accuracy of its checks by referring to relevant literature related to the creative work. For example, the checking unit may refer to laws and regulations related to the creative work when performing its checks. For example, the checking unit may refer to marketing guidelines related to the creative work when performing its checks. For example, the checking unit may refer to past cases related to the creative work when performing its checks. This improves the accuracy of the checks by referring to relevant literature. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit may input data on relevant literature related to the creative work into the AI, and the AI ​​may improve the accuracy of the checks.

[0085] The regulation verification unit can estimate the user's emotions and adjust the regulation verification method based on the estimated user emotions. For example, if the user is tense, the regulation verification unit will perform the verification using strict criteria. For example, if the user is relaxed, the regulation verification unit will perform the verification using flexible criteria. For example, if the user is in a hurry, the regulation verification unit will set criteria for rapid verification. This allows for more appropriate verification by adjusting the regulation verification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the regulation verification unit may be performed using AI or not. For example, the regulation verification unit inputs user emotion data into the AI, and the AI ​​adjusts the regulation verification method.

[0086] The regulation verification unit can predict current regulations by referring to past regulation data during regulation verification. For example, the regulation verification unit predicts the trend of current regulations based on past regulation data. For example, the regulation verification unit verifies current regulations by referring to past regulation change history. For example, the regulation verification unit verifies current regulations based on past regulation violation cases. This allows for prediction of current regulation trends by referring to past regulation data, enabling more appropriate verification. Some or all of the above processes in the regulation verification unit may be performed using AI, or not. For example, the regulation verification unit inputs past regulation data into AI, and the AI ​​predicts current regulations.

[0087] The regulation verification unit can apply different verification methods to each creative category during regulation verification. For example, in the case of text-based creatives, the regulation verification unit focuses on proofreading and legal checks. For example, in the case of image-based creatives, the regulation verification unit focuses on checking the image content and copyright. For example, in the case of banner-based creatives, the regulation verification unit focuses on checking the design and marketing aspects. By applying different verification methods to each creative category, more appropriate verification can be performed. Some or all of the above processes in the regulation verification unit may be performed using AI or not. For example, the regulation verification unit inputs creative category data into the AI, and the AI ​​applies different verification methods.

[0088] The regulation verification unit can estimate the user's emotions and adjust the importance of the regulations based on the estimated emotions. For example, if the user is nervous, the regulation verification unit will prioritize checking important regulations. For example, if the user is relaxed, the regulation verification unit will sequentially check detailed regulations. For example, if the user is in a hurry, the regulation verification unit will prioritize checking the most important regulations. This allows for more appropriate verification by adjusting the importance of regulations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the regulation verification unit may be performed using AI or not. For example, the regulation verification unit inputs user emotion data into the AI, and the AI ​​adjusts the importance of the regulations.

[0089] The regulation verification unit can analyze changes in regulations based on the creative submission date when verifying regulations. For example, if the submission date falls around the time of a specific legal amendment, the regulation verification unit will verify the regulations based on that amendment. For example, if the submission date falls around the time of a specific event, the regulation verification unit will verify the regulations related to that event. For example, if the submission date falls within a specific season, the regulation verification unit will verify the regulations related to that season. This allows for more appropriate verification by analyzing changes in regulations based on the submission date. Some or all of the above-described processes in the regulation verification unit may be performed using AI or not. For example, the regulation verification unit inputs the creative submission date data into the AI, and the AI ​​analyzes the changes in regulations.

[0090] The regulation verification unit can analyze regulations by referring to relevant market data for creative works during regulation verification. For example, the regulation verification unit can verify regulations by referring to the regulations of the market targeted by the creative work. For example, the regulation verification unit can verify regulations by referring to the trends of the market targeted by the creative work. For example, the regulation verification unit can verify regulations by referring to the examples of competitors in the market targeted by the creative work. This improves the accuracy of regulation analysis by referring to relevant market data. Some or all of the above processing in the regulation verification unit may be performed using AI or not. For example, the regulation verification unit inputs relevant market data for creative works into AI, and the AI ​​analyzes the regulations.

[0091] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is tense, the feedback unit will provide feedback in gentle language. If the user is relaxed, the feedback unit will provide detailed feedback. If the user is in a hurry, the feedback unit will provide concise and quick feedback. This allows for more appropriate feedback by adjusting the feedback method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit inputs user emotion data into the AI, and the AI ​​adjusts the feedback method.

[0092] The feedback unit can analyze the user's past feedback history to select the optimal feedback method when providing feedback. For example, the feedback unit can provide feedback in a similar format based on the user's preferred feedback format in the past. For example, the feedback unit can analyze the content of feedback the user has received in the past and focus on providing feedback on areas for improvement. For example, the feedback unit can provide feedback at an appropriate time based on the frequency of feedback the user has received in the past. This allows for the selection of a more appropriate feedback method by analyzing past feedback history. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's past feedback history into AI, and the AI ​​can select the optimal feedback method.

[0093] The feedback unit can customize the feedback content based on the user's current project status. For example, the feedback unit can provide appropriate feedback according to the progress of the project the user is currently working on. For example, the feedback unit can provide specific solutions to challenges the user is facing. For example, the feedback unit can evaluate the degree of achievement and provide feedback based on the goals set by the user. This allows for more appropriate feedback by customizing the feedback content based on the current project status. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's current project status data into the AI, and the AI ​​can customize the feedback content.

[0094] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit will prioritize providing important feedback. For example, if the user is relaxed, the feedback unit will sequentially provide detailed feedback. For example, if the user is in a hurry, the feedback unit will prioritize providing the most important feedback. This allows for more appropriate feedback by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit inputs user emotion data into the AI, and the AI ​​determines the priority of feedback.

[0095] The feedback unit can select the optimal feedback method by considering the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit will provide feedback tailored to the characteristics of that region. For example, if the user is traveling, the feedback unit will provide feedback tailored to the situation at the travel destination. For example, if the user is participating in a specific event, the feedback unit will provide feedback related to that event. This allows for more appropriate feedback by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal feedback method.

[0096] The feedback unit can analyze a user's social media activity and suggest methods for providing feedback. For example, the feedback unit can provide relevant feedback based on the user's recent posts. For example, the feedback unit can analyze the trends of accounts the user follows and provide feedback tailored to those trends. For example, the feedback unit can provide relevant feedback based on topics in online communities the user participates in. By analyzing social media activity, it can suggest more appropriate methods for providing feedback. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's social media activity data into an AI, which then suggests methods for providing feedback.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The generation unit can estimate the user's emotions and adjust the tone of the creative content it generates based on those emotions. For example, if the user is relaxed, the generation AI will generate text and images with a soft tone. If the user is excited, the generation AI will generate creative content with an energetic tone. If the user is sad, the generation AI will generate creative content with a comforting tone. This allows for the provision of more appropriate creative content by generating content with a tone that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the tone of the creative content.

[0099] The generation unit can analyze the user's past creative history during generation and select the optimal generation method. For example, the generation unit can analyze the style of creatives the user has created in the past and generate new creatives in a similar style. For example, the generation unit can use the user's preferred colors and fonts to create new creatives using a generation AI. For example, the generation unit can incorporate elements of creatives that have received high ratings from the user in the past to generate new creatives. This allows for the selection of a more appropriate generation method by analyzing the past creative history. Generation AIs include, for example, text generation AIs (e.g., LLM), image generation AIs, or multimodal generation AIs. Some or all of the above processes in the generation unit are performed using generation AIs. For example, the generation unit inputs the user's past creative history into the generation AI, which then selects the optimal generation method.

[0100] The generation unit can customize the generated content based on the user's current projects and areas of interest during the generation process. For example, the generation unit can use a generation AI to create creative content that matches the theme of the project the user is currently working on. For example, the generation unit can use a generation AI to generate creative content by incorporating information related to the user's areas of interest. For example, the generation unit can use a generation AI to generate highly relevant creative content based on keywords specified by the user. This allows for the provision of more relevant creative content by customizing the generated content based on the user's current projects and areas of interest. Generation AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generation AI. For example, the generation unit inputs data about the user's current projects and areas of interest into the generation AI, and the generation AI customizes the generated content.

[0101] The generation unit can estimate the user's emotions and determine the priority of the creative content to generate based on the estimated emotions. For example, if the user is stressed, the generation AI will prioritize generating relaxing creative content. If the user is excited, the generation AI will prioritize generating energetic creative content. If the user is calm, the generation AI will prioritize generating creative content with a calm tone. This allows for the provision of more appropriate creative content by prioritizing creative content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI determines the priority of the creative content.

[0102] The generation unit can generate highly relevant creative content by considering the user's geographical location during the generation process. For example, if the user is in a specific region, the generation AI will create creative content by incorporating information relevant to that region. For example, if the user is traveling, the generation AI will generate creative content based on tourist information of the travel destination. For example, if the user is attending a specific event, the generation AI will create creative content related to that event. This allows for the provision of more relevant creative content by considering geographical location information. The generation AI includes, for example, text generation AI (e.g., LLM), image generation AI, or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, and the generation AI generates highly relevant creative content.

[0103] The checking unit can estimate the user's emotions and adjust the checking criteria based on the estimated emotions. For example, if the user is nervous, the checking unit will check the creative using strict criteria. If the user is relaxed, the checking unit will check the creative using flexible criteria. If the user is in a hurry, the checking unit will set criteria for quick checking. This allows for more appropriate checking by adjusting the checking criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit inputs user emotion data into the AI, and the AI ​​adjusts the checking criteria.

[0104] The checking unit can improve the accuracy of its checks by considering the interrelationships of creative elements during the checking process. For example, the checking unit checks the relationship between text and images to ensure consistency. For example, the checking unit checks whether the banner and page designs match. For example, the checking unit checks whether the overall tone and manner of the creative elements are consistent. This improves the accuracy of the checks by considering the interrelationships of the creative elements. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit can input data on the interrelationships of creative elements into the AI, which can then improve the accuracy of the checks.

[0105] The checking unit can perform checks while considering the attribute information of the creative submitter. For example, if the submitter is a new employee, the checking unit will focus on basic check items. If the submitter is an experienced employee, the checking unit will focus on detailed check items. If the submitter belongs to a specific department, the checking unit will perform checks tailored to the characteristics of that department. This allows for more appropriate checks by considering the submitter's attribute information. Some or all of the above processes in the checking unit may be performed using AI or not. For example, the checking unit inputs the submitter's attribute information into the AI, and the AI ​​adjusts the check criteria.

[0106] The checking unit can estimate the user's emotions and adjust the order in which the check results are displayed based on the estimated emotions. For example, if the user is nervous, the checking unit will display important check results first. If the user is relaxed, the checking unit will display detailed check results sequentially. If the user is in a hurry, the checking unit will prioritize displaying the most important check results. This allows for more appropriate feedback by adjusting the order in which the check results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit inputs user emotion data into the AI, and the AI ​​adjusts the order in which the check results are displayed.

[0107] The checking unit can perform checks while considering the geographical distribution of the creative work. For example, if the creative work is aimed at a specific region, the checking unit will perform checks based on the culture and regulations of that region. For example, if the creative work is aimed at a multinational audience, the checking unit will perform checks based on the regulations of each country. For example, if the creative work is aimed at a specific city, the checking unit will perform checks based on the characteristics of that city. This allows for more appropriate checks by considering geographical distribution. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit inputs geographical distribution data of the creative work into the AI, and the AI ​​adjusts the check criteria.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The generation unit generates creative content based on user input data. The generation unit generates creative content using, for example, a generative AI. The generation unit generates text using, for example, a text generation AI (e.g., LLM). The generation unit can also generate images using an image generation AI. The generation unit can generate high-quality images using, for example, a GAN (Generative Opposite Network). The generation unit can generate natural-sounding text using, for example, a Transformer model. Step 2: The checking team checks the generated creative from a general perspective. The checking team performs proofreading, for example. The checking team can perform grammar checks and spell checks. The checking team performs checks from a legal perspective, for example. The checking team can perform copyright and trademark checks. The checking team performs social media risk checks, for example. The checking team can assess the risk of online backlash and verify privacy. The checking team performs checks from a marketing perspective, for example. The checking team can verify the suitability for the target audience and the consistency of the brand message. Step 3: The Regulation Verification Unit verifies the regulations set by the user. The Regulation Verification Unit verifies the creative based on the constraints set by the user, for example. The Regulation Verification Unit can verify restrictions on the use of colors and fonts, for example. The Regulation Verification Unit can verify brand guidelines and legal restrictions, for example. Step 4: The feedback unit provides feedback on the issues identified by the checking unit and the regulation verification unit. The feedback unit, for example, provides feedback to the user on the identified issues and encourages them to make corrections. The feedback unit may, for example, point out grammatical errors or design inconsistencies. The feedback unit may, for example, provide correction priorities and specific instructions.

[0110] 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.

[0111] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the generation unit, check unit, regulation confirmation unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the smart device 14 or the processor 28 of the data processing unit 12, and generates creative content using generation AI. The check unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and checks the generated creative content from a general perspective. The regulation confirmation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and verifies the regulations set by the user. The feedback unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and provides feedback to the user on the identified problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0117] 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.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0119] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] 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.

[0121] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the generation unit, checking unit, regulation confirmation unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the smart glasses 214 or the processor 28 of the data processing unit 12, and generates creative content using generation AI. The checking unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and checks the generated creative content from a general perspective. The regulation confirmation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and verifies the regulations set by the user. The feedback unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and provides feedback to the user on the identified problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0133] 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0135] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] 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.

[0137] 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.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the generation unit, checking unit, regulation confirmation unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the headset terminal 314 or the processor 28 of the data processing unit 12, and generates creative content using generation AI. The checking unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and checks the generated creative content from a general perspective. The regulation confirmation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and verifies the regulations set by the user. The feedback unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and provides feedback to the user on the identified problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0151] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] 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.

[0153] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] 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.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the generation unit, checking unit, regulation confirmation unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the robot 414 or the processor 28 of the data processing unit 12, and generates creatives using generation AI. The checking unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and checks the generated creatives from a general perspective. The regulation confirmation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and verifies the regulations set by the user. The feedback unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and provides feedback to the user on the identified problems. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] 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.

[0164] Figure 9 shows the 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.

[0165] 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.

[0166] 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.

[0167] 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, and motorcycles, 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 based, for example, 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.

[0168] 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."

[0169] 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.

[0170] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] 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 other things 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.

[0180] 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 to be incorporated by reference.

[0181] (Note 1) A generation unit that generates creatives based on user input data, A checking section that reviews the generated creative from a general perspective, A regulation verification unit that checks the regulations set by the user, The system includes a feedback unit that provides feedback on the problems identified by the check unit and the regulation confirmation unit. A system characterized by the following features. (Note 2) The generating unit is Generate creative content using AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned checking unit is We perform checks on aspects such as proofreading, legal considerations, social media risks, and marketing considerations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The regulation verification unit is, Review the creative based on the constraints set by the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is We will provide feedback to users about the identified issues and encourage them to make corrections. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is It estimates the user's emotions and adjusts the tone of the creative generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is During generation, the system analyzes the user's past creative history and selects the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is During generation, the generated content is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is It estimates user emotions and determines the priority of creative content to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During generation, the system considers the user's geographical location to generate highly relevant creative content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, the system analyzes the user's social media activity and generates relevant creative content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned checking unit is The system estimates the user's emotions and adjusts the check criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned checking unit is During the review process, we improve the accuracy of the review by considering the interrelationships between creative elements. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned checking unit is During the review process, the attribute information of the creative submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned checking unit is It estimates the user's emotions and adjusts the order in which the check results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned checking unit is During the review process, the geographical distribution of the creative content will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned checking unit is During the review process, we refer to relevant literature related to the creative work to improve the accuracy of the review. The system described in Appendix 1, characterized by the features described herein. (Note 18) The regulation verification unit is, We estimate user sentiment and adjust the regulation verification method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The regulation verification unit is, When checking regulations, we predict current regulations by referring to past regulation data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The regulation verification unit is, When checking regulations, different verification methods will be applied to each creative category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The regulation verification unit is, It estimates user sentiment and adjusts the importance of regulations based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The regulation verification unit is, When reviewing the regulations, we analyze any changes in the regulations based on the creative submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 23) The regulation verification unit is, When reviewing regulations, we analyze them by referring to relevant market data for creative work. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, the system analyzes the user's past feedback history to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, customize the feedback content based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, the optimal feedback method is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest ways to provide feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that generates creatives based on user input data, A checking section that reviews the generated creative from a general perspective, A regulation verification unit that checks the regulations set by the user, The system includes a feedback unit that provides feedback on the problems identified by the check unit and the regulation confirmation unit. A system characterized by the following features.

2. The generating unit is Generating creative content using generative AI. The system according to feature 1.

3. The aforementioned checking unit is We perform checks on aspects such as proofreading, legal considerations, social media risks, and marketing considerations. The system according to feature 1.

4. The regulation verification unit is, Review the creative based on the constraints set by the user. The system according to feature 1.

5. The aforementioned feedback unit is We will provide feedback to users about the identified issues and encourage them to make corrections. The system according to feature 1.

6. The generating unit is It estimates the user's emotions and adjusts the tone of the creative generated based on those estimated emotions. The system according to feature 1.

7. The generating unit is During generation, the system analyzes the user's past creative history and selects the optimal generation method. The system according to feature 1.

8. The generating unit is During generation, the generated content is customized based on the user's current projects and areas of interest. The system according to feature 1.

9. The generating unit is It estimates user emotions and determines the priority of creative content to generate based on those estimated emotions. The system according to feature 1.

10. The generating unit is During generation, the system considers the user's geographical location to generate highly relevant creative content. The system according to feature 1.

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