system

Abstract

The system according to the embodiment aims to automatically redesign creatives of different sizes. [Solution] The system according to the embodiment comprises an analysis unit, a redesign unit, and a supply unit. The analysis unit analyzes the original creative. The redesign unit redesigns the creative to a specified size based on the results of the analysis by the analysis unit. The supply unit provides the creative generated by the redesign unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the task of manually redesigning multiple creatives of different sizes was cumbersome and inefficient.

[0005] The system according to the embodiment aims to automatically redesign creatives of different sizes. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a redesign unit, and a provision unit. The analysis unit analyzes the original creative. The redesign unit redesigns the original creative to fit a specified size based on the results of the analysis by the analysis unit. The provision unit provides the creative generated by the redesign unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically redesign creatives of different sizes. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A creative redesign system according to an embodiment of the present invention automates the task of redesigning a single creative in multiple different sizes using a generation AI. In this creative redesign system, a user inputs a source creative and specifies the size of the creative they want to generate. The generation AI then analyzes the original creative and redesigns it to fit the specified size. The generated creative is provided to the user, who can modify or adjust it as needed. This eliminates the need for manual redesign and allows the user to efficiently generate creatives in multiple sizes. The generation AI analyzes the layout and design elements of the original creative and automatically calculates the optimal placement. For example, it performs image cropping and text resizing to generate a creative suitable for the specified size. The generated creative is provided to the user, who can modify or adjust it as needed. This improves work efficiency in the fields of advertising and marketing and maintains consistency in creatives. Furthermore, the use of generation AI enables high-quality redesigns and improves user satisfaction. This eliminates the need for manual redesigns and allows the creative redesign system to efficiently generate creatives in multiple sizes.

[0029] The creative redesign system according to this embodiment comprises an analysis unit, a redesign unit, and a provision unit. The analysis unit analyzes the original creative. The original creative includes, but is not limited to, images, videos, and text. The analysis unit analyzes the layout and design elements of the original creative using, for example, image analysis technology. The analysis unit can also analyze the text elements of the original creative using text analysis technology. Furthermore, the analysis unit can also analyze the data of the original creative using data analysis technology. The redesign unit redesigns the creative to a specified size based on the results analyzed by the analysis unit. The redesign is performed by, for example, resizing, changing the layout, and adjusting the colors. The redesign unit changes the size of the original creative using, for example, resizing technology. The redesign unit can also change the layout of the original creative using layout change technology. Furthermore, the redesign unit can adjust the colors of the original creative using color adjustment technology. The provision unit provides the creative generated by the redesign unit. Provision is performed by, for example, digital distribution, printing, and online sharing, but is not limited to these methods. The service provider can, for example, provide creative works generated using digital distribution technology in digital format. The service provider can also provide creative works generated using printing technology in printed format. Furthermore, the service provider can share creative works generated using online sharing technology online. As a result, the creative redesign system according to this embodiment can analyze the original creative work, redesign it to a specified size, and provide the generated creative work.

[0030] The redesign unit includes a cropping unit that performs image cropping. The cropping unit performs image cropping. Cropping includes, but is not limited to, methods for cutting out a portion of the image and specifying the cropping range. The cropping unit performs cropping using, for example, a technique for cutting out a portion of the image. The cropping unit can also perform cropping using a technique for specifying the cropping range. Furthermore, the cropping unit can perform cropping using a technique for confirming the cropping result. As a result, the redesign unit can generate creatives optimized for a specified size by cropping the image.

[0031] The redesign unit includes a resizing unit that resizes text. The resizing unit resizes text. Resizing includes, but is not limited to, changing font size or image size. For example, the resizing unit can resize text using font size change technology. The resizing unit can also resize text using image size change technology. Furthermore, the resizing unit can resize text using technology to verify the resizing result. As a result, the redesign unit can generate creative content optimized for a specified size by resizing text.

[0032] The service provider includes an evaluation unit that assesses the quality of the generated creative. The evaluation unit assesses the quality of the generated creative. Quality evaluation includes, but is not limited to, resolution, color, and layout consistency. For example, the evaluation unit can evaluate the quality of the generated creative using a resolution evaluation technique. The evaluation unit can also evaluate the quality of the generated creative using a color evaluation technique. Furthermore, the evaluation unit can also evaluate the quality of the generated creative using a layout consistency evaluation technique. In this way, the service provider can provide high-quality creative by evaluating the quality of the generated creative.

[0033] The providing unit includes an interface unit that provides an interface for the user to modify the generated creative. The interface unit provides the interface for the user to modify the generated creative. The interface includes, for example, a screen, a button, a menu, etc. for the user to operate, but is not limited to these examples. The interface unit provides the interface using, for example, a technology that provides a screen for the user to operate. The interface unit can also provide the interface using a technology that provides buttons for the user to operate. Furthermore, the interface unit can also provide the interface using a technology that provides a menu for the user to operate. In this way, the providing unit provides an interface for the user to modify the generated creative, making it easier for the user to modify the creative.

[0034] The analysis unit can perform a detailed analysis of the color and font characteristics of the original creative and reflect them in the redesign. For example, the analysis unit can analyze the color palette of the original creative and use the same colors when redesigning. The analysis unit can also analyze the font style of the original creative and use the same font when redesigning. Furthermore, the analysis unit can analyze the color balance of the original creative and maintain the same balance when redesigning. In this way, by analyzing the color and font characteristics of the original creative in detail, consistency can be maintained when redesigning.

[0035] The analysis unit can evaluate the importance of the content of the original creative and determine the priority of analysis based on the importance. For example, the analysis unit can prioritize analysis of important text elements and emphasize them during redesign. The analysis unit can also prioritize analysis of important image elements and place them in prominent positions during redesign. Furthermore, the analysis unit can prioritize analysis of important brand elements and maintain consistency during redesign. In this way, by evaluating the importance of the content of the original creative, important elements can be prioritized for analysis.

[0036] The analysis unit can analyze the metadata of the original creative and extract information necessary for redesign. For example, the analysis unit analyzes the creation date and creator information of the original creative and uses this information as reference when redesigning. The analysis unit can also analyze the purpose of use and target audience information of the original creative and reflect this information when redesigning. Furthermore, the analysis unit can analyze the usage history of the original creative and propose the optimal design when redesigning. In this way, by analyzing the metadata of the original creative, the information necessary for redesign can be efficiently extracted.

[0037] The analysis unit analyzes the usage history of the original creative and can improve the accuracy of the analysis based on the past usage status. For example, the analysis unit analyzes what media the original creative was used in and suggests the optimal media when redesigning. The analysis unit can also analyze what target audience the original creative was used for and suggest the optimal design when redesigning. Furthermore, the analysis unit can analyze what effect the original creative had and suggest a design that maximizes that effect when redesigning. In this way, by analyzing the usage history of the original creative, the accuracy of the analysis can be improved based on the past usage status.

[0038] During redesign, the redesign unit can optimize the original creative layout to a specified size while maintaining it. For example, the redesign unit can maintain the original creative layout and adjust the position of elements to match the specified size. The redesign unit can also maintain the original creative layout and adjust the size of elements to match the specified size. Furthermore, the redesign unit can maintain the original creative layout and adjust the margins to match the specified size. This allows for a consistent redesign by optimizing the original creative layout to a specified size while maintaining it.

[0039] The redesign department can retain the brand elements of the original creative when redesigning, ensuring brand consistency. For example, the redesign department can retain the logo of the original creative and place it in a prominent position when redesigning. The redesign department can also retain the color palette of the original creative and use the same colors when redesigning. Furthermore, the redesign department can retain the font style of the original creative and use the same font when redesigning. This allows the redesign to ensure brand consistency by retaining the brand elements of the original creative.

[0040] The redesign department can take into account the animation elements of the original creative when redesigning. For example, the redesign department can retain the animation elements of the original creative and apply the same animations when redesigning. The redesign department can also analyze the animation elements of the original creative and propose optimal animations when redesigning. Furthermore, the redesign department can adjust the animation elements of the original creative and apply new animations when redesigning. This allows for a redesign that retains the animation elements by taking into account the animation elements of the original creative.

[0041] The redesign department can preserve the interactive elements of the original creative during redesign, thereby improving the user experience. For example, the redesign department can preserve the interactive elements of the original creative and apply the same interactive elements during redesign. Furthermore, the redesign department can analyze the interactive elements of the original creative and suggest the most suitable interactive elements during redesign. In addition, the redesign department can adjust the interactive elements of the original creative and apply new interactive elements during redesign. This allows for redesigns that improve the user experience while preserving the interactive elements of the original creative.

[0042] The service provider can suggest usage scenarios for the generated creative at the time of delivery. For example, if the generated creative is for advertising, the service provider can suggest the most suitable advertising medium. Furthermore, if the generated creative is for social media, the service provider can suggest the optimal posting time. Additionally, if the generated creative is for a presentation, the service provider can suggest the optimal slide layout. This allows users to choose the most suitable usage method by suggesting usage scenarios for the generated creative.

[0043] The service provider can record the usage history of the generated creative at the time of delivery and reflect this in the next redesign. For example, the service provider can record the usage history of the generated creative and use it as a reference for the next redesign. Furthermore, the service provider can record the effectiveness of the generated creative and propose the optimal design for the next redesign. In addition, the service provider can record the usage status of the generated creative and propose the optimal medium for the next redesign. This allows the service provider to record the usage history of the generated creative and use it as a reference for the next redesign.

[0044] The providing unit can provide usage guidelines for the generated creative when providing the creative. For example, the providing unit provides the optimal way to use the generated creative as a guideline. The providing unit can also provide points to note when using the generated creative as a guideline. Furthermore, the providing unit can also provide examples of how to use the generated creative as a guideline. In this way, by providing the usage guidelines for the generated creative, the user can understand the optimal way to use it.

[0045] The providing unit provides a function for sharing the generated creative content at the time of providing the creative content, thereby promoting collaboration within a team. For example, the providing unit provides a function for sharing the generated creative content with team members. The providing unit can also provide a function for receiving feedback on the generated creative content from team members. Furthermore, the providing unit can also provide a function for collaboratively editing the generated creative content with team members. In this way, by providing a function for sharing the generated creative content, it is possible to promote collaboration within a team.

[0046] The trimming unit can remove unnecessary portions while preserving important elements of the original creative during trimming. For example, the trimming unit can preserve important text elements of the original creative and remove unnecessary background. The trimming unit can also preserve important image elements of the original creative and remove unnecessary white space. Furthermore, the trimming unit can preserve important brand elements of the original creative and remove unnecessary decorations. This allows for visually effective trimming by preserving important elements of the original creative and removing unnecessary portions.

[0047] The trimming unit can maintain the composition of the original creative and ensure visual balance when trimming. For example, the trimming unit can maintain the composition of the original creative and adjust the position of elements when trimming. The trimming unit can also maintain the composition of the original creative and adjust the size of elements when trimming. Furthermore, the trimming unit can maintain the composition of the original creative and adjust margins when trimming. This allows trimming to be performed while maintaining visual balance by maintaining the composition of the original creative.

[0048] The trimming unit can take into account the background of the original creative when trimming. For example, the trimming unit can preserve the background of the original creative and highlight important elements when trimming. The trimming unit can also analyze the background of the original creative and suggest an optimal background when trimming. Furthermore, the trimming unit can adjust the background of the original creative and apply a new background when trimming. This allows trimming that makes the most of the background by taking the background of the original creative into consideration.

[0049] The trimming unit can retain the text elements of the original creative when trimming, ensuring consistency of information. For example, the trimming unit can retain the text elements of the original creative to avoid deleting important information when trimming. The trimming unit can also analyze the text elements of the original creative and suggest an optimal arrangement when trimming. Furthermore, the trimming unit can adjust the text elements of the original creative and apply the new arrangement when trimming. This allows trimming to be performed while ensuring consistency of information by retaining the text elements of the original creative.

[0050] The resizing unit can optimize the font size of the original creative when resizing to improve legibility. For example, the resizing unit maintains the font size of the original creative to ensure legibility when resizing. The resizing unit can also analyze the font size of the original creative and suggest an optimal font size when resizing. Furthermore, the resizing unit can adjust the font size of the original creative and apply the new font size when resizing. This allows for resizing with improved legibility by optimizing the font size of the original creative.

[0051] The resizing unit can maintain the image resolution of the original creative image during resizing to ensure quality. For example, the resizing unit can maintain the image resolution of the original creative image during resizing to ensure quality. The resizing unit can also analyze the image resolution of the original creative image and propose an optimal resolution during resizing. Furthermore, the resizing unit can adjust the image resolution of the original creative image and apply the new resolution during resizing. This allows for quality-ensuring resizing by maintaining the image resolution of the original creative image.

[0052] The resizing unit can maintain the aspect ratio of the original creative when resizing, ensuring design consistency. For example, the resizing unit can maintain the aspect ratio of the original creative when resizing, ensuring design consistency. The resizing unit can also analyze the aspect ratio of the original creative and propose an optimal aspect ratio when resizing. Furthermore, the resizing unit can adjust the aspect ratio of the original creative and apply the new aspect ratio when resizing. This allows resizing to maintain the aspect ratio of the original creative, ensuring design consistency.

[0053] The resizing function can maintain the original creative's color palette during resizing, ensuring brand consistency. For example, it can retain the original creative's color palette and use the same colors during resizing. It can also analyze the original creative's color palette and suggest optimal colors during resizing. Furthermore, it can adjust the original creative's color palette and apply new colors during resizing. This allows for resizing while maintaining brand consistency by preserving the original creative's color palette.

[0054] The evaluation unit can assess the visual appeal of the generated creative during the evaluation process. For example, the evaluation unit can evaluate the color balance of the generated creative. It can also evaluate the consistency of the layout of the generated creative. Furthermore, the evaluation unit can evaluate the legibility of the fonts in the generated creative. By evaluating the visual appeal of the generated creative, it is possible to provide visually superior creative.

[0055] The evaluation unit can assess the suitability of the generated creative to its intended use during the evaluation process. For example, if the generated creative is for advertising, the evaluation unit can evaluate its effectiveness. It can also assess the suitability of the generated creative if it is for social media. Furthermore, if the generated creative is for presentations, the evaluation unit can evaluate its visibility. By evaluating the suitability of the generated creative to its intended use, the evaluation unit can provide creative that is appropriate for the purpose.

[0056] During the evaluation, the evaluation unit can evaluate the market acceptability of the generated creative. For example, the evaluation unit evaluates the degree to which the generated creative will be accepted in the target market. The evaluation unit can also evaluate how superior the generated creative is compared to the creatives of competitors. Furthermore, the evaluation unit can also evaluate the degree to which the generated creative will improve brand recognition in the market. In this way, by evaluating the market acceptability of the generated creative, it is possible to provide creative that is suitable for the market.

[0057] During the evaluation, the evaluation unit can compare the generated creative with competitors' creatives. For example, the evaluation unit evaluates how superior the generated creative is compared to the competitors' creatives. The evaluation unit can also evaluate how visually appealing the generated creative is compared to the competitors' creatives. Furthermore, the evaluation unit can also evaluate how effective the generated creative is compared to the competitors' creatives. In this way, by comparing the generated creative with competitors' creatives, it is possible to provide a competitive creative.

[0058] When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. For example, the interface unit can preferentially display a layout that the user has used in the past. The interface unit can also suggest the most efficient display method based on the user's past operation history. Furthermore, the interface unit can also preferentially display functions that the user has used in the past. In this way, by referring to the user's past operation history, it is possible to provide the optimal display method for the user.

[0059] The interface unit can select the optimal display method when displaying the interface, taking into consideration the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the interface unit can provide a display method optimized for a wide screen. In this way, by taking into consideration the user's device information, it is possible to provide a display method optimized for the device.

[0060] The interface unit can select the optimal display method when displaying the interface, taking into consideration the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the interface unit can provide a display method optimized for a wide screen. In this way, by taking into consideration the user's device information, it is possible to provide a display method optimized for the device.

[0061] The interface unit can provide a function of reflecting user feedback in real time when displaying the interface. For example, the interface unit reflects feedback provided by the user on the interface in real time. The interface unit can also reflect evaluations provided by the user on the usability of the interface in real time. Furthermore, the interface unit can also reflect suggestions provided by the user on the design of the interface in real time. In this way, by reflecting user feedback in real time, it is possible to provide an interface that immediately reflects the user's opinions.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The analysis unit can analyze the user's past creative creation history and reflect it in the redesign. For example, it can analyze the style and layout of creatives created by the user in the past and apply a similar style when redesigning. The analysis unit can also analyze the colors and fonts used by the user in the past and use the same elements when redesigning. Furthermore, the analysis unit can analyze the effectiveness of creatives created by the user in the past and propose a design that maximizes that effectiveness when redesigning. In this way, by analyzing the user's past creative creation history, it is possible to perform a redesign that reflects the user's preferences and effective designs.

[0064] The providing unit can suggest a usage scenario for the generated creative. For example, if the generated creative is for advertising, the providing unit can suggest the optimal advertising medium. Also, if the generated creative is for social media, the providing unit can suggest the optimal posting time. Furthermore, if the generated creative is for a presentation, the providing unit can suggest the optimal slide arrangement. In this way, by suggesting usage scenarios for the generated creative, the user can select the optimal usage method.

[0065] The redesign department can redesign while taking into account the animation elements of the original creative. For example, the animation elements of the original creative can be preserved and the same animations can be applied during the redesign. The department can also analyze the animation elements of the original creative and propose the optimal animations during the redesign. Furthermore, the department can adjust the animation elements of the original creative and apply new animations during the redesign. This allows for a redesign that preserves the animation elements by taking into account the animation elements of the original creative.

[0066] The analysis unit can analyze the metadata of the original creative and extract the information necessary for redesign. For example, it can analyze the creation date and creator information of the original creative and use it as reference during the redesign. It can also analyze the purpose of use and target audience information of the original creative and reflect it in the redesign. Furthermore, it can analyze the usage history of the original creative and suggest the optimal design during the redesign. In this way, by analyzing the metadata of the original creative, the information necessary for redesign can be extracted efficiently.

[0067] The redesign department can preserve the interactive elements of the original creative during redesign, thereby improving the user experience. For example, it can retain the interactive elements of the original creative and apply the same interactive elements during redesign. It can also analyze the interactive elements of the original creative and suggest the most suitable interactive elements during redesign. Furthermore, it can adjust the interactive elements of the original creative and apply new interactive elements during redesign. In this way, it is possible to perform redesigns that improve the user experience by preserving the interactive elements of the original creative.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The analysis unit analyzes the original creative. The original creative includes images, videos, text, etc. The analysis unit uses image analysis technology to analyze the layout and design elements of the original creative, text analysis technology to analyze the text elements, and data analysis technology to analyze the data. Step 2: The redesign department redesigns the original creative to the specified size based on the results of the analysis conducted by the analysis department. The redesign is carried out by methods such as resizing, changing the layout, and adjusting the colors. The redesign department redesigns the original creative using resizing techniques, layout techniques, and color adjustment techniques. Step 3: The provision department provides the creative generated by the redesign department. The provision is performed by methods such as digital distribution, printing, online sharing, etc. The provision department provides the generated creative using digital distribution technology, printing technology, and online sharing technology.

[0070] (Example 2) A creative redesign system according to an embodiment of the present invention automates the task of redesigning a single creative in multiple different sizes using a generation AI. In this creative redesign system, a user inputs a source creative and specifies the size of the creative they want to generate. The generation AI then analyzes the original creative and redesigns it to fit the specified size. The generated creative is provided to the user, who can modify or adjust it as needed. This eliminates the need for manual redesign and allows the user to efficiently generate creatives in multiple sizes. The generation AI analyzes the layout and design elements of the original creative and automatically calculates the optimal placement. For example, it performs image cropping and text resizing to generate a creative suitable for the specified size. The generated creative is provided to the user, who can modify or adjust it as needed. This improves work efficiency in the fields of advertising and marketing and maintains consistency in creatives. Furthermore, the use of generation AI enables high-quality redesigns and improves user satisfaction. This eliminates the need for manual redesigns and allows the creative redesign system to efficiently generate creatives in multiple sizes.

[0071] The creative redesign system according to this embodiment comprises an analysis unit, a redesign unit, and a provision unit. The analysis unit analyzes the original creative. The original creative includes, but is not limited to, images, videos, and text. The analysis unit analyzes the layout and design elements of the original creative using, for example, image analysis technology. The analysis unit can also analyze the text elements of the original creative using text analysis technology. Furthermore, the analysis unit can also analyze the data of the original creative using data analysis technology. The redesign unit redesigns the creative to a specified size based on the results analyzed by the analysis unit. The redesign is performed by, for example, resizing, changing the layout, and adjusting the colors. The redesign unit changes the size of the original creative using, for example, resizing technology. The redesign unit can also change the layout of the original creative using layout change technology. Furthermore, the redesign unit can adjust the colors of the original creative using color adjustment technology. The provision unit provides the creative generated by the redesign unit. Provision is performed by, for example, digital distribution, printing, and online sharing, but is not limited to these methods. The service provider can, for example, provide creative works generated using digital distribution technology in digital format. The service provider can also provide creative works generated using printing technology in printed format. Furthermore, the service provider can share creative works generated using online sharing technology online. As a result, the creative redesign system according to this embodiment can analyze the original creative work, redesign it to a specified size, and provide the generated creative work.

[0072] The redesign unit includes a cropping unit that performs image cropping. The cropping unit performs image cropping. Cropping includes, but is not limited to, methods for cutting out a portion of the image and specifying the cropping range. The cropping unit performs cropping using, for example, a technique for cutting out a portion of the image. The cropping unit can also perform cropping using a technique for specifying the cropping range. Furthermore, the cropping unit can perform cropping using a technique for confirming the cropping result. As a result, the redesign unit can generate creatives optimized for a specified size by cropping the image.

[0073] The redesign unit includes a resizing unit that resizes text. The resizing unit resizes text. Resizing includes, but is not limited to, changing font size or image size. For example, the resizing unit can resize text using font size change technology. The resizing unit can also resize text using image size change technology. Furthermore, the resizing unit can resize text using technology to verify the resizing result. As a result, the redesign unit can generate creative content optimized for a specified size by resizing text.

[0074] The service provider includes an evaluation unit that assesses the quality of the generated creative. The evaluation unit assesses the quality of the generated creative. Quality evaluation includes, but is not limited to, resolution, color, and layout consistency. For example, the evaluation unit can evaluate the quality of the generated creative using a resolution evaluation technique. The evaluation unit can also evaluate the quality of the generated creative using a color evaluation technique. Furthermore, the evaluation unit can also evaluate the quality of the generated creative using a layout consistency evaluation technique. In this way, the service provider can provide high-quality creative by evaluating the quality of the generated creative.

[0075] The providing unit includes an interface unit that provides an interface for the user to modify the generated creative. The interface unit provides the interface for the user to modify the generated creative. The interface includes, for example, a screen, a button, a menu, etc. for the user to operate, but is not limited to these examples. The interface unit provides the interface using, for example, a technology that provides a screen for the user to operate. The interface unit can also provide the interface using a technology that provides buttons for the user to operate. Furthermore, the interface unit can also provide the interface using a technology that provides a menu for the user to operate. In this way, the providing unit provides an interface for the user to modify the generated creative, making it easier for the user to modify the creative.

[0076] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can shallow the analysis depth and quickly redesign the content. Conversely, if the user is relaxed, the analysis unit can deepen the analysis depth and perform a more detailed redesign. Furthermore, if the user is in a hurry, the analysis unit can minimize the analysis depth and quickly provide results. This allows for analysis tailored to the user's situation by adjusting the analysis depth according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The analysis unit can perform a detailed analysis of the color and font characteristics of the original creative and reflect them in the redesign. For example, the analysis unit can analyze the color palette of the original creative and use the same colors when redesigning. The analysis unit can also analyze the font style of the original creative and use the same font when redesigning. Furthermore, the analysis unit can analyze the color balance of the original creative and maintain the same balance when redesigning. In this way, by analyzing the color and font characteristics of the original creative in detail, consistency can be maintained when redesigning.

[0078] The analysis unit can evaluate the importance of the content of the original creative and determine the priority of analysis based on the importance. For example, the analysis unit can prioritize analysis of important text elements and emphasize them during redesign. The analysis unit can also prioritize analysis of important image elements and place them in prominent positions during redesign. Furthermore, the analysis unit can prioritize analysis of important brand elements and maintain consistency during redesign. In this way, by evaluating the importance of the content of the original creative, important elements can be prioritized for analysis.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display that is easy for the user to understand can be provided. 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.

[0080] The analysis unit can analyze the metadata of the original creative and extract information necessary for redesign. For example, the analysis unit analyzes the creation date and creator information of the original creative and uses this information as reference when redesigning. The analysis unit can also analyze the purpose of use and target audience information of the original creative and reflect this information when redesigning. Furthermore, the analysis unit can analyze the usage history of the original creative and propose the optimal design when redesigning. In this way, by analyzing the metadata of the original creative, the information necessary for redesign can be efficiently extracted.

[0081] The analysis unit analyzes the usage history of the original creative and can improve the accuracy of the analysis based on the past usage status. For example, the analysis unit analyzes what media the original creative was used in and suggests the optimal media when redesigning. The analysis unit can also analyze what target audience the original creative was used for and suggest the optimal design when redesigning. Furthermore, the analysis unit can analyze what effect the original creative had and suggest a design that maximizes that effect when redesigning. In this way, by analyzing the usage history of the original creative, the accuracy of the analysis can be improved based on the past usage status.

[0082] The redesign unit can estimate the user's emotions and adjust the redesign style based on the estimated user emotions. For example, if the user is relaxed, the redesign unit generates a design that makes extensive use of soft colors and curves. Furthermore, if the user is excited, the redesign unit can generate a design that uses vivid colors and movement. Furthermore, if the user is calm, the redesign unit can generate a simple and sophisticated design. This allows for a redesign that is suited to the user's situation by adjusting the redesign style according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] During redesign, the redesign unit can optimize the original creative layout to a specified size while maintaining it. For example, the redesign unit can maintain the original creative layout and adjust the position of elements to match the specified size. The redesign unit can also maintain the original creative layout and adjust the size of elements to match the specified size. Furthermore, the redesign unit can maintain the original creative layout and adjust the margins to match the specified size. This allows for a consistent redesign by optimizing the original creative layout to a specified size while maintaining it.

[0084] The redesign department can retain the brand elements of the original creative when redesigning, ensuring brand consistency. For example, the redesign department can retain the logo of the original creative and place it in a prominent position when redesigning. The redesign department can also retain the color palette of the original creative and use the same colors when redesigning. Furthermore, the redesign department can retain the font style of the original creative and use the same font when redesigning. This allows the redesign to ensure brand consistency by retaining the brand elements of the original creative.

[0085] The redesign unit can estimate the user's emotions and adjust the redesign speed based on the estimated emotions. For example, if the user is in a hurry, the redesign unit can speed up the redesign to provide results quickly. Conversely, if the user is relaxed, the redesign unit can slow down the redesign speed to make detailed adjustments. Furthermore, if the user is stressed, the redesign unit can adjust the redesign speed to provide results at an appropriate pace. This allows for redesigns tailored to the user's situation by adjusting the redesign speed according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The redesign department can take into account the animation elements of the original creative when redesigning. For example, the redesign department can retain the animation elements of the original creative and apply the same animations when redesigning. The redesign department can also analyze the animation elements of the original creative and propose optimal animations when redesigning. Furthermore, the redesign department can adjust the animation elements of the original creative and apply new animations when redesigning. This allows for a redesign that retains the animation elements by taking into account the animation elements of the original creative.

[0087] The redesign department can preserve the interactive elements of the original creative during redesign, thereby improving the user experience. For example, the redesign department can preserve the interactive elements of the original creative and apply the same interactive elements during redesign. Furthermore, the redesign department can analyze the interactive elements of the original creative and suggest the most suitable interactive elements during redesign. In addition, the redesign department can adjust the interactive elements of the original creative and apply new interactive elements during redesign. This allows for redesigns that improve the user experience while preserving the interactive elements of the original creative.

[0088] The service provider can estimate the user's emotions and adjust the order in which it delivers creative content based on those emotions. For example, if the user is in a hurry, the service provider will prioritize delivering the most important creative content. If the user is relaxed, the service provider can also deliver detailed creative content sequentially. Furthermore, if the user is stressed, the service provider can prioritize delivering simple and highly visible creative content. By adjusting the order in which creative content is delivered according to the user's emotions, the service provider can deliver creative content that is appropriate for the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The service provider can suggest usage scenarios for the generated creative at the time of delivery. For example, if the generated creative is for advertising, the service provider can suggest the most suitable advertising medium. Furthermore, if the generated creative is for social media, the service provider can suggest the optimal posting time. Additionally, if the generated creative is for a presentation, the service provider can suggest the optimal slide layout. This allows users to choose the most suitable usage method by suggesting usage scenarios for the generated creative.

[0090] The service provider can record the usage history of the generated creative at the time of delivery and reflect this in the next redesign. For example, the service provider can record the usage history of the generated creative and use it as a reference for the next redesign. Furthermore, the service provider can record the effectiveness of the generated creative and propose the optimal design for the next redesign. In addition, the service provider can record the usage status of the generated creative and propose the optimal medium for the next redesign. This allows the service provider to record the usage history of the generated creative and use it as a reference for the next redesign.

[0091] The providing unit can estimate the user's emotions and adjust the format of the creative to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide a simple, highly visible format. If the user is relaxed, the providing unit can also provide a format including detailed information. Furthermore, if the user is feeling stressed, the providing unit can also provide a visually calming format. In this way, by adjusting the format of the creative to be provided according to the user's emotions, it is possible to provide a format appropriate for the user's situation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] The providing unit can provide usage guidelines for the generated creative when providing the creative. For example, the providing unit provides the optimal way to use the generated creative as a guideline. The providing unit can also provide points to note when using the generated creative as a guideline. Furthermore, the providing unit can also provide examples of how to use the generated creative as a guideline. In this way, by providing the usage guidelines for the generated creative, the user can understand the optimal way to use it.

[0093] The providing unit provides a function for sharing the generated creative content at the time of providing the creative content, thereby promoting collaboration within a team. For example, the providing unit provides a function for sharing the generated creative content with team members. The providing unit can also provide a function for receiving feedback on the generated creative content from team members. Furthermore, the providing unit can also provide a function for collaboratively editing the generated creative content with team members. In this way, by providing a function for sharing the generated creative content, it is possible to promote collaboration within a team.

[0094] The trimming unit can estimate the user's emotions and adjust the trimming range based on the estimated emotions. For example, if the user is relaxed, the trimming unit can perform detailed trimming and remove unnecessary parts. If the user is in a hurry, the trimming unit can perform simple trimming and provide results quickly. Furthermore, if the user is stressed, the trimming unit can adjust the trimming range to maintain an appropriate balance. This allows for trimming that is appropriate to the user's situation by adjusting the trimming range according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The trimming unit can remove unnecessary portions while preserving important elements of the original creative during trimming. For example, the trimming unit can preserve important text elements of the original creative and remove unnecessary background. The trimming unit can also preserve important image elements of the original creative and remove unnecessary white space. Furthermore, the trimming unit can preserve important brand elements of the original creative and remove unnecessary decorations. This allows for visually effective trimming by preserving important elements of the original creative and removing unnecessary portions.

[0096] The trimming unit can maintain the composition of the original creative and ensure visual balance when trimming. For example, the trimming unit can maintain the composition of the original creative and adjust the position of elements when trimming. The trimming unit can also maintain the composition of the original creative and adjust the size of elements when trimming. Furthermore, the trimming unit can maintain the composition of the original creative and adjust margins when trimming. This allows trimming to be performed while maintaining visual balance by maintaining the composition of the original creative.

[0097] The trimming unit can estimate the user's emotions and adjust the trimming speed based on the estimated user emotions. For example, if the user is in a hurry, the trimming unit can speed up the trimming speed to provide quick results. Furthermore, if the user is relaxed, the trimming unit can slow down the trimming speed to perform detailed adjustments. Furthermore, if the user is feeling stressed, the trimming unit can adjust the trimming speed to provide results at an appropriate pace. Thus, by adjusting the trimming speed according to the user's emotions, trimming appropriate for the user's situation can be performed. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The trimming unit can take into account the background of the original creative when trimming. For example, the trimming unit can preserve the background of the original creative and highlight important elements when trimming. The trimming unit can also analyze the background of the original creative and suggest an optimal background when trimming. Furthermore, the trimming unit can adjust the background of the original creative and apply a new background when trimming. This allows trimming that makes the most of the background by taking the background of the original creative into consideration.

[0099] The trimming unit can retain the text elements of the original creative when trimming, ensuring consistency of information. For example, the trimming unit can retain the text elements of the original creative to avoid deleting important information when trimming. The trimming unit can also analyze the text elements of the original creative and suggest an optimal arrangement when trimming. Furthermore, the trimming unit can adjust the text elements of the original creative and apply the new arrangement when trimming. This allows trimming to be performed while ensuring consistency of information by retaining the text elements of the original creative.

[0100] The resizing unit can estimate the user's emotions and adjust the accuracy of resizing based on the estimated user emotions. For example, if the user is in a hurry, the resizing unit can lower the resizing accuracy to provide quick results. Furthermore, if the user is relaxed, the resizing unit can increase the resizing accuracy to perform more detailed adjustments. Furthermore, if the user is feeling stressed, the resizing unit can adjust the resizing accuracy to provide results at an appropriate pace. This allows resizing appropriate to the user's situation by adjusting the resizing accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The resizing unit can optimize the font size of the original creative when resizing to improve legibility. For example, the resizing unit maintains the font size of the original creative to ensure legibility when resizing. The resizing unit can also analyze the font size of the original creative and suggest an optimal font size when resizing. Furthermore, the resizing unit can adjust the font size of the original creative and apply the new font size when resizing. This allows for resizing with improved legibility by optimizing the font size of the original creative.

[0102] The resizing unit can maintain the image resolution of the original creative image during resizing to ensure quality. For example, the resizing unit can maintain the image resolution of the original creative image during resizing to ensure quality. The resizing unit can also analyze the image resolution of the original creative image and propose an optimal resolution during resizing. Furthermore, the resizing unit can adjust the image resolution of the original creative image and apply the new resolution during resizing. This allows for quality-ensuring resizing by maintaining the image resolution of the original creative image.

[0103] The resizing unit can estimate the user's emotions and adjust the resizing speed based on the estimated user emotions. For example, if the user is in a hurry, the resizing unit can speed up the resizing speed to provide quick results. Furthermore, if the user is relaxed, the resizing unit can slow down the resizing speed to perform more detailed adjustments. Furthermore, if the user is feeling stressed, the resizing unit can adjust the resizing speed to provide results at an appropriate pace. This allows resizing to be tailored to the user's situation by adjusting the resizing speed according to 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The resizing unit can maintain the aspect ratio of the original creative when resizing, ensuring design consistency. For example, the resizing unit can maintain the aspect ratio of the original creative when resizing, ensuring design consistency. The resizing unit can also analyze the aspect ratio of the original creative and propose an optimal aspect ratio when resizing. Furthermore, the resizing unit can adjust the aspect ratio of the original creative and apply the new aspect ratio when resizing. This allows resizing to maintain the aspect ratio of the original creative, ensuring design consistency.

[0105] The resizing function can maintain the original creative's color palette during resizing, ensuring brand consistency. For example, it can retain the original creative's color palette and use the same colors during resizing. It can also analyze the original creative's color palette and suggest optimal colors during resizing. Furthermore, it can adjust the original creative's color palette and apply new colors during resizing. This allows for resizing while maintaining brand consistency by preserving the original creative's color palette.

[0106] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on those emotions. For example, if the user is relaxed, the evaluation unit can use detailed evaluation criteria to evaluate the creative work. If the user is in a hurry, the evaluation unit can also use simple evaluation criteria to quickly evaluate the creative work. Furthermore, if the user is stressed, the evaluation unit can adjust the evaluation criteria to conduct the evaluation at an appropriate pace. This allows for evaluations that are appropriate to the user's situation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The evaluation unit can assess the visual appeal of the generated creative during the evaluation process. For example, the evaluation unit can evaluate the color balance of the generated creative. It can also evaluate the consistency of the layout of the generated creative. Furthermore, the evaluation unit can evaluate the legibility of the fonts in the generated creative. By evaluating the visual appeal of the generated creative, it is possible to provide visually superior creative.

[0108] The evaluation unit can assess the suitability of the generated creative to its intended use during the evaluation process. For example, if the generated creative is for advertising, the evaluation unit can evaluate its effectiveness. It can also assess the suitability of the generated creative if it is for social media. Furthermore, if the generated creative is for presentations, the evaluation unit can evaluate its visibility. By evaluating the suitability of the generated creative to its intended use, the evaluation unit can provide creative that is appropriate for the purpose.

[0109] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit provides a simple, highly visible display method. If the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the evaluation results according to the user's emotions, it is possible to provide a display that is easy for the user to view. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0110] During the evaluation, the evaluation unit can evaluate the market acceptability of the generated creative. For example, the evaluation unit evaluates the degree to which the generated creative will be accepted in the target market. The evaluation unit can also evaluate how superior the generated creative is compared to the creatives of competitors. Furthermore, the evaluation unit can also evaluate the degree to which the generated creative will improve brand recognition in the market. In this way, by evaluating the market acceptability of the generated creative, it is possible to provide creative that is suitable for the market.

[0111] During the evaluation, the evaluation unit can compare the generated creative with competitors' creatives. For example, the evaluation unit evaluates how superior the generated creative is compared to the competitors' creatives. The evaluation unit can also evaluate how visually appealing the generated creative is compared to the competitors' creatives. Furthermore, the evaluation unit can also evaluate how effective the generated creative is compared to the competitors' creatives. In this way, by comparing the generated creative with competitors' creatives, it is possible to provide a competitive creative.

[0112] The interface unit can estimate the user's emotions and adjust the interface layout based on the estimated user's emotions. For example, if the user is nervous, the interface unit provides a simple, highly visible layout. If the user is relaxed, the interface unit can also provide a layout including detailed information. Furthermore, if the user is in a hurry, the interface unit can also provide a layout that focuses on the main points. In this way, by adjusting the interface layout according to the user's emotions, it is possible to provide an interface that is easy for the user to use. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0113] When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. For example, the interface unit can preferentially display a layout that the user has used in the past. The interface unit can also suggest the most efficient display method based on the user's past operation history. Furthermore, the interface unit can also preferentially display functions that the user has used in the past. In this way, by referring to the user's past operation history, it is possible to provide the optimal display method for the user.

[0114] The interface unit can select the optimal display method when displaying the interface, taking into consideration the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the interface unit can provide a display method optimized for a wide screen. In this way, by taking into consideration the user's device information, it is possible to provide a display method optimized for the device.

[0115] The interface unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user emotions. For example, if the user is nervous, the interface unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the interface unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the interface unit can provide procedures that can be operated quickly. In this way, by adjusting the interface operation procedures according to the user's emotions, it is possible to provide operation procedures that are easy for the user to use. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0116] The interface unit can select the optimal display method when displaying the interface, taking into consideration the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the interface unit can provide a display method optimized for a wide screen. In this way, by taking into consideration the user's device information, it is possible to provide a display method optimized for the device.

[0117] The interface unit can provide a function of reflecting user feedback in real time when displaying the interface. For example, the interface unit reflects feedback provided by the user on the interface in real time. The interface unit can also reflect evaluations provided by the user on the usability of the interface in real time. Furthermore, the interface unit can also reflect suggestions provided by the user on the design of the interface in real time. In this way, by reflecting user feedback in real time, it is possible to provide an interface that immediately reflects the user's opinions. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, redesign unit, provision unit, trimming unit, resizing unit, evaluation unit, and interface unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The redesign unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The trimming unit and the resizing unit are realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The evaluation unit and the interface unit are realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, redesign unit, providing unit, trimming unit, resizing unit, evaluation unit, and interface unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The redesign unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The trimming unit and the resizing unit are realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The evaluation unit and the interface unit are realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, redesign unit, provision unit, trimming unit, resizing unit, evaluation unit, and interface unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The redesign unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The trimming unit and resizing unit are realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The evaluation unit and interface unit are realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, redesign unit, provision unit, trimming unit, resizing unit, evaluation unit, and interface unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The redesign unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The trimming unit and the resizing unit are realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The evaluation unit and the interface unit are realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The analysis unit can analyze the user's past creative creation history and reflect it in the redesign. For example, it can analyze the style and layout of creatives created by the user in the past and apply a similar style when redesigning. The analysis unit can also analyze the colors and fonts used by the user in the past and use the same elements when redesigning. Furthermore, the analysis unit can analyze the effectiveness of creatives created by the user in the past and propose a design that maximizes that effectiveness when redesigning. In this way, by analyzing the user's past creative creation history, it is possible to perform a redesign that reflects the user's preferences and effective designs.

[0120] The redesign department can estimate the user's emotions and adjust the redesign's colors based on those emotions. For example, if the user is relaxed, soft colors can be used. If the user is excited, vibrant colors can be used. Furthermore, if the user is calm, simple and sophisticated colors can be used. In this way, by adjusting the redesign's colors according to the user's emotions, it is possible to provide colors that are appropriate for the user's situation.

[0121] The providing unit can suggest a usage scenario for the generated creative. For example, if the generated creative is for advertising, the providing unit can suggest the optimal advertising medium. Also, if the generated creative is for social media, the providing unit can suggest the optimal posting time. Furthermore, if the generated creative is for a presentation, the providing unit can suggest the optimal slide arrangement. In this way, by suggesting usage scenarios for the generated creative, the user can select the optimal usage method.

[0122] The analysis unit can estimate the user's emotions and adjust the analysis priority based on those emotions. For example, if the user is stressed, it can prioritize analyzing important elements and quickly redesign the design. If the user is relaxed, it can perform a detailed analysis and make a more accurate redesign. Furthermore, if the user is in a hurry, it can adjust the analysis priority to provide results quickly. In this way, by adjusting the analysis priority according to the user's emotions, the system can perform analysis that is appropriate for the user's situation.

[0123] The redesign department can redesign while taking into account the animation elements of the original creative. For example, the animation elements of the original creative can be preserved and the same animations can be applied during the redesign. The department can also analyze the animation elements of the original creative and propose the optimal animations during the redesign. Furthermore, the department can adjust the animation elements of the original creative and apply new animations during the redesign. This allows for a redesign that preserves the animation elements by taking into account the animation elements of the original creative.

[0124] The providing unit can estimate the user's emotions and adjust the order of creatives to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the most important creatives can be provided preferentially. Also, if the user is relaxed, detailed creatives can be provided sequentially. Furthermore, if the user is feeling stressed, simple creatives with high visibility can be provided preferentially. In this way, by adjusting the order of creatives to be provided according to the user's emotions, creatives suited to the user's situation can be provided.

[0125] The redesign unit can estimate the user's emotions and adjust the speed of the redesign based on the estimated user's emotions. For example, if the user is in a hurry, the speed of the redesign can be increased to provide quick results. Alternatively, if the user is relaxed, the speed of the redesign can be decreased to allow for more detailed adjustments. Furthermore, if the user is feeling stressed, the speed of the redesign can be adjusted to provide results at an appropriate pace. In this way, by adjusting the speed of the redesign according to the user's emotions, a redesign suited to the user's situation can be performed.

[0126] The analysis unit can analyze the metadata of the original creative and extract the information necessary for redesign. For example, it can analyze the creation date and creator information of the original creative and use it as reference during the redesign. It can also analyze the purpose of use and target audience information of the original creative and reflect it in the redesign. Furthermore, it can analyze the usage history of the original creative and suggest the optimal design during the redesign. In this way, by analyzing the metadata of the original creative, the information necessary for redesign can be extracted efficiently.

[0127] The redesign department can preserve the interactive elements of the original creative during redesign, thereby improving the user experience. For example, it can retain the interactive elements of the original creative and apply the same interactive elements during redesign. It can also analyze the interactive elements of the original creative and suggest the most suitable interactive elements during redesign. Furthermore, it can adjust the interactive elements of the original creative and apply new interactive elements during redesign. In this way, it is possible to perform redesigns that improve the user experience by preserving the interactive elements of the original creative.

[0128] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. For example, if the user is relaxed, detailed evaluation criteria can be used to evaluate creative works. If the user is in a hurry, simple evaluation criteria can be used to quickly evaluate creative works. Furthermore, if the user is feeling stressed, the evaluation criteria can be adjusted to allow evaluation at an appropriate pace. In this way, by adjusting the evaluation criteria according to the user's emotions, it is possible to perform evaluation appropriate to the user's situation.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The analysis unit analyzes the original creative. The original creative includes images, videos, text, etc. The analysis unit uses image analysis technology to analyze the layout and design elements of the original creative, text analysis technology to analyze the text elements, and data analysis technology to analyze the data. Step 2: The redesign department redesigns the original creative to the specified size based on the results of the analysis conducted by the analysis department. The redesign is carried out by methods such as resizing, changing the layout, and adjusting the colors. The redesign department redesigns the original creative using resizing techniques, layout techniques, and color adjustment techniques. Step 3: The provision department provides the creative generated by the redesign department. The provision is performed by methods such as digital distribution, printing, online sharing, etc. The provision department provides the generated creative using digital distribution technology, printing technology, and online sharing technology.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0133] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0175] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0182] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0187] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0192] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0193] 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.

[0194] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0195] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0196] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0197] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0200] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0202] [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The analysis unit analyzes the original creative, A redesign unit that redesigns the product to a specified size based on the results of the analysis performed by the aforementioned analysis unit, The system comprises a providing unit that provides the creative generated by the redesign unit. A system characterized by:

2. The redesign department It includes a cropping section for cropping images. The system of claim 1 .

3. The redesign department It includes a resizing section for resizing text. The system of claim 1 .

4. The providing unit It includes an evaluation unit that assesses the quality of the generated creative. The system of claim 1 .

5. The providing unit It includes an interface section that provides an interface for users to modify the creatives they generate. The system of claim 1 .

6. The analysis unit It estimates the user's emotions and adjusts the depth of the analysis based on the estimated emotions. The system of claim 1 .

7. The analysis unit We thoroughly analyze the color and font characteristics of the original creative and reflect them in the redesign. The system of claim 1 .

8. The analysis unit Evaluate the importance of the original creative content and determine the priority of analysis based on that importance. The system of claim 1 .

9. The analysis unit It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system of claim 1 .

Images