system

Abstract

The system according to the embodiment aims to streamline the advertisement production process and quickly create creative advertisements. [Solution] The system according to the embodiment includes a reception unit, a generation unit, an editing unit, and an output unit. The reception unit inputs information about the purpose of the advertisement or the target audience. The generation unit analyzes the information input by the reception unit and generates a concept and design proposal for the advertisement. The editing unit allows the user to make revisions based on the proposal generated by the generation unit. The output unit outputs the advertisement revised by the editing unit in a format suitable for various advertising media.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback that the advertising production process is time-consuming and labor-intensive, making it difficult to quickly create creative advertisements.

[0005] The system according to the embodiment aims to streamline the advertisement production process and quickly create creative advertisements. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an editing unit, and an output unit. The reception unit inputs information about the purpose of the advertisement or the target audience. The generation unit analyzes the information input by the reception unit and generates a concept and design proposal for the advertisement. The editing unit allows the user to make revisions based on the proposal generated by the generation unit. The output unit outputs the advertisement revised by the editing unit in a format suitable for various advertising media. [Effects of the Invention]

[0007] The system according to the embodiment streamlines the advertisement production process and enables the rapid creation of creative advertisements. [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) An advertising production system according to an embodiment of the present invention streamlines the advertising production process and quickly creates creative advertisements. In the advertising production system, a user inputs basic information, such as the purpose of the advertisement and the target audience. A generation AI analyzes the information to generate an advertising concept and design proposal, and the user makes necessary modifications to complete the final advertisement. For example, in the advertising production system, a user inputs the purpose of the advertisement, such as promoting a new product or announcing a specific event. The user also inputs information such as the target audience's age group and interests. This information is input into the generation AI, which analyzes it and generates an advertising concept and design proposal. Based on the generated proposal, the user makes modifications, such as changing colors and fonts or adding text, to complete the final advertisement. The completed advertisement is optimized by the generation AI and output in a format suitable for various advertising media. This allows the advertising production system to significantly reduce advertising production time and quickly realize creative ideas. Furthermore, the generation AI utilizes past data and trend information to create effective advertisements. For example, when creating a promotional ad for a new product, generative AI can refer to past successes and propose optimal designs, thereby maximizing the effectiveness of the ad.

[0029] An advertising production system according to an embodiment includes a reception unit, a generation unit, an editing unit, and an output unit. The reception unit receives basic information, such as the purpose of the advertisement and the target audience. For example, the reception unit clarifies the purpose of the advertisement, such as promoting a new product or announcing a specific event. The reception unit can also receive information, such as the target audience's age group and interests. The generation unit analyzes the information received by the reception unit and generates an advertisement concept and design proposal using a generation AI. For example, the generation AI proposes an optimal advertisement concept based on past advertising data and trend information. The generation AI generates the advertisement concept and design proposal using a text generation AI (e.g., LLM) or a multimodal generation AI. The editing unit allows the user to make revisions based on the proposals generated by the generation unit. For example, the editing unit can change colors and fonts, add text, and so on. The editing unit can easily make revisions using editing tools provided by the generation AI. The output unit optimizes the advertisement revised by the editing unit and outputs it in a format suitable for various advertising media. For example, the output unit can output in various formats such as web advertisements, social media advertisements, print advertisements, etc. This allows the advertising production system according to the embodiment to streamline the advertising production process and quickly create creative advertisements.

[0030] The generation unit can propose advertising concepts based on past advertising data or trend information. The generation unit can propose advertising concepts based on, for example, the results of past advertising campaigns or advertising performance data. The generation unit can also propose advertising concepts based on current market trends and consumer interests. For example, the generation unit can generate effective advertising concepts by referring to past success stories. The generation unit can also generate design proposals that incorporate the latest trend information. This makes it possible to create effective advertisements by utilizing past data and trend information. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input past advertising data and trend information into the generation AI and have the generation AI generate an advertising concept.

[0031] The generation unit can generate design proposals that reflect the product's features. The generation unit can generate design proposals that reflect, for example, the product's features, design, price, etc. For example, the generation unit can generate design proposals that emphasize the product's features. The generation unit can also generate design proposals that appeal to a target audience based on the product's features. For example, the generation unit generates design proposals that emphasize the product's unique features. This can maximize the effectiveness of advertising by emphasizing the product's features. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the product's features into the generation AI and cause the generation AI to generate design proposals.

[0032] The editorial department can change the color or font, or add text. For example, the editorial department can change the color of the design proposal proposed by the generation AI. For example, the editorial department can change the color to match the theme of the advertisement. The editorial department can also change the font. For example, the editorial department can select a font that is appropriate for the advertisement's message. The editorial department can also add text. For example, the editorial department can add a catchphrase or detailed information for the advertisement. This allows the user to easily modify the advertisement. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, the generation AI. For example, the editorial department can change the color or font, or add text using an editing tool provided by the generation AI.

[0033] The output unit can output advertisements in various formats, such as web advertisements, social media advertisements, and print advertisements. For example, the output unit can output banner advertisements and video advertisements as web advertisements. The output unit can also output image advertisements and story advertisements as social media advertisements. The output unit can also output posters and flyers as print advertisements. For example, the output unit selects the optimal output method depending on the advertisement format. This allows advertisements to be output in formats suitable for various advertising media. Some or all of the above-described processing in the output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the output unit outputs advertisements optimized by the generation AI in formats suitable for various advertising media.

[0034] The reception unit can provide input assistance by referring to the user's past input history when inputting information about the purpose of the advertisement and the target audience. For example, the reception unit can automatically display information about the purpose of the advertisement and the target audience previously input by the user as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information about the purpose of the advertisement and the target audience to be used in a specific time period based on the user's past input history. This can improve the efficiency of input work by referring to the user's past input history. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI perform input assistance.

[0035] The reception unit can provide input guidance based on the user's industry and market trends when entering information about the purpose of the advertisement and the target audience. For example, the reception unit can provide guidance for entering information about the purpose of the advertisement and the target audience based on the latest trend information in the user's industry. The reception unit can also suggest the optimal input method based on successful examples of advertisements created by the user in the past. Furthermore, the reception unit can provide input guidance based on industry best practices for the information entered by the user. This allows for the creation of more effective advertisements by providing input guidance based on industry and market trends. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input industry trend information into the generation AI and have the generation AI provide input guidance.

[0036] The reception unit can select the optimal input means depending on the user's input method when inputting information about the purpose of the advertisement or the target audience. For example, when a user inputs information about the purpose of the advertisement or the target audience by voice, the reception unit automatically converts the information into text using voice recognition technology. Furthermore, when a user inputs information using an image, the reception unit can also extract the necessary information using image recognition technology. Furthermore, when a user inputs information using text, the reception unit can analyze the input content in real time and suggest the optimal input means. This can improve the efficiency of input work by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input voice data to a generation AI and have the generation AI perform text conversion.

[0037] When inputting information about the purpose of an advertisement or a target audience, the reception unit can prioritize input of highly relevant information by taking into account the user's geographical location information. For example, when a user intends to advertise in a specific region, the reception unit can provide input guidance based on the market trends of that region. Furthermore, when a user intends to set a geographically close target audience, the reception unit can also suggest input content taking into account the characteristics of that region. Furthermore, when a user intends to advertise in a specific city, the reception unit can also provide input guidance based on consumer behavior in that city. This allows for the creation of effective advertisements tailored to regional characteristics by taking geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input geographical location information into the generation AI and cause the generation AI to provide input guidance.

[0038] When inputting information about the advertisement purpose and the target audience, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit can identify the interests of the target audience based on the user's social media activity and suggest input content. The reception unit can also analyze the user's social media posts and input information related to the advertisement purpose. Furthermore, the reception unit can input target audience information based on the activity of the user's friends on social media. In this way, by analyzing social media activity, effective advertisements based on the interests of the target audience can be created. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input social media activity data into the generation AI and cause the generation AI to input related information.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting information about the advertising purpose and the target audience. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by reflecting the user's past feedback. Furthermore, the reception unit can improve the input method to resolve problems pointed out by the user in the past. In this way, by reflecting past feedback, the optimal input method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input past feedback data into the generation AI and have the generation AI customize the input method.

[0040] When generating advertising concepts and design proposals, the generation unit can optimize the generation algorithm based on the purpose of the advertisement. For example, in the case of a promotional advertisement for a new product, the generation unit generates a design proposal that emphasizes the product's features. In addition, in the case of an advertisement announcing a specific event, the generation unit can generate a design proposal that focuses on detailed information about the event. Furthermore, in the case of an advertisement aimed at improving a brand image, the generation unit can generate a design proposal that emphasizes the value of the brand. This makes it possible to generate optimal design proposals based on the purpose of the advertisement. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the purpose of the advertisement into the generation AI and cause the generation AI to optimize the generation algorithm.

[0041] The generation unit can take into consideration the target audience's attribute information when generating advertising concepts and design proposals. The generation unit, for example, generates design proposals according to the target audience's age group. The generation unit can also generate advertising concepts based on the target audience's interests. Furthermore, the generation unit can generate design proposals that take into consideration the target audience's regional characteristics. This makes it possible to create more effective advertisements by taking into consideration the target audience's attribute information. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the target audience's attribute information into the generation AI and cause the generation AI to generate advertising concepts and design proposals.

[0042] When generating advertising concepts and design proposals, the generation unit can improve the accuracy of generation by referring to past advertising data and trend information. The generation unit, for example, generates effective advertising concepts based on past success stories. The generation unit can also generate design proposals that incorporate the latest trend information. Furthermore, the generation unit can analyze past advertising data and generate optimal advertising concepts. In this way, by referring to past data and trend information, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input past advertising data and trend information into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] When generating advertising concepts and design proposals, the generation unit can determine the generation priority based on the submission date of the advertisement. For example, the generation unit prioritizes the generation of advertising projects with an approaching deadline. The generation unit can also prioritize the generation of advertising concepts that match seasons or events. Furthermore, the generation unit can determine the optimal generation order based on the submission date specified by the user. This allows advertisements to be created efficiently by determining the generation priority based on the submission date. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input submission date data into the generation AI and have the generation AI determine the generation priority.

[0044] When generating advertising concepts and design proposals, the generation unit can adjust the order of generation based on the relevance of the advertisement. For example, the generation unit prioritizes generating concepts that are most relevant to the purpose of the advertisement. The generation unit can also prioritize generating design proposals that are most relevant to the attributes of the target audience. Furthermore, the generation unit can also prioritize generating concepts that are most suitable for the advertising medium. In this way, by adjusting the order of generation based on the relevance of the advertisement, it is possible to create effective advertisements. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input advertisement relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0045] When generating advertising concepts and design proposals, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user is an expert in the advertising industry, the generation unit can generate concepts that use a lot of technical terminology. Alternatively, if the user is unfamiliar with the advertising industry, the generation unit can generate simple concepts that avoid technical terminology. Furthermore, the generation unit can adjust the use of optimal technical terminology based on the user's past input history. This allows the creation of optimal advertisements by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0046] When revising an advertisement, the editorial department can provide editing assistance by referring to the user's past editing history. For example, the editorial department can suggest the optimal editing method based on the revisions made by the user in the past. The editorial department can also preferentially suggest editing tools that the user has used in the past. Furthermore, the editorial department can predict and suggest revisions to be made in a specific time period based on the user's past editing history. This makes it possible to improve the efficiency of editing work by referring to the user's past editing history. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, a generation AI. For example, the editorial department can input past editing history data into the generation AI and have the generation AI perform editing assistance.

[0047] When revising an advertisement, the editorial department can provide editing guidance based on the user's industry and market trends. For example, the editorial department can suggest modifications to the advertisement based on the latest trend information in the user's industry. The editorial department can also suggest the optimal modification method based on successful examples of advertisements created by the user in the past. Furthermore, the editorial department can provide editing guidance based on industry best practices for the information entered by the user. This allows for the creation of more effective advertisements by providing editing guidance based on industry and market trends. Some or all of the above-mentioned processing in the editorial department can be performed using, or without, a generation AI. For example, the editorial department can input industry trend information into the generation AI and have the generation AI provide the editing guidance.

[0048] When correcting an advertisement, the editorial department can select the optimal editing method depending on the user's input method. For example, when a user inputs the advertisement corrections by voice, the editorial department automatically converts the voice into text using voice recognition technology. In addition, when a user inputs the corrections using an image, the editorial department can also extract necessary information using image recognition technology. Furthermore, when a user inputs the corrections using text, the editorial department can analyze the input content in real time and suggest the optimal editing method. This makes it possible to improve the efficiency of editing work by providing the optimal editing method depending on the user's input method. Some or all of the above-mentioned processing in the editorial department may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the editorial department can input voice data into a generation AI and have the generation AI perform text conversion.

[0049] When revising an advertisement, the editorial department can prioritize highly relevant revisions by taking into account the user's geographical location information. For example, if a user runs an advertisement in a specific region, the editorial department can suggest revisions based on market trends in that region. In addition, if a user sets a geographically close target audience, the editorial department can also suggest revisions taking into account the characteristics of that region. Furthermore, if a user runs an advertisement in a specific city, the editorial department can also suggest revisions based on consumer behavior in that city. In this way, by taking geographical location information into account, it is possible to create effective advertisements tailored to regional characteristics. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, a generation AI. For example, the editorial department can input geographical location information into the generation AI and have the generation AI execute the suggested revisions.

[0050] When revising an advertisement, the editorial department can analyze the user's social media activity and make relevant revisions. For example, the editorial department can identify the interests of the target audience based on the user's social media activity and suggest revisions. The editorial department can also analyze the user's social media posts and suggest revisions related to the advertisement's purpose. Furthermore, the editorial department can also revise the target audience information based on the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to create effective advertisements based on the interests of the target audience. Some or all of the above-mentioned processing by the editorial department can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the editorial department can input social media activity data into the generation AI and have the generation AI perform relevant revisions.

[0051] When revising an advertisement, the editorial department can customize the revision method by reflecting the user's past feedback. For example, the editorial department can propose an optimal revision method based on feedback provided by the user in the past. The editorial department can also customize the editing interface by reflecting the user's past feedback. Furthermore, the editorial department can improve the revision method to resolve problems previously pointed out by the user. In this way, by reflecting past feedback, it is possible to provide the user with an optimal revision method. Some or all of the above-mentioned processing in the editorial department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the editorial department can input past feedback data into the generation AI and have the generation AI customize the revision method.

[0052] When outputting an advertisement, the output unit can provide output assistance by referring to the user's past output history. For example, the output unit can suggest an optimal output method based on the format of advertisements previously output by the user. The output unit can also preferentially suggest output tools that the user has used in the past. Furthermore, the output unit can predict and suggest output content to be performed in a specific time period based on the user's past output history. This makes it possible to improve the efficiency of output work by referring to the user's past output history. Some or all of the above-mentioned processing in the output unit can be performed using, or without, a generation AI. For example, the output unit can input past output history data into the generation AI and have the generation AI perform output assistance.

[0053] When outputting an advertisement, the output unit can provide an output guide based on the user's industry and market trends. For example, the output unit can suggest advertisement output content based on the latest trend information for the user's industry. The output unit can also suggest an optimal output method based on successful examples of advertisements created by the user in the past. Furthermore, the output unit can provide an output guide that references industry best practices for the information input by the user. This allows for the creation of more effective advertisements by providing an output guide based on industry and market trends. Some or all of the above-described processing in the output unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the output unit can input industry trend information to the generation AI and have the generation AI provide the output guide.

[0054] When outputting an advertisement, the output unit can select the optimal output means depending on the user's input method. For example, when a user inputs the advertisement output content by voice, the output unit automatically converts it into text using voice recognition technology. Furthermore, when a user inputs the output content using an image, the output unit can also extract necessary information using image recognition technology. Furthermore, when a user inputs the output content as text, the output unit can analyze the input content in real time and suggest the optimal output means. This can improve the efficiency of the output process by providing the optimal output means depending on the user's input method. Some or all of the above-described processing in the output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the output unit can input voice data to a generation AI and have the generation AI perform text conversion.

[0055] When outputting an advertisement, the output unit can prioritize highly relevant output by taking into account the user's geographical location information. For example, if a user runs an advertisement in a specific region, the output unit can suggest output content based on the market trends of that region. In addition, if a user sets a geographically close target audience, the output unit can also suggest output content by taking into account the characteristics of that region. Furthermore, if a user runs an advertisement in a specific city, the output unit can also suggest output content based on consumer behavior in that city. In this way, by taking into account the geographical location information, it is possible to create an effective advertisement tailored to the regional characteristics. Some or all of the above-described processing in the output unit may be performed using, or without, a generation AI. For example, the output unit can input geographical location information to the generation AI and have the generation AI execute the output content suggestion.

[0056] When outputting an advertisement, the output unit can analyze the user's social media activity and provide related output. For example, the output unit can identify the interests of the target audience based on the user's social media activity and suggest output content. The output unit can also analyze the user's social media posts and suggest output content related to the advertisement's purpose. Furthermore, the output unit can output information about the target audience based on the activity of the user's friends on social media. This allows for the creation of effective advertisements based on the interests of the target audience by analyzing social media activity. Some or all of the above-described processing in the output unit can be performed using, or without, a generation AI. For example, the output unit can input social media activity data into the generation AI and cause the generation AI to execute related output.

[0057] When outputting an advertisement, the output unit can customize the output method by reflecting the user's past feedback. The output unit, for example, suggests an optimal output method based on feedback provided by the user in the past. The output unit can also customize the output interface by reflecting the user's past feedback. Furthermore, the output unit can improve the output method to resolve problems previously pointed out by the user. In this way, by reflecting past feedback, it is possible to provide the user with an optimal output method. Some or all of the above-described processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can input past feedback data into the generation AI and have the generation AI customize the output method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can display relevant success stories of advertising campaigns in real time based on the user's input. For example, if the user inputs a promotion for a new product, past successful advertising cases for similar promotions can be displayed. Also, if the user inputs an announcement for a specific event, success stories related to that event can be displayed. Furthermore, the reception unit can display success stories targeting the same target audience based on the target audience information input by the user. This allows the user to create effective advertisements while referring to past success stories.

[0060] The generation unit can predict the effectiveness of an advertisement based on the advertisement purpose and target audience information input by the user. For example, the generation unit can predict the degree of effectiveness of similar advertisements based on past advertisement data. The generation unit can also predict the effectiveness of an advertisement taking current market trends into consideration. Furthermore, the generation unit can predict the effectiveness of an advertisement based on attribute information of the target audience. This allows the user to understand the effectiveness of the advertisement in advance and create the optimal advertisement.

[0061] When a user makes changes to an ad, the editorial department can display a preview of the changes in real time. For example, if a user changes the color, the change is immediately reflected in the preview. Also, if a user adds text, the text can be immediately displayed in the preview. Furthermore, if a user changes the font, the font can be immediately reflected in the preview. This allows users to efficiently modify their ads while checking the changes in real time.

[0062] The receiving unit can support voice input when a user inputs information about the purpose of an advertisement or the target audience. For example, when a user inputs the purpose of an advertisement by voice, the information can be automatically converted into text using speech recognition technology. Also, when a user inputs information about a target audience by voice, the information can be automatically converted into text. Furthermore, using speech input allows a user to input information without using their hands, thereby streamlining input work. This allows a user to input advertisement information quickly and efficiently using speech input.

[0063] When generating advertising concepts and design proposals, the generation unit can refer to the user's past advertising production history. For example, a new advertising proposal can be generated based on the design and concept of an advertisement created by the user in the past. An effective advertising proposal can also be generated based on data on the user's successful advertising campaigns in the past. Furthermore, specific patterns and trends can be extracted from the user's past advertising production history, and new advertising proposals can be generated based on them. This makes it possible to create more effective advertisements by utilizing the user's past experience.

[0064] When a user makes changes to an advertisement, the editorial department can provide real-time feedback on the changes. For example, if a user changes a color, the editorial department can provide feedback on the impact of the change on the entire advertisement. If a user adds text, the editorial department can provide feedback on how the text will be received by the target audience. Furthermore, if a user changes a font, the editorial department can provide feedback on how the font will affect the message of the advertisement. This allows users to effectively revise their advertisements while receiving feedback on their changes.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The user inputs basic information such as the purpose of the advertisement and the target audience into the reception unit. For example, the reception unit clarifies the purpose of the advertisement, such as promoting a new product or announcing a specific event. The reception unit can also input information such as the target audience's age group and interests. Step 2: The generation unit uses a generation AI to analyze the information input by the reception unit and generate advertising concepts and design proposals. For example, the generation AI proposes optimal advertising concepts based on past advertising data and trend information. The generation AI generates advertising concepts and design proposals using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: In the editorial department, users make revisions based on the ideas generated by the generation department. For example, the editorial department can change colors and fonts, add text, etc. The editorial department can easily make revisions using the editing tools provided by the generation AI. Step 4: The output unit optimizes the advertisements revised by the editorial department and outputs them in a format suitable for various advertising media. For example, the output unit can output in various formats such as web advertisements, social media advertisements, and print advertisements.

[0067] (Example 2) An advertising production system according to an embodiment of the present invention streamlines the advertising production process and quickly creates creative advertisements. In the advertising production system, a user inputs basic information, such as the purpose of the advertisement and the target audience. A generation AI analyzes the information to generate an advertising concept and design proposal, and the user makes necessary modifications to complete the final advertisement. For example, in the advertising production system, a user inputs the purpose of the advertisement, such as promoting a new product or announcing a specific event. The user also inputs information such as the target audience's age group and interests. This information is input into the generation AI, which analyzes it and generates an advertising concept and design proposal. Based on the generated proposal, the user makes modifications, such as changing colors and fonts or adding text, to complete the final advertisement. The completed advertisement is optimized by the generation AI and output in a format suitable for various advertising media. This allows the advertising production system to significantly reduce advertising production time and quickly realize creative ideas. Furthermore, the generation AI utilizes past data and trend information to create effective advertisements. For example, when creating a promotional ad for a new product, generative AI can refer to past successes and propose optimal designs, thereby maximizing the effectiveness of the ad.

[0068] An advertising production system according to an embodiment includes a reception unit, a generation unit, an editing unit, and an output unit. The reception unit receives basic information, such as the purpose of the advertisement and the target audience. For example, the reception unit clarifies the purpose of the advertisement, such as promoting a new product or announcing a specific event. The reception unit can also receive information, such as the target audience's age group and interests. The generation unit analyzes the information received by the reception unit and generates an advertisement concept and design proposal using a generation AI. For example, the generation AI proposes an optimal advertisement concept based on past advertising data and trend information. The generation AI generates the advertisement concept and design proposal using a text generation AI (e.g., LLM) or a multimodal generation AI. The editing unit allows the user to make revisions based on the proposals generated by the generation unit. For example, the editing unit can change colors and fonts, add text, and so on. The editing unit can easily make revisions using editing tools provided by the generation AI. The output unit optimizes the advertisement revised by the editing unit and outputs it in a format suitable for various advertising media. For example, the output unit can output in various formats such as web advertisements, social media advertisements, print advertisements, etc. This allows the advertising production system according to the embodiment to streamline the advertising production process and quickly create creative advertisements.

[0069] The generation unit can propose advertising concepts based on past advertising data or trend information. The generation unit can propose advertising concepts based on, for example, the results of past advertising campaigns or advertising performance data. The generation unit can also propose advertising concepts based on current market trends and consumer interests. For example, the generation unit can generate effective advertising concepts by referring to past success stories. The generation unit can also generate design proposals that incorporate the latest trend information. This makes it possible to create effective advertisements by utilizing past data and trend information. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input past advertising data and trend information into the generation AI and have the generation AI generate an advertising concept.

[0070] The generation unit can generate design proposals that reflect the product's features. The generation unit can generate design proposals that reflect, for example, the product's features, design, price, etc. For example, the generation unit can generate design proposals that emphasize the product's features. The generation unit can also generate design proposals that appeal to a target audience based on the product's features. For example, the generation unit generates design proposals that emphasize the product's unique features. This can maximize the effectiveness of advertising by emphasizing the product's features. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the product's features into the generation AI and cause the generation AI to generate design proposals.

[0071] The editorial department can change the color or font, or add text. For example, the editorial department can change the color of the design proposal proposed by the generation AI. For example, the editorial department can change the color to match the theme of the advertisement. The editorial department can also change the font. For example, the editorial department can select a font that is appropriate for the advertisement's message. The editorial department can also add text. For example, the editorial department can add a catchphrase or detailed information for the advertisement. This allows the user to easily modify the advertisement. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, the generation AI. For example, the editorial department can change the color or font, or add text using an editing tool provided by the generation AI.

[0072] The output unit can output advertisements in various formats, such as web advertisements, social media advertisements, and print advertisements. For example, the output unit can output banner advertisements and video advertisements as web advertisements. The output unit can also output image advertisements and story advertisements as social media advertisements. The output unit can also output posters and flyers as print advertisements. For example, the output unit selects the optimal output method depending on the advertisement format. This allows advertisements to be output in formats suitable for various advertising media. Some or all of the above-described processing in the output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the output unit outputs advertisements optimized by the generation AI in formats suitable for various advertising media.

[0073] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly enter information about the advertising purpose and target audience. This makes it possible to provide an optimal input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0074] The reception unit can provide input assistance by referring to the user's past input history when inputting information about the purpose of the advertisement and the target audience. For example, the reception unit can automatically display information about the purpose of the advertisement and the target audience previously input by the user as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information about the purpose of the advertisement and the target audience to be used in a specific time period based on the user's past input history. This can improve the efficiency of input work by referring to the user's past input history. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI perform input assistance.

[0075] The reception unit can provide input guidance based on the user's industry and market trends when entering information about the purpose of the advertisement and the target audience. For example, the reception unit can provide guidance for entering information about the purpose of the advertisement and the target audience based on the latest trend information in the user's industry. The reception unit can also suggest the optimal input method based on successful examples of advertisements created by the user in the past. Furthermore, the reception unit can provide input guidance based on industry best practices for the information entered by the user. This allows for the creation of more effective advertisements by providing input guidance based on industry and market trends. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input industry trend information into the generation AI and have the generation AI provide input guidance.

[0076] The reception unit can select the optimal input means depending on the user's input method when inputting information about the purpose of the advertisement or the target audience. For example, when a user inputs information about the purpose of the advertisement or the target audience by voice, the reception unit automatically converts the information into text using voice recognition technology. Furthermore, when a user inputs information using an image, the reception unit can also extract the necessary information using image recognition technology. Furthermore, when a user inputs information using text, the reception unit can analyze the input content in real time and suggest the optimal input means. This can improve the efficiency of input work by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input voice data to a generation AI and have the generation AI perform text conversion.

[0077] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is nervous, the reception unit can guide the user to prioritize input of important information. Furthermore, if the user is relaxed, the reception unit can also prompt the user to input detailed information. Furthermore, if the user is in a hurry, the reception unit can suggest inputting the most important information first. Thus, by prioritizing input content according to the user's emotions, important information can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0078] When inputting information about the purpose of an advertisement or a target audience, the reception unit can prioritize input of highly relevant information by taking into account the user's geographical location information. For example, when a user intends to advertise in a specific region, the reception unit can provide input guidance based on the market trends of that region. Furthermore, when a user intends to set a geographically close target audience, the reception unit can also suggest input content taking into account the characteristics of that region. Furthermore, when a user intends to advertise in a specific city, the reception unit can also provide input guidance based on consumer behavior in that city. This allows for the creation of effective advertisements tailored to regional characteristics by taking geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input geographical location information into the generation AI and cause the generation AI to provide input guidance.

[0079] When inputting information about the advertisement purpose and the target audience, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit can identify the interests of the target audience based on the user's social media activity and suggest input content. The reception unit can also analyze the user's social media posts and input information related to the advertisement purpose. Furthermore, the reception unit can input target audience information based on the activity of the user's friends on social media. In this way, by analyzing social media activity, effective advertisements based on the interests of the target audience can be created. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input social media activity data into the generation AI and cause the generation AI to input related information.

[0080] The reception unit can customize the input method by reflecting the user's past feedback when inputting information about the advertising purpose and the target audience. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by reflecting the user's past feedback. Furthermore, the reception unit can improve the input method to resolve problems pointed out by the user in the past. In this way, by reflecting past feedback, the optimal input method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input past feedback data into the generation AI and have the generation AI customize the input method.

[0081] The generation unit can estimate the user's emotions and adjust the expression method of the generated advertising concept based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an advertising concept with a soft tone. Furthermore, if the user is excited, the generation unit can generate an advertising concept with an energetic expression. Furthermore, if the user is calm, the generation unit can generate a simple and sophisticated advertising concept. This allows the generation of an optimal advertising concept according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the expression method of the advertising concept.

[0082] When generating advertising concepts and design proposals, the generation unit can optimize the generation algorithm based on the purpose of the advertisement. For example, in the case of a promotional advertisement for a new product, the generation unit generates a design proposal that emphasizes the product's features. In addition, in the case of an advertisement announcing a specific event, the generation unit can generate a design proposal that focuses on detailed information about the event. Furthermore, in the case of an advertisement aimed at improving a brand image, the generation unit can generate a design proposal that emphasizes the value of the brand. This makes it possible to generate optimal design proposals based on the purpose of the advertisement. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the purpose of the advertisement into the generation AI and cause the generation AI to optimize the generation algorithm.

[0083] The generation unit can take into consideration the target audience's attribute information when generating advertising concepts and design proposals. The generation unit, for example, generates design proposals according to the target audience's age group. The generation unit can also generate advertising concepts based on the target audience's interests. Furthermore, the generation unit can generate design proposals that take into consideration the target audience's regional characteristics. This makes it possible to create more effective advertisements by taking into consideration the target audience's attribute information. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the target audience's attribute information into the generation AI and cause the generation AI to generate advertising concepts and design proposals.

[0084] When generating advertising concepts and design proposals, the generation unit can improve the accuracy of generation by referring to past advertising data and trend information. The generation unit, for example, generates effective advertising concepts based on past success stories. The generation unit can also generate design proposals that incorporate the latest trend information. Furthermore, the generation unit can analyze past advertising data and generate optimal advertising concepts. In this way, by referring to past data and trend information, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input past advertising data and trend information into the generation AI and cause the generation AI to improve the accuracy of generation.

[0085] The generation unit can estimate the user's emotions and adjust the length of the generated advertising concept based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point advertising concept. If the user is relaxed, the generation unit can generate a longer advertising concept with detailed explanations. If the user is excited, the generation unit can generate an advertising concept with visually stimulating effects. This allows the creation of an optimal advertisement by adjusting the length of the advertising concept according to the user's emotions. The emotion estimation is realized 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. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the length of the advertising concept.

[0086] When generating advertising concepts and design proposals, the generation unit can determine the generation priority based on the submission date of the advertisement. For example, the generation unit prioritizes the generation of advertising projects with an approaching deadline. The generation unit can also prioritize the generation of advertising concepts that match seasons or events. Furthermore, the generation unit can determine the optimal generation order based on the submission date specified by the user. This allows advertisements to be created efficiently by determining the generation priority based on the submission date. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input submission date data into the generation AI and have the generation AI determine the generation priority.

[0087] When generating advertising concepts and design proposals, the generation unit can adjust the order of generation based on the relevance of the advertisement. For example, the generation unit prioritizes generating concepts that are most relevant to the purpose of the advertisement. The generation unit can also prioritize generating design proposals that are most relevant to the attributes of the target audience. Furthermore, the generation unit can also prioritize generating concepts that are most suitable for the advertising medium. In this way, by adjusting the order of generation based on the relevance of the advertisement, it is possible to create effective advertisements. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input advertisement relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0088] When generating advertising concepts and design proposals, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user is an expert in the advertising industry, the generation unit can generate concepts that use a lot of technical terminology. Alternatively, if the user is unfamiliar with the advertising industry, the generation unit can generate simple concepts that avoid technical terminology. Furthermore, the generation unit can adjust the use of optimal technical terminology based on the user's past input history. This allows the creation of optimal advertisements by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0089] The editing unit can estimate the user's emotions and adjust the display method of the editing interface based on the estimated user emotions. For example, if the user is feeling stressed, the editing unit can provide a simple interface and minimize editing steps. Furthermore, if the user is relaxed, the editing unit can provide detailed editing options and suggest customizable editing methods. Furthermore, if the user is in a hurry, the editing unit can prioritize voice input to enable quick editing. This allows for an optimal editing interface to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the editing unit can be performed using, for example, the generation AI, or without the generation AI. For example, the editing unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] When revising an advertisement, the editorial department can provide editing assistance by referring to the user's past editing history. For example, the editorial department can suggest the optimal editing method based on the revisions made by the user in the past. The editorial department can also preferentially suggest editing tools that the user has used in the past. Furthermore, the editorial department can predict and suggest revisions to be made in a specific time period based on the user's past editing history. This makes it possible to improve the efficiency of editing work by referring to the user's past editing history. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, a generation AI. For example, the editorial department can input past editing history data into the generation AI and have the generation AI perform editing assistance.

[0091] When revising an advertisement, the editorial department can provide editing guidance based on the user's industry and market trends. For example, the editorial department can suggest modifications to the advertisement based on the latest trend information in the user's industry. The editorial department can also suggest the optimal modification method based on successful examples of advertisements created by the user in the past. Furthermore, the editorial department can provide editing guidance based on industry best practices for the information entered by the user. This allows for the creation of more effective advertisements by providing editing guidance based on industry and market trends. Some or all of the above-mentioned processing in the editorial department can be performed using, or without, a generation AI. For example, the editorial department can input industry trend information into the generation AI and have the generation AI provide the editing guidance.

[0092] When correcting an advertisement, the editorial department can select the optimal editing method depending on the user's input method. For example, when a user inputs the advertisement corrections by voice, the editorial department automatically converts the voice into text using voice recognition technology. In addition, when a user inputs the corrections using an image, the editorial department can also extract necessary information using image recognition technology. Furthermore, when a user inputs the corrections using text, the editorial department can analyze the input content in real time and suggest the optimal editing method. This makes it possible to improve the efficiency of editing work by providing the optimal editing method depending on the user's input method. Some or all of the above-mentioned processing in the editorial department may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the editorial department can input voice data into a generation AI and have the generation AI perform text conversion.

[0093] The editorial department can estimate the user's emotions and prioritize revisions based on the estimated user emotions. For example, if the user is nervous, the editorial department can guide the user to prioritize important revisions. Furthermore, if the user is relaxed, the editorial department can encourage the user to make detailed revisions. Furthermore, if the user is in a hurry, the editorial department can suggest making the most important revisions first. Thus, by prioritizing revisions according to the user's emotions, important revisions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the editorial department can be performed using, for example, the generation AI. For example, the editorial department can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0094] When revising an advertisement, the editorial department can prioritize highly relevant revisions by taking into account the user's geographical location information. For example, if a user runs an advertisement in a specific region, the editorial department can suggest revisions based on market trends in that region. In addition, if a user sets a geographically close target audience, the editorial department can also suggest revisions taking into account the characteristics of that region. Furthermore, if a user runs an advertisement in a specific city, the editorial department can also suggest revisions based on consumer behavior in that city. In this way, by taking geographical location information into account, it is possible to create effective advertisements tailored to regional characteristics. Some or all of the above-mentioned processing in the editorial department may be performed using, or without, a generation AI. For example, the editorial department can input geographical location information into the generation AI and have the generation AI execute the suggested revisions.

[0095] When revising an advertisement, the editorial department can analyze the user's social media activity and make relevant revisions. For example, the editorial department can identify the interests of the target audience based on the user's social media activity and suggest revisions. The editorial department can also analyze the user's social media posts and suggest revisions related to the advertisement's purpose. Furthermore, the editorial department can also revise the target audience information based on the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to create effective advertisements based on the interests of the target audience. Some or all of the above-mentioned processing by the editorial department can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the editorial department can input social media activity data into the generation AI and have the generation AI perform relevant revisions.

[0096] When revising an advertisement, the editorial department can customize the revision method by reflecting the user's past feedback. For example, the editorial department can propose an optimal revision method based on feedback provided by the user in the past. The editorial department can also customize the editing interface by reflecting the user's past feedback. Furthermore, the editorial department can improve the revision method to resolve problems previously pointed out by the user. In this way, by reflecting past feedback, it is possible to provide the user with an optimal revision method. Some or all of the above-mentioned processing in the editorial department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the editorial department can input past feedback data into the generation AI and have the generation AI customize the revision method.

[0097] The output unit can estimate the user's emotions and adjust the display method of the output format based on the estimated user emotions. For example, if the user is feeling stressed, the output unit can provide a simple output format and minimize output procedures. Furthermore, if the user is relaxed, the output unit can provide detailed output options and suggest customizable output methods. Furthermore, if the user is in a hurry, the output unit can prioritize voice input and enable quick output format selection. This allows the optimal output format to be provided according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the output unit can be performed using, for example, the generation AI. For example, the output unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0098] When outputting an advertisement, the output unit can provide output assistance by referring to the user's past output history. For example, the output unit can suggest an optimal output method based on the format of advertisements previously output by the user. The output unit can also preferentially suggest output tools that the user has used in the past. Furthermore, the output unit can predict and suggest output content to be performed in a specific time period based on the user's past output history. This makes it possible to improve the efficiency of output work by referring to the user's past output history. Some or all of the above-mentioned processing in the output unit can be performed using, or without, a generation AI. For example, the output unit can input past output history data into the generation AI and have the generation AI perform output assistance.

[0099] When outputting an advertisement, the output unit can provide an output guide based on the user's industry and market trends. For example, the output unit can suggest advertisement output content based on the latest trend information for the user's industry. The output unit can also suggest an optimal output method based on successful examples of advertisements created by the user in the past. Furthermore, the output unit can provide an output guide that references industry best practices for the information input by the user. This allows for the creation of more effective advertisements by providing an output guide based on industry and market trends. Some or all of the above-described processing in the output unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the output unit can input industry trend information to the generation AI and have the generation AI provide the output guide.

[0100] When outputting an advertisement, the output unit can select the optimal output means depending on the user's input method. For example, when a user inputs the advertisement output content by voice, the output unit automatically converts it into text using voice recognition technology. Furthermore, when a user inputs the output content using an image, the output unit can also extract necessary information using image recognition technology. Furthermore, when a user inputs the output content as text, the output unit can analyze the input content in real time and suggest the optimal output means. This can improve the efficiency of the output process by providing the optimal output means depending on the user's input method. Some or all of the above-described processing in the output unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the output unit can input voice data to a generation AI and have the generation AI perform text conversion.

[0101] The output unit can estimate the user's emotions and prioritize output content based on the estimated user emotions. For example, if the user is nervous, the output unit can guide the user to prioritize important output content. Furthermore, if the user is relaxed, the output unit can also prompt the user to perform detailed output content. Furthermore, if the user is in a hurry, the output unit can suggest performing the most important output content first. Thus, by prioritizing output content according to the user's emotions, important information can be output preferentially. Emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the output unit can be performed using, for example, the generation AI. For example, the output unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0102] When outputting an advertisement, the output unit can prioritize highly relevant output by taking into account the user's geographical location information. For example, if a user runs an advertisement in a specific region, the output unit can suggest output content based on the market trends of that region. In addition, if a user sets a geographically close target audience, the output unit can also suggest output content by taking into account the characteristics of that region. Furthermore, if a user runs an advertisement in a specific city, the output unit can also suggest output content based on consumer behavior in that city. In this way, by taking into account the geographical location information, it is possible to create an effective advertisement tailored to the regional characteristics. Some or all of the above-described processing in the output unit may be performed using, or without, a generation AI. For example, the output unit can input geographical location information to the generation AI and have the generation AI execute the output content suggestion.

[0103] When outputting an advertisement, the output unit can analyze the user's social media activity and provide related output. For example, the output unit can identify the interests of the target audience based on the user's social media activity and suggest output content. The output unit can also analyze the user's social media posts and suggest output content related to the advertisement's purpose. Furthermore, the output unit can output information about the target audience based on the activity of the user's friends on social media. This allows for the creation of effective advertisements based on the interests of the target audience by analyzing social media activity. Some or all of the above-described processing in the output unit can be performed using, or without, a generation AI. For example, the output unit can input social media activity data into the generation AI and cause the generation AI to execute related output.

[0104] When outputting an advertisement, the output unit can customize the output method by reflecting the user's past feedback. The output unit, for example, suggests an optimal output method based on feedback provided by the user in the past. The output unit can also customize the output interface by reflecting the user's past feedback. Furthermore, the output unit can improve the output method to resolve problems previously pointed out by the user. In this way, by reflecting past feedback, it is possible to provide the user with an optimal output method. Some or all of the above-described processing in the output unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the output unit can input past feedback data into the generation AI and have the generation AI customize the output method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, editing unit, and output unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user inputs basic information such as the purpose of the advertisement and the target audience. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an advertisement concept and design proposal using a generation AI. The editing unit is realized by the control unit 46A of the smart device 14, and the user makes revisions based on the generated proposal. The output unit is realized by the specific processing unit 290 of the data processing device 12, and outputs the optimized advertisement in a format suitable for various advertising media. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, editing unit, and output unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user inputs basic information such as the purpose of the advertisement and the target audience. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an advertisement concept and design proposal using a generation AI. The editing unit is realized by the control unit 46A of the smart glasses 214, and the user makes revisions based on the generated proposal. The output unit is realized by the specific processing unit 290 of the data processing device 12, and outputs the optimized advertisement in a format suitable for various advertising media. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, editing unit, and output unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user inputs basic information such as the purpose of the advertisement and the target audience. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an advertisement concept and design proposal using a generation AI. The editing unit is realized by the control unit 46A of the headset type terminal 314, and the user makes revisions based on the generated proposal. The output unit is realized by the specific processing unit 290 of the data processing device 12, and outputs the optimized advertisement in a format suitable for various advertising media. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, editing unit, and output unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user inputs basic information such as the purpose of the advertisement and the target audience. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an advertisement concept and design proposal using a generation AI. The editing unit is realized by the control unit 46A of the robot 414, and the user makes revisions based on the generated proposal. The output unit is realized by the specific processing unit 290 of the data processing device 12, and outputs the optimized advertisement in a format suitable for various advertising media.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can display relevant success stories of advertising campaigns in real time based on the user's input. For example, if the user inputs a promotion for a new product, past successful advertising cases for similar promotions can be displayed. Also, if the user inputs an announcement for a specific event, success stories related to that event can be displayed. Furthermore, the reception unit can display success stories targeting the same target audience based on the target audience information input by the user. This allows the user to create effective advertisements while referring to past success stories.

[0107] The generation unit can predict the effectiveness of an advertisement based on the advertisement purpose and target audience information input by the user. For example, the generation unit can predict the degree of effectiveness of similar advertisements based on past advertisement data. The generation unit can also predict the effectiveness of an advertisement taking current market trends into consideration. Furthermore, the generation unit can predict the effectiveness of an advertisement based on attribute information of the target audience. This allows the user to understand the effectiveness of the advertisement in advance and create the optimal advertisement.

[0108] The generation unit can estimate the user's emotions and adjust the proposed advertisement design based on the estimated user's emotions. For example, if the user is excited, it can suggest vibrant colors and dynamic designs. If the user is relaxed, it can suggest calm colors and simple designs. Furthermore, if the user is stressed, it can suggest designs that are visually relaxing. In this way, it is possible to provide optimal design proposals according to the user's emotions.

[0109] When a user makes changes to an ad, the editorial department can display a preview of the changes in real time. For example, if a user changes the color, the change is immediately reflected in the preview. Also, if a user adds text, the text can be immediately displayed in the preview. Furthermore, if a user changes the font, the font can be immediately reflected in the preview. This allows users to efficiently modify their ads while checking the changes in real time.

[0110] When selecting an advertisement output format, the output unit can estimate the user's emotions and suggest the optimal output format based on the estimated emotions. For example, if the user is in a hurry, the output unit can suggest an easy and quick output format. If the user is relaxed, the output unit can suggest an output format that allows detailed customization. Furthermore, if the user is stressed, the output unit can suggest a simple and easy-to-use output format. In this way, the output unit can provide the optimal output format according to the user's emotions.

[0111] The receiving unit can support voice input when a user inputs information about the purpose of an advertisement or the target audience. For example, when a user inputs the purpose of an advertisement by voice, the information can be automatically converted into text using speech recognition technology. Also, when a user inputs information about a target audience by voice, the information can be automatically converted into text. Furthermore, using speech input allows a user to input information without using their hands, thereby streamlining input work. This allows a user to input advertisement information quickly and efficiently using speech input.

[0112] When generating advertising concepts and design proposals, the generation unit can refer to the user's past advertising production history. For example, a new advertising proposal can be generated based on the design and concept of an advertisement created by the user in the past. An effective advertising proposal can also be generated based on data on the user's successful advertising campaigns in the past. Furthermore, specific patterns and trends can be extracted from the user's past advertising production history, and new advertising proposals can be generated based on them. This makes it possible to create more effective advertisements by utilizing the user's past experience.

[0113] The generation unit can estimate the user's emotions and adjust the advertising concept based on the estimated user's emotions. For example, if the user is excited, an energetic and lively concept can be proposed. If the user is relaxed, a calm concept can be proposed. Furthermore, if the user is stressed, a simple and easy-to-understand concept can be proposed. This makes it possible to provide the optimal advertising concept according to the user's emotions.

[0114] When a user makes changes to an advertisement, the editorial department can provide real-time feedback on the changes. For example, if a user changes a color, the editorial department can provide feedback on the impact of the change on the entire advertisement. If a user adds text, the editorial department can provide feedback on how the text will be received by the target audience. Furthermore, if a user changes a font, the editorial department can provide feedback on how the font will affect the message of the advertisement. This allows users to effectively revise their advertisements while receiving feedback on their changes.

[0115] The output unit can estimate the user's emotions when selecting an advertisement output format and adjust the output procedure based on the estimated emotions. For example, if the user is in a hurry, the output unit can suggest a format that allows for output in the shortest procedure. If the user is relaxed, the output unit can suggest a procedure that allows for detailed customization. Furthermore, if the user is stressed, the output unit can suggest a simple and intuitive procedure. This makes it possible to provide the optimal output procedure according to the user's emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The user inputs basic information such as the purpose of the advertisement and the target audience into the reception unit. For example, the reception unit clarifies the purpose of the advertisement, such as promoting a new product or announcing a specific event. The reception unit can also input information such as the target audience's age group and interests. Step 2: The generation unit uses a generation AI to analyze the information input by the reception unit and generate advertising concepts and design proposals. For example, the generation AI proposes optimal advertising concepts based on past advertising data and trend information. The generation AI generates advertising concepts and design proposals using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: In the editorial department, users make revisions based on the ideas generated by the generation department. For example, the editorial department can change colors and fonts, add text, etc. The editorial department can easily make revisions using the editing tools provided by the generation AI. Step 4: The output unit optimizes the advertisements revised by the editorial department and outputs them in a format suitable for various advertising media. For example, the output unit can output in various formats such as web advertisements, social media advertisements, and print advertisements.

[0118] 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.

[0119] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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. A system comprising: a reception unit for inputting information on the purpose of an advertisement or the target audience; a generation unit for analyzing the information input by the reception unit and generating an advertisement concept and design proposal; an editing unit for allowing a user to make revisions based on the proposal generated by the generation unit; and an output unit for outputting the advertisement revised by the editing unit in a format suitable for various advertising media.

2. The system according to claim 1 , wherein the generating unit proposes an advertising concept based on past advertising data or trend information.

3. The system according to claim 1 , wherein the generation unit generates a design proposal that reflects the characteristics of the product.

4. The system of claim 1 , wherein the editing section changes colors or fonts, or adds text.

5. The system of claim 1 , wherein the output unit outputs in various formats, such as web or social media advertisements, print advertisements, and the like.

6. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering information about the purpose of the advertisement and the target audience, the system provides input assistance by referring to the user's past input history.

2. The system of claim 1.

8. The reception unit Provides guidance based on the user's industry and market trends when entering advertising objectives and target audience information 2. The system of claim 1.

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