system

Abstract

The system according to this embodiment aims to enable small businesses to easily create and distribute effective web advertisements. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and an update unit. The reception unit receives input for the content to be used to create an advertisement. The generation unit automatically generates advertisement text and visual content based on the information entered by the reception unit. The update unit updates the advertisement generated by the generation unit based on viewer response data.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for small-scale stores to easily create and distribute effective web advertisements, and there is room for improvement.

[0005] The system according to the embodiment aims to enable small-scale stores to easily create and distribute effective web advertisements.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and an update unit. The reception unit inputs the content for which an advertisement is to be created. The generation unit automatically generates an advertisement text and visual content based on the information input by the reception unit. The update unit updates the advertisement generated by the generation unit based on the reaction data of viewers. [Effects of the Invention]

[0007] The system according to this embodiment allows small businesses to easily create and distribute effective web advertisements. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) An advertising creation platform according to an embodiment of the present invention is a system that enables small businesses to easily create and distribute effective web advertisements using generative AI. In this system, the user inputs the content they wish to create an advertisement for, and the generative AI analyzes the input information to automatically generate advertisement text and visual content. The generative AI generates advertisement text using LLM and generates images and videos using multimodal technology. The generated advertisements are frequently updated based on viewer response data. The generative AI analyzes viewer response data and optimizes the content of the advertisements. Furthermore, the generative AI also assists in optimizing media placement and managing advertising budgets. This platform allows small businesses to easily create and distribute effective web advertisements, enabling users to focus on maximizing advertising effectiveness. For example, by creating advertisements that visually convey the characteristics of the store and frequently updating them based on viewer response data, it is possible to respond to a wider range of customer needs. Additionally, since the AI ​​assists in optimizing media placement and managing advertising budgets, users can focus on maximizing advertising effectiveness. Thus, the advertising creation platform enables small businesses to easily create and distribute effective web advertisements, allowing users to focus on maximizing advertising effectiveness.

[0029] The advertising creation platform according to this embodiment comprises a reception unit, a generation unit, and an update unit. The reception unit receives input for the content to be used to create an advertisement. This content includes, but is not limited to, product information, service details, and promotional information. The reception unit can, for example, analyze the information entered by the user and collect data necessary for creating the advertisement. The generation unit uses a generation AI to automatically generate ad copy and visual content based on the information entered by the reception unit. The generation unit can, for example, generate ad copy using LLM and generate images and videos using multimodal technology. The generation unit can, for example, use LLM to generate ad copy based on information entered by the user. The generation unit can also use multimodal technology to generate images and videos based on information entered by the user. When generating ad copy using LLM, the generation unit can, for example, optimize grammar and vocabulary using natural language processing technology. The update unit updates the advertisement generated by the generation unit based on viewer response data. The update unit can, for example, improve ad copy and visual content based on data such as viewer click-through rates and viewing time. The update unit can, for example, analyze viewer response data and optimize the content of advertisements. The update unit can also frequently update the content of advertisements based on viewer response data. This allows the advertisement creation platform according to the embodiment to enable small businesses to easily create and distribute effective web advertisements, and allows users to focus on maximizing the effectiveness of their advertisements.

[0030] The reception desk receives input for the content to be used in the advertisement. This content includes, but is not limited to, product information, service details, and promotional information. The reception desk can, for example, analyze the information entered by the user and collect the data necessary for creating the advertisement. Specifically, the information entered by the user includes details of the product or service, target audience, and advertising objectives in text format. This information is entered through the reception desk interface and centrally managed within the system. Furthermore, the reception desk can analyze the information entered by the user using natural language processing technology and extract keywords and phrases necessary for creating the advertisement. For example, it can extract product features and benefits from product information, details and benefits of the services offered from service details, and discounts and benefits from promotional information. This allows the reception desk to efficiently organize the information provided by the user and provide the basic data for the generation desk to create the advertisement. The reception desk also has a function to provide feedback on the information entered by the user and notify the user of any missing or additional information. This ensures that the user provides all the necessary information for creating the advertisement, preparing the generation desk to generate high-quality advertisements. Furthermore, the reception desk can save a user's past ad history and provide it as a reusable template. This allows users to create ads efficiently and save time and effort.

[0031] The generation unit uses generation AI to automatically generate ad copy and visual content based on information entered by the reception unit. For example, the generation unit generates ad copy using LLM (Large-Scale Language Model) and images and videos using multimodal technology. Specifically, the LLM generates ad copy optimized for the target audience based on user input. LLM utilizes natural language processing technology to create grammatically accurate and engaging ad copy. For example, it generates phrases that emphasize product features and benefits, stimulating purchase intent. Furthermore, the generation unit uses multimodal technology to generate images and videos that match the ad copy. For example, it automatically generates visual content including product images and service usage scenarios to enhance the visual appeal of the ad. Based on the information provided by the user, the generation unit can select appropriate colors and design elements to create visually consistent advertisements. In addition, the generation unit provides an interface that allows users to preview, review, and modify the generated ad copy and visual content. This allows users to review the generated ad and make adjustments as needed. The ad generation unit continuously learns from user feedback to improve ad quality. This allows the generation unit to consistently provide high-quality ads that are in line with the latest trends and user needs.

[0032] The update unit updates the ads generated by the generation unit based on viewer response data. For example, the update unit can improve ad copy and visual content based on data such as viewer click-through rates and viewing time. Specifically, it analyzes in detail how viewers reacted to the ads and evaluates their effectiveness. For example, if the click-through rate is low, it will revise the ad copy and visual content design to make them more appealing. If viewing time is short, it will adjust the length and structure of the ad to keep viewers interested. The update unit can collect this data in real time and quickly update the ad content. Furthermore, the update unit can also improve the targeting accuracy of ads based on viewer response data. For example, it can identify ads that are effective for specific age groups or regions and generate customized ads for those groups. This maximizes the effectiveness of the ads and improves the return on investment. The update unit can also collect viewer feedback and conduct surveys and research to identify areas for improvement in ads. This allows for the provision of ads that meet viewer needs and expectations, improving satisfaction. The update department automates these processes and efficiently updates ad content, providing an environment where users can focus on maximizing ad effectiveness.

[0033] The support department can assist in optimizing advertising media and managing advertising budgets. For example, the support department can propose the optimal advertising delivery plan considering the characteristics of the advertising media. For example, the support department can assist in managing advertising budgets and optimize budget allocation. For example, the support department can analyze the effectiveness of advertising media and optimize advertising delivery. For example, the support department can assist in managing advertising budgets and optimize budget allocation. For example, the support department can propose the optimal advertising delivery plan considering the characteristics of the advertising media. For example, the support department can assist in managing advertising budgets and optimize budget allocation. This allows for a focus on maximizing advertising effectiveness. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can optimize advertising delivery using an AI model that proposes the optimal advertising delivery plan considering the characteristics of the advertising media.

[0034] The generation unit can generate ad copy using LLM and generate images and videos using multimodal technology. For example, the generation unit can generate ad copy based on information entered by the user using LLM. For example, when generating ad copy using LLM, the generation unit can optimize grammar and vocabulary using natural language processing technology. For example, the generation unit can generate images and videos based on information entered by the user using multimodal technology. For example, the generation unit can generate ads that integrate ad copy and visual content using multimodal technology. This automates the generation of ad copy and visual content. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input user-entered information as prompts to the generation AI and have the generation AI execute the generation of ad copy and visual content.

[0035] The update unit can analyze viewer response data and optimize the ad content. For example, the update unit can improve ad copy and visual content based on data such as viewer click-through rates and viewing time. For example, the update unit can frequently update the ad content based on viewer response data. For example, the update unit can analyze viewer response data and optimize the ad content. This optimizes the ad content based on viewer responses. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can use viewer response data as input and optimize the ad content using an AI model that optimizes ad content.

[0036] The generation unit can generate advertisements that visually convey the characteristics of a store and the appeal of its products. For example, the generation unit can generate images and videos that emphasize the characteristics of a store. For example, the generation unit can generate advertisements with designs that highlight the appeal of products. For example, the generation unit can use a generation AI to generate advertisements in order to visually convey the characteristics of a store and the appeal of its products. For example, the generation unit can use a generation AI to generate images and videos in order to visually convey the characteristics of a store. For example, the generation unit can use a generation AI to generate advertisements in order to visually convey the appeal of products. This makes it possible to visually convey the characteristics of a store and the appeal of its products. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can input prompts into the generation AI to generate advertisements in order to visually convey the characteristics of a store and the appeal of its products.

[0037] The update unit can improve ad copy and visual content based on data such as viewer click-through rates and viewing time. For example, the update unit can improve ad copy based on viewer click-through rates. For example, the update unit can improve visual content based on viewer viewing time. The update unit can frequently update ad copy and visual content based on data such as viewer click-through rates and viewing time. This ensures that ad copy and visual content are improved based on viewer data. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can optimize ad content using an AI model that takes viewer click-through rates and viewing time as input and improves ad copy and visual content.

[0038] The reception desk can analyze the user's past ad creation history and suggest the optimal input method. For example, the reception desk can automatically display ad templates that the user has frequently used in the past as candidates. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest ad content to be used at a specific time of day based on the user's past ad creation history. This allows the reception desk to suggest the optimal input method based on the user's past history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can suggest an input method using an AI model that takes the user's past ad creation history as input and suggests the optimal input method.

[0039] The reception desk can filter the user's current business situation and market trends when they input advertising content. For example, the reception desk can prioritize displaying relevant advertising content according to the user's business situation. For example, the reception desk can analyze market trends and suggest advertising content that matches current trends. For example, the reception desk can combine the user's business situation and market trends to filter for the most suitable advertising content. This allows the reception desk to provide advertising content based on business conditions and market trends. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can optimize advertising content using an AI model that takes the user's business situation and market trends as input and filters the advertising content.

[0040] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting advertising content. For example, the reception desk can suggest region-specific advertising content based on the user's current location. For example, the reception desk can refer to advertising content of nearby competitors based on the user's geographical location. For example, the reception desk can suggest advertising content related to local events and festivals by considering the user's geographical location. This allows for the provision of advertising content based on geographical location information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can optimize advertising content using an AI model that takes the user's geographical location as input and prioritizes inputting highly relevant information.

[0041] The reception desk can analyze the user's social media activity and input relevant information when inputting advertising content. For example, the reception desk can analyze the user's social media posts and suggest relevant advertising content. For example, the reception desk can customize advertising content by referring to the activities of the user's followers and friends. For example, the reception desk can optimize advertising content based on the user's popular posts on social media. This allows for the provision of advertising content based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can optimize advertising content using an AI model that takes the user's social media activity as input and inputs relevant information.

[0042] The generation unit can adjust the level of detail based on the importance of the product when generating ad copy and visual content. For example, the generation unit can generate detailed descriptions and high-resolution images for important products. For example, the generation unit can generate concise descriptions and standard images for general products. For example, the generation unit can generate visual content with special designs and detailed information for specific campaign products. This allows for the provision of ad copy and visual content tailored to the importance of the product. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can use a generation AI model that takes product importance as input and adjusts the level of detail to generate ad copy and visual content.

[0043] The generation unit can apply different generation algorithms depending on the product category when generating ad copy and visual content. For example, for food products, the generation unit can generate appetizing images and mouthwatering ad copy. For fashion products, for example, the generation unit can generate stylish images and trendy ad copy. For home appliance products, for example, the generation unit can generate images that emphasize functionality and ad copy that includes detailed descriptions. This allows for the provision of ad copy and visual content tailored to the product category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can use a generation AI model that takes the product category as input and applies different generation algorithms to generate ad copy and visual content.

[0044] The generation unit can prioritize the generation of ad copy and visual content based on the product's submission timing. For example, the generation unit can prioritize the generation of ad copy and visual content for new products. For example, for seasonal products, the generation unit can generate ad copy and visual content according to the season. For example, for sale products, the generation unit can generate ad copy and visual content according to the sale period. This allows for the provision of ad copy and visual content that are appropriate for the product's submission timing. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can take the product's submission timing as input and generate ad copy and visual content using a generation AI model that determines priorities.

[0045] The generation unit can adjust the order of ad copy and visual content based on product relevance when generating them. For example, the generation unit can generate ad copy and visual content that displays highly relevant products first. For example, the generation unit can generate ad copy and visual content for less relevant products at a later date. For example, the generation unit can optimize the display order of ad copy and visual content based on product relevance. This allows for the provision of ad copy and visual content that are appropriate to the relevance of the products. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can generate ad copy and visual content using a generation AI model that takes product relevance as input and adjusts the order.

[0046] The update unit can select the optimal update method when updating an advertisement by referring to past viewer response data. For example, the update unit can prioritize updating effective ad content based on viewer click-through rates. For example, the update unit can update ad content to be more engaging by referring to viewer viewing time. For example, the update unit can analyze past viewer response data and select the most effective update method. This allows the update unit to provide the optimal update method based on past viewer response data. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can update advertisements using an AI model that takes past viewer response data as input and selects the optimal update method.

[0047] The update unit can customize the content of an ad based on the viewer's attribute information when updating an ad. For example, the update unit can customize the ad content according to the viewer's age group. For example, the update unit can adjust the ad content based on the viewer's gender. For example, the update unit can optimize the ad content according to the viewer's interests. This allows the update unit to provide content based on the viewer's attribute information. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can update an ad using an AI model that takes viewer attribute information as input and customizes the content of the update.

[0048] The update unit can adjust the update content when updating advertisements, taking into account the geographical distribution of the audience. For example, the update unit can customize the advertisement content according to the audience's region. For example, the update unit can update the advertisement content to include region-specific information based on the geographical distribution of the audience. For example, the update unit can suggest optimal advertisement content, taking into account the geographical distribution of the audience. This allows for the provision of advertisement content based on the geographical distribution of the audience. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can update advertisements using an AI model that takes the geographical distribution of the audience as input and adjusts the update content.

[0049] The update unit can improve the accuracy of updates by referring to the viewer's relevant literature when updating advertisements. For example, the update unit can update advertisement content based on literature related to the viewer's interests. For example, the update unit can propose optimal advertisement content by combining the viewer's past response data with relevant literature. For example, the update unit can improve the accuracy of advertisement content by referring to the viewer's relevant literature. This allows for the provision of advertisement content based on the viewer's relevant literature. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can update advertisements using an AI model that takes the viewer's relevant literature as input and improves the accuracy of updates.

[0050] The support department can select the optimal management method when managing advertising budgets by referring to past budget data. For example, the support department can propose the optimal budget allocation based on past budget data. For example, the support department can adjust budget management methods by referring to the results of past advertising campaigns. For example, the support department can analyze past budget data and select an effective budget management method. This allows the support department to provide the optimal management method based on past budget data. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can manage advertising budgets using an AI model that takes past budget data as input and selects the optimal management method.

[0051] The support department can select the optimal management method when managing advertising budgets, taking into account the characteristics of the media in which the ads are placed. For example, the support department can propose the optimal budget allocation according to the characteristics of the media in which the ads are placed. For example, the support department can analyze the effectiveness of the media in which the ads are placed and adjust the budget management method. For example, the support department can select an effective budget management method, taking into account the characteristics of the media in which the ads are placed. This allows the support department to provide the optimal management method based on the characteristics of the media in which the ads are placed. Some or all of the above processes performed by the support department may be performed using AI or not. For example, the support department can manage advertising budgets using an AI model that takes the characteristics of the media in which the ads are placed as input and selects the optimal management method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] An ad creation platform can analyze a user's past ad viewing history and suggest the most suitable ad content. For example, it can prioritize displaying relevant ads based on the content of ads the user has frequently viewed in the past. It can also suggest customized ads based on products and services the user has shown interest in in the past. Furthermore, it can predict the ad content to display at specific times based on the user's past ad viewing history and deliver ads at the optimal time. This allows for the suggestion of the most suitable ad content based on the user's past history. Some or all of the above processes in the ad creation platform may be performed using AI or not. For example, the ad creation platform can use an AI model that takes the user's past ad viewing history as input and suggests the most suitable ad content.

[0054] An advertising creation platform can generate region-specific advertising content by taking into account the user's geographical location. For example, it can generate advertisements related to local events and festivals based on the user's current location. It can also prioritize displaying advertisements for nearby stores and services based on the user's geographical location. Furthermore, it can suggest advertising designs tailored to the characteristics of the region, taking the user's geographical location into account. This allows for the provision of geographically-based advertising content. Some or all of the above processes in the advertising creation platform may be performed using AI or not. For example, the advertising creation platform can optimize advertising content using an AI model that takes the user's geographical location as input and generates region-specific advertising content.

[0055] An advertising creation platform can analyze a user's social media activity and generate relevant advertising content. For example, it can analyze a user's social media posts and suggest advertisements for relevant products or services. It can also generate customized advertisements by referencing the activity of the user's followers and friends. Furthermore, it can optimize advertising content based on the user's popular social media posts. This allows for the provision of advertising content tailored to social media activity. Some or all of the above processes in the advertising creation platform may be performed using AI or not. For example, the advertising creation platform can optimize advertising content using an AI model that takes a user's social media activity as input and generates relevant advertising content.

[0056] An advertising creation platform can optimize ad content based on the user's business situation and market trends. For example, it can prioritize displaying relevant ad content according to the user's business situation. It can also analyze market trends and suggest ad content that matches current trends. Furthermore, it can combine the user's business situation and market trends to generate optimal ad content. This allows for the provision of ad content based on business conditions and market trends. Some or all of the above processes in the advertising creation platform may be performed using AI or not. For example, the advertising creation platform can optimize ad content using an AI model that takes the user's business situation and market trends as input.

[0057] An ad creation platform can analyze a user's past ad creation history and suggest the optimal input method. For example, it can automatically display ad templates that the user has frequently used in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest ad content to be used at specific times based on the user's past ad creation history. This allows the platform to suggest the optimal input method based on the user's past history. Some or all of the above processes in the ad creation platform may be performed using AI or not. For example, the ad creation platform can suggest input methods using an AI model that takes the user's past ad creation history as input and suggests the optimal input method.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives the content for which the advertisement will be created. This content may include, for example, product information, service details, and promotional information. The reception desk analyzes the information entered by the user and collects the data necessary for creating the advertisement. Step 2: The generation unit uses generation AI to automatically generate ad copy and visual content based on the information entered by the reception unit. The generation unit generates ad copy using LLM and generates images and videos using multimodal technology. The generation unit can optimize grammar and vocabulary using natural language processing technology. Step 3: The update unit updates the ads generated by the generation unit based on viewer response data. The update unit improves ad copy and visual content based on viewer data such as click-through rates and viewing time, optimizing the ad content. The update unit can frequently update the ad content based on viewer response data.

[0060] (Example of form 2) An advertising creation platform according to an embodiment of the present invention is a system that enables small businesses to easily create and distribute effective web advertisements using generative AI. In this system, the user inputs the content they wish to create an advertisement for, and the generative AI analyzes the input information to automatically generate advertisement text and visual content. The generative AI generates advertisement text using LLM and generates images and videos using multimodal technology. The generated advertisements are frequently updated based on viewer response data. The generative AI analyzes viewer response data and optimizes the content of the advertisements. Furthermore, the generative AI also assists in optimizing media placement and managing advertising budgets. This platform allows small businesses to easily create and distribute effective web advertisements, enabling users to focus on maximizing advertising effectiveness. For example, by creating advertisements that visually convey the characteristics of the store and frequently updating them based on viewer response data, it is possible to respond to a wider range of customer needs. Additionally, since the AI ​​assists in optimizing media placement and managing advertising budgets, users can focus on maximizing advertising effectiveness. Thus, the advertising creation platform enables small businesses to easily create and distribute effective web advertisements, allowing users to focus on maximizing advertising effectiveness.

[0061] The advertising creation platform according to this embodiment comprises a reception unit, a generation unit, and an update unit. The reception unit receives input for the content to be used to create an advertisement. This content includes, but is not limited to, product information, service details, and promotional information. The reception unit can, for example, analyze the information entered by the user and collect data necessary for creating the advertisement. The generation unit uses a generation AI to automatically generate ad copy and visual content based on the information entered by the reception unit. The generation unit can, for example, generate ad copy using LLM and generate images and videos using multimodal technology. The generation unit can, for example, use LLM to generate ad copy based on information entered by the user. The generation unit can also use multimodal technology to generate images and videos based on information entered by the user. When generating ad copy using LLM, the generation unit can, for example, optimize grammar and vocabulary using natural language processing technology. The update unit updates the advertisement generated by the generation unit based on viewer response data. The update unit can, for example, improve ad copy and visual content based on data such as viewer click-through rates and viewing time. The update unit can, for example, analyze viewer response data and optimize the content of advertisements. The update unit can also frequently update the content of advertisements based on viewer response data. This allows the advertisement creation platform according to the embodiment to enable small businesses to easily create and distribute effective web advertisements, and allows users to focus on maximizing the effectiveness of their advertisements.

[0062] The reception desk receives input for the content to be used in the advertisement. This content includes, but is not limited to, product information, service details, and promotional information. The reception desk can, for example, analyze the information entered by the user and collect the data necessary for creating the advertisement. Specifically, the information entered by the user includes details of the product or service, target audience, and advertising objectives in text format. This information is entered through the reception desk interface and centrally managed within the system. Furthermore, the reception desk can analyze the information entered by the user using natural language processing technology and extract keywords and phrases necessary for creating the advertisement. For example, it can extract product features and benefits from product information, details and benefits of the services offered from service details, and discounts and benefits from promotional information. This allows the reception desk to efficiently organize the information provided by the user and provide the basic data for the generation desk to create the advertisement. The reception desk also has a function to provide feedback on the information entered by the user and notify the user of any missing or additional information. This ensures that the user provides all the necessary information for creating the advertisement, preparing the generation desk to generate high-quality advertisements. Furthermore, the reception desk can save a user's past ad history and provide it as a reusable template. This allows users to create ads efficiently and save time and effort.

[0063] The generation unit uses generation AI to automatically generate ad copy and visual content based on information entered by the reception unit. For example, the generation unit generates ad copy using LLM (Large-Scale Language Model) and images and videos using multimodal technology. Specifically, the LLM generates ad copy optimized for the target audience based on user input. LLM utilizes natural language processing technology to create grammatically accurate and engaging ad copy. For example, it generates phrases that emphasize product features and benefits, stimulating purchase intent. Furthermore, the generation unit uses multimodal technology to generate images and videos that match the ad copy. For example, it automatically generates visual content including product images and service usage scenarios to enhance the visual appeal of the ad. Based on the information provided by the user, the generation unit can select appropriate colors and design elements to create visually consistent advertisements. In addition, the generation unit provides an interface that allows users to preview, review, and modify the generated ad copy and visual content. This allows users to review the generated ad and make adjustments as needed. The ad generation unit continuously learns from user feedback to improve ad quality. This allows the generation unit to consistently provide high-quality ads that are in line with the latest trends and user needs.

[0064] The update unit updates the ads generated by the generation unit based on viewer response data. For example, the update unit can improve ad copy and visual content based on data such as viewer click-through rates and viewing time. Specifically, it analyzes in detail how viewers reacted to the ads and evaluates their effectiveness. For example, if the click-through rate is low, it will revise the ad copy and visual content design to make them more appealing. If viewing time is short, it will adjust the length and structure of the ad to keep viewers interested. The update unit can collect this data in real time and quickly update the ad content. Furthermore, the update unit can also improve the targeting accuracy of ads based on viewer response data. For example, it can identify ads that are effective for specific age groups or regions and generate customized ads for those groups. This maximizes the effectiveness of the ads and improves the return on investment. The update unit can also collect viewer feedback and conduct surveys and research to identify areas for improvement in ads. This allows for the provision of ads that meet viewer needs and expectations, improving satisfaction. The update department automates these processes and efficiently updates ad content, providing an environment where users can focus on maximizing ad effectiveness.

[0065] The support department can assist in optimizing advertising media and managing advertising budgets. For example, the support department can propose the optimal advertising delivery plan considering the characteristics of the advertising media. For example, the support department can assist in managing advertising budgets and optimize budget allocation. For example, the support department can analyze the effectiveness of advertising media and optimize advertising delivery. For example, the support department can assist in managing advertising budgets and optimize budget allocation. For example, the support department can propose the optimal advertising delivery plan considering the characteristics of the advertising media. For example, the support department can assist in managing advertising budgets and optimize budget allocation. This allows for a focus on maximizing advertising effectiveness. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can optimize advertising delivery using an AI model that proposes the optimal advertising delivery plan considering the characteristics of the advertising media.

[0066] The generation unit can generate ad copy using LLM and generate images and videos using multimodal technology. For example, the generation unit can generate ad copy based on information entered by the user using LLM. For example, when generating ad copy using LLM, the generation unit can optimize grammar and vocabulary using natural language processing technology. For example, the generation unit can generate images and videos based on information entered by the user using multimodal technology. For example, the generation unit can generate ads that integrate ad copy and visual content using multimodal technology. This automates the generation of ad copy and visual content. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input user-entered information as prompts to the generation AI and have the generation AI execute the generation of ad copy and visual content.

[0067] The update unit can analyze viewer response data and optimize the ad content. For example, the update unit can improve ad copy and visual content based on data such as viewer click-through rates and viewing time. For example, the update unit can frequently update the ad content based on viewer response data. For example, the update unit can analyze viewer response data and optimize the ad content. This optimizes the ad content based on viewer responses. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can use viewer response data as input and optimize the ad content using an AI model that optimizes ad content.

[0068] The generation unit can generate advertisements that visually convey the characteristics of a store and the appeal of its products. For example, the generation unit can generate images and videos that emphasize the characteristics of a store. For example, the generation unit can generate advertisements with designs that highlight the appeal of products. For example, the generation unit can use a generation AI to generate advertisements in order to visually convey the characteristics of a store and the appeal of its products. For example, the generation unit can use a generation AI to generate images and videos in order to visually convey the characteristics of a store. For example, the generation unit can use a generation AI to generate advertisements in order to visually convey the appeal of products. This makes it possible to visually convey the characteristics of a store and the appeal of its products. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can input prompts into the generation AI to generate advertisements in order to visually convey the characteristics of a store and the appeal of its products.

[0069] The update unit can improve ad copy and visual content based on data such as viewer click-through rates and viewing time. For example, the update unit can improve ad copy based on viewer click-through rates. For example, the update unit can improve visual content based on viewer viewing time. The update unit can frequently update ad copy and visual content based on data such as viewer click-through rates and viewing time. This ensures that ad copy and visual content are improved based on viewer data. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can optimize ad content using an AI model that takes viewer click-through rates and viewing time as input and improves ad copy and visual content.

[0070] The reception desk can estimate the user's emotions and adjust the input interface for the advertisement content based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of the advertisement content. This provides an input interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can use facial recognition technology or voice analysis technology to estimate the user's emotions.

[0071] The reception desk can analyze the user's past ad creation history and suggest the optimal input method. For example, the reception desk can automatically display ad templates that the user has frequently used in the past as candidates. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest ad content to be used at a specific time of day based on the user's past ad creation history. This allows the reception desk to suggest the optimal input method based on the user's past history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can suggest an input method using an AI model that takes the user's past ad creation history as input and suggests the optimal input method.

[0072] The reception desk can filter the user's current business situation and market trends when they input advertising content. For example, the reception desk can prioritize displaying relevant advertising content according to the user's business situation. For example, the reception desk can analyze market trends and suggest advertising content that matches current trends. For example, the reception desk can combine the user's business situation and market trends to filter for the most suitable advertising content. This allows the reception desk to provide advertising content based on business conditions and market trends. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can optimize advertising content using an AI model that takes the user's business situation and market trends as input and filters the advertising content.

[0073] The reception unit can estimate the user's emotions and determine the priority of input content based on the estimated emotions. For example, if the user is nervous, the reception unit can prioritize displaying important input items. For example, if the user is relaxed, the reception unit can sequentially display detailed input items. For example, if the user is in a hurry, the reception unit can display the most important input items first. This provides priority of input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can use facial recognition technology or voice analysis technology to estimate the user's emotions.

[0074] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting advertising content. For example, the reception desk can suggest region-specific advertising content based on the user's current location. For example, the reception desk can refer to advertising content of nearby competitors based on the user's geographical location. For example, the reception desk can suggest advertising content related to local events and festivals by considering the user's geographical location. This allows for the provision of advertising content based on geographical location information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can optimize advertising content using an AI model that takes the user's geographical location as input and prioritizes inputting highly relevant information.

[0075] The reception desk can analyze the user's social media activity and input relevant information when inputting advertising content. For example, the reception desk can analyze the user's social media posts and suggest relevant advertising content. For example, the reception desk can customize advertising content by referring to the activities of the user's followers and friends. For example, the reception desk can optimize advertising content based on the user's popular posts on social media. This allows for the provision of advertising content based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can optimize advertising content using an AI model that takes the user's social media activity as input and inputs relevant information.

[0076] The generation unit can estimate the user's emotions and adjust the expression of ad copy and visual content based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate ad copy with a soft tone. For example, if the user is excited, the generation unit can generate ad copy using energetic expressions. For example, if the user is stressed, the generation unit can generate visual content with a simple and calm design. This allows for the provision of ad copy and visual content that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generation AI. For example, the generation unit can use facial recognition technology or voice analysis technology to estimate the user's emotions.

[0077] The generation unit can adjust the level of detail based on the importance of the product when generating ad copy and visual content. For example, the generation unit can generate detailed descriptions and high-resolution images for important products. For example, the generation unit can generate concise descriptions and standard images for general products. For example, the generation unit can generate visual content with special designs and detailed information for specific campaign products. This allows for the provision of ad copy and visual content tailored to the importance of the product. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can use a generation AI model that takes product importance as input and adjusts the level of detail to generate ad copy and visual content.

[0078] The generation unit can apply different generation algorithms depending on the product category when generating ad copy and visual content. For example, for food products, the generation unit can generate appetizing images and mouthwatering ad copy. For fashion products, for example, the generation unit can generate stylish images and trendy ad copy. For home appliance products, for example, the generation unit can generate images that emphasize functionality and ad copy that includes detailed descriptions. This allows for the provision of ad copy and visual content tailored to the product category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can use a generation AI model that takes the product category as input and applies different generation algorithms to generate ad copy and visual content.

[0079] The generation unit can estimate the user's emotions and adjust the length of ad copy and visual content based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise ad copy. For example, if the user is relaxed, the generation unit can generate longer ad copy with detailed explanations. For example, if the user is excited, the generation unit can generate visually stimulating effects. This allows for the provision of ad copy and visual content lengths that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can use facial recognition technology or speech analysis technology to estimate the user's emotions.

[0080] The generation unit can prioritize the generation of ad copy and visual content based on the product's submission timing. For example, the generation unit can prioritize the generation of ad copy and visual content for new products. For example, for seasonal products, the generation unit can generate ad copy and visual content according to the season. For example, for sale products, the generation unit can generate ad copy and visual content according to the sale period. This allows for the provision of ad copy and visual content that are appropriate for the product's submission timing. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can take the product's submission timing as input and generate ad copy and visual content using a generation AI model that determines priorities.

[0081] The generation unit can adjust the order of ad copy and visual content based on product relevance when generating them. For example, the generation unit can generate ad copy and visual content that displays highly relevant products first. For example, the generation unit can generate ad copy and visual content for less relevant products at a later date. For example, the generation unit can optimize the display order of ad copy and visual content based on product relevance. This allows for the provision of ad copy and visual content that are appropriate to the relevance of the products. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can generate ad copy and visual content using a generation AI model that takes product relevance as input and adjusts the order.

[0082] The update unit can estimate the user's emotions and adjust the ad update frequency based on the estimated emotions. For example, if the user is stressed, the update unit can reduce the update frequency to alleviate the burden. For example, if the user is relaxed, the update unit can increase the update frequency to provide new information. For example, if the user is in a hurry, the update unit can prioritize only important updates. This allows for ad update frequencies tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can use facial recognition technology or voice analysis technology to estimate the user's emotions.

[0083] The update unit can select the optimal update method when updating an advertisement by referring to past viewer response data. For example, the update unit can prioritize updating effective ad content based on viewer click-through rates. For example, the update unit can update ad content to be more engaging by referring to viewer viewing time. For example, the update unit can analyze past viewer response data and select the most effective update method. This allows the update unit to provide the optimal update method based on past viewer response data. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can update advertisements using an AI model that takes past viewer response data as input and selects the optimal update method.

[0084] The update unit can customize the content of an ad based on the viewer's attribute information when updating an ad. For example, the update unit can customize the ad content according to the viewer's age group. For example, the update unit can adjust the ad content based on the viewer's gender. For example, the update unit can optimize the ad content according to the viewer's interests. This allows the update unit to provide content based on the viewer's attribute information. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can update an ad using an AI model that takes viewer attribute information as input and customizes the content of the update.

[0085] The update unit can estimate the user's emotions and determine the content of the ad update based on the estimated emotions. For example, if the user is relaxed, the update unit can update the ad content to incorporate new ideas. For example, if the user is stressed, the update unit can update the ad content to be simple and effective. For example, if the user is in a hurry, the update unit can update the ad content to include only essential information. This allows for the provision of ad updates that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can use facial recognition technology or voice analysis technology to estimate the user's emotions.

[0086] The update unit can adjust the update content when updating advertisements, taking into account the geographical distribution of the audience. For example, the update unit can customize the advertisement content according to the audience's region. For example, the update unit can update the advertisement content to include region-specific information based on the geographical distribution of the audience. For example, the update unit can suggest optimal advertisement content, taking into account the geographical distribution of the audience. This allows for the provision of advertisement content based on the geographical distribution of the audience. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can update advertisements using an AI model that takes the geographical distribution of the audience as input and adjusts the update content.

[0087] The update unit can improve the accuracy of updates by referring to the viewer's relevant literature when updating advertisements. For example, the update unit can update advertisement content based on literature related to the viewer's interests. For example, the update unit can propose optimal advertisement content by combining the viewer's past response data with relevant literature. For example, the update unit can improve the accuracy of advertisement content by referring to the viewer's relevant literature. This allows for the provision of advertisement content based on the viewer's relevant literature. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can update advertisements using an AI model that takes the viewer's relevant literature as input and improves the accuracy of updates.

[0088] The support unit can estimate the user's emotions and adjust the advertising budget management method based on the estimated user emotions. For example, if the user is stressed, the support unit can provide a simple budget management method. For example, if the user is relaxed, the support unit can provide detailed budget management options. For example, if the user is in a hurry, the support unit can suggest a way to manage the budget quickly. This provides an advertising budget management method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can use facial recognition technology or voice analysis technology to estimate the user's emotions.

[0089] The support department can select the optimal management method when managing advertising budgets by referring to past budget data. For example, the support department can propose the optimal budget allocation based on past budget data. For example, the support department can adjust budget management methods by referring to the results of past advertising campaigns. For example, the support department can analyze past budget data and select an effective budget management method. This allows the support department to provide the optimal management method based on past budget data. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can manage advertising budgets using an AI model that takes past budget data as input and selects the optimal management method.

[0090] The support unit can estimate the user's emotions and prioritize the advertising budget based on those emotions. For example, if the user is relaxed, the support unit can suggest a detailed budget allocation. For example, if the user is stressed, the support unit can prioritize the management of important budget items. For example, if the user is in a hurry, the support unit can provide a way to quickly determine the budget allocation. This allows for prioritizing the advertising budget according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can use facial recognition technology or voice analysis technology to estimate the user's emotions.

[0091] The support department can select the optimal management method when managing advertising budgets, taking into account the characteristics of the media in which the ads are placed. For example, the support department can propose the optimal budget allocation according to the characteristics of the media in which the ads are placed. For example, the support department can analyze the effectiveness of the media in which the ads are placed and adjust the budget management method. For example, the support department can select an effective budget management method, taking into account the characteristics of the media in which the ads are placed. This allows the support department to provide the optimal management method based on the characteristics of the media in which the ads are placed. Some or all of the above processes performed by the support department may be performed using AI or not. For example, the support department can manage advertising budgets using an AI model that takes the characteristics of the media in which the ads are placed as input and selects the optimal management method.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] An ad creation platform can estimate a user's emotions and adjust the timing of ad delivery based on those emotions. For example, if a user is relaxed, ad delivery can be delayed to allow the user to view the ad more attentively. If a user is stressed, ad delivery can be sped up so the user can get information sooner. Furthermore, if a user is in a hurry, ad delivery can be adjusted to the optimal timing so the user can quickly get the information they need. This allows for ad delivery timing that is tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad creation platform may be performed using AI or not. For example, an ad creation platform can use facial recognition technology or voice analysis technology to estimate a user's emotions.

[0094] An ad creation platform can analyze a user's past ad viewing history and suggest the most suitable ad content. For example, it can prioritize displaying relevant ads based on the content of ads the user has frequently viewed in the past. It can also suggest customized ads based on products and services the user has shown interest in in the past. Furthermore, it can predict the ad content to display at specific times based on the user's past ad viewing history and deliver ads at the optimal time. This allows for the suggestion of the most suitable ad content based on the user's past history. Some or all of the above processes in the ad creation platform may be performed using AI or not. For example, the ad creation platform can use an AI model that takes the user's past ad viewing history as input and suggests the most suitable ad content.

[0095] An ad creation platform can estimate a user's emotions and adjust the ad design based on those emotions. For example, if a user is relaxed, it can generate an ad with soft colors and a simple design. If a user is excited, it can generate an ad with vibrant colors and a dynamic design. Furthermore, if a user is stressed, it can generate an ad with calm colors and a simple design. This allows for ad designs tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad creation platform may be performed using AI or not. For example, an ad creation platform can use facial recognition technology or voice analysis technology to estimate a user's emotions.

[0096] An advertising creation platform can generate region-specific advertising content by taking into account the user's geographical location. For example, it can generate advertisements related to local events and festivals based on the user's current location. It can also prioritize displaying advertisements for nearby stores and services based on the user's geographical location. Furthermore, it can suggest advertising designs tailored to the characteristics of the region, taking the user's geographical location into account. This allows for the provision of geographically-based advertising content. Some or all of the above processes in the advertising creation platform may be performed using AI or not. For example, the advertising creation platform can optimize advertising content using an AI model that takes the user's geographical location as input and generates region-specific advertising content.

[0097] An ad creation platform can estimate a user's emotions and adjust the ad display format based on those emotions. For example, if a user is relaxed, still image ads can be prioritized. If a user is excited, video ads can be prioritized. Furthermore, if a user is stressed, text ads can be prioritized. This allows for the provision of ad display formats tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad creation platform may be performed using AI or not. For example, an ad creation platform can use facial recognition technology or voice analysis technology to estimate a user's emotions.

[0098] An advertising creation platform can analyze a user's social media activity and generate relevant advertising content. For example, it can analyze a user's social media posts and suggest advertisements for relevant products or services. It can also generate customized advertisements by referencing the activity of the user's followers and friends. Furthermore, it can optimize advertising content based on the user's popular social media posts. This allows for the provision of advertising content tailored to social media activity. Some or all of the above processes in the advertising creation platform may be performed using AI or not. For example, the advertising creation platform can optimize advertising content using an AI model that takes a user's social media activity as input and generates relevant advertising content.

[0099] An ad creation platform can estimate a user's emotions and adjust ad targeting based on those emotions. For example, if a user is relaxed, ads can be delivered to a broad target audience. If a user is excited, ads can be delivered to a target audience with specific interests. Furthermore, if a user is stressed, simple and effective targeting can be implemented. This allows for ad targeting that is tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad creation platform may be performed using AI or not. For example, an ad creation platform can use facial recognition technology or voice analysis technology to estimate a user's emotions.

[0100] An advertising creation platform can optimize ad content based on the user's business situation and market trends. For example, it can prioritize displaying relevant ad content according to the user's business situation. It can also analyze market trends and suggest ad content that matches current trends. Furthermore, it can combine the user's business situation and market trends to generate optimal ad content. This allows for the provision of ad content based on business conditions and market trends. Some or all of the above processes in the advertising creation platform may be performed using AI or not. For example, the advertising creation platform can optimize ad content using an AI model that takes the user's business situation and market trends as input.

[0101] An ad creation platform can estimate a user's emotions and adjust the ad update frequency based on that estimation. For example, if a user is stressed, the update frequency can be reduced to lessen their burden. Conversely, if a user is relaxed, the update frequency can be increased to provide new information. Furthermore, if a user is in a hurry, only important updates can be prioritized. This allows for ad update frequencies tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad creation platform may be performed using AI or not. For example, an ad creation platform can use facial recognition technology or voice analysis technology to estimate a user's emotions.

[0102] An ad creation platform can analyze a user's past ad creation history and suggest the optimal input method. For example, it can automatically display ad templates that the user has frequently used in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest ad content to be used at specific times based on the user's past ad creation history. This allows the platform to suggest the optimal input method based on the user's past history. Some or all of the above processes in the ad creation platform may be performed using AI or not. For example, the ad creation platform can suggest input methods using an AI model that takes the user's past ad creation history as input and suggests the optimal input method.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The reception desk receives the content for which the advertisement will be created. This content may include, for example, product information, service details, and promotional information. The reception desk analyzes the information entered by the user and collects the data necessary for creating the advertisement. Step 2: The generation unit uses generation AI to automatically generate ad copy and visual content based on the information entered by the reception unit. The generation unit generates ad copy using LLM and generates images and videos using multimodal technology. The generation unit can optimize grammar and vocabulary using natural language processing technology. Step 3: The update unit updates the ads generated by the generation unit based on viewer response data. The update unit improves ad copy and visual content based on viewer data such as click-through rates and viewing time, optimizing the ad content. The update unit can frequently update the ad content based on viewer response data.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the reception unit, generation unit, update unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs the content they want to create an advertisement for. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where it automatically generates advertisement text and visual content using generation AI. The update unit is implemented by the specific processing unit 290 of the data processing unit 12, where it updates the advertisement based on viewer response data. The support unit is implemented by the specific processing unit 290 of the data processing unit 12, where it assists in optimizing the media for placement and managing the advertising budget. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the reception unit, generation unit, update unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs the content they want to create an advertisement by voice. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which automatically generates advertisement text and visual content using generation AI. The update unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which updates the advertisement based on viewer response data. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which assists in optimizing the media for placement and managing the advertising budget. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, generation unit, update unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs the content they want to create an advertisement by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates advertisement text and visual content using generation AI. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which updates the advertisement based on viewer response data. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which assists in optimizing the media for placement and managing the advertising budget. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, generation unit, update unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs the content they want to create an advertisement by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates advertisement text and visual content using generation AI. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which updates the advertisement based on viewer response data. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which assists in optimizing the media for placement and managing the advertising budget. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) The reception desk is where you enter the details of the advertisement you want to create, A generation unit that automatically generates advertising copy and visual content based on the information entered by the reception unit, An update unit updates the advertisement generated by the generation unit based on viewer response data, Equipped with A system characterized by the following features. (Note 2) We have a support department that assists with optimizing media placement and managing advertising budgets. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is LLM is used to generate ad copy, and multimodal technology is used to generate images and videos. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned update unit is, Analyze viewer response data to optimize ad content. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate advertisements that visually convey the store's features and the appeal of its products. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned update unit is, We improve ad copy and visual content based on viewer data such as click-through rates and viewing time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the ad content input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past ad creation history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter ad content, filtering is performed based on their current business situation and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering ad content, the system prioritizes inputting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering ad content, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the way ad copy and visual content are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating ad copy and visual content, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating ad copy and visual content, different generation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of ad copy and visual content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Prioritize the creation of ad copy and visual content based on the product submission date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating ad copy and visual content, the order is adjusted based on product relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned update unit is, It estimates the user's emotions and adjusts the ad refresh frequency based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned update unit is, When updating ads, the system selects the optimal update method by referring to past viewer response data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned update unit is, When updating ads, customize the update content based on the audience's demographics. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned update unit is, The system estimates user sentiment and determines ad updates based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is, When updating ads, adjust the content to take into account the geographical distribution of the audience. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is, When updating ads, we refer to the viewer's relevant literature to improve the accuracy of the updates. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit, We estimate user sentiment and adjust how advertising budgets are managed based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned support unit, When managing advertising budgets, refer to past budget data to select the optimal management method. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned support unit, It estimates user sentiment and prioritizes the advertising budget based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned support unit, When managing advertising budgets, select the optimal management method by considering the characteristics of the media outlets where the ads are placed. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk is where you enter the details of the advertisement you want to create, A generation unit that automatically generates advertising copy and visual content based on the information entered by the reception unit, An update unit updates the advertisement generated by the generation unit based on viewer response data, Equipped with A system characterized by the following features.

2. We have a support department that assists with optimizing media placement and managing advertising budgets. The system according to feature 1.

3. The generating unit is LLM is used to generate ad copy, and multimodal technology is used to generate images and videos. The system according to feature 1.

4. The aforementioned update unit is, Analyze viewer response data to optimize ad content. The system according to feature 1.

5. The generating unit is Generate advertisements that visually convey the store's features and the appeal of its products. The system according to feature 1.

6. The aforementioned update unit is, We improve ad copy and visual content based on viewer data such as click-through rates and viewing time. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the ad content input interface based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past ad creation history and suggest the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users enter ad content, filtering is performed based on their current business situation and market trends. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

Images