System

Abstract

An object of the system according to the embodiment is to automatically generate and optimize advertisement content in real time.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a provision unit, and an optimization unit. The reception unit receives a purpose of the advertisement, a target group, and basic information of product information. The generation unit analyzes the information received by the reception unit and automatically generates advertisement content. The providing unit provides the advertisement content generated by the generating unit in the form of text, image, or video. The optimization unit monitors the effect of the advertisement content provided by the provision unit in real time, and automatically corrects and optimizes the advertisement content as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of taking time and money to generate and optimize advertising content.

[0005] The system according to the embodiment aims to automatically generate advertising content and optimize it in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and an optimization unit. The reception unit receives basic information such as the purpose of the advertisement, the target demographic, and product information. The generation unit analyzes the information received by the reception unit and automatically generates advertisement content. The provision unit provides the advertisement content generated by the generation unit in the form of text, images, or videos. The optimization unit monitors the effectiveness of the advertisement content provided by the provision unit in real time and automatically modifies and optimizes the advertisement content as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate advertising content and optimize it in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automatic advertisement generation system according to an embodiment of the present invention automatically generates advertisement content without the involvement of an advertising agency. In this automatic advertisement generation system, a user inputs basic information, such as the purpose of the advertisement, target demographic, and product information. A generation AI analyzes this information to automatically generate optimal advertisement content and provide it in the form of text, images, video, or other formats. Furthermore, the automatic advertisement generation system monitors the effectiveness of the advertisement in real time and automatically modifies and optimizes the advertisement content as needed. This allows the automatic advertisement generation system to quickly and efficiently create advertisements without the involvement of an advertising agency. For example, a user may want to promote a new product, specifying women in their 20s as the target demographic and cosmetics product details. This information is input into the generation AI, which analyzes the information and automatically generates optimal advertisement content. The generated advertisement content is provided in the form of text, images, video, or other formats. This allows users to quickly create advertisements in a variety of formats. Furthermore, the generation AI can monitor the effectiveness of the advertisement in real time and automatically modify and optimize the advertisement content as needed. This maximizes the effectiveness of the advertisement and optimizes the approach to the target demographic. For example, by monitoring the click-through rate and conversion rate of advertisements and modifying the advertisement content if the effectiveness is low, it is possible to realize an effective advertising campaign.

[0029] An automatic advertisement generation system according to an embodiment includes a receiving unit, a generating unit, a providing unit, and an optimizing unit. The receiving unit receives basic information, such as the purpose of the advertisement, target demographic, and product information. For example, the user may input basic information, such as the purpose of the advertisement, target demographic, and product information, such as women in their 20s, in order to promote a new product. The generating unit uses a generation AI to analyze the information received by the receiving unit and automatically generate optimal advertisement content. For example, the generation AI generates advertisement content that is most effective for the target demographic based on past advertising data and market trends. The generating unit generates advertisement content in response to a prompt from the generation AI, such as "generate advertisement text and images that emphasize the usage and effects of cosmetics for women in their 20s." The providing unit provides the advertisement content generated by the generating unit in the form of text, images, video, or the like. For example, it is also possible to automatically generate a promotional video based on the advertisement text created by the generation AI. The optimizing unit monitors the effectiveness of the advertisement content provided by the providing unit in real time and automatically corrects and optimizes the advertisement content as necessary. For example, the optimization unit monitors the click-through rate and conversion rate of the advertisement and automatically corrects and optimizes the advertisement content if the effectiveness is low. As a result, the automatic advertisement generation system according to the embodiment can quickly and efficiently create advertisements without going through an advertising agency. For example, a user can create advertisements themselves, reducing the effort and cost of requesting an advertising agency. Furthermore, since the generation AI generates advertisement content that reflects market trends, effective advertisements can be created. For example, if you want to quickly promote a new product, you can use the generation AI to create a high-quality advertisement in a short period of time.

[0030] The reception unit analyzes the user's past ad creation history and suggests the optimal input method. For example, the reception unit automatically displays as candidates the purpose and target demographic of ads that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the purpose and target demographic of ads to be used in a specific time period based on the user's past ad creation history. This enables efficient ad creation by suggesting the optimal input method based on the past ad creation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past ad creation history data into the generation AI and have the generation AI suggest the optimal input method.

[0031] The reception unit provides the user with current market conditions and competitive information when the user inputs the advertising purpose and target demographic. For example, the reception unit displays current market trends for reference when the user inputs the advertising purpose. The reception unit can also display competitors' advertising strategies for reference when the user inputs the target demographic. Furthermore, the reception unit can provide market data on products in the same category when the user inputs product information. This allows for the generation of more effective advertising content by providing market conditions and competitive information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input current market trend data to a generation AI and have the generation AI generate information to be provided to the user.

[0032] The reception unit selects the optimal input means according to the user's input method when inputting the purpose and target demographic of the advertisement. For example, if the user selects voice input, the reception unit inputs the purpose and target demographic of the advertisement using voice recognition technology. Furthermore, if the user selects text input, the reception unit can provide an input completion function to enable efficient input. Furthermore, if the user selects image input, the reception unit can automatically extract the purpose and target demographic of the advertisement using image analysis technology. This enables efficient advertisement creation by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data to a generation AI and have the generation AI convert the voice data into text data.

[0033] The reception unit prioritizes inputting relevant information in consideration of the user's geographical location information when inputting the purpose of the advertisement and the target demographic. For example, when the user is running an advertisement in a specific region, the reception unit prioritizes displaying market data for that region. The reception unit can also provide demographic data for the region when the user inputs the target demographic for that region. Furthermore, the reception unit can also provide competitive information for that region when the user inputs product information for that region. This allows for more effective advertising content to be generated by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to generate information to be provided to the user.

[0034] When the user inputs the advertising purpose and target demographic, the reception unit analyzes the user's social media activity and inputs related information. The reception unit, for example, suggests the advertising purpose and target demographic based on the user's social media activity. The reception unit can also set the target demographic based on the user's social media follower information. Furthermore, the reception unit can analyze the user's social media posts and suggest the advertising purpose and product information. This makes it possible to generate more effective advertising content by analyzing social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input social media activity data to a generation AI and cause the generation AI to generate information to be provided to the user.

[0035] The reception unit customizes the input method by reflecting the user's past feedback when inputting the purpose of the advertisement and the target demographic. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially display specific input items based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. This makes it possible to generate more effective advertising content by reflecting past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data into a generation AI and have the generation AI customize the input method.

[0036] When generating advertising content, the generation unit optimizes the generation algorithm based on the reaction data of the target demographic. The generation unit generates optimal advertising content based on, for example, click-through rate data of the target demographic. The generation unit can also generate effective advertising content based on conversion rate data of the target demographic. Furthermore, the generation unit can generate attractive advertising content based on engagement data of the target demographic. In this way, by optimizing the generation algorithm based on the reaction data of the target demographic, more effective advertising content can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the reaction data of the target demographic into the generation AI and cause the generation AI to optimize the generation algorithm.

[0037] When generating advertising content, the generation unit applies different generation algorithms according to different advertising formats. For example, in the case of a text advertisement, the generation unit applies an algorithm that generates an effective catchphrase. In addition, in the case of an image advertisement, the generation unit can also apply an algorithm that generates a visually appealing design. Furthermore, in the case of a video advertisement, the generation unit can also apply an algorithm that generates content that emphasizes storytelling. In this way, by applying generation algorithms according to different advertising formats, more effective advertising content can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input advertising format data to the generation AI and cause the generation AI to apply the generation algorithm.

[0038] When generating advertising content, the generation unit improves the accuracy of generation by referring to the user's past advertising results. The generation unit generates effective advertising content based on, for example, successful examples of the user's past advertising campaigns. The generation unit can also extract improvements from the user's past advertising results and reflect them in the generation algorithm. Furthermore, the generation unit can analyze the user's past advertising data and generate optimal advertising content. By referring to past advertising results, the accuracy of generation can be improved, and more effective advertising content can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input past advertising result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0039] When generating advertising content, the generation unit customizes the content based on the age group and gender of the target demographic. For example, if the target demographic is young, the generation unit generates advertising content that reflects trends. Furthermore, if the target demographic is elderly, the generation unit can also generate simple and easy-to-understand advertising content. Furthermore, if the target demographic is women, the generation unit can also generate advertising content that emphasizes products and services for women. This makes it possible to generate more effective advertising content by providing advertising content based on the age group and gender of the target demographic. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the age group and gender of the target demographic into the generation AI and have the generation AI customize the generated content.

[0040] When generating advertising content, the generation unit adjusts the generated content according to the season or an event. For example, the generation unit generates advertising content that emphasizes seasonal products or services. The generation unit can also generate advertising content tailored to specific events (Christmas, Halloween, etc.). Furthermore, the generation unit can generate advertising content that adds visual effects related to the season or event. This allows for the generation of more effective advertising content by providing advertising content tailored to the season or event. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input seasonal and event data into the generation AI and have the generation AI adjust the generated content.

[0041] When generating advertising content, the generation unit adjusts the use of technical terms in the generated content according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit generates advertising content that uses a lot of technical terms. Furthermore, if the user is a general consumer, the generation unit can also generate advertising content using easy-to-understand language. Furthermore, the generation unit can adjust the frequency of use of technical terms according to the user's level of expertise. This allows for the generation of more effective advertising content by providing technical terms appropriate to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0042] When providing advertisement content, the provision unit selects the optimal provision method by referring to the user's past advertisement viewing history. The provision unit selects the optimal provision method, for example, based on the format of advertisements viewed by the user in the past. The provision unit can also prioritize the provision of advertisement content that is likely to be of interest to the user based on the user's past advertisement viewing history. Furthermore, the provision unit can analyze the user's past advertisement viewing history and select the most effective provision method. As a result, more effective advertisement content can be provided by selecting the optimal provision method based on the past advertisement viewing history. Some or all of the above-described processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input past advertisement viewing history data into a generation AI and cause the generation AI to select the optimal provision method.

[0043] When providing advertisement content, the providing unit customizes the advertisement content based on the user's current device information. For example, if the user is using a smartphone, the providing unit provides advertisement content optimized for the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide advertisement content optimized for the tablet. Furthermore, if the user is using a desktop, the providing unit can also provide advertisement content optimized for the desktop. This makes it possible to provide more effective advertisement content by providing advertisement content based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input device information data to a generation AI and cause the generation AI to customize the advertisement content.

[0044] The provision unit improves the provision method by reflecting user feedback when providing advertising content. For example, if a user provides feedback on the provided advertising content, the provision unit improves the provision method based on the feedback. The provision unit can also analyze the user feedback and propose an optimal provision method. Furthermore, the provision unit can adjust the display format of the advertising content based on the user feedback. In this way, more effective advertising content can be provided by reflecting the user feedback. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input feedback data into a generation AI and cause the generation AI to improve the provision method.

[0045] The provision unit selects the optimal provision method when providing advertisement content, taking into account the user's geographical location information. For example, if the user is in a specific area, the provision unit provides advertisement content related to that area. The provision unit can also select the optimal advertisement display method based on the user's geographical location information. Furthermore, if the user is moving, the provision unit can update the geographical location information in real time and provide optimal advertisement content. This makes it possible to provide more effective advertisement content by taking the geographical location information into consideration. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input geographical location information data to a generation AI and cause the generation AI to select the optimal provision method.

[0046] When providing advertising content, the providing unit analyzes the user's social media activity and customizes the content to be provided. For example, the providing unit provides advertising content related to places where the user has checked in on social media. The providing unit can also analyze the content posted by the user on social media and provide related advertising content. Furthermore, the providing unit can also provide related advertising content by referring to the activities of the user's friends on social media. In this way, more effective advertising content can be provided by analyzing social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data into a generation AI and have the generation AI customize the content to be provided.

[0047] The provision unit customizes the provision method by reflecting the user's past feedback when providing advertising content. The provision unit, for example, suggests an optimal provision method based on feedback provided by the user in the past. The provision unit can also analyze the user's past feedback and customize the provision method. Furthermore, the provision unit can also adjust the display format of the advertising content based on the user's past feedback. In this way, more effective advertising content can be provided by reflecting past feedback. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input past feedback data into a generation AI and cause the generation AI to customize the provision method.

[0048] When monitoring the effectiveness of an advertisement, the optimization unit optimizes the optimization algorithm by referring to past advertising data. The optimization unit optimizes optimal advertising content based on, for example, successful examples of past advertising campaigns. The optimization unit can also extract improvements from past advertising data and reflect them in the optimization algorithm. Furthermore, the optimization unit can analyze past advertising data and optimize the most effective advertising content. As a result, more effective advertising content can be provided by optimizing the optimization algorithm based on past advertising data. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input past advertising data into a generation AI and cause the generation AI to optimize the optimization algorithm.

[0049] When monitoring the effectiveness of an advertisement, the optimization unit applies different optimization methods depending on the different advertisement formats. For example, in the case of a text advertisement, the optimization unit applies a method for optimizing an effective catchphrase. In addition, in the case of an image advertisement, the optimization unit can also apply a method for optimizing a visually attractive design. Furthermore, in the case of a video advertisement, the optimization unit can also apply a method for optimizing content that emphasizes storytelling. In this way, by applying optimization methods according to different advertisement formats, more effective advertisement content can be provided. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input advertisement format data to a generation AI and cause the generation AI to apply the optimization method.

[0050] When monitoring the effectiveness of an advertisement, the optimization unit improves the accuracy of optimization by referring to the user's past advertising results. The optimization unit, for example, optimizes effective advertising content based on successful examples of the user's past advertising campaigns. The optimization unit can also extract improvements from the user's past advertising results and reflect them in the optimization algorithm. Furthermore, the optimization unit can analyze the user's past advertising data and optimize the most effective advertising content. By referring to past advertising results, the optimization accuracy can be improved and more effective advertising content can be provided. Some or all of the above-described processing in the optimization unit may be performed, for example, using AI or without AI. For example, the optimization unit can input past advertising result data into the generation AI and cause the generation AI to improve the optimization accuracy.

[0051] When monitoring the effectiveness of an advertisement, the optimization unit selects an optimization method taking into account the user's geographic location information. For example, if the user is in a specific area, the optimization unit optimizes advertisement content related to that area. The optimization unit can also select an optimal advertisement display method based on the user's geographic location information. Furthermore, if the user is moving, the optimization unit can update the geographic location information in real time and provide optimal advertisement content. This makes it possible to provide more effective advertisement content by taking the geographic location information into consideration. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input geographic location information data to a generation AI and cause the generation AI to select an optimization method.

[0052] When monitoring the effectiveness of advertisements, the optimization unit analyzes the user's social media activity to customize the optimization method. For example, the optimization unit optimizes advertisement content related to locations where the user has checked in on social media. The optimization unit can also analyze the user's social media posts and optimize related advertisement content. Furthermore, the optimization unit can also optimize related advertisement content with reference to the activities of the user's friends on social media. In this way, more effective advertisement content can be provided by analyzing social media activity. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input social media activity data into the generation AI and cause the generation AI to customize the optimization method.

[0053] When monitoring the effectiveness of an advertisement, the optimization unit customizes the optimization method by reflecting the user's past feedback. The optimization unit, for example, proposes an optimal optimization method based on feedback provided by the user in the past. The optimization unit can also analyze the user's past feedback and customize the optimization method. Furthermore, the optimization unit can adjust the display format of the advertisement content based on the user's past feedback. In this way, more effective advertisement content can be provided by reflecting the past feedback. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input past feedback data into the generation AI and cause the generation AI to customize the optimization method.

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

[0055] The reception unit can present relevant examples of past successful advertising campaigns based on the user's input. For example, if the user inputs information for the purpose of promoting a new product, examples of successful advertising campaigns in the past for a similar purpose can be displayed and used as reference. In addition, when the user inputs a target demographic, the reception unit can present past successful examples aimed at the same target demographic. Furthermore, when the user inputs product information, the reception unit can provide successful examples of products in the same category. This allows for the generation of more effective advertising content by referring to past successful examples.

[0056] The reception unit not only analyzes the user's past ad creation history and suggests the optimal input method, but also suggests improvements to the input content based on past ad effectiveness data. For example, it analyzes the click rates and conversion rates of ads created by the user in the past, points out areas that were less effective, and suggests areas for improvement. The reception unit can also compare the effectiveness of ad formats used by the user in the past and suggest the most effective format. Furthermore, the reception unit can suggest ad content that is effective for a specific time period based on the user's past ad creation history. This makes it possible to generate more effective ad content based on the past ad creation history and effectiveness data.

[0057] When the user inputs the purpose of advertising and the target demographic, the reception unit not only provides the user with current market conditions and competitive information, but also provides future market trend forecasts. For example, when the user inputs the purpose of advertising, a future market trend forecast is displayed for reference. Furthermore, when the user inputs the target demographic, the reception unit can also display a future competitor's advertising strategy forecast for reference. Furthermore, when the user inputs product information, the reception unit can also provide future market data forecasts. In this way, by providing future market trends and competitive information, more effective advertising content can be generated.

[0058] When the user inputs the purpose and target demographic of the advertisement, the reception unit not only selects the optimal input means according to the user's input method, but also monitors the user's input speed and accuracy in real time and provides appropriate feedback. For example, if the user selects voice input, the reception unit uses voice recognition technology to input the purpose and target demographic of the advertisement, and if the input speed or accuracy is low, points out areas for improvement. Furthermore, if the user selects text input, the reception unit not only provides an input completion function to enable efficient input, but also monitors the input speed and accuracy and provides appropriate feedback. Furthermore, if the user selects image input, the reception unit can automatically extract the purpose and target demographic of the advertisement using image analysis technology, monitor the input speed and accuracy, and provide appropriate feedback. This allows for efficient advertisement creation by providing the optimal input means and feedback according to the user's input method.

[0059] When inputting the purpose and target demographic of an advertisement, the reception unit not only prioritizes inputting highly relevant information taking into account the user's geographical location information, but also provides advice that takes into account the culture and customs unique to the region based on the user's geographical location information. For example, when a user runs an advertisement in a specific region, the reception unit suggests advertisement content based on the culture and customs of that region. Furthermore, when a user inputs the target demographic of a specific region, the reception unit can also suggest target demographic settings that take into account the culture and customs of that region. Furthermore, when a user inputs product information, the reception unit can also suggest product information based on the culture and customs of that region. This makes it possible to generate more effective advertisement content by taking into account the geographical location information and the culture and customs unique to the region.

[0060] When inputting the purpose and target demographic of an advertisement, the reception unit not only analyzes the user's social media activity and inputs related information, but also evaluates the user's influence on social media and proposes an optimal advertising strategy. For example, a user sets the purpose and target demographic of an advertisement based on the number of followers and engagement rate on social media. The reception unit can also analyze the content of the user's social media posts and propose the most effective advertising format. Furthermore, the reception unit can evaluate the user's influence on social media and optimize the timing and frequency of ad delivery. This makes it possible to generate more effective advertising content by taking social media activity and influence into consideration.

[0061] The receiving unit not only customizes the input method by reflecting the user's past feedback when inputting the advertisement purpose and target demographic, but also automatically adjusts the settings of the advertisement purpose and target demographic based on the user's feedback. For example, the receiving unit suggests optimal advertisement purpose and target demographic based on the user's past feedback. The receiving unit can also prioritize displaying specific input items based on the user's past feedback to simplify the input procedure. Furthermore, the receiving unit can analyze the user's past feedback and automatically adjust the settings of the advertisement purpose and target demographic. In this way, more effective advertisement content can be generated by reflecting past feedback.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: In the reception unit, the user inputs basic information such as the purpose of the advertisement, target demographic, product information, etc. For example, the user inputs basic information such as the purpose of advertising, target demographic, product information, etc. For example, the user inputs details of cosmetics as the product information for the promotion of a new product, with the target demographic being women in their 20s. Step 2: The generation unit uses the generation AI to analyze the information received by the reception unit and automatically generate optimal advertising content. For example, the generation AI generates advertising content that is most effective for the target demographic based on past advertising data and market trends. The generation unit receives a prompt from the generation AI to "generate advertising text and images that emphasize the usage and effects of cosmetics for women in their 20s," and generates the advertising content. Step 3: The provider provides the advertising content generated by the generator in the form of text, images, videos, etc. For example, it is possible to automatically generate a promotional video based on the advertising text created by the generation AI. Step 4: The optimization unit monitors the effectiveness of the advertising content provided by the provider in real time and automatically modifies and optimizes the advertising content as necessary. For example, it monitors the click-through rate and conversion rate of the advertisement and modifies the advertising content if the effectiveness is low.

[0064] (Example 2) An automatic advertisement generation system according to an embodiment of the present invention automatically generates advertisement content without the involvement of an advertising agency. In this automatic advertisement generation system, a user inputs basic information, such as the purpose of the advertisement, target demographic, and product information. A generation AI analyzes this information to automatically generate optimal advertisement content and provide it in the form of text, images, video, or other formats. Furthermore, the automatic advertisement generation system monitors the effectiveness of the advertisement in real time and automatically modifies and optimizes the advertisement content as needed. This allows the automatic advertisement generation system to quickly and efficiently create advertisements without the involvement of an advertising agency. For example, a user may want to promote a new product, specifying women in their 20s as the target demographic and cosmetics product details. This information is input into the generation AI, which analyzes the information and automatically generates optimal advertisement content. The generated advertisement content is provided in the form of text, images, video, or other formats. This allows users to quickly create advertisements in a variety of formats. Furthermore, the generation AI can monitor the effectiveness of the advertisement in real time and automatically modify and optimize the advertisement content as needed. This maximizes the effectiveness of the advertisement and optimizes the approach to the target demographic. For example, by monitoring the click-through rate and conversion rate of advertisements and modifying the advertisement content if the effectiveness is low, it is possible to realize an effective advertising campaign.

[0065] An automatic advertisement generation system according to an embodiment includes a receiving unit, a generating unit, a providing unit, and an optimizing unit. The receiving unit receives basic information, such as the purpose of the advertisement, target demographic, and product information. For example, the user may input basic information, such as the purpose of the advertisement, target demographic, and product information, such as women in their 20s, in order to promote a new product. The generating unit uses a generation AI to analyze the information received by the receiving unit and automatically generate optimal advertisement content. For example, the generation AI generates advertisement content that is most effective for the target demographic based on past advertising data and market trends. The generating unit generates advertisement content in response to a prompt from the generation AI, such as "generate advertisement text and images that emphasize the usage and effects of cosmetics for women in their 20s." The providing unit provides the advertisement content generated by the generating unit in the form of text, images, video, or the like. For example, it is also possible to automatically generate a promotional video based on the advertisement text created by the generation AI. The optimizing unit monitors the effectiveness of the advertisement content provided by the providing unit in real time and automatically corrects and optimizes the advertisement content as necessary. For example, the optimization unit monitors the click-through rate and conversion rate of the advertisement and automatically corrects and optimizes the advertisement content if the effectiveness is low. As a result, the automatic advertisement generation system according to the embodiment can quickly and efficiently create advertisements without going through an advertising agency. For example, a user can create advertisements themselves, reducing the effort and cost of requesting an advertising agency. Furthermore, since the generation AI generates advertisement content that reflects market trends, effective advertisements can be created. For example, if you want to quickly promote a new product, you can use the generation AI to create a high-quality advertisement in a short period of time.

[0066] The reception unit estimates the user's emotions and guides the user in entering the purpose and target demographic of the advertisement based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly enter the purpose and target demographic of the advertisement. This allows for the generation of more appropriate advertisement content by providing input guidance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0067] The reception unit analyzes the user's past ad creation history and suggests the optimal input method. For example, the reception unit automatically displays as candidates the purpose and target demographic of ads that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the purpose and target demographic of ads to be used in a specific time period based on the user's past ad creation history. This enables efficient ad creation by suggesting the optimal input method based on the past ad creation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past ad creation history data into the generation AI and have the generation AI suggest the optimal input method.

[0068] The reception unit provides the user with current market conditions and competitive information when the user inputs the advertising purpose and target demographic. For example, the reception unit displays current market trends for reference when the user inputs the advertising purpose. The reception unit can also display competitors' advertising strategies for reference when the user inputs the target demographic. Furthermore, the reception unit can provide market data on products in the same category when the user inputs product information. This allows for the generation of more effective advertising content by providing market conditions and competitive information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input current market trend data to a generation AI and have the generation AI generate information to be provided to the user.

[0069] The reception unit selects the optimal input means according to the user's input method when inputting the purpose and target demographic of the advertisement. For example, if the user selects voice input, the reception unit inputs the purpose and target demographic of the advertisement using voice recognition technology. Furthermore, if the user selects text input, the reception unit can provide an input completion function to enable efficient input. Furthermore, if the user selects image input, the reception unit can automatically extract the purpose and target demographic of the advertisement using image analysis technology. This enables efficient advertisement creation by providing the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data to a generation AI and have the generation AI convert the voice data into text data.

[0070] The reception unit estimates the user's emotions and prioritizes input content based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes displaying important input fields to simplify the input procedure. Furthermore, when the user is relaxed, the reception unit can display detailed input fields and provide a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying the most important input fields to enable quick input. This enables efficient advertisement creation by prioritizing input content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0071] The reception unit prioritizes inputting relevant information in consideration of the user's geographical location information when inputting the purpose of the advertisement and the target demographic. For example, when the user is running an advertisement in a specific region, the reception unit prioritizes displaying market data for that region. The reception unit can also provide demographic data for the region when the user inputs the target demographic for that region. Furthermore, the reception unit can also provide competitive information for that region when the user inputs product information for that region. This allows for more effective advertising content to be generated by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to generate information to be provided to the user.

[0072] When the user inputs the advertising purpose and target demographic, the reception unit analyzes the user's social media activity and inputs related information. The reception unit, for example, suggests the advertising purpose and target demographic based on the user's social media activity. The reception unit can also set the target demographic based on the user's social media follower information. Furthermore, the reception unit can analyze the user's social media posts and suggest the advertising purpose and product information. This makes it possible to generate more effective advertising content by analyzing social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input social media activity data to a generation AI and cause the generation AI to generate information to be provided to the user.

[0073] The reception unit customizes the input method by reflecting the user's past feedback when inputting the purpose of the advertisement and the target demographic. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially display specific input items based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. This makes it possible to generate more effective advertising content by reflecting past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data into a generation AI and have the generation AI customize the input method.

[0074] The generation unit estimates the user's emotions and adjusts the way the advertisement content is presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit may present the advertisement content in a soft tone. Furthermore, if the user is excited, the generation unit may present the advertisement content in an energetic tone. Furthermore, if the user is stressed, the generation unit may present the advertisement content in a simple, calm tone. This allows for the generation of more effective advertisement content by providing an expression method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to adjust the way the advertisement content is presented.

[0075] When generating advertising content, the generation unit optimizes the generation algorithm based on the reaction data of the target demographic. The generation unit generates optimal advertising content based on, for example, click-through rate data of the target demographic. The generation unit can also generate effective advertising content based on conversion rate data of the target demographic. Furthermore, the generation unit can generate attractive advertising content based on engagement data of the target demographic. In this way, by optimizing the generation algorithm based on the reaction data of the target demographic, more effective advertising content can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the reaction data of the target demographic into the generation AI and cause the generation AI to optimize the generation algorithm.

[0076] When generating advertising content, the generation unit applies different generation algorithms according to different advertising formats. For example, in the case of a text advertisement, the generation unit applies an algorithm that generates an effective catchphrase. In addition, in the case of an image advertisement, the generation unit can also apply an algorithm that generates a visually appealing design. Furthermore, in the case of a video advertisement, the generation unit can also apply an algorithm that generates content that emphasizes storytelling. In this way, by applying generation algorithms according to different advertising formats, more effective advertising content can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input advertising format data to the generation AI and cause the generation AI to apply the generation algorithm.

[0077] When generating advertising content, the generation unit improves the accuracy of generation by referring to the user's past advertising results. The generation unit generates effective advertising content based on, for example, successful examples of the user's past advertising campaigns. The generation unit can also extract improvements from the user's past advertising results and reflect them in the generation algorithm. Furthermore, the generation unit can analyze the user's past advertising data and generate optimal advertising content. By referring to past advertising results, the accuracy of generation can be improved, and more effective advertising content can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input past advertising result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0078] The generation unit estimates the user's emotions and adjusts the length of the advertisement content based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates longer advertisement content including detailed explanations. If the user is in a hurry, the generation unit can also generate shorter advertisement content that focuses on the main points. Furthermore, if the user is excited, the generation unit can also generate advertisement content with visually stimulating effects. This allows for the generation of more effective advertisement content by providing advertisement content lengths that correspond to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to adjust the length of the advertisement content.

[0079] When generating advertising content, the generation unit customizes the content based on the age group and gender of the target demographic. For example, if the target demographic is young, the generation unit generates advertising content that reflects trends. Furthermore, if the target demographic is elderly, the generation unit can also generate simple and easy-to-understand advertising content. Furthermore, if the target demographic is women, the generation unit can also generate advertising content that emphasizes products and services for women. This makes it possible to generate more effective advertising content by providing advertising content based on the age group and gender of the target demographic. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the age group and gender of the target demographic into the generation AI and have the generation AI customize the generated content.

[0080] When generating advertising content, the generation unit adjusts the generated content according to the season or an event. For example, the generation unit generates advertising content that emphasizes seasonal products or services. The generation unit can also generate advertising content tailored to specific events (Christmas, Halloween, etc.). Furthermore, the generation unit can generate advertising content that adds visual effects related to the season or event. This allows for the generation of more effective advertising content by providing advertising content tailored to the season or event. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input seasonal and event data into the generation AI and have the generation AI adjust the generated content.

[0081] When generating advertising content, the generation unit adjusts the use of technical terms in the generated content according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit generates advertising content that uses a lot of technical terms. Furthermore, if the user is a general consumer, the generation unit can also generate advertising content using easy-to-understand language. Furthermore, the generation unit can adjust the frequency of use of technical terms according to the user's level of expertise. This allows for the generation of more effective advertising content by providing technical terms appropriate to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0082] The provision unit estimates the user's emotions and adjusts the method of providing advertising content based on the estimated user emotions. For example, when the user is relaxed, the provision unit provides advertising content including detailed information. Furthermore, when the user is in a hurry, the provision unit can also provide concise advertising content that focuses on the main points. Furthermore, when the user is excited, the provision unit can also provide visually stimulating advertising content. This allows for more effective advertising content by providing a method of providing advertising that matches the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the provision unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the provision unit may input the user's facial expression data into the generation AI and cause the generation AI to adjust the method of providing advertising content.

[0083] When providing advertisement content, the provision unit selects the optimal provision method by referring to the user's past advertisement viewing history. The provision unit selects the optimal provision method, for example, based on the format of advertisements viewed by the user in the past. The provision unit can also prioritize the provision of advertisement content that is likely to be of interest to the user based on the user's past advertisement viewing history. Furthermore, the provision unit can analyze the user's past advertisement viewing history and select the most effective provision method. As a result, more effective advertisement content can be provided by selecting the optimal provision method based on the past advertisement viewing history. Some or all of the above-described processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input past advertisement viewing history data into a generation AI and cause the generation AI to select the optimal provision method.

[0084] When providing advertisement content, the providing unit customizes the advertisement content based on the user's current device information. For example, if the user is using a smartphone, the providing unit provides advertisement content optimized for the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide advertisement content optimized for the tablet. Furthermore, if the user is using a desktop, the providing unit can also provide advertisement content optimized for the desktop. This makes it possible to provide more effective advertisement content by providing advertisement content based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input device information data to a generation AI and cause the generation AI to customize the advertisement content.

[0085] The provision unit improves the provision method by reflecting user feedback when providing advertising content. For example, if a user provides feedback on the provided advertising content, the provision unit improves the provision method based on the feedback. The provision unit can also analyze the user feedback and propose an optimal provision method. Furthermore, the provision unit can adjust the display format of the advertising content based on the user feedback. In this way, more effective advertising content can be provided by reflecting the user feedback. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input feedback data into a generation AI and cause the generation AI to improve the provision method.

[0086] The providing unit estimates the user's emotions and determines the priority of advertisement content to be provided based on the estimated user emotions. For example, when the user is relaxed, the providing unit prioritizes providing advertisement content containing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing concise advertisement content that focuses on the main points. Furthermore, when the user is excited, the providing unit can prioritize providing visually stimulating advertisement content. This allows for more effective advertisement content to be provided by prioritizing advertisement content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's facial expression data into the generation AI and cause the generation AI to determine the priority of advertisement content.

[0087] The provision unit selects the optimal provision method when providing advertisement content, taking into account the user's geographical location information. For example, if the user is in a specific area, the provision unit provides advertisement content related to that area. The provision unit can also select the optimal advertisement display method based on the user's geographical location information. Furthermore, if the user is moving, the provision unit can update the geographical location information in real time and provide optimal advertisement content. This makes it possible to provide more effective advertisement content by taking the geographical location information into consideration. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input geographical location information data to a generation AI and cause the generation AI to select the optimal provision method.

[0088] When providing advertising content, the providing unit analyzes the user's social media activity and customizes the content to be provided. For example, the providing unit provides advertising content related to places where the user has checked in on social media. The providing unit can also analyze the content posted by the user on social media and provide related advertising content. Furthermore, the providing unit can also provide related advertising content by referring to the activities of the user's friends on social media. In this way, more effective advertising content can be provided by analyzing social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data into a generation AI and have the generation AI customize the content to be provided.

[0089] The provision unit customizes the provision method by reflecting the user's past feedback when providing advertising content. The provision unit, for example, suggests an optimal provision method based on feedback provided by the user in the past. The provision unit can also analyze the user's past feedback and customize the provision method. Furthermore, the provision unit can also adjust the display format of the advertising content based on the user's past feedback. In this way, more effective advertising content can be provided by reflecting past feedback. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input past feedback data into a generation AI and cause the generation AI to customize the provision method.

[0090] The optimization unit estimates the user's emotions and adjusts the optimization method of the advertising content based on the estimated user emotions. For example, when the user is relaxed, the optimization unit optimizes advertising content that includes detailed information. Furthermore, when the user is in a hurry, the optimization unit can optimize concise advertising content that focuses on the main points. Furthermore, when the user is excited, the optimization unit can optimize visually stimulating advertising content. This allows for an optimization method tailored to the user's emotions, thereby providing more effective advertising content. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the optimization unit may input the user's facial expression data into the generation AI and cause the generation AI to adjust the optimization method of the advertising content.

[0091] When monitoring the effectiveness of an advertisement, the optimization unit optimizes the optimization algorithm by referring to past advertising data. The optimization unit optimizes optimal advertising content based on, for example, successful examples of past advertising campaigns. The optimization unit can also extract improvements from past advertising data and reflect them in the optimization algorithm. Furthermore, the optimization unit can analyze past advertising data and optimize the most effective advertising content. As a result, more effective advertising content can be provided by optimizing the optimization algorithm based on past advertising data. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input past advertising data into a generation AI and cause the generation AI to optimize the optimization algorithm.

[0092] When monitoring the effectiveness of an advertisement, the optimization unit applies different optimization methods depending on the different advertisement formats. For example, in the case of a text advertisement, the optimization unit applies a method for optimizing an effective catchphrase. In addition, in the case of an image advertisement, the optimization unit can also apply a method for optimizing a visually attractive design. Furthermore, in the case of a video advertisement, the optimization unit can also apply a method for optimizing content that emphasizes storytelling. In this way, by applying optimization methods according to different advertisement formats, more effective advertisement content can be provided. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input advertisement format data to a generation AI and cause the generation AI to apply the optimization method.

[0093] When monitoring the effectiveness of an advertisement, the optimization unit improves the accuracy of optimization by referring to the user's past advertising results. The optimization unit, for example, optimizes effective advertising content based on successful examples of the user's past advertising campaigns. The optimization unit can also extract improvements from the user's past advertising results and reflect them in the optimization algorithm. Furthermore, the optimization unit can analyze the user's past advertising data and optimize the most effective advertising content. By referring to past advertising results, the optimization accuracy can be improved and more effective advertising content can be provided. Some or all of the above-described processing in the optimization unit may be performed, for example, using AI or without AI. For example, the optimization unit can input past advertising result data into the generation AI and cause the generation AI to improve the optimization accuracy.

[0094] The optimization unit estimates the user's emotions and determines the priority of optimization of advertising content based on the estimated user emotions. For example, when the user is relaxed, the optimization unit prioritizes optimization of advertising content that includes detailed information. Furthermore, when the user is in a hurry, the optimization unit can prioritize optimization of concise advertising content that focuses on the main points. Furthermore, when the user is excited, the optimization unit can prioritize optimization of visually stimulating advertising content. This allows for providing optimization priorities according to the user's emotions, thereby providing more effective advertising content. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or without AI. For example, the optimization unit may input the user's facial expression data into the generation AI and cause the generation AI to determine the priority of optimization of advertising content.

[0095] When monitoring the effectiveness of an advertisement, the optimization unit selects an optimization method taking into account the user's geographic location information. For example, if the user is in a specific area, the optimization unit optimizes advertisement content related to that area. The optimization unit can also select an optimal advertisement display method based on the user's geographic location information. Furthermore, if the user is moving, the optimization unit can update the geographic location information in real time and provide optimal advertisement content. This makes it possible to provide more effective advertisement content by taking the geographic location information into consideration. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input geographic location information data to a generation AI and cause the generation AI to select an optimization method.

[0096] When monitoring the effectiveness of advertisements, the optimization unit analyzes the user's social media activity to customize the optimization method. For example, the optimization unit optimizes advertisement content related to locations where the user has checked in on social media. The optimization unit can also analyze the user's social media posts and optimize related advertisement content. Furthermore, the optimization unit can also optimize related advertisement content with reference to the activities of the user's friends on social media. In this way, more effective advertisement content can be provided by analyzing social media activity. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input social media activity data into the generation AI and cause the generation AI to customize the optimization method.

[0097] When monitoring the effectiveness of an advertisement, the optimization unit customizes the optimization method by reflecting the user's past feedback. The optimization unit, for example, proposes an optimal optimization method based on feedback provided by the user in the past. The optimization unit can also analyze the user's past feedback and customize the optimization method. Furthermore, the optimization unit can adjust the display format of the advertisement content based on the user's past feedback. In this way, more effective advertisement content can be provided by reflecting the past feedback. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input past feedback data into the generation AI and cause the generation AI to customize the optimization method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and optimization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user inputs basic information such as the purpose of the advertisement, target demographic, and product information. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and automatically generates optimal advertisement content using a generation AI. The provision unit is realized by the output device 40 of the smart device 14, and provides the generated advertisement content in the form of text, image, video, or the like. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and monitors the effectiveness of the advertisement in real time and automatically corrects and optimizes the advertisement content as necessary. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and optimization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user inputs basic information such as the purpose of the advertisement, target demographic, and product information. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and automatically generates optimal advertisement content using a generation AI. The provision unit is realized by the speaker 240 of the smart glasses 214, and provides the generated advertisement content in the form of text, image, video, or the like. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and monitors the effectiveness of the advertisement in real time and automatically corrects and optimizes the advertisement content as necessary. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and optimization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user inputs basic information such as the purpose of the advertisement, target demographic, and product information. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and automatically generates optimal advertisement content using a generation AI. The provision unit is realized by the display 343 of the headset type terminal 314, and provides the generated advertisement content in the form of text, image, video, or the like. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and monitors the effectiveness of the advertisement in real time and automatically corrects and optimizes the advertisement content as necessary. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and optimization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user inputs basic information such as the purpose of the advertisement, target demographic, and product information. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and automatically generates optimal advertisement content using a generation AI. The provision unit is realized by the speaker 240 of the robot 414, and provides the generated advertisement content in the form of text, image, video, or the like. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and monitors the effectiveness of the advertisement in real time and automatically corrects and optimizes the advertisement content as necessary.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can present relevant examples of past successful advertising campaigns based on the user's input. For example, if the user inputs information for the purpose of promoting a new product, examples of successful advertising campaigns in the past for a similar purpose can be displayed and used as reference. In addition, when the user inputs a target demographic, the reception unit can present past successful examples aimed at the same target demographic. Furthermore, when the user inputs product information, the reception unit can provide successful examples of products in the same category. This allows for the generation of more effective advertising content by referring to past successful examples.

[0100] The reception unit can estimate the user's emotions and provide appropriate advice based on the estimated user emotions when the user inputs the purpose of the advertisement or the target demographic. For example, if the user is feeling anxious, the reception unit can provide advice that gives a sense of security and supports the input. If the user is excited, the reception unit can also provide advice that encourages calm judgment. Furthermore, if the user is relaxed, the reception unit can also provide detailed information and advice that encourages deeper understanding. In this way, more appropriate advertisement content can be generated by providing advice according to the user's emotions.

[0101] The reception unit not only analyzes the user's past ad creation history and suggests the optimal input method, but also suggests improvements to the input content based on past ad effectiveness data. For example, it analyzes the click rates and conversion rates of ads created by the user in the past, points out areas that were less effective, and suggests areas for improvement. The reception unit can also compare the effectiveness of ad formats used by the user in the past and suggest the most effective format. Furthermore, the reception unit can suggest ad content that is effective for a specific time period based on the user's past ad creation history. This makes it possible to generate more effective ad content based on the past ad creation history and effectiveness data.

[0102] When the user inputs the purpose of advertising and the target demographic, the reception unit not only provides the user with current market conditions and competitive information, but also provides future market trend forecasts. For example, when the user inputs the purpose of advertising, a future market trend forecast is displayed for reference. Furthermore, when the user inputs the target demographic, the reception unit can also display a future competitor's advertising strategy forecast for reference. Furthermore, when the user inputs product information, the reception unit can also provide future market data forecasts. In this way, by providing future market trends and competitive information, more effective advertising content can be generated.

[0103] When the user inputs the purpose and target demographic of the advertisement, the reception unit not only selects the optimal input means according to the user's input method, but also monitors the user's input speed and accuracy in real time and provides appropriate feedback. For example, if the user selects voice input, the reception unit uses voice recognition technology to input the purpose and target demographic of the advertisement, and if the input speed or accuracy is low, points out areas for improvement. Furthermore, if the user selects text input, the reception unit not only provides an input completion function to enable efficient input, but also monitors the input speed and accuracy and provides appropriate feedback. Furthermore, if the user selects image input, the reception unit can automatically extract the purpose and target demographic of the advertisement using image analysis technology, monitor the input speed and accuracy, and provide appropriate feedback. This allows for efficient advertisement creation by providing the optimal input means and feedback according to the user's input method.

[0104] The reception unit not only estimates the user's emotions and prioritizes input content based on the estimated user emotions, but also dynamically changes the interface design according to the user's emotions. For example, if the user is feeling stressed, a simple and calm interface can be provided to simplify the input procedure. Alternatively, if the user is relaxed, a colorful and detailed interface can be provided to provide a customizable input method. Furthermore, if the user is in a hurry, an interface designed to highlight the most important input items can be provided to enable quick input. This allows for efficient advertisement creation by providing an interface design according to the user's emotions.

[0105] When inputting the purpose and target demographic of an advertisement, the reception unit not only prioritizes inputting highly relevant information taking into account the user's geographical location information, but also provides advice that takes into account the culture and customs unique to the region based on the user's geographical location information. For example, when a user runs an advertisement in a specific region, the reception unit suggests advertisement content based on the culture and customs of that region. Furthermore, when a user inputs the target demographic of a specific region, the reception unit can also suggest target demographic settings that take into account the culture and customs of that region. Furthermore, when a user inputs product information, the reception unit can also suggest product information based on the culture and customs of that region. This makes it possible to generate more effective advertisement content by taking into account the geographical location information and the culture and customs unique to the region.

[0106] When inputting the purpose and target demographic of an advertisement, the reception unit not only analyzes the user's social media activity and inputs related information, but also evaluates the user's influence on social media and proposes an optimal advertising strategy. For example, a user sets the purpose and target demographic of an advertisement based on the number of followers and engagement rate on social media. The reception unit can also analyze the content of the user's social media posts and propose the most effective advertising format. Furthermore, the reception unit can evaluate the user's influence on social media and optimize the timing and frequency of ad delivery. This makes it possible to generate more effective advertising content by taking social media activity and influence into consideration.

[0107] The receiving unit not only customizes the input method by reflecting the user's past feedback when inputting the advertisement purpose and target demographic, but also automatically adjusts the settings of the advertisement purpose and target demographic based on the user's feedback. For example, the receiving unit suggests optimal advertisement purpose and target demographic based on the user's past feedback. The receiving unit can also prioritize displaying specific input items based on the user's past feedback to simplify the input procedure. Furthermore, the receiving unit can analyze the user's past feedback and automatically adjust the settings of the advertisement purpose and target demographic. In this way, more effective advertisement content can be generated by reflecting past feedback.

[0108] The generation unit not only estimates the user's emotions and adjusts the way in which the advertisement content is presented based on the estimated user's emotions, but also optimizes the timing of advertisement delivery according to the user's emotions. For example, if the user is relaxed, the advertisement delivery timing can be adjusted so that the user can receive the advertisement in a relaxed state. Also, if the user is excited, the advertisement content can be presented in an energetic tone so that the user can receive the advertisement in an excited state. Furthermore, if the user is stressed, the advertisement content can be presented in a simple, calm tone so that the delivery timing can be set to reduce stress. In this way, more effective advertisement content can be generated by providing advertisement delivery timing according to the user's emotions.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: In the reception unit, the user inputs basic information such as the purpose of the advertisement, target demographic, product information, etc. For example, the user inputs basic information such as the purpose of advertising, target demographic, product information, etc. For example, the user inputs details of cosmetics as the product information for the promotion of a new product, with the target demographic being women in their 20s. Step 2: The generation unit uses the generation AI to analyze the information received by the reception unit and automatically generate optimal advertising content. For example, the generation AI generates advertising content that is most effective for the target demographic based on past advertising data and market trends. The generation unit receives a prompt from the generation AI to "generate advertising text and images that emphasize the usage and effects of cosmetics for women in their 20s," and generates the advertising content. Step 3: The provider provides the advertising content generated by the generator in the form of text, images, videos, etc. For example, it is possible to automatically generate a promotional video based on the advertising text created by the generation AI. Step 4: The optimization unit monitors the effectiveness of the advertising content provided by the provider in real time and automatically modifies and optimizes the advertising content as necessary. For example, it monitors the click-through rate and conversion rate of the advertisement and modifies the advertising content if the effectiveness is low.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] 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.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives basic information such as the purpose of advertising, target demographic, and product information; a generation unit that analyzes the information received by the reception unit and automatically generates advertisement content; a providing unit that provides the advertisement content generated by the generating unit in the form of text, image, or video; an optimization unit that monitors in real time the effectiveness of the advertisement content provided by the provision unit and automatically corrects and optimizes the advertisement content as necessary. A system characterized by:

2. The reception unit Estimate user emotions and guide input of advertising objectives and target demographics based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyzes the user's past ad creation history and suggests the optimal input method 2. The system of claim 1.

4. The reception unit Provides users with current market and competitive information when entering advertising objectives and target audiences 2. The system of claim 1.

5. The reception unit When entering advertising objectives and target demographics, select the most appropriate input method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering advertising objectives and target audiences, the system prioritizes relevant information based on the user's geographic location.

2. The system of claim 1.

8. The reception unit When entering advertising objectives and target audience, analyze users' social media activity and enter relevant information 2. The system of claim 1.

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