System

Abstract

An object of the system according to the embodiment is to enable a small-scale shop to easily create and distribute an effective WEB advertisement.SOLUTION: A system includes a commodity information acquisition part, an advertisement sentence generation part, a target analysis part, and an advertisement distribution optimization part. The product information acquisition unit acquires information on a product or a service from a user. The advertisement copy generation unit generates an advertisement copy based on the information acquired by the product information acquisition unit. The target analyzing unit analyzes information of a target audience. The advertisement distribution optimization unit proposes an optimal advertisement placement medium based on the information analyzed by the target analysis unit.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 technology has made it difficult for small businesses to easily create and distribute effective web advertisements.

[0005] The system according to the embodiment aims to enable small businesses to easily create and distribute effective web advertisements. [Means for solving the problem]

[0006] The system according to the embodiment includes a product information acquisition unit, an advertising copy generation unit, a target analysis unit, and an advertising delivery optimization unit. The product information acquisition unit acquires information about products and services from users. The advertising copy generation unit generates advertising copy based on the information acquired by the product information acquisition unit. The target analysis unit analyzes information about the target audience. The advertising delivery optimization unit suggests optimal advertising media based on the information analyzed by the target analysis unit. [Effects of the Invention]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The web advertisement creation and distribution platform according to an embodiment of the present invention is a system in which a generation AI automatically generates advertisement copy based on product or service information provided by users, and provides content tailored to the preferences of the target audience. As a result, the web advertisement creation and distribution platform allows small businesses to easily create and distribute effective web advertisements, maximizing the effectiveness of their advertisements.

[0029] A web advertising creation and distribution platform according to an embodiment includes a product information acquisition unit, an advertisement copy generation unit, a target analysis unit, and an advertisement delivery optimization unit. The product information acquisition unit acquires product and service information from users. For example, the product information acquisition unit collects text and image information input by the user. It can also acquire user reviews provided by users. The advertisement copy generation unit generates advertisement copy based on the information acquired by the product information acquisition unit. For example, based on the user-input information "We sell fresh vegetables," the generation AI generates advertisement copy such as "We deliver fresh and delicious vegetables!". The generation AI can also generate advertisement copy in different formats, such as banner ads and video ads. The target analysis unit analyzes information about the target audience. For example, it collects and analyzes data such as the target audience's age, gender, interests, and purchasing history. Based on this data, the target analysis unit generates advertisement copy tailored to the target audience's preferences. The advertisement delivery optimization unit suggests optimal advertisement media based on the information analyzed by the target analysis unit. For example, advertisements aimed at younger generations are distributed primarily through social media, while advertisements aimed at older people are distributed through newspapers and television. The ad distribution optimization department selects the optimal distribution media, taking into account the regional characteristics and cultural background of the target audience. This allows the web ad creation and distribution platform to automatically generate ad copy using AI based on information about the products and services provided by users, and provide content tailored to the preferences of the target audience.

[0030] The ad copy generation unit can learn the user's past advertising history and generate optimal ad copy based on past successes. For example, the ad copy generation unit's generation AI analyzes the user's past advertising history and learns patterns of successful ad copy. For example, it extracts characteristics of ad copy that have recorded high click-through rates in the past and reflects them in new ad copy. It also builds a database of ad copy created by the user in the past, and the generation AI generates optimal ad copy based on that data. For example, if a specific keyword or phrase was effective, it will incorporate it into the new ad copy. The generation AI also analyzes the results of past advertising campaigns and identifies the factors that contributed to their success. For example, it will apply expressions and tones that were effective for a specific target audience to the new ad copy. This can increase the effectiveness of advertising by generating optimal ad copy based on past successes.

[0031] The ad copy generation unit can generate more professional and reliable ad copy by inputting detailed information about a product or service. For example, a user inputs detailed information about a product or service, and the generation AI generates professional ad copy based on that information. For example, the ad copy generation unit creates ad copy that includes details about ingredients and manufacturing processes. The generation AI also generates ad copy that emphasizes the technical features and advantages of the product or service. For example, it highlights how a specific technology or manufacturing method differs from other products. The generation AI also generates reliable ad copy based on detailed information about the product or service. For example, it includes information about certification or quality assurance from a third-party organization in the ad copy. This allows the credibility of the advertisement to be improved by generating professional and reliable ad copy based on detailed information.

[0032] The ad copy generation unit can input images and videos of products and services and generate ad copy based on that visual information. For example, a user can upload images of a product or service, and the generation AI can analyze the images to generate ad copy. For example, it can create a catchy slogan that emphasizes the product's features. Alternatively, a video of the product or service can be input, and the generation AI can generate ad copy based on the content of the video. For example, it can reflect the key points introduced in the video in the ad copy. The generation AI can also analyze the visual information of images and videos to generate visually appealing ad copy. For example, it can use expressions that emphasize the beauty and ease of use of the product. In this way, by generating ad copy based on visual information, it is possible to create visually appealing advertisements.

[0033] The ad copy generation unit supports the generation of ad copy in different languages ​​and can automatically generate advertisements for international markets. In the ad copy generation unit, for example, the generation AI automatically generates ad copy in different languages ​​to create advertisements for international markets. For example, it supports multiple languages ​​such as English, French, and Chinese. In addition, the generation AI generates multilingual ad copy based on information about products and services provided by users. For example, it uses expressions that are suited to the culture and customs of each language. In addition, the generation AI generates ad copy in different languages ​​to support advertising campaigns for international markets. For example, it creates ad copy that meets the market needs of each country. In this way, by supporting the generation of ad copy in different languages, it is possible to automatically generate advertisements for international markets.

[0034] The target analysis unit can analyze the target audience's past purchasing history and behavioral data, and generate optimal ad copy based on that. In the target analysis unit, for example, the generation AI analyzes the target audience's past purchasing history and generates optimal ad copy based on that data. For example, it creates ad copy related to products purchased in the past. In addition, the target audience's behavioral data is analyzed, and the generation AI generates ad copy based on that data. For example, it creates ad copy based on the browsing history of a specific website. In addition, the generation AI generates personalized ad copy based on the target audience's purchasing history and behavioral data. For example, it creates ad copy that suggests related products based on past purchasing patterns. In this way, by generating optimal ad copy based on past purchasing history and behavioral data, it is possible to provide effective ads to the target audience.

[0035] The target analysis unit can analyze the social media activity of the target audience and generate ad copy that reflects trends and interests. In the target analysis unit, for example, the generation AI analyzes the social media activity of the target audience and generates ad copy based on that data. For example, it creates ad copy that reflects recent trends and interests. In addition, the generation AI analyzes the content of the target audience's social media posts and generates ad copy based on that data. For example, it creates ad copy that includes specific hashtags and keywords. In addition, the generation AI generates ad copy that reflects trends and interests based on the target audience's social media activity data. For example, it creates ad copy related to recent topics and events. In this way, by analyzing social media activity and generating ad copy that reflects trends and interests, it is possible to provide ads that are likely to resonate with the target audience.

[0036] The target analysis unit can generate advertising copy that takes into account the cultural background and regional characteristics of the target audience. For example, the generation AI in the target analysis unit takes into account the cultural background of the target audience and generates advertising copy based on that data. For example, it uses expressions that suit specific cultures and customs. The generation AI also analyzes the regional characteristics of the target audience, and generates advertising copy based on that data. For example, it creates advertising copy related to local events or local specialties. The generation AI also generates personalized advertising copy based on the cultural background and regional characteristics of the target audience. For example, it creates advertising copy that incorporates local words and dialects. In this way, by generating advertising copy that takes into account the cultural background and regional characteristics, it is possible to provide advertisements that are appropriate for the target audience.

[0037] The target analysis unit can generate customized advertisements that match the lifestyles and hobbies of the target audience. In the target analysis unit, for example, the generation AI analyzes the lifestyles of the target audience and generates customized advertisements based on that data. For example, it creates advertising copy that matches a health-conscious lifestyle. In addition, the generation AI analyzes the hobbies and interests of the target audience and generates customized advertisements based on that data. For example, it creates advertising copy that introduces products and services related to a specific hobby. In addition, the generation AI generates personalized customized advertisements based on the lifestyles and hobbies of the target audience. For example, it creates advertising copy for outdoor enthusiasts. In this way, customized advertisements that match the lifestyles and hobbies can be generated, making it possible to provide advertisements that are suitable for the target audience.

[0038] The ad delivery optimization unit can analyze past ad delivery data and identify the most effective delivery timing. In the ad delivery optimization unit, for example, the generation AI analyzes past ad delivery data and identifies the most effective delivery timing. For example, delivery during specific time periods or days of the week increases effectiveness. In addition, a system is built in which the generation AI suggests the optimal delivery timing based on past ad delivery data. For example, the delivery timing is adjusted based on past click rates and conversion rates. In addition, the generation AI analyzes past ad delivery data and identifies the optimal delivery timing based on the behavior patterns of the target audience. For example, it delivers ads to users who are active during specific time periods. In this way, the effectiveness of advertising can be maximized by analyzing past data and identifying the most effective delivery timing.

[0039] The ad delivery optimization unit can consider the regional characteristics of ad delivery and propose the optimal delivery media for each region. For example, the ad delivery optimization unit uses a generation AI to analyze the regional characteristics of ad delivery and propose the optimal delivery media for each region. For example, social media is used in urban areas, while newspapers and radio are used in rural areas. A system can also be built in which the generation AI proposes the optimal delivery media based on the regional characteristics of ad delivery. For example, it can analyze media consumption patterns for each region and select the optimal media. The generation AI can also consider the regional characteristics of ad delivery and propose delivery media customized for each region. For example, it can use media that is popular in a specific region. This makes it possible to maximize the effectiveness of advertising by considering regional characteristics and proposing the optimal delivery media for each region.

[0040] The ad delivery optimization unit can accommodate different ad formats and propose the optimal format. In the ad delivery optimization unit, for example, the generation AI analyzes different ad formats and proposes the optimal format. For example, it selects banner ads or video ads that are effective for the target audience. It also builds a system that analyzes the effectiveness of ad formats and the generation AI proposes the optimal format. For example, it evaluates the effectiveness of formats based on past data. The generation AI also accommodates different ad formats and proposes the optimal format for the target audience. For example, it selects a format that is effective for a specific age group. This allows it to accommodate different ad formats and propose the optimal format, maximizing the effectiveness of advertising.

[0041] The ad delivery optimization unit can monitor the effectiveness of ad delivery in real time and instantly adjust the delivery strategy as needed. For example, the ad delivery optimization unit builds a system in which a generation AI monitors the effectiveness of ad delivery in real time and instantly adjusts the delivery strategy as needed. For example, the delivery strategy is changed based on click-through rate and conversion rate. The effectiveness of ad delivery is also analyzed in real time, and the generation AI proposes the optimal delivery strategy. For example, it stops ads that are ineffective and prioritizes the delivery of ads that are highly effective. The generation AI also monitors the effectiveness of ad delivery in real time and adjusts the delivery strategy according to the response of the target audience. For example, it delivers ads that are highly effective during specific time periods. This makes it possible to maximize the effectiveness of advertising by monitoring the effectiveness of ad delivery in real time and instantly adjusting the delivery strategy as needed.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The web ad creation and distribution platform can also learn from a user's past advertising history and generate optimal ad copy based on past successes. For example, it can extract the characteristics of ad copy that has previously recorded high click-through rates and reflect them in new ad copy. It can also build a database of ad copy created by users in the past, and the generation AI can use that data to generate optimal ad copy. It can also analyze the results of past advertising campaigns and identify the factors that led to success. This can increase the effectiveness of advertising by generating optimal ad copy based on past successes.

[0044] The web ad creation and distribution platform can also generate more professional and reliable ad copy by inputting detailed product or service information. For example, ad copy can be created that includes details about ingredients and manufacturing processes. It can also generate ad copy that emphasizes the technical features and benefits of products and services. It can also include information about third-party certifications and quality assurance. This allows the platform to generate professional and reliable ad copy based on detailed information, improving the credibility of ads.

[0045] The web advertising creation and distribution platform can also input images and videos of products and services and generate ad copy based on that visual information. For example, a user can upload an image of a product or service, and the generation AI can analyze the image and generate ad copy. It is also possible to input a video of the product or service and have the generation AI generate ad copy based on the content of the video. Furthermore, the visual information of the image or video can be analyzed to generate visually appealing ad copy. This allows for the creation of visually appealing ads by generating ad copy based on visual information.

[0046] The web advertising creation and distribution platform also supports the generation of ad copy in different languages, making it possible to automatically generate ads for international markets. For example, the generation AI can automatically generate ad copy in different languages ​​to create ads for international markets. The generation AI can also generate multilingual ad copy based on product and service information provided by users. Furthermore, it can use expressions that are tailored to the culture and customs of each language. This makes it possible to automatically generate ads for international markets by supporting the generation of ad copy in different languages.

[0047] The web ad creation and distribution platform can also analyze the target audience's past purchase history and behavioral data, and generate optimal ad copy based on that. For example, the generation AI can analyze the target audience's past purchase history and generate optimal ad copy based on that data. It is also possible for the generation AI to analyze the target audience's behavioral data and generate ad copy based on that data. Furthermore, personalized ad copy can be generated based on the target audience's purchase history and behavioral data. This allows for effective advertising to be provided to the target audience by generating optimal ad copy based on past purchase history and behavioral data.

[0048] The web ad creation and distribution platform can also analyze the target audience's social media activity and generate ad copy that reflects trends and interests. For example, the generation AI can analyze the target audience's social media activity and generate ad copy based on that data. It is also possible for the generation AI to analyze the target audience's social media posts and generate ad copy based on that data. Furthermore, it can generate ad copy that reflects trends and interests based on the target audience's social media activity data. This allows for the provision of ads that are more likely to resonate with the target audience by analyzing social media activity and generating ad copy that reflects trends and interests.

[0049] The web ad creation and distribution platform can also generate ad copy that takes into account the cultural background and regional characteristics of the target audience. For example, the generation AI can take into account the cultural background of the target audience and generate ad copy based on that data. It is also possible for the generation AI to analyze the regional characteristics of the target audience and generate ad copy based on that data. Furthermore, it is possible to generate personalized ad copy based on the cultural background and regional characteristics of the target audience. This allows for the provision of ads that are appropriate for the target audience by generating ad copy that takes into account the cultural background and regional characteristics.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The product information acquisition unit acquires product and service information from the user. For example, the user inputs text information or image information, and the product information acquisition unit collects that information. It can also acquire user reviews provided by the user. Step 2: The ad copy generation unit generates ad copy based on the information acquired by the product information acquisition unit. For example, based on the information "We sell fresh vegetables" entered by the user, the generation AI generates ad copy such as "We deliver fresh and delicious vegetables!". The generation AI can also generate ad copy in different formats, such as banner ads and video ads. Step 3: The target analysis department analyzes information about the target audience. For example, it collects and analyzes data such as the target audience's age, gender, interests, and purchasing history. Based on this data, the target analysis department generates advertising copy that matches the preferences of the target audience. Step 4: The Ad Delivery Optimization Department proposes the optimal advertising media based on the information analyzed by the Target Analysis Department. For example, advertisements aimed at younger generations may be distributed primarily through social media, while advertisements aimed at older people may be distributed through newspapers and television. The Ad Delivery Optimization Department selects the optimal distribution media, taking into account the regional characteristics and cultural background of the target audience.

[0052] (Example 2) The web advertisement creation and distribution platform according to an embodiment of the present invention is a system in which a generation AI automatically generates advertisement copy based on product or service information provided by users, and provides content tailored to the preferences of the target audience. As a result, the web advertisement creation and distribution platform allows small businesses to easily create and distribute effective web advertisements, maximizing the effectiveness of their advertisements.

[0053] A web advertising creation and distribution platform according to an embodiment includes a product information acquisition unit, an advertisement copy generation unit, a target analysis unit, and an advertisement delivery optimization unit. The product information acquisition unit acquires product and service information from users. For example, the product information acquisition unit collects text and image information input by the user. It can also acquire user reviews provided by users. The advertisement copy generation unit generates advertisement copy based on the information acquired by the product information acquisition unit. For example, based on the user-input information "We sell fresh vegetables," the generation AI generates advertisement copy such as "We deliver fresh and delicious vegetables!". The generation AI can also generate advertisement copy in different formats, such as banner ads and video ads. The target analysis unit analyzes information about the target audience. For example, it collects and analyzes data such as the target audience's age, gender, interests, and purchasing history. Based on this data, the target analysis unit generates advertisement copy tailored to the target audience's preferences. The advertisement delivery optimization unit suggests optimal advertisement media based on the information analyzed by the target analysis unit. For example, advertisements aimed at younger generations are distributed primarily through social media, while advertisements aimed at older people are distributed through newspapers and television. The ad distribution optimization department selects the optimal distribution media, taking into account the regional characteristics and cultural background of the target audience. This allows the web ad creation and distribution platform to automatically generate ad copy using AI based on information about the products and services provided by users, and provide content tailored to the preferences of the target audience.

[0054] The ad copy generation unit can learn the user's past advertising history and generate optimal ad copy based on past successes. For example, the ad copy generation unit's generation AI analyzes the user's past advertising history and learns patterns of successful ad copy. For example, it extracts characteristics of ad copy that have recorded high click-through rates in the past and reflects them in new ad copy. It also builds a database of ad copy created by the user in the past, and the generation AI generates optimal ad copy based on that data. For example, if a specific keyword or phrase was effective, it will incorporate it into the new ad copy. The generation AI also analyzes the results of past advertising campaigns and identifies the factors that contributed to their success. For example, it will apply expressions and tones that were effective for a specific target audience to the new ad copy. This can increase the effectiveness of advertising by generating optimal ad copy based on past successes.

[0055] The ad copy generation unit can generate more professional and reliable ad copy by inputting detailed information about a product or service. For example, a user inputs detailed information about a product or service, and the generation AI generates professional ad copy based on that information. For example, the ad copy generation unit creates ad copy that includes details about ingredients and manufacturing processes. The generation AI also generates ad copy that emphasizes the technical features and advantages of the product or service. For example, it highlights how a specific technology or manufacturing method differs from other products. The generation AI also generates reliable ad copy based on detailed information about the product or service. For example, it includes information about certification or quality assurance from a third-party organization in the ad copy. This allows the credibility of the advertisement to be improved by generating professional and reliable ad copy based on detailed information.

[0056] The ad copy generation unit can use the emotion estimation function to analyze emotions toward the information entered by the user and generate ad copy that elicits positive emotions. For example, the generation AI in the ad copy generation unit analyzes emotions toward the information entered by the user and generates ad copy that elicits positive emotions. For example, if a user enters "fresh vegetables," the unit generates ad copy such as "We deliver fresh and delicious vegetables!". The emotion estimation function also analyzes emotions toward the information entered by the user in real time and selects expressions that elicit positive emotions. For example, it generates ad copy that emphasizes "healthy ingredients." The generation AI also generates ad copy that elicits positive emotions based on the results of the emotion analysis of the information entered by the user. For example, it creates ad copy that emphasizes "safe and secure products." This generates ad copy that elicits positive emotions, thereby increasing the effectiveness of advertising.

[0057] The ad copy generation unit can input images and videos of products and services and generate ad copy based on that visual information. For example, a user can upload images of a product or service, and the generation AI can analyze the images to generate ad copy. For example, it can create a catchy slogan that emphasizes the product's features. Alternatively, a video of the product or service can be input, and the generation AI can generate ad copy based on the content of the video. For example, it can reflect the key points introduced in the video in the ad copy. The generation AI can also analyze the visual information of images and videos to generate visually appealing ad copy. For example, it can use expressions that emphasize the beauty and ease of use of the product. In this way, by generating ad copy based on visual information, it is possible to create visually appealing advertisements.

[0058] The ad copy generation unit supports the generation of ad copy in different languages ​​and can automatically generate advertisements for international markets. In the ad copy generation unit, for example, the generation AI automatically generates ad copy in different languages ​​to create advertisements for international markets. For example, it supports multiple languages ​​such as English, French, and Chinese. In addition, the generation AI generates multilingual ad copy based on information about products and services provided by users. For example, it uses expressions that are suited to the culture and customs of each language. In addition, the generation AI generates ad copy in different languages ​​to support advertising campaigns for international markets. For example, it creates ad copy that meets the market needs of each country. In this way, by supporting the generation of ad copy in different languages, it is possible to automatically generate advertisements for international markets.

[0059] The ad copy generation unit uses the emotion estimation function to analyze the emotions of users when they enter information in real time and make suggestions to optimize the input content. The ad copy generation unit, for example, uses the emotion estimation function to analyze the emotions of users when they enter information about products or services in real time. For example, if the user has positive emotions, suggestions are made to emphasize those emotions. The emotion of the user when they enter information is also analyzed, and the generation AI suggests the optimal input content. For example, if the user has negative emotions, positive expressions are suggested. The emotion estimation function is also used to build a system that makes suggestions to optimize the user's input content. For example, appropriate keywords and phrases are suggested depending on the user's emotions. In this way, the quality of ad copy can be improved by analyzing the user's emotions in real time and making suggestions to optimize the input content.

[0060] The target analysis unit can analyze the target audience's past purchasing history and behavioral data, and generate optimal ad copy based on that. In the target analysis unit, for example, the generation AI analyzes the target audience's past purchasing history and generates optimal ad copy based on that data. For example, it creates ad copy related to products purchased in the past. In addition, the target audience's behavioral data is analyzed, and the generation AI generates ad copy based on that data. For example, it creates ad copy based on the browsing history of a specific website. In addition, the generation AI generates personalized ad copy based on the target audience's purchasing history and behavioral data. For example, it creates ad copy that suggests related products based on past purchasing patterns. In this way, by generating optimal ad copy based on past purchasing history and behavioral data, it is possible to provide effective ads to the target audience.

[0061] The target analysis unit can analyze the social media activity of the target audience and generate ad copy that reflects trends and interests. In the target analysis unit, for example, the generation AI analyzes the social media activity of the target audience and generates ad copy based on that data. For example, it creates ad copy that reflects recent trends and interests. In addition, the generation AI analyzes the content of the target audience's social media posts and generates ad copy based on that data. For example, it creates ad copy that includes specific hashtags and keywords. In addition, the generation AI generates ad copy that reflects trends and interests based on the target audience's social media activity data. For example, it creates ad copy related to recent topics and events. In this way, by analyzing social media activity and generating ad copy that reflects trends and interests, it is possible to provide ads that are likely to resonate with the target audience.

[0062] The target analysis unit can use the emotion estimation function to analyze the emotions of the target audience and generate advertising copy that is likely to resonate emotionally. In the target analysis unit, for example, the generation AI analyzes the emotions of the target audience and generates advertising copy that is likely to resonate emotionally. For example, it uses expressions that elicit positive emotions. In addition, the emotion estimation function is used to analyze the emotions of the target audience in real time and generate advertising copy that is likely to resonate emotionally. For example, it creates a catchy slogan that appeals to emotions. In addition, the generation AI generates advertising copy that is likely to resonate emotionally based on the emotional data of the target audience. For example, it uses expressions that elicit emotion or joy. In this way, the effectiveness of advertising can be increased by analyzing the emotions of the target audience and generating advertising copy that is likely to resonate emotionally.

[0063] The target analysis unit can generate advertising copy that takes into account the cultural background and regional characteristics of the target audience. For example, the generation AI in the target analysis unit takes into account the cultural background of the target audience and generates advertising copy based on that data. For example, it uses expressions that suit specific cultures and customs. The generation AI also analyzes the regional characteristics of the target audience, and generates advertising copy based on that data. For example, it creates advertising copy related to local events or local specialties. The generation AI also generates personalized advertising copy based on the cultural background and regional characteristics of the target audience. For example, it creates advertising copy that incorporates local words and dialects. In this way, by generating advertising copy that takes into account the cultural background and regional characteristics, it is possible to provide advertisements that are appropriate for the target audience.

[0064] The target analysis unit can generate customized advertisements that match the lifestyles and hobbies of the target audience. In the target analysis unit, for example, the generation AI analyzes the lifestyles of the target audience and generates customized advertisements based on that data. For example, it creates advertising copy that matches a health-conscious lifestyle. In addition, the generation AI analyzes the hobbies and interests of the target audience and generates customized advertisements based on that data. For example, it creates advertising copy that introduces products and services related to a specific hobby. In addition, the generation AI generates personalized customized advertisements based on the lifestyles and hobbies of the target audience. For example, it creates advertising copy for outdoor enthusiasts. In this way, customized advertisements that match the lifestyles and hobbies can be generated, making it possible to provide advertisements that are suitable for the target audience.

[0065] The target analysis unit can use the emotion estimation function to analyze the real-time emotions of the target audience and dynamically change the ad copy based on that. The target analysis unit, for example, uses the emotion estimation function to analyze the real-time emotions of the target audience and dynamically change the ad copy based on that data. For example, expressions that elicit positive emotions are added. In addition, a system is built in which the generative AI analyzes the real-time emotional data of the target audience and dynamically changes the ad copy. For example, the catchy copy is changed according to the emotions. In addition, the emotion estimation function is used to dynamically change the ad copy according to changes in the emotions of the target audience. For example, a positive message is sent to a target with negative emotions. In this way, by analyzing real-time emotions and dynamically changing the ad copy based on that, it is possible to provide ads that are appropriate for the target audience.

[0066] The ad delivery optimization unit can analyze past ad delivery data and identify the most effective delivery timing. In the ad delivery optimization unit, for example, the generation AI analyzes past ad delivery data and identifies the most effective delivery timing. For example, delivery during specific time periods or days of the week increases effectiveness. In addition, a system is built in which the generation AI suggests the optimal delivery timing based on past ad delivery data. For example, the delivery timing is adjusted based on past click rates and conversion rates. In addition, the generation AI analyzes past ad delivery data and identifies the optimal delivery timing based on the behavior patterns of the target audience. For example, it delivers ads to users who are active during specific time periods. In this way, the effectiveness of advertising can be maximized by analyzing past data and identifying the most effective delivery timing.

[0067] The ad delivery optimization unit can consider the regional characteristics of ad delivery and propose the optimal delivery media for each region. For example, the ad delivery optimization unit uses a generation AI to analyze the regional characteristics of ad delivery and propose the optimal delivery media for each region. For example, social media is used in urban areas, while newspapers and radio are used in rural areas. A system can also be built in which the generation AI proposes the optimal delivery media based on the regional characteristics of ad delivery. For example, it can analyze media consumption patterns for each region and select the optimal media. The generation AI can also consider the regional characteristics of ad delivery and propose delivery media customized for each region. For example, it can use media that is popular in a specific region. This makes it possible to maximize the effectiveness of advertising by considering regional characteristics and proposing the optimal delivery media for each region.

[0068] The ad delivery optimization unit can use the emotion estimation function to analyze the emotional reactions of the target audience after ad delivery and reflect this in the next delivery strategy. The ad delivery optimization unit, for example, uses the emotion estimation function to analyze the emotional reactions of the target audience after ad delivery. For example, ads with a high number of positive emotional reactions are reflected in the next delivery strategy. In addition, a generation AI analyzes emotional data after ad delivery and builds a system to optimize the next delivery strategy. For example, the content and delivery timing of ads are adjusted based on the emotional reactions. In addition, the emotion estimation function is used to monitor the emotional reactions of the target audience after ad delivery in real time and reflect this data in the next delivery strategy. For example, ads with a low number of negative emotional reactions are preferentially delivered. In this way, the effectiveness of advertising can be maximized by analyzing emotional reactions after ad delivery and reflecting this in the next delivery strategy.

[0069] The ad delivery optimization unit can accommodate different ad formats and propose the optimal format. In the ad delivery optimization unit, for example, the generation AI analyzes different ad formats and proposes the optimal format. For example, it selects banner ads or video ads that are effective for the target audience. It also builds a system that analyzes the effectiveness of ad formats and the generation AI proposes the optimal format. For example, it evaluates the effectiveness of formats based on past data. The generation AI also accommodates different ad formats and proposes the optimal format for the target audience. For example, it selects a format that is effective for a specific age group. This allows it to accommodate different ad formats and propose the optimal format, maximizing the effectiveness of advertising.

[0070] The ad delivery optimization unit can monitor the effectiveness of ad delivery in real time and instantly adjust the delivery strategy as needed. For example, the ad delivery optimization unit builds a system in which a generation AI monitors the effectiveness of ad delivery in real time and instantly adjusts the delivery strategy as needed. For example, the delivery strategy is changed based on click-through rate and conversion rate. The effectiveness of ad delivery is also analyzed in real time, and the generation AI proposes the optimal delivery strategy. For example, it stops ads that are ineffective and prioritizes the delivery of ads that are highly effective. The generation AI also monitors the effectiveness of ad delivery in real time and adjusts the delivery strategy according to the response of the target audience. For example, it delivers ads that are highly effective during specific time periods. This makes it possible to maximize the effectiveness of advertising by monitoring the effectiveness of ad delivery in real time and instantly adjusting the delivery strategy as needed.

[0071] The ad delivery optimization unit can use the emotion estimation function to predict the emotions of the target audience before delivering ads and propose the optimal delivery timing. The ad delivery optimization unit, for example, uses the emotion estimation function to build a system that predicts the emotions of the target audience before delivering ads and proposes the optimal delivery timing. For example, ads are delivered during times when positive emotions are high. In addition, the generation AI analyzes the emotional data of the target audience and proposes the optimal delivery timing. For example, ads are delivered when emotions are positive. In addition, the emotion estimation function is used to predict the emotions of the target audience and propose the optimal delivery timing based on that data. For example, ads are delivered to coincide with specific events or seasons. In this way, the effectiveness of ads can be maximized by predicting the emotions of the target audience before delivering ads and proposing the optimal delivery timing.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The web ad creation and distribution platform can also learn from a user's past advertising history and generate optimal ad copy based on past successes. For example, it can extract the characteristics of ad copy that has previously recorded high click-through rates and reflect them in new ad copy. It can also build a database of ad copy created by users in the past, and the generation AI can use that data to generate optimal ad copy. It can also analyze the results of past advertising campaigns and identify the factors that led to success. This can increase the effectiveness of advertising by generating optimal ad copy based on past successes.

[0074] The web ad creation and distribution platform can also generate more professional and reliable ad copy by inputting detailed product or service information. For example, ad copy can be created that includes details about ingredients and manufacturing processes. It can also generate ad copy that emphasizes the technical features and benefits of products and services. It can also include information about third-party certifications and quality assurance. This allows the platform to generate professional and reliable ad copy based on detailed information, improving the credibility of ads.

[0075] The web ad creation and distribution platform can also use its emotion estimation function to analyze the emotions felt by users regarding the information they enter and generate ad copy that elicits positive emotions. For example, if a user enters "fresh vegetables," it can generate ad copy such as "We deliver fresh and delicious vegetables!". The emotion estimation function can also be used to analyze the emotions felt by users regarding the information they enter in real time and select expressions that elicit positive emotions. Furthermore, the generation AI can generate ad copy that elicits positive emotions based on the results of the emotion analysis of the information entered by the user. This can increase the effectiveness of advertising by generating ad copy that elicits positive emotions.

[0076] The web advertising creation and distribution platform can also input images and videos of products and services and generate ad copy based on that visual information. For example, a user can upload an image of a product or service, and the generation AI can analyze the image and generate ad copy. It is also possible to input a video of the product or service and have the generation AI generate ad copy based on the content of the video. Furthermore, the visual information of the image or video can be analyzed to generate visually appealing ad copy. This allows for the creation of visually appealing ads by generating ad copy based on visual information.

[0077] The web advertising creation and distribution platform also supports the generation of ad copy in different languages, making it possible to automatically generate ads for international markets. For example, the generation AI can automatically generate ad copy in different languages ​​to create ads for international markets. The generation AI can also generate multilingual ad copy based on product and service information provided by users. Furthermore, it can use expressions that are tailored to the culture and customs of each language. This makes it possible to automatically generate ads for international markets by supporting the generation of ad copy in different languages.

[0078] The web ad creation and distribution platform can also use its emotion estimation function to analyze users' emotions in real time as they enter information and make suggestions to optimize the content they enter. For example, the emotion estimation function can be used to analyze users' emotions in real time as they enter product or service information. It is also possible to analyze users' emotions as they enter information and have the generation AI suggest optimal content. It can also suggest appropriate keywords and phrases based on the user's emotions. This allows for real-time analysis of user emotions and suggestions to optimize the content they enter, improving the quality of ad copy.

[0079] The web ad creation and distribution platform can also analyze the target audience's past purchase history and behavioral data, and generate optimal ad copy based on that. For example, the generation AI can analyze the target audience's past purchase history and generate optimal ad copy based on that data. It is also possible for the generation AI to analyze the target audience's behavioral data and generate ad copy based on that data. Furthermore, personalized ad copy can be generated based on the target audience's purchase history and behavioral data. This allows for effective advertising to be provided to the target audience by generating optimal ad copy based on past purchase history and behavioral data.

[0080] The web ad creation and distribution platform can also analyze the target audience's social media activity and generate ad copy that reflects trends and interests. For example, the generation AI can analyze the target audience's social media activity and generate ad copy based on that data. It is also possible for the generation AI to analyze the target audience's social media posts and generate ad copy based on that data. Furthermore, it can generate ad copy that reflects trends and interests based on the target audience's social media activity data. This allows for the provision of ads that are more likely to resonate with the target audience by analyzing social media activity and generating ad copy that reflects trends and interests.

[0081] The web advertising creation and distribution platform can also use an emotion estimation function to analyze the emotions of the target audience and generate ad copy that is likely to resonate with them emotionally. For example, the generation AI can analyze the emotions of the target audience and generate ad copy that is likely to resonate with them emotionally. It is also possible to use the emotion estimation function to analyze the emotions of the target audience in real time and generate ad copy that is likely to resonate with them. Furthermore, it is possible to generate ad copy that is likely to resonate with them emotionally based on the emotional data of the target audience. This allows for the effectiveness of advertising to be increased by analyzing the emotions of the target audience and generating ad copy that is likely to resonate with them emotionally.

[0082] The web ad creation and distribution platform can also generate ad copy that takes into account the cultural background and regional characteristics of the target audience. For example, the generation AI can take into account the cultural background of the target audience and generate ad copy based on that data. It is also possible for the generation AI to analyze the regional characteristics of the target audience and generate ad copy based on that data. Furthermore, it is possible to generate personalized ad copy based on the cultural background and regional characteristics of the target audience. This allows for the provision of ads that are appropriate for the target audience by generating ad copy that takes into account the cultural background and regional characteristics.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The product information acquisition unit acquires product and service information from the user. For example, the user inputs text information or image information, and the product information acquisition unit collects that information. It can also acquire user reviews provided by the user. Step 2: The ad copy generation unit generates ad copy based on the information acquired by the product information acquisition unit. For example, based on the information "We sell fresh vegetables" entered by the user, the generation AI generates ad copy such as "We deliver fresh and delicious vegetables!". The generation AI can also generate ad copy in different formats, such as banner ads and video ads. Step 3: The target analysis department analyzes information about the target audience. For example, it collects and analyzes data such as the target audience's age, gender, interests, and purchasing history. Based on this data, the target analysis department generates advertising copy that matches the preferences of the target audience. Step 4: The Ad Delivery Optimization Department proposes the optimal advertising media based on the information analyzed by the Target Analysis Department. For example, advertisements aimed at younger generations may be distributed primarily through social media, while advertisements aimed at older people may be distributed through newspapers and television. The Ad Delivery Optimization Department selects the optimal distribution media, taking into account the regional characteristics and cultural background of the target audience.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 robot 414 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 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 product information acquisition unit that acquires product and service information from a user; an advertisement copy generation unit that generates advertisement copy based on the information acquired by the product information acquisition unit; A target analysis department that analyzes information about the target audience; an advertisement distribution optimization unit that proposes optimal advertisement placement media based on the information analyzed by the target analysis unit; A system characterized by:

2. The advertisement copy generation unit Learn the user's past advertising history and generate optimal ad copy based on past successes 2. The system of claim 1.

3. The advertisement copy generation unit Enter detailed information about your product or service to generate more professional and reliable ad copy 2. The system of claim 1.

4. The advertisement copy generation unit Analyze the emotions of the user regarding the information entered by the user and generate advertising copy that elicits positive emotions 2. The system of claim 1.

5. The advertisement copy generation unit Input images and videos of the product or service and generate advertising copy based on that visual information 2. The system of claim 1.

Images