System

Abstract

An object of the system according to the embodiment is to generate and adjust a personalized advertisement in real time based on individual data of a user.SOLUTION: A system includes a data collection part, an analysis part, an advertisement generation part, and an advertisement adjustment part. The data collection unit collects individual data of a user. The analysis unit analyzes the individual data collected by the data collection unit. The advertisement generation unit generates a personalized advertisement based on the data analyzed by the analysis unit. The advertisement adjustment unit adjusts the advertisement generated by the advertisement generation unit in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to generate and adjust personalized advertisements in real time based on individual user data.

[0005] The system according to the embodiment aims to generate and adjust personalized advertisements in real time based on individual data of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an advertisement generation unit, and an advertisement adjustment unit. The data collection unit collects individual data of users. The analysis unit analyzes the individual data collected by the data collection unit. The advertisement generation unit generates personalized advertisements based on the data analyzed by the analysis unit. The advertisement adjustment unit adjusts the advertisements generated by the advertisement generation unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can generate and adjust personalized advertisements in real time based on individual user data. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A next-generation video advertising system according to an embodiment of the present invention is a system that analyzes individual user data in real time, generates personalized advertisements using a generation AI, and adjusts them in real time, thereby attracting user attention, maximizing engagement, and improving the ROI of advertising campaigns.

[0029] A next-generation video advertising system according to an embodiment includes a data collection unit, an analysis unit, an advertisement generation unit, and an advertisement adjustment unit. The data collection unit collects individual data about a user. For example, the data collection unit collects the user's browsing history, search history, purchase history, social media activity, and the like. The data collection unit can collect this data in real time. For example, the data collection unit collects the user's browsing history in real time while the user is browsing a website. The analysis unit analyzes the individual data collected by the data collection unit. For example, a generation AI analyzes the user's interests using data mining technology. The analysis unit can also analyze data using a machine learning algorithm. For example, the generation AI analyzes the user's purchase history and identifies products that the user is interested in. The advertisement generation unit generates personalized advertisements based on the data analyzed by the analysis unit. For example, the generation AI generates advertisements based on the user's interests. The advertisement generation unit can also generate video advertisements. For example, the generation AI generates video advertisements related to products recently searched for by the user. The advertisement adjustment unit adjusts the advertisements generated by the advertisement generation unit in real time. For example, the generation AI adjusts the content of advertisements based on user feedback and interactions. The advertisement adjustment unit can also adjust the timing and frequency of advertisement delivery by having the generation AI analyze advertisement performance data. For example, the generation AI analyzes advertisement click-through rates and changes advertisement content during times when click-through rates are low. This allows the next-generation video advertising system according to the embodiment to generate and adjust personalized advertisements in real time based on individual user data. For example, by providing advertisements related to products that users are interested in in real time, the effectiveness of advertisements can be increased. Furthermore, adjusting advertisement content based on user feedback can improve the user experience. Furthermore, by analyzing performance data of advertising campaigns and proposing optimal strategies, advertisers' ROI can be maximized.

[0030] The data collection unit collects the user's real-time location information, and the analysis unit analyzes the location information to generate advertisements relevant to that location. For example, the data collection unit collects GPS data from the user's smartphone in real time and inputs the location information into the generation AI. The generation AI generates advertisements relevant to the user's current location. For example, if the user is in a shopping mall, it generates advertisements for stores in the mall. The data collection unit also collects Wi-Fi location information and inputs that data into the generation AI. The generation AI analyzes the Wi-Fi location information and generates advertisements relevant to the user's location. For example, if the user is in a cafe, it generates a promotional advertisement for that cafe. The data collection unit also collects the user's location information using beacon technology and inputs that data into the generation AI. The generation AI analyzes the beacon location information and generates advertisements relevant to the user's location. For example, if the user is in a specific store, it generates an advertisement containing sales information for that store. This enables more targeted advertising by providing advertisements relevant to the user's current location.

[0031] The data collection unit collects the user's voice data, the analysis unit analyzes the voice data to infer the user's interests, and the advertisement generation unit generates advertisements based on the interests. For example, the data collection unit collects the user's voice data in real time, and the generation AI analyzes the voice. For example, the interest and concern are inferred from what the user is saying and advertisements are generated based on that. The data collection unit also collects the user's voice tone and inputs that data into the generation AI. The generation AI analyzes the voice tone and infers the user's emotional state. For example, if the user is speaking in an excited tone, an energetic advertisement is generated. The data collection unit also converts the user's voice data into text and inputs the text data into the generation AI. The generation AI analyzes the text data and infers the user's interests. For example, if the user is talking about a specific brand, an advertisement related to that brand is generated. This allows for more targeted advertisements to be provided by generating advertisements based on the user's voice data.

[0032] The data collection unit collects the user's purchase history and return history, the analysis unit analyzes the purchase history and return history to identify products that the user does not like, and the advertisement generation unit can generate advertisements that avoid the disliked products. The data collection unit, for example, collects the user's purchase history and return history, and the generation AI analyzes the data. For example, an advertisement is generated that avoids products that are frequently returned. The data collection unit also collects the user's purchase history and inputs that data into the generation AI. The generation AI analyzes the purchase history to identify products that the user does not like. For example, an advertisement is generated that avoids products that have been purchased but returned in the past. The data collection unit also collects the user's return history and inputs that data into the generation AI. The generation AI analyzes the return history to identify products that the user does not like. For example, an advertisement is generated that avoids products with low ratings. This allows for more effective advertisements to be provided by generating advertisements that avoid products that the user does not like.

[0033] The advertisement generation unit can analyze a user's past advertisement viewing history, extract the most effective advertisement elements, and reflect them in a new advertisement. For example, the advertisement generation unit collects a user's past advertisement viewing history, and the generation AI analyzes that data. For example, it extracts elements of advertisements that were viewed for a long time and reflects them in a new advertisement. The advertisement generation unit also collects a user's advertisement viewing history and inputs that data into the generation AI. The generation AI analyzes the advertisement viewing history and identifies the most effective advertisement elements. For example, if a particular design or message is effective, it reflects those elements in a new advertisement. The advertisement generation unit also analyzes a user's advertisement viewing history and extracts the most effective advertisement elements. For example, if a particular music or video is effective, it reflects those elements in a new advertisement. This makes it possible to provide more effective advertisements by generating new advertisements based on past advertisement viewing history.

[0034] The advertisement generation unit can analyze a user's device usage patterns and identify the optimal timing for displaying advertisements. For example, the advertisement generation unit collects a user's device usage patterns, and the generation AI analyzes the data. For example, the advertisement generation unit identifies the time periods when the user is most active and displays advertisements during those time periods. The advertisement generation unit also collects the user's device usage frequency and inputs the data into the generation AI. The generation AI analyzes the usage frequency and identifies the optimal timing for displaying advertisements. For example, advertisements are displayed during times when the user frequently uses the device. The advertisement generation unit also collects the applications used by the user's device and inputs the data into the generation AI. The generation AI analyzes the applications used and identifies the optimal timing for displaying advertisements. For example, advertisements are displayed during times when the user is using a specific application. This allows for more effective advertisements to be provided by identifying the optimal timing for displaying advertisements based on the user's device usage patterns.

[0035] The advertisement generation unit can add interactive elements to advertisements based on the user's hobbies and interests, allowing the user to interact directly with the advertisement. For example, the advertisement generation unit analyzes the user's hobbies and interests and generates an interactive advertisement based on the data. For example, if the user likes games, the advertisement generation unit generates an advertisement including game elements. The advertisement generation unit also collects the user's hobbies and interests and inputs the data into the generation AI. The generation AI analyzes the hobbies and interests and adds interactive elements. For example, if the user likes music, the advertisement generation unit generates an advertisement including a music quiz. The advertisement generation unit also collects the user's interest data and inputs the data into the generation AI. The generation AI analyzes the interest data and allows the user to interact with the advertisement. For example, if the user likes traveling, the advertisement generation unit generates an advertisement offering options for travel destinations. This allows for higher engagement by providing interactive advertisements based on the user's hobbies and interests.

[0036] The advertisement generation unit can analyze data on the user's friends and family and generate advertisements that utilize social networks. For example, the advertisement generation unit collects data on the user's friends and family, and the generation AI analyzes that data. For example, advertisements related to products that the friends and family are interested in are generated. The advertisement generation unit also collects the user's social network data and inputs that data into the generation AI. The generation AI analyzes the social network data and generates advertisements that the user's friends and family will be interested in. For example, advertisements related to products shared by friends are generated. The advertisement generation unit also collects the user's friend list and inputs that data into the generation AI. The generation AI analyzes the friend list and generates advertisements that the friends and family will be interested in. For example, advertisements related to products purchased by family members are generated. This makes it possible to achieve higher engagement by providing advertisements that utilize the user's social network.

[0037] The ad adjustment unit can analyze the specific actions taken by users on ads and optimize the ad content based on the actions. The ad adjustment unit collects actions taken by users on ads, such as clicking or skipping, and the generation AI analyzes that data. For example, it improves elements of skipped ads. The ad adjustment unit also collects user action data and inputs that data into the generation AI. The generation AI analyzes the action data and optimizes the ad content. For example, it strengthens elements of ads with high click-through rates. The ad adjustment unit also collects user action history and inputs that data into the generation AI. The generation AI analyzes the action history and optimizes the ad content. For example, it reflects elements of shared ads in new ads. This makes it possible to provide more effective ads by optimizing the ad content based on user actions.

[0038] The ad adjustment unit can collect user feedback in real time and instantly change the ad content based on the feedback. For example, the ad adjustment unit collects user feedback in real time and the generation AI analyzes the data. For example, if a user gives positive feedback about an ad, those elements are strengthened. The ad adjustment unit also collects user feedback data and inputs that data into the generation AI. The generation AI analyzes the feedback data and instantly changes the ad content. For example, if there is a lot of negative feedback, the ad content is improved. The ad adjustment unit also collects user feedback in real time and inputs that data into the generation AI. The generation AI analyzes the feedback and instantly changes the ad content. For example, if a user likes a particular element, that element is strengthened. This makes it possible to provide more effective ads by instantly changing the ad content based on user feedback.

[0039] The ad adjustment unit provides an interface that allows users to customize advertisements, enabling users to select advertisement content themselves. The ad adjustment unit, for example, provides an interface that allows users to customize advertisement content. For example, it allows users to select products or services that interest them. The ad adjustment unit also collects user customization data and inputs that data to the generation AI. The generation AI analyzes the customization data and generates advertisement content selected by the user. For example, if the user selects a specific brand, it generates advertisements related to that brand. The ad adjustment unit also collects the user's customization history and inputs that data to the generation AI. The generation AI analyzes the customization history and generates advertisement content that the user prefers. For example, it reflects advertisement elements that the user has previously selected in new advertisements. This allows users to customize advertisement content, making it possible to provide more personalized advertisements.

[0040] The ad adjustment unit can cause the generation AI to generate multiple ad variations based on user feedback, allowing the user to select the variation they like best. The ad adjustment unit, for example, collects user feedback and causes the generation AI to generate multiple ad variations based on that data. For example, it generates ads with different designs and messages. The ad adjustment unit also collects user feedback data and inputs that data into the generation AI. The generation AI analyzes the feedback data and generates multiple ad variations. For example, it generates ads using different colors and fonts. The ad adjustment unit also causes the generation AI to generate multiple ad variations based on user feedback, allowing the user to select the variation they like best. For example, it reflects ad elements selected by the user in the new ad. This makes it possible to provide more personalized ads by providing multiple ad variations based on user feedback.

[0041] The ad tailoring unit can analyze performance data of ad campaigns and propose optimal strategies to maximize ROI. For example, the ad tailoring unit collects performance data of ad campaigns, and the generation AI analyzes the data. For example, it proposes optimal strategies based on click-through rates and conversion rates. The ad tailoring unit also collects performance data of ad campaigns and inputs the data into the generation AI. The generation AI analyzes the performance data and identifies optimal strategies to maximize ROI. For example, it proposes a strategy that focuses on a specific target audience. The ad tailoring unit also analyzes performance data of ad campaigns and proposes optimal timing and frequency of ad delivery. For example, it proposes a strategy to deliver ads during times when click-through rates are high. This makes it possible to maximize ROI by proposing optimal strategies based on the performance data of ad campaigns.

[0042] The ad tailoring unit can optimize the target audience based on the performance data of the advertising campaign. For example, the ad tailoring unit analyzes the performance data of the advertising campaign and identifies the most effective target audience. For example, focusing on a specific age group or region. The ad tailoring unit also collects performance data of the advertising campaign and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes the target audience. For example, focusing on users with specific interests. The ad tailoring unit also proposes a strategy to optimize the target audience based on the performance data of the advertising campaign. For example, delivering advertisements focused on a specific demographic. This makes it possible to provide more effective advertisements by optimizing the target audience based on the performance data of the advertising campaign.

[0043] The ad adjustment unit can optimize ad creatives based on performance data of ad campaigns. For example, the ad adjustment unit analyzes performance data of ad campaigns and identifies the most effective ad creatives. For example, if a particular design or message is effective, it strengthens those elements. The ad adjustment unit also collects performance data of ad campaigns and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes ad creatives. For example, it generates ads that use particular colors and fonts. The ad adjustment unit also proposes a strategy to optimize ad creatives based on performance data of ad campaigns. For example, it delivers ads with strengthened particular visual elements. In this way, by optimizing ad creatives based on performance data of ad campaigns, more effective ads can be provided.

[0044] The ad adjustment unit can optimize the timing of ad delivery based on performance data of the ad campaign. For example, the ad adjustment unit analyzes performance data of the ad campaign and identifies the most effective timing for ad delivery. For example, it delivers ads during times when click-through rates are high. The ad adjustment unit also collects performance data of the ad campaign and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes the timing of ad delivery. For example, it delivers ads during times when users are most active. The ad adjustment unit also proposes a strategy for optimizing the timing of ad delivery based on the performance data of the ad campaign. For example, it delivers ads during specific events. In this way, by optimizing the timing of ad delivery based on the performance data of the ad campaign, more effective ads can be provided.

[0045] The ad adjustment unit can optimize the frequency of ad delivery based on performance data of the ad campaign. For example, the ad adjustment unit analyzes performance data of the ad campaign and identifies the most effective frequency of ad delivery. For example, it identifies the frequency with a high click-through rate or conversion rate. The ad adjustment unit also collects performance data of the ad campaign and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes the frequency of ad delivery. For example, it identifies how many times per day or how many times per week to display the ad. The ad adjustment unit also proposes a strategy to optimize the frequency of ad delivery based on the performance data of the ad campaign. For example, it may deliver ads intensively during specific time periods. In this way, by optimizing the frequency of ad delivery based on the performance data of the ad campaign, more effective ads can be provided.

[0046] The advertising tailoring unit can optimize advertising delivery channels based on advertising campaign performance data. For example, the advertising tailoring unit analyzes advertising campaign performance data and identifies the most effective advertising delivery channel. For example, if a specific social media platform or website is effective, the advertising tailoring unit focuses advertising on that channel. The advertising tailoring unit also collects advertising campaign performance data and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes advertising delivery channels. For example, if a specific search engine or email marketing is effective, the advertising tailoring unit delivers ads to that channel. The advertising tailoring unit also proposes a strategy to optimize advertising delivery channels based on advertising campaign performance data. For example, delivering ads to specific devices or applications. In this way, by optimizing advertising delivery channels based on advertising campaign performance data, more effective advertising can be provided.

[0047] The ad adjustment unit monitors ad performance in real time and can adjust ad content and delivery methods as needed. For example, the ad adjustment unit collects ad performance data in real time and the generation AI analyzes that data. For example, if the click-through rate or conversion rate drops, the ad content is changed. The ad adjustment unit also collects ad performance data in real time and inputs that data into the generation AI. The generation AI analyzes the performance data and adjusts ad content and delivery methods as needed. For example, it changes ad content during specific time periods. The ad adjustment unit also monitors ad performance in real time and proposes strategies to adjust ad content and delivery methods as needed. For example, it delivers ads to specific devices or applications. This makes it possible to provide more effective ads by monitoring ad performance in real time and adjusting ad content and delivery methods as needed.

[0048] The ad adjustment unit can optimize ad creatives in real time based on ad performance data. For example, the ad adjustment unit collects ad performance data in real time and the generation AI analyzes that data. For example, if the click-through rate or conversion rate drops, the ad creative is changed. The ad adjustment unit also collects ad performance data in real time and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes the ad creatives in real time. For example, if a particular design or message is effective, those elements are enhanced. The ad adjustment unit also proposes a strategy to optimize the ad creatives in real time based on the ad performance data. For example, an ad with specific visual elements enhanced is delivered. This makes it possible to provide more effective ads by optimizing the ad creatives in real time based on the ad performance data.

[0049] The ad adjustment unit can optimize the timing of ad delivery in real time based on ad performance data. For example, the ad adjustment unit collects ad performance data in real time, and the generation AI analyzes that data. For example, if the click-through rate or conversion rate drops, the timing of ad delivery is changed. The ad adjustment unit also collects ad performance data in real time and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes the timing of ad delivery in real time. For example, it delivers ads during times when users are most active. The ad adjustment unit also proposes a strategy to optimize the timing of ad delivery in real time based on ad performance data. For example, it delivers ads during specific events. This makes it possible to provide more effective ads by optimizing the timing of ad delivery in real time based on ad performance data.

[0050] The ad adjustment unit can optimize ad delivery channels in real time based on ad performance data. For example, the ad adjustment unit collects ad performance data in real time, and the generation AI analyzes that data. For example, if the click-through rate or conversion rate drops, the ad delivery channel is changed. The ad adjustment unit also collects ad performance data in real time and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes ad delivery channels in real time. For example, if a specific social media platform or website is effective, the ad adjustment unit will focus ads on that channel. The ad adjustment unit also proposes a strategy to optimize ad delivery channels in real time based on ad performance data. For example, delivering ads to specific devices or applications. This makes it possible to provide more effective ads by optimizing ad delivery channels in real time based on ad performance data.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The data collection unit collects the user's purchase history and return history, the analysis unit analyzes the purchase history and return history to identify products that the user does not like, and the advertisement generation unit can generate advertisements that avoid the disliked products. The data collection unit, for example, collects the user's purchase history and return history, and the generation AI analyzes the data. For example, an advertisement is generated that avoids products that are frequently returned. The data collection unit also collects the user's purchase history and inputs that data into the generation AI. The generation AI analyzes the purchase history to identify products that the user does not like. For example, an advertisement is generated that avoids products that have been purchased but returned in the past. The data collection unit also collects the user's return history and inputs that data into the generation AI. The generation AI analyzes the return history to identify products that the user does not like. For example, an advertisement is generated that avoids products with low ratings. This allows for more effective advertisements to be provided by generating advertisements that avoid products that the user does not like.

[0053] The advertisement generation unit can analyze a user's past advertisement viewing history, extract the most effective advertisement elements, and reflect them in a new advertisement. For example, the advertisement generation unit collects a user's past advertisement viewing history, and the generation AI analyzes that data. For example, it extracts elements of advertisements that were viewed for a long time and reflects them in a new advertisement. The advertisement generation unit also collects a user's advertisement viewing history and inputs that data into the generation AI. The generation AI analyzes the advertisement viewing history and identifies the most effective advertisement elements. For example, if a particular design or message is effective, it reflects those elements in a new advertisement. The advertisement generation unit also analyzes a user's advertisement viewing history and extracts the most effective advertisement elements. For example, if a particular music or video is effective, it reflects those elements in a new advertisement. This makes it possible to provide more effective advertisements by generating new advertisements based on past advertisement viewing history.

[0054] The advertisement generation unit can analyze a user's device usage patterns and identify the optimal timing for displaying advertisements. For example, the advertisement generation unit collects a user's device usage patterns, and the generation AI analyzes the data. For example, the advertisement generation unit identifies the time periods when the user is most active and displays advertisements during those time periods. The advertisement generation unit also collects the user's device usage frequency and inputs the data into the generation AI. The generation AI analyzes the usage frequency and identifies the optimal timing for displaying advertisements. For example, advertisements are displayed during times when the user frequently uses the device. The advertisement generation unit also collects the applications used by the user's device and inputs the data into the generation AI. The generation AI analyzes the applications used and identifies the optimal timing for displaying advertisements. For example, advertisements are displayed during times when the user is using a specific application. This allows for more effective advertisements to be provided by identifying the optimal timing for displaying advertisements based on the user's device usage patterns.

[0055] The advertisement generation unit can add interactive elements to advertisements based on the user's hobbies and interests, allowing the user to interact directly with the advertisement. For example, the advertisement generation unit analyzes the user's hobbies and interests and generates an interactive advertisement based on the data. For example, if the user likes games, the advertisement generation unit generates an advertisement including game elements. The advertisement generation unit also collects the user's hobbies and interests and inputs the data into the generation AI. The generation AI analyzes the hobbies and interests and adds interactive elements. For example, if the user likes music, the advertisement generation unit generates an advertisement including a music quiz. The advertisement generation unit also collects the user's interest data and inputs the data into the generation AI. The generation AI analyzes the interest data and allows the user to interact with the advertisement. For example, if the user likes traveling, the advertisement generation unit generates an advertisement offering options for travel destinations. This allows for higher engagement by providing interactive advertisements based on the user's hobbies and interests.

[0056] The advertisement generation unit can analyze data on the user's friends and family and generate advertisements that utilize social networks. For example, the advertisement generation unit collects data on the user's friends and family, and the generation AI analyzes that data. For example, advertisements related to products that the friends and family are interested in are generated. The advertisement generation unit also collects the user's social network data and inputs that data into the generation AI. The generation AI analyzes the social network data and generates advertisements that the user's friends and family will be interested in. For example, advertisements related to products shared by friends are generated. The advertisement generation unit also collects the user's friend list and inputs that data into the generation AI. The generation AI analyzes the friend list and generates advertisements that the friends and family will be interested in. For example, advertisements related to products purchased by family members are generated. This makes it possible to achieve higher engagement by providing advertisements that utilize the user's social network.

[0057] The ad adjustment unit can analyze the specific actions taken by users on ads and optimize the ad content based on the actions. The ad adjustment unit collects actions taken by users on ads, such as clicking or skipping, and the generation AI analyzes that data. For example, it improves elements of skipped ads. The ad adjustment unit also collects user action data and inputs that data into the generation AI. The generation AI analyzes the action data and optimizes the ad content. For example, it strengthens elements of ads with high click-through rates. The ad adjustment unit also collects user action history and inputs that data into the generation AI. The generation AI analyzes the action history and optimizes the ad content. For example, it reflects elements of shared ads in new ads. This makes it possible to provide more effective ads by optimizing the ad content based on user actions.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The data collection unit collects individual data about the user. For example, the data collection unit collects the user's browsing history, search history, purchase history, social media activity, etc. The data collection unit can also collect this data in real time. For example, the data collection unit can collect the browsing history in real time while the user is browsing a website. Step 2: The analysis unit analyzes the individual data collected by the data collection unit. For example, the generation AI uses data mining technology to analyze the user's interests. The analysis unit can also analyze the data using machine learning algorithms. For example, the generation AI analyzes the user's purchasing history and identifies products that the user is interested in. Step 3: The advertisement generation unit generates personalized advertisements based on the data analyzed by the analysis unit. For example, the generation AI generates advertisements based on the user's interests. The advertisement generation unit can also generate video advertisements. For example, the generation AI generates video advertisements related to products recently searched for by the user. Step 4: The ad adjustment unit adjusts the ads generated by the ad generation unit in real time. For example, the generation AI adjusts the ad content based on user feedback and interactions. The ad adjustment unit can also adjust the timing and frequency of ad delivery by having the generation AI analyze ad performance data. For example, the generation AI analyzes ad click rates and changes ad content during times when click rates are low.

[0060] (Example 2) A next-generation video advertising system according to an embodiment of the present invention is a system that analyzes individual user data in real time, generates personalized advertisements using a generation AI, and adjusts them in real time, thereby attracting user attention, maximizing engagement, and improving the ROI of advertising campaigns.

[0061] A next-generation video advertising system according to an embodiment includes a data collection unit, an analysis unit, an advertisement generation unit, and an advertisement adjustment unit. The data collection unit collects individual data about a user. For example, the data collection unit collects the user's browsing history, search history, purchase history, social media activity, and the like. The data collection unit can collect this data in real time. For example, the data collection unit collects the user's browsing history in real time while the user is browsing a website. The analysis unit analyzes the individual data collected by the data collection unit. For example, a generation AI analyzes the user's interests using data mining technology. The analysis unit can also analyze data using a machine learning algorithm. For example, the generation AI analyzes the user's purchase history and identifies products that the user is interested in. The advertisement generation unit generates personalized advertisements based on the data analyzed by the analysis unit. For example, the generation AI generates advertisements based on the user's interests. The advertisement generation unit can also generate video advertisements. For example, the generation AI generates video advertisements related to products recently searched for by the user. The advertisement adjustment unit adjusts the advertisements generated by the advertisement generation unit in real time. For example, the generation AI adjusts the content of advertisements based on user feedback and interactions. The advertisement adjustment unit can also adjust the timing and frequency of advertisement delivery by having the generation AI analyze advertisement performance data. For example, the generation AI analyzes advertisement click-through rates and changes advertisement content during times when click-through rates are low. This allows the next-generation video advertising system according to the embodiment to generate and adjust personalized advertisements in real time based on individual user data. For example, by providing advertisements related to products that users are interested in in real time, the effectiveness of advertisements can be increased. Furthermore, adjusting advertisement content based on user feedback can improve the user experience. Furthermore, by analyzing performance data of advertising campaigns and proposing optimal strategies, advertisers' ROI can be maximized.

[0062] The data collection unit collects the user's biometric data, the analysis unit analyzes the biometric data to estimate the user's emotional state, and the advertisement generation unit generates advertisements based on the user's emotional state. The data collection unit, for example, monitors the user's heart rate in real time and inputs the data into the generation AI. The generation AI analyzes heart rate fluctuations to estimate whether the user is excited or relaxed. For example, if the heart rate is elevated, it determines that the user is excited and generates an energetic advertisement. The data collection unit also measures electrodermal activity and inputs the data into the generation AI. The generation AI analyzes electrodermal activity fluctuations to estimate the user's emotional state. For example, if the electrodermal activity is high, it determines that the user is stressed and generates an advertisement with a relaxing effect. The data collection unit also measures brain waves and inputs the data into the generation AI. The generation AI analyzes brain wave fluctuations to estimate the user's emotional state. For example, if there are a lot of alpha waves, it determines that the user is relaxed and generates an advertisement with a relaxing effect. This allows for more effective advertisements to be provided by generating advertisements based on the user's emotional state.

[0063] The data collection unit collects the user's real-time location information, and the analysis unit analyzes the location information to generate advertisements relevant to that location. For example, the data collection unit collects GPS data from the user's smartphone in real time and inputs the location information into the generation AI. The generation AI generates advertisements relevant to the user's current location. For example, if the user is in a shopping mall, it generates advertisements for stores in the mall. The data collection unit also collects Wi-Fi location information and inputs that data into the generation AI. The generation AI analyzes the Wi-Fi location information and generates advertisements relevant to the user's location. For example, if the user is in a cafe, it generates a promotional advertisement for that cafe. The data collection unit also collects the user's location information using beacon technology and inputs that data into the generation AI. The generation AI analyzes the beacon location information and generates advertisements relevant to the user's location. For example, if the user is in a specific store, it generates an advertisement containing sales information for that store. This enables more targeted advertising by providing advertisements relevant to the user's current location.

[0064] The data collection unit collects the user's social media posts, the analysis unit analyzes the social media posts to infer the user's emotional state, and the advertisement generation unit generates advertisements based on the emotional state. For example, the data collection unit collects the user's social media posts, and the generation AI analyzes the content of the posts. For example, it analyzes the keywords and context contained in the posts to infer the user's emotional state. If there are many positive emotions, it generates an upbeat advertisement. The data collection unit also collects the frequency and time of the user's social media posts and inputs that data into the generation AI. The generation AI analyzes the frequency and time of posts to infer the user's emotional state. For example, if there are many posts at night, it generates an advertisement with a relaxing effect. The data collection unit also collects the user's social media interactions (likes, comments, shares, etc.) and inputs that data into the generation AI. The generation AI analyzes the content of the interactions to infer the user's emotional state. For example, if there are many positive comments, it generates a positive advertisement. This allows for more personalized advertisements to be provided by generating advertisements based on the user's social media posts.

[0065] The data collection unit collects the user's voice data, the analysis unit analyzes the voice data to infer the user's interests, and the advertisement generation unit generates advertisements based on the interests. For example, the data collection unit collects the user's voice data in real time, and the generation AI analyzes the voice. For example, the interest and concern are inferred from what the user is saying and advertisements are generated based on that. The data collection unit also collects the user's voice tone and inputs that data into the generation AI. The generation AI analyzes the voice tone and infers the user's emotional state. For example, if the user is speaking in an excited tone, an energetic advertisement is generated. The data collection unit also converts the user's voice data into text and inputs the text data into the generation AI. The generation AI analyzes the text data and infers the user's interests. For example, if the user is talking about a specific brand, an advertisement related to that brand is generated. This allows for more targeted advertisements to be provided by generating advertisements based on the user's voice data.

[0066] The data collection unit collects the user's purchase history and return history, the analysis unit analyzes the purchase history and return history to identify products that the user does not like, and the advertisement generation unit can generate advertisements that avoid the disliked products. The data collection unit, for example, collects the user's purchase history and return history, and the generation AI analyzes the data. For example, an advertisement is generated that avoids products that are frequently returned. The data collection unit also collects the user's purchase history and inputs that data into the generation AI. The generation AI analyzes the purchase history to identify products that the user does not like. For example, an advertisement is generated that avoids products that have been purchased but returned in the past. The data collection unit also collects the user's return history and inputs that data into the generation AI. The generation AI analyzes the return history to identify products that the user does not like. For example, an advertisement is generated that avoids products with low ratings. This allows for more effective advertisements to be provided by generating advertisements that avoid products that the user does not like.

[0067] The data collection unit collects facial expressions while the user is viewing an advertisement, the analysis unit analyzes the expressions to estimate the user's emotional state, and the advertisement adjustment unit adjusts advertisement content in real time based on the user's emotional state. For example, the data collection unit monitors the user's facial expressions while viewing an advertisement using a camera, and the generation AI analyzes the expressions. For example, if the user smiles frequently, positive advertisements are continuously displayed. The data collection unit also collects the user's facial expression data and inputs the data into the generation AI. The generation AI analyzes the facial expression data and estimates the user's emotional state. For example, if the user shows many surprised expressions, an advertisement that induces surprise is generated. The data collection unit also collects changes in the user's facial expression in real time and inputs the data into the generation AI. The generation AI analyzes the changes in facial expression and estimates the user's emotional state. For example, if the user shows many angry expressions, an advertisement with a relaxing effect is generated. This allows for more effective advertisements to be provided by adjusting advertisement content in real time based on the user's facial expressions.

[0068] The advertisement generation unit can analyze a user's past advertisement viewing history, extract the most effective advertisement elements, and reflect them in a new advertisement. For example, the advertisement generation unit collects a user's past advertisement viewing history, and the generation AI analyzes that data. For example, it extracts elements of advertisements that were viewed for a long time and reflects them in a new advertisement. The advertisement generation unit also collects a user's advertisement viewing history and inputs that data into the generation AI. The generation AI analyzes the advertisement viewing history and identifies the most effective advertisement elements. For example, if a particular design or message is effective, it reflects those elements in a new advertisement. The advertisement generation unit also analyzes a user's advertisement viewing history and extracts the most effective advertisement elements. For example, if a particular music or video is effective, it reflects those elements in a new advertisement. This makes it possible to provide more effective advertisements by generating new advertisements based on past advertisement viewing history.

[0069] The advertisement generation unit can analyze a user's device usage patterns and identify the optimal timing for displaying advertisements. For example, the advertisement generation unit collects a user's device usage patterns, and the generation AI analyzes the data. For example, the advertisement generation unit identifies the time periods when the user is most active and displays advertisements during those time periods. The advertisement generation unit also collects the user's device usage frequency and inputs the data into the generation AI. The generation AI analyzes the usage frequency and identifies the optimal timing for displaying advertisements. For example, advertisements are displayed during times when the user frequently uses the device. The advertisement generation unit also collects the applications used by the user's device and inputs the data into the generation AI. The generation AI analyzes the applications used and identifies the optimal timing for displaying advertisements. For example, advertisements are displayed during times when the user is using a specific application. This allows for more effective advertisements to be provided by identifying the optimal timing for displaying advertisements based on the user's device usage patterns.

[0070] The advertisement generation unit can analyze the user's emotions in real time while watching an advertisement and dynamically change the advertisement content based on the emotions. For example, the advertisement generation unit analyzes the user's emotions in real time while watching an advertisement and dynamically change the advertisement content based on the data. For example, if the user is excited, an energetic advertisement is displayed. The advertisement generation unit also collects the user's emotional data and inputs the data into the generation AI. The generation AI analyzes the emotional data and identifies the user's emotional state. For example, if the user is relaxed, an advertisement with a relaxing effect is displayed. The advertisement generation unit also collects the user's emotional changes in real time and inputs the data into the generation AI. The generation AI analyzes the emotional changes and dynamically change the advertisement content. For example, if the user is surprised, an advertisement that causes surprise is displayed. This makes it possible to provide more effective advertisements by dynamically changing the advertisement content based on the user's emotions.

[0071] The advertisement generation unit can add interactive elements to advertisements based on the user's hobbies and interests, allowing the user to interact directly with the advertisement. For example, the advertisement generation unit analyzes the user's hobbies and interests and generates an interactive advertisement based on the data. For example, if the user likes games, the advertisement generation unit generates an advertisement including game elements. The advertisement generation unit also collects the user's hobbies and interests and inputs the data into the generation AI. The generation AI analyzes the hobbies and interests and adds interactive elements. For example, if the user likes music, the advertisement generation unit generates an advertisement including a music quiz. The advertisement generation unit also collects the user's interest data and inputs the data into the generation AI. The generation AI analyzes the interest data and allows the user to interact with the advertisement. For example, if the user likes traveling, the advertisement generation unit generates an advertisement offering options for travel destinations. This allows for higher engagement by providing interactive advertisements based on the user's hobbies and interests.

[0072] The advertisement generation unit can analyze data on the user's friends and family and generate advertisements that utilize social networks. For example, the advertisement generation unit collects data on the user's friends and family, and the generation AI analyzes that data. For example, advertisements related to products that the friends and family are interested in are generated. The advertisement generation unit also collects the user's social network data and inputs that data into the generation AI. The generation AI analyzes the social network data and generates advertisements that the user's friends and family will be interested in. For example, advertisements related to products shared by friends are generated. The advertisement generation unit also collects the user's friend list and inputs that data into the generation AI. The generation AI analyzes the friend list and generates advertisements that the friends and family will be interested in. For example, advertisements related to products purchased by family members are generated. This makes it possible to achieve higher engagement by providing advertisements that utilize the user's social network.

[0073] The advertisement generation unit can analyze the voice tone of the user while watching an advertisement and adjust the advertisement content based on the voice tone. For example, the advertisement generation unit analyzes the voice tone of the user while watching an advertisement in real time and adjusts the advertisement content based on the data. For example, if the user speaks in an excited tone, an energetic advertisement is displayed. The advertisement generation unit also collects the user's voice tone data and inputs the data to the generation AI. The generation AI analyzes the voice tone data and identifies the user's emotional state. For example, if the user speaks in a relaxed tone, an advertisement with a relaxing effect is displayed. The advertisement generation unit also collects changes in the user's voice tone in real time and inputs the data to the generation AI. The generation AI analyzes the changes in voice tone and adjusts the advertisement content. For example, if the user speaks in a surprised tone, an advertisement that provokes surprise is displayed. This allows for more effective advertisements to be provided by adjusting the advertisement content based on the user's voice tone.

[0074] The ad adjustment unit can analyze the specific actions taken by users on ads and optimize the ad content based on the actions. The ad adjustment unit collects actions taken by users on ads, such as clicking or skipping, and the generation AI analyzes that data. For example, it improves elements of skipped ads. The ad adjustment unit also collects user action data and inputs that data into the generation AI. The generation AI analyzes the action data and optimizes the ad content. For example, it strengthens elements of ads with high click-through rates. The ad adjustment unit also collects user action history and inputs that data into the generation AI. The generation AI analyzes the action history and optimizes the ad content. For example, it reflects elements of shared ads in new ads. This makes it possible to provide more effective ads by optimizing the ad content based on user actions.

[0075] The ad adjustment unit can collect user feedback in real time and instantly change the ad content based on the feedback. For example, the ad adjustment unit collects user feedback in real time and the generation AI analyzes the data. For example, if a user gives positive feedback about an ad, those elements are strengthened. The ad adjustment unit also collects user feedback data and inputs that data into the generation AI. The generation AI analyzes the feedback data and instantly changes the ad content. For example, if there is a lot of negative feedback, the ad content is improved. The ad adjustment unit also collects user feedback in real time and inputs that data into the generation AI. The generation AI analyzes the feedback and instantly changes the ad content. For example, if a user likes a particular element, that element is strengthened. This makes it possible to provide more effective ads by instantly changing the ad content based on user feedback.

[0076] The advertisement adjustment unit can analyze the emotions of a user while viewing an advertisement in real time and dynamically adjust the advertisement content based on the emotions. For example, the advertisement adjustment unit analyzes the emotions of a user while viewing an advertisement in real time and dynamically changes the advertisement content based on the data. For example, if the user is excited, an energetic advertisement is displayed. The advertisement adjustment unit also collects user emotional data and inputs the data to the generation AI. The generation AI analyzes the emotional data and identifies the user's emotional state. For example, if the user is relaxed, an advertisement with a relaxing effect is displayed. The advertisement adjustment unit also collects user emotional changes in real time and inputs the data to the generation AI. The generation AI analyzes the emotional changes and dynamically changes the advertisement content. For example, if the user is surprised, an advertisement that causes surprise is displayed. This makes it possible to provide more effective advertisements by dynamically adjusting the advertisement content based on the user's emotions.

[0077] The ad adjustment unit provides an interface that allows users to customize advertisements, enabling users to select advertisement content themselves. The ad adjustment unit, for example, provides an interface that allows users to customize advertisement content. For example, it allows users to select products or services that interest them. The ad adjustment unit also collects user customization data and inputs that data to the generation AI. The generation AI analyzes the customization data and generates advertisement content selected by the user. For example, if the user selects a specific brand, it generates advertisements related to that brand. The ad adjustment unit also collects the user's customization history and inputs that data to the generation AI. The generation AI analyzes the customization history and generates advertisement content that the user prefers. For example, it reflects advertisement elements that the user has previously selected in new advertisements. This allows users to customize advertisement content, making it possible to provide more personalized advertisements.

[0078] The ad adjustment unit can cause the generation AI to generate multiple ad variations based on user feedback, allowing the user to select the variation they like best. The ad adjustment unit, for example, collects user feedback and causes the generation AI to generate multiple ad variations based on that data. For example, it generates ads with different designs and messages. The ad adjustment unit also collects user feedback data and inputs that data into the generation AI. The generation AI analyzes the feedback data and generates multiple ad variations. For example, it generates ads using different colors and fonts. The ad adjustment unit also causes the generation AI to generate multiple ad variations based on user feedback, allowing the user to select the variation they like best. For example, it reflects ad elements selected by the user in the new ad. This makes it possible to provide more personalized ads by providing multiple ad variations based on user feedback.

[0079] The ad tailoring unit can analyze performance data of ad campaigns and propose optimal strategies to maximize ROI. For example, the ad tailoring unit collects performance data of ad campaigns, and the generation AI analyzes the data. For example, it proposes optimal strategies based on click-through rates and conversion rates. The ad tailoring unit also collects performance data of ad campaigns and inputs the data into the generation AI. The generation AI analyzes the performance data and identifies optimal strategies to maximize ROI. For example, it proposes a strategy that focuses on a specific target audience. The ad tailoring unit also analyzes performance data of ad campaigns and proposes optimal timing and frequency of ad delivery. For example, it proposes a strategy to deliver ads during times when click-through rates are high. This makes it possible to maximize ROI by proposing optimal strategies based on the performance data of ad campaigns.

[0080] The ad tailoring unit can optimize the target audience based on the performance data of the advertising campaign. For example, the ad tailoring unit analyzes the performance data of the advertising campaign and identifies the most effective target audience. For example, focusing on a specific age group or region. The ad tailoring unit also collects performance data of the advertising campaign and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes the target audience. For example, focusing on users with specific interests. The ad tailoring unit also proposes a strategy to optimize the target audience based on the performance data of the advertising campaign. For example, delivering advertisements focused on a specific demographic. This makes it possible to provide more effective advertisements by optimizing the target audience based on the performance data of the advertising campaign.

[0081] The ad adjustment unit can optimize ad creatives based on performance data of ad campaigns. For example, the ad adjustment unit analyzes performance data of ad campaigns and identifies the most effective ad creatives. For example, if a particular design or message is effective, it strengthens those elements. The ad adjustment unit also collects performance data of ad campaigns and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes ad creatives. For example, it generates ads that use particular colors and fonts. The ad adjustment unit also proposes a strategy to optimize ad creatives based on performance data of ad campaigns. For example, it delivers ads with strengthened particular visual elements. In this way, by optimizing ad creatives based on performance data of ad campaigns, more effective ads can be provided.

[0082] The ad adjustment unit can optimize the timing of ad delivery based on performance data of the ad campaign. For example, the ad adjustment unit analyzes performance data of the ad campaign and identifies the most effective timing for ad delivery. For example, it delivers ads during times when click-through rates are high. The ad adjustment unit also collects performance data of the ad campaign and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes the timing of ad delivery. For example, it delivers ads during times when users are most active. The ad adjustment unit also proposes a strategy for optimizing the timing of ad delivery based on the performance data of the ad campaign. For example, it delivers ads during specific events. In this way, by optimizing the timing of ad delivery based on the performance data of the ad campaign, more effective ads can be provided.

[0083] The ad adjustment unit can optimize the frequency of ad delivery based on performance data of the ad campaign. For example, the ad adjustment unit analyzes performance data of the ad campaign and identifies the most effective frequency of ad delivery. For example, it identifies the frequency with a high click-through rate or conversion rate. The ad adjustment unit also collects performance data of the ad campaign and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes the frequency of ad delivery. For example, it identifies how many times per day or how many times per week to display the ad. The ad adjustment unit also proposes a strategy to optimize the frequency of ad delivery based on the performance data of the ad campaign. For example, it may deliver ads intensively during specific time periods. In this way, by optimizing the frequency of ad delivery based on the performance data of the ad campaign, more effective ads can be provided.

[0084] The advertising tailoring unit can optimize advertising delivery channels based on advertising campaign performance data. For example, the advertising tailoring unit analyzes advertising campaign performance data and identifies the most effective advertising delivery channel. For example, if a specific social media platform or website is effective, the advertising tailoring unit focuses advertising on that channel. The advertising tailoring unit also collects advertising campaign performance data and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes advertising delivery channels. For example, if a specific search engine or email marketing is effective, the advertising tailoring unit delivers ads to that channel. The advertising tailoring unit also proposes a strategy to optimize advertising delivery channels based on advertising campaign performance data. For example, delivering ads to specific devices or applications. In this way, by optimizing advertising delivery channels based on advertising campaign performance data, more effective advertising can be provided.

[0085] The ad adjustment unit monitors ad performance in real time and can adjust ad content and delivery methods as needed. For example, the ad adjustment unit collects ad performance data in real time and the generation AI analyzes that data. For example, if the click-through rate or conversion rate drops, the ad content is changed. The ad adjustment unit also collects ad performance data in real time and inputs that data into the generation AI. The generation AI analyzes the performance data and adjusts ad content and delivery methods as needed. For example, it changes ad content during specific time periods. The ad adjustment unit also monitors ad performance in real time and proposes strategies to adjust ad content and delivery methods as needed. For example, it delivers ads to specific devices or applications. This makes it possible to provide more effective ads by monitoring ad performance in real time and adjusting ad content and delivery methods as needed.

[0086] The ad adjustment unit can optimize ad creatives in real time based on ad performance data. For example, the ad adjustment unit collects ad performance data in real time and the generation AI analyzes that data. For example, if the click-through rate or conversion rate drops, the ad creative is changed. The ad adjustment unit also collects ad performance data in real time and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes the ad creatives in real time. For example, if a particular design or message is effective, those elements are enhanced. The ad adjustment unit also proposes a strategy to optimize the ad creatives in real time based on the ad performance data. For example, an ad with specific visual elements enhanced is delivered. This makes it possible to provide more effective ads by optimizing the ad creatives in real time based on the ad performance data.

[0087] The ad adjustment unit can optimize the timing of ad delivery in real time based on ad performance data. For example, the ad adjustment unit collects ad performance data in real time, and the generation AI analyzes that data. For example, if the click-through rate or conversion rate drops, the timing of ad delivery is changed. The ad adjustment unit also collects ad performance data in real time and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes the timing of ad delivery in real time. For example, it delivers ads during times when users are most active. The ad adjustment unit also proposes a strategy to optimize the timing of ad delivery in real time based on ad performance data. For example, it delivers ads during specific events. This makes it possible to provide more effective ads by optimizing the timing of ad delivery in real time based on ad performance data.

[0088] The ad adjustment unit can optimize ad delivery channels in real time based on ad performance data. For example, the ad adjustment unit collects ad performance data in real time, and the generation AI analyzes that data. For example, if the click-through rate or conversion rate drops, the ad delivery channel is changed. The ad adjustment unit also collects ad performance data in real time and inputs that data into the generation AI. The generation AI analyzes the performance data and optimizes ad delivery channels in real time. For example, if a specific social media platform or website is effective, the ad adjustment unit will focus ads on that channel. The ad adjustment unit also proposes a strategy to optimize ad delivery channels in real time based on ad performance data. For example, delivering ads to specific devices or applications. This makes it possible to provide more effective ads by optimizing ad delivery channels in real time based on ad performance data.

[0089] The ad adjustment unit can use the emotion estimation function to monitor ad performance in real time and continuously search for optimal ad content. The ad adjustment unit, for example, uses the emotion estimation function to develop a system that monitors ad performance in real time. For example, it analyzes a user's facial expressions and voice and calculates an emotion score. The ad adjustment unit also collects ad performance data in real time and inputs that data into the generation AI. The generation AI analyzes the performance data and continuously searches for optimal ad content. For example, it identifies ad content with high click-through rates and conversion rates. The ad adjustment unit also uses the emotion estimation function to monitor ad performance in real time and proposes a strategy to continuously search for optimal ad content. For example, it adjusts ad content based on the user's emotional state. In this way, by using the emotion estimation function to monitor ad performance in real time and continuously searching for optimal ad content, more effective ads can be provided.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The data collection unit collects the user's voice data, the analysis unit analyzes the voice data to infer the user's interests, and the advertisement generation unit generates advertisements based on the interests. For example, the data collection unit collects the user's voice data in real time, and the generation AI analyzes the voice. For example, the interest and concern are inferred from what the user is saying and advertisements are generated based on that. The data collection unit also collects the user's voice tone and inputs that data into the generation AI. The generation AI analyzes the voice tone and infers the user's emotional state. For example, if the user is speaking in an excited tone, an energetic advertisement is generated. The data collection unit also converts the user's voice data into text and inputs the text data into the generation AI. The generation AI analyzes the text data and infers the user's interests. For example, if the user is talking about a specific brand, an advertisement related to that brand is generated. This allows for more targeted advertisements to be provided by generating advertisements based on the user's voice data.

[0092] The data collection unit collects the user's purchase history and return history, the analysis unit analyzes the purchase history and return history to identify products that the user does not like, and the advertisement generation unit can generate advertisements that avoid the disliked products. The data collection unit, for example, collects the user's purchase history and return history, and the generation AI analyzes the data. For example, an advertisement is generated that avoids products that are frequently returned. The data collection unit also collects the user's purchase history and inputs that data into the generation AI. The generation AI analyzes the purchase history to identify products that the user does not like. For example, an advertisement is generated that avoids products that have been purchased but returned in the past. The data collection unit also collects the user's return history and inputs that data into the generation AI. The generation AI analyzes the return history to identify products that the user does not like. For example, an advertisement is generated that avoids products with low ratings. This allows for more effective advertisements to be provided by generating advertisements that avoid products that the user does not like.

[0093] The data collection unit collects facial expressions while the user is viewing an advertisement, the analysis unit analyzes the expressions to estimate the user's emotional state, and the advertisement adjustment unit adjusts advertisement content in real time based on the user's emotional state. For example, the data collection unit monitors the user's facial expressions while viewing an advertisement using a camera, and the generation AI analyzes the expressions. For example, if the user smiles frequently, positive advertisements are continuously displayed. The data collection unit also collects the user's facial expression data and inputs the data into the generation AI. The generation AI analyzes the facial expression data and estimates the user's emotional state. For example, if the user shows many surprised expressions, an advertisement that induces surprise is generated. The data collection unit also collects changes in the user's facial expression in real time and inputs the data into the generation AI. The generation AI analyzes the changes in facial expression and estimates the user's emotional state. For example, if the user shows many angry expressions, an advertisement with a relaxing effect is generated. This allows for more effective advertisements to be provided by adjusting advertisement content in real time based on the user's facial expressions.

[0094] The advertisement generation unit can analyze a user's past advertisement viewing history, extract the most effective advertisement elements, and reflect them in a new advertisement. For example, the advertisement generation unit collects a user's past advertisement viewing history, and the generation AI analyzes that data. For example, it extracts elements of advertisements that were viewed for a long time and reflects them in a new advertisement. The advertisement generation unit also collects a user's advertisement viewing history and inputs that data into the generation AI. The generation AI analyzes the advertisement viewing history and identifies the most effective advertisement elements. For example, if a particular design or message is effective, it reflects those elements in a new advertisement. The advertisement generation unit also analyzes a user's advertisement viewing history and extracts the most effective advertisement elements. For example, if a particular music or video is effective, it reflects those elements in a new advertisement. This makes it possible to provide more effective advertisements by generating new advertisements based on past advertisement viewing history.

[0095] The advertisement generation unit can analyze a user's device usage patterns and identify the optimal timing for displaying advertisements. For example, the advertisement generation unit collects a user's device usage patterns, and the generation AI analyzes the data. For example, the advertisement generation unit identifies the time periods when the user is most active and displays advertisements during those time periods. The advertisement generation unit also collects the user's device usage frequency and inputs the data into the generation AI. The generation AI analyzes the usage frequency and identifies the optimal timing for displaying advertisements. For example, advertisements are displayed during times when the user frequently uses the device. The advertisement generation unit also collects the applications used by the user's device and inputs the data into the generation AI. The generation AI analyzes the applications used and identifies the optimal timing for displaying advertisements. For example, advertisements are displayed during times when the user is using a specific application. This allows for more effective advertisements to be provided by identifying the optimal timing for displaying advertisements based on the user's device usage patterns.

[0096] The advertisement generation unit can analyze the user's emotions in real time while watching an advertisement and dynamically change the advertisement content based on the emotions. For example, the advertisement generation unit analyzes the user's emotions in real time while watching an advertisement and dynamically change the advertisement content based on the data. For example, if the user is excited, an energetic advertisement is displayed. The advertisement generation unit also collects the user's emotional data and inputs the data into the generation AI. The generation AI analyzes the emotional data and identifies the user's emotional state. For example, if the user is relaxed, an advertisement with a relaxing effect is displayed. The advertisement generation unit also collects the user's emotional changes in real time and inputs the data into the generation AI. The generation AI analyzes the emotional changes and dynamically change the advertisement content. For example, if the user is surprised, an advertisement that causes surprise is displayed. This makes it possible to provide more effective advertisements by dynamically changing the advertisement content based on the user's emotions.

[0097] The advertisement generation unit can add interactive elements to advertisements based on the user's hobbies and interests, allowing the user to interact directly with the advertisement. For example, the advertisement generation unit analyzes the user's hobbies and interests and generates an interactive advertisement based on the data. For example, if the user likes games, the advertisement generation unit generates an advertisement including game elements. The advertisement generation unit also collects the user's hobbies and interests and inputs the data into the generation AI. The generation AI analyzes the hobbies and interests and adds interactive elements. For example, if the user likes music, the advertisement generation unit generates an advertisement including a music quiz. The advertisement generation unit also collects the user's interest data and inputs the data into the generation AI. The generation AI analyzes the interest data and allows the user to interact with the advertisement. For example, if the user likes traveling, the advertisement generation unit generates an advertisement offering options for travel destinations. This allows for higher engagement by providing interactive advertisements based on the user's hobbies and interests.

[0098] The advertisement generation unit can analyze data on the user's friends and family and generate advertisements that utilize social networks. For example, the advertisement generation unit collects data on the user's friends and family, and the generation AI analyzes that data. For example, advertisements related to products that the friends and family are interested in are generated. The advertisement generation unit also collects the user's social network data and inputs that data into the generation AI. The generation AI analyzes the social network data and generates advertisements that the user's friends and family will be interested in. For example, advertisements related to products shared by friends are generated. The advertisement generation unit also collects the user's friend list and inputs that data into the generation AI. The generation AI analyzes the friend list and generates advertisements that the friends and family will be interested in. For example, advertisements related to products purchased by family members are generated. This makes it possible to achieve higher engagement by providing advertisements that utilize the user's social network.

[0099] The advertisement generation unit can analyze the voice tone of the user while watching an advertisement and adjust the advertisement content based on the voice tone. For example, the advertisement generation unit analyzes the voice tone of the user while watching an advertisement in real time and adjusts the advertisement content based on the data. For example, if the user speaks in an excited tone, an energetic advertisement is displayed. The advertisement generation unit also collects the user's voice tone data and inputs the data to the generation AI. The generation AI analyzes the voice tone data and identifies the user's emotional state. For example, if the user speaks in a relaxed tone, an advertisement with a relaxing effect is displayed. The advertisement generation unit also collects changes in the user's voice tone in real time and inputs the data to the generation AI. The generation AI analyzes the changes in voice tone and adjusts the advertisement content. For example, if the user speaks in a surprised tone, an advertisement that provokes surprise is displayed. This allows for more effective advertisements to be provided by adjusting the advertisement content based on the user's voice tone.

[0100] The ad adjustment unit can analyze the specific actions taken by users on ads and optimize the ad content based on the actions. The ad adjustment unit collects actions taken by users on ads, such as clicking or skipping, and the generation AI analyzes that data. For example, it improves elements of skipped ads. The ad adjustment unit also collects user action data and inputs that data into the generation AI. The generation AI analyzes the action data and optimizes the ad content. For example, it strengthens elements of ads with high click-through rates. The ad adjustment unit also collects user action history and inputs that data into the generation AI. The generation AI analyzes the action history and optimizes the ad content. For example, it reflects elements of shared ads in new ads. This makes it possible to provide more effective ads by optimizing the ad content based on user actions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The data collection unit collects individual data about the user. For example, the data collection unit collects the user's browsing history, search history, purchase history, social media activity, etc. The data collection unit can also collect this data in real time. For example, the data collection unit can collect the browsing history in real time while the user is browsing a website. Step 2: The analysis unit analyzes the individual data collected by the data collection unit. For example, the generation AI uses data mining technology to analyze the user's interests. The analysis unit can also analyze the data using machine learning algorithms. For example, the generation AI analyzes the user's purchasing history and identifies products that the user is interested in. Step 3: The advertisement generation unit generates personalized advertisements based on the data analyzed by the analysis unit. For example, the generation AI generates advertisements based on the user's interests. The advertisement generation unit can also generate video advertisements. For example, the generation AI generates video advertisements related to products recently searched for by the user. Step 4: The ad adjustment unit adjusts the ads generated by the ad generation unit in real time. For example, the generation AI adjusts the ad content based on user feedback and interactions. The ad adjustment unit can also adjust the timing and frequency of ad delivery by having the generation AI analyze ad performance data. For example, the generation AI analyzes ad click rates and changes ad content during times when click rates are low.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 data collection unit that collects individual data of users; an analysis unit that analyzes the individual data collected by the data collection unit; an advertisement generation unit that generates a personalized advertisement based on the data analyzed by the analysis unit; an advertisement adjustment unit that adjusts the advertisement generated by the advertisement generation unit in real time; A system characterized by:

2. The data collection unit Collect real-time location information of users, The analysis unit Analyzing the location information to generate advertisements relevant to that location 2. The system of claim 1.

3. The data collection unit Collecting user voice data; The analysis unit Analyzing the voice data to estimate the user's interests and concerns; The advertisement generation unit Generate advertisements based on those interests 2. The system of claim 1.

4. The advertisement generation unit Analyze the user's past ad viewing history, Extract the most effective advertising elements and incorporate them into new ads 2. The system of claim 1.

5. The advertisement adjustment unit Analyze the specific actions users take in response to ads, Optimize ad content based on said actions 2. The system of claim 1.

6. The advertisement adjustment unit Analyze users' emotions in real time while they are watching ads, Dynamically adjusting advertising content based on the emotion 2. The system of claim 1.

7. The advertisement adjustment unit Using the emotion estimation function, Monitor your ad performance in real time Continuously search for optimal advertising content 2. The system of claim 1.

Images