system

Abstract

A system in accordance with an embodiment is directed to automating the generation and presentation of advertising creatives to efficiently manage advertising.SOLUTION: A system according to an embodiment includes a generation AI, an advertisement display unit, and a reward giving unit. The generation AI automatically generates an advertisement creative of an image or a moving image using the generation AI. The advertisement posting unit posts the advertisement generated by the generation AI on the MDM installed smartphone. The reward giving unit gives a reward based on the advertisement displayed by the advertisement display unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of generating advertising creatives and displaying ads was often done manually, which led to issues of inefficiency.

[0005] The system according to the embodiment aims to automate the generation and display of advertising creatives and to efficiently manage advertising. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an advertisement display unit, and a reward granting unit. The generation AI automatically generates advertisement creatives such as images and videos using the generation AI. The advertisement displaying unit displays advertisements generated by the generation AI on smartphones with MDM installed. The reward granting unit grants rewards based on the advertisements displayed by the advertisement displaying unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the generation and display of advertising creatives, enabling efficient advertising management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The advertising display system according to an embodiment of the present invention uses AI to automatically generate image and video advertising creatives, displaying advertisements on smartphones with MDM installed for each hour of use and providing rewards. This allows the advertising display system to gently limit excessive smartphone use among young people through periodic advertisements, strengthening the advertising business and increasing subscriber numbers.

[0029] An advertisement display system according to an embodiment includes a generation AI, an advertisement display unit, and a reward granting unit. The generation AI automatically generates advertisement creatives for images and videos. For example, the generation AI receives prompts containing user instructions as input and generates appropriate advertisement creatives. The generation AI can also generate advertisements using technologies such as deep learning and GAN (generative artificial network). The generation AI can also adapt the content and format of the advertisement creatives to images, videos, text advertisements, etc. The advertisement displaying unit displays advertisements generated by the generation AI on smartphones with MDM installed. For example, the advertisements generated for each hour of use on smartphones with MDM installed can be automatically displayed. The advertisement displaying unit can also analyze smartphone usage patterns and display advertisements at optimal times. The advertisement displaying unit can also display advertisements that are highly relevant to the user's use of a specific app based on the user's app usage history. The reward granting unit grants rewards based on the advertisements displayed by the advertisement displaying unit. For example, the reward granting unit may grant a basic fee of 990 yen for 3GB of data in PayPay based on an advertisement with a CPC (cost per click) of 990 yen. The reward granting unit may also analyze user click data and dynamically adjust the optimal reward. The reward granting unit may also provide benefits tailored to the user's purchasing habits based on PayPay usage history. As a result, the advertising display system according to the embodiment is capable of automatically generating advertising creatives using generation AI, displaying advertisements on smartphones with MDM installed, and granting rewards.

[0030] Generative AI can analyze a user's past ad click history and generate ad creatives optimized for the user. For example, generative AI can analyze a user's past ad click history and extract the characteristics of ads with high click rates. For example, it can identify that certain colors, designs, and messages are effective and generate new ad creatives based on that. Generative AI can also identify a user's interests based on their past click history and generate ad creatives tailored to those interests. For example, for a user interested in sports equipment, it can generate ads for the latest sports gear. Generative AI can also analyze click history and learn patterns of ads to which the user has responded in the past. For example, it can generate ads with high click rates during specific times of the day or on specific days of the week, providing ads tailored to the user's behavioral patterns. This allows it to generate ad creatives optimized based on the user's past ad click history.

[0031] Generative AI can analyze a user's current context in real time and generate advertisements based on that. For example, generative AI can analyze a user's location information and generate advertisements related to that location. For example, if a user is in a shopping mall, it can display advertisements about sales at stores in the mall. Generative AI can also analyze a user's current activity and generate advertisements related to that. For example, if a user is running, it can display advertisements for sports drinks and running shoes. Generative AI can also analyze a user's context in real time and generate optimal advertisements. For example, if a user is at a movie theater, it can display advertisements for trailers and related goods for the movie currently showing. This makes it possible to generate advertisements based on a user's current context in real time.

[0032] The generation AI can generate advertisements that are compatible with different media formats and meet the user's preferences. For example, the generation AI generates audio advertisements and customizes the audio message according to the user's preferences. For example, if a user likes music, it generates audio advertisements related to music. The generation AI can also generate interactive advertisements that allow users to interact with the advertisements. For example, it can generate quiz-style advertisements that allow users to receive rewards by answering questions. The generation AI can also generate advertisements that are compatible with different media formats and deliver them according to the user's preferences. For example, it can generate video and animated advertisements to provide visually appealing advertisements. This makes it possible to generate advertisements that are compatible with different media formats and meet the user's preferences.

[0033] Generative AI can generate advertising creatives that correspond to different languages ​​and cultures, appealing to international users. Generative AI can, for example, generate advertising creatives that correspond to different languages. For example, it can generate advertisements tailored to the user's native language, such as English, French, or Chinese. Generative AI can also generate advertising creatives that correspond to different cultures, appealing to international users. For example, it can generate advertisements that take cultural backgrounds and customs into consideration. Generative AI can also generate advertisements that reflect regional characteristics for international users. For example, it can generate advertisements tailored to events or holidays in a specific region. This allows it to generate advertising creatives that correspond to different languages ​​and cultures, appealing to international users.

[0034] The advertisement display unit can analyze a user's smartphone usage patterns and display advertisements at the optimal timing. For example, an MDM system analyzes a user's smartphone usage patterns and displays advertisements at the optimal timing based on usage frequency and time of day. For example, advertisements are displayed during times when the user frequently uses their smartphone. The advertisement display unit also displays advertisements that are highly relevant to the use of a specific app based on the user's app usage history. For example, when the user is using a game app, game-related advertisements are displayed. The advertisement display unit also analyzes smartphone usage patterns and displays advertisements at times when the user is most likely to respond to advertisements. For example, advertisements are displayed during times when the user is relaxing. This allows advertisements to be displayed at the optimal timing based on the user's smartphone usage patterns.

[0035] The advertisement display unit can display highly relevant advertisements while the user is using a specific app based on the user's app usage history. For example, an MDM system analyzes the user's app usage history and displays highly relevant advertisements while the user is using a specific app. For example, when the user is using a shopping app, advertisements for related products are displayed. The advertisement display unit also displays advertisements tailored to the user's interests based on the app usage history. For example, when the user is using a fitness app, fitness-related advertisements are displayed. The advertisement display unit also displays advertisements to which the user is most likely to respond while using a specific app. For example, when the user is using a news app, advertisements related to the latest news are displayed. This makes it possible to display highly relevant advertisements based on the user's app usage history.

[0036] The advertisement display unit can cooperate with other smartphone functions to provide a more interactive advertising experience. For example, the MDM system cooperates with the smartphone's camera function to display relevant advertisements when the user is using the camera. For example, an advertisement for a photo printing service is displayed while the user is taking a photo. The advertisement display unit also cooperates with the GPS function to display advertisements based on the user's current location. For example, if the user is near a specific store, sales information for that store is displayed. The advertisement display unit also cooperates with other smartphone functions to provide an interactive advertising experience. For example, when the user is listening to music, an advertisement for a related music event is displayed. This cooperates with other smartphone functions to provide an interactive advertising experience.

[0037] The ad display unit is compatible with different devices and can display ads across multiple devices. For example, the ad display unit's MDM system can also support tablets and smartwatches, displaying ads across multiple devices. For example, the same ad can be displayed when the user is using a tablet. The ad display unit also links ads across different devices to provide a seamless advertising experience. For example, when a user taps on an ad displayed on a smartwatch, detailed information can be displayed on a smartphone. The ad display unit also builds an ad display system compatible with multiple devices, providing a consistent advertising experience regardless of which device the user is using. For example, the same ad campaign can be deployed on smartphones, tablets, and smartwatches. This allows the ad display unit to support different devices and display ads across multiple devices.

[0038] The reward granting unit can analyze CPC click data and dynamically adjust the optimal reward for each user. For example, the reward granting unit analyzes CPC click data and builds a system that dynamically adjusts the optimal reward for each user. For example, additional data capacity is provided to users with a high click rate. The reward granting unit also customizes the type and amount of reward based on the user's click data. For example, points or gift cards are provided to users who click many times on a specific advertisement. The reward granting unit also analyzes click data in real time and dynamically adjusts rewards according to user behavior. For example, a user who clicks many times during a specific time period is provided with a special benefit for that time period. This makes it possible to dynamically adjust the optimal reward for each user based on the CPC click data.

[0039] The reward granting unit can provide rewards tailored to a user's purchasing habits based on their PayPay usage history. For example, the reward granting unit analyzes PayPay usage history and builds a system that provides rewards tailored to a user's purchasing habits. For example, a user who frequently purchases products in a specific category is provided with rewards related to that category. The reward granting unit also provides personalized rewards based on the user's PayPay usage history. For example, a user who frequently uses PayPay at a specific store is provided with coupons that can be used at that store. The reward granting unit also analyzes PayPay usage data and customizes rewards based on the user's purchasing patterns. For example, a user who frequently uses PayPay on weekends is provided with weekend-only rewards. This makes it possible to provide rewards tailored to a user's purchasing habits based on their PayPay usage history.

[0040] The reward granting unit can also make CPC rewards compatible with other digital currencies and point systems. For example, the reward granting unit builds a system that makes CPC rewards compatible with other digital currencies and point systems. For example, it allows users to earn Rakuten points or Amazon gift cards by clicking. The reward granting unit also provides multiple reward options that users can choose from. For example, it allows users to choose from PayPay points, Rakuten points, and Amazon gift cards as click rewards. The reward granting unit also develops a system that works with other digital currencies and point systems and allows users to freely select rewards. For example, it allows users to customize rewards to suit their preferences. This makes it possible to make CPC rewards compatible with other digital currencies and point systems.

[0041] The reward granting unit can provide CPC rewards as multiple options that users can choose from. For example, the reward granting unit provides CPC rewards as multiple options and builds a system that allows users to choose from. For example, the reward granting unit allows users to choose from data capacity, music streaming service subscriptions, movie tickets, etc. The reward granting unit also allows users to select rewards that suit their needs. For example, the reward granting unit provides additional data capacity to users who need data capacity, and music streaming service subscriptions to users who want to enjoy entertainment. The reward granting unit also develops a system that provides multiple reward options and allows users to freely choose. For example, the reward granting unit allows users to choose from data capacity, music streaming services, and movie tickets as click rewards. This makes it possible to provide CPC rewards as multiple options that users can choose from.

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

[0043] The advertisement display system may further include a health management unit that monitors the user's health status. For example, the health management unit collects data from a smartwatch or fitness tracker and analyzes the user's heart rate, number of steps, and sleep patterns. This allows health-related advertisements to be displayed based on the user's health status. For example, if the user is not getting enough exercise, advertisements for fitness gyms may be displayed. Also, if the user is not getting enough sleep, advertisements for sleep-improvement products and services may be displayed. Furthermore, the health management unit may provide personalized health advice based on the user's health data. For example, if the user is feeling stressed, advertisements suggesting relaxation methods or stress-relief products may be displayed.

[0044] The advertisement display system may further include a purchase history analysis unit that analyzes a user's purchase history. For example, the purchase history analysis unit collects data on products purchased by the user in the past and analyzes the user's purchasing trends. This makes it possible to display highly relevant advertisements based on the user's purchase history. For example, advertisements related to product categories frequently purchased by the user may be displayed. The purchase history analysis unit may also learn the user's purchasing patterns and display advertisements tailored to specific times or events. For example, if a user purchases a specific product every Christmas, Christmas-related advertisements may be displayed to coincide with that time of year. Furthermore, the purchase history analysis unit may provide special offers and discount information based on the user's purchase history. For example, an advertisement offering a discount coupon for a product purchased by the user in the past may be displayed.

[0045] The advertisement display system may further include a social media analysis unit that analyzes the user's social media activity. For example, the social media analysis unit may analyze the user's posts and the accounts the user follows to identify the user's interests. This allows highly relevant advertisements to be displayed based on the user's social media activity. For example, if the user frequently posts about a particular brand or product, advertisements for that brand or product may be displayed. The social media analysis unit may also analyze the activities of the user's followers and friends to display advertisements that will influence the user. For example, if a user's friend purchases a particular product, an advertisement related to that product may be displayed. Furthermore, the social media analysis unit may provide information about special offers and campaigns based on the user's social media activity. For example, if a user posts using a particular hashtag, an advertisement for a campaign related to that hashtag may be displayed.

[0046] The advertisement display system may further include a location information analysis unit that analyzes the user's location information. For example, the location information analysis unit may analyze the user's current location and display advertisements related to that location. For example, if the user is in a shopping mall, sale information for stores in the mall may be displayed. Also, if the user is in a tourist spot, advertisements related to the tourist spot may be displayed. Furthermore, the location information analysis unit may analyze the user's movement patterns and display advertisements that are optimal for the user's location. For example, if the user is commuting, advertisements related to the user's commuting route may be displayed. Also, if the user is at home, advertisements for products and services that can be used at home may be displayed. In this way, optimal advertisements may be displayed based on the user's location information.

[0047] The advertisement display system may further include a device usage history analysis unit that analyzes a user's device usage history. For example, the device usage history analysis unit collects data on devices used by the user in the past and analyzes the user's device usage patterns. This allows for displaying highly relevant advertisements based on the user's device usage history. For example, advertisements related to devices frequently used by the user may be displayed. The device usage history analysis unit may also learn the user's device usage patterns and display advertisements that are optimal for the use of a specific device. For example, when the user is using a tablet, advertisements for accessories related to the tablet may be displayed. Furthermore, the device usage history analysis unit may provide special offers and discount information based on the user's device usage history. For example, an advertisement offering a discount coupon for a device previously purchased by the user may be displayed.

[0048] The advertisement display system may further include an app usage history analysis unit that analyzes a user's app usage history. For example, the app usage history analysis unit collects data on apps used by the user in the past and analyzes the user's app usage patterns. This makes it possible to display highly relevant advertisements based on the user's app usage history. For example, advertisements related to apps frequently used by the user may be displayed. The app usage history analysis unit may also learn the user's app usage patterns and display advertisements that are optimal for specific app usage. For example, when a user uses a fitness app, advertisements for fitness-related products and services may be displayed. Furthermore, the app usage history analysis unit may provide rewards and discount information based on the user's app usage history. For example, advertisements offering rewards for apps used by the user in the past may be displayed.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: Generative AI automatically generates image and video ad creatives. For example, generative AI receives prompts containing instructions from users as input and generates appropriate ad creatives. Generative AI can also generate ads using technologies such as deep learning and GAN (generative adversarial networks). Furthermore, generative AI can adapt the content and format of ad creatives to images, videos, text ads, etc. Step 2: The ad display unit displays the ads generated by the generation AI on smartphones with MDM installed. For example, the ads generated for each hour of use are automatically displayed on smartphones with MDM installed. The ad display unit can also analyze smartphone usage patterns and display ads at optimal times. Furthermore, the ad display unit can display highly relevant ads while using a specific app based on the user's app usage history. Step 3: The reward granting unit grants rewards based on the advertisements posted by the advertisement posting unit. For example, the reward granting unit grants a basic fee of 990 yen for 3GB of data in PayPay based on an advertisement with a CPC (cost per click) of 990 yen. The reward granting unit can also analyze user click data and dynamically adjust the optimal reward. Furthermore, the reward granting unit can provide benefits tailored to the user's purchasing habits based on their PayPay usage history.

[0051] (Example 2) The advertising display system according to an embodiment of the present invention uses AI to automatically generate image and video advertising creatives, displaying advertisements on smartphones with MDM installed for each hour of use and providing rewards. This allows the advertising display system to gently limit excessive smartphone use among young people through periodic advertisements, strengthening the advertising business and increasing subscriber numbers.

[0052] An advertisement display system according to an embodiment includes a generation AI, an advertisement display unit, and a reward granting unit. The generation AI automatically generates advertisement creatives for images and videos. For example, the generation AI receives prompts containing user instructions as input and generates appropriate advertisement creatives. The generation AI can also generate advertisements using technologies such as deep learning and GAN (generative artificial network). The generation AI can also adapt the content and format of the advertisement creatives to images, videos, text advertisements, etc. The advertisement displaying unit displays advertisements generated by the generation AI on smartphones with MDM installed. For example, the advertisements generated for each hour of use on smartphones with MDM installed can be automatically displayed. The advertisement displaying unit can also analyze smartphone usage patterns and display advertisements at optimal times. The advertisement displaying unit can also display advertisements that are highly relevant to the user's use of a specific app based on the user's app usage history. The reward granting unit grants rewards based on the advertisements displayed by the advertisement displaying unit. For example, the reward granting unit may grant a basic fee of 990 yen for 3GB of data in PayPay based on an advertisement with a CPC (cost per click) of 990 yen. The reward granting unit may also analyze user click data and dynamically adjust the optimal reward. The reward granting unit may also provide benefits tailored to the user's purchasing habits based on PayPay usage history. As a result, the advertising display system according to the embodiment is capable of automatically generating advertising creatives using generation AI, displaying advertisements on smartphones with MDM installed, and granting rewards.

[0053] Generative AI can analyze a user's past ad click history and generate ad creatives optimized for the user. For example, generative AI can analyze a user's past ad click history and extract the characteristics of ads with high click rates. For example, it can identify that certain colors, designs, and messages are effective and generate new ad creatives based on that. Generative AI can also identify a user's interests based on their past click history and generate ad creatives tailored to those interests. For example, for a user interested in sports equipment, it can generate ads for the latest sports gear. Generative AI can also analyze click history and learn patterns of ads to which the user has responded in the past. For example, it can generate ads with high click rates during specific times of the day or on specific days of the week, providing ads tailored to the user's behavioral patterns. This allows it to generate ad creatives optimized based on the user's past ad click history.

[0054] Generative AI can analyze a user's current context in real time and generate advertisements based on that. For example, generative AI can analyze a user's location information and generate advertisements related to that location. For example, if a user is in a shopping mall, it can display advertisements about sales at stores in the mall. Generative AI can also analyze a user's current activity and generate advertisements related to that. For example, if a user is running, it can display advertisements for sports drinks and running shoes. Generative AI can also analyze a user's context in real time and generate optimal advertisements. For example, if a user is at a movie theater, it can display advertisements for trailers and related goods for the movie currently showing. This makes it possible to generate advertisements based on a user's current context in real time.

[0055] The generation AI can use its emotion estimation function to estimate a user's current emotional state and generate advertising creatives that best match that emotion. For example, the generation AI uses its emotion estimation function to analyze a user's facial expressions and voice to estimate their current emotional state. For example, if a user is relaxed, it generates advertisements for products and services that have a relaxing effect. The generation AI also adjusts the tone and message of the advertisement based on the user's emotional state. For example, if a user is feeling stressed, it generates advertisements related to relaxation and stress relief. The generation AI also generates advertising creatives that best match the user's emotions based on the emotion estimation data. For example, if a user is happy, it generates advertisements with positive messages and upbeat designs. This makes it possible to generate optimal advertising creatives based on the user's emotional state.

[0056] The generation AI can generate advertisements that are compatible with different media formats and meet the user's preferences. For example, the generation AI generates audio advertisements and customizes the audio message according to the user's preferences. For example, if a user likes music, it generates audio advertisements related to music. The generation AI can also generate interactive advertisements that allow users to interact with the advertisements. For example, it can generate quiz-style advertisements that allow users to receive rewards by answering questions. The generation AI can also generate advertisements that are compatible with different media formats and deliver them according to the user's preferences. For example, it can generate video and animated advertisements to provide visually appealing advertisements. This makes it possible to generate advertisements that are compatible with different media formats and meet the user's preferences.

[0057] Generative AI can generate advertising creatives that correspond to different languages ​​and cultures, appealing to international users. Generative AI can, for example, generate advertising creatives that correspond to different languages. For example, it can generate advertisements tailored to the user's native language, such as English, French, or Chinese. Generative AI can also generate advertising creatives that correspond to different cultures, appealing to international users. For example, it can generate advertisements that take cultural backgrounds and customs into consideration. Generative AI can also generate advertisements that reflect regional characteristics for international users. For example, it can generate advertisements tailored to events or holidays in a specific region. This allows it to generate advertising creatives that correspond to different languages ​​and cultures, appealing to international users.

[0058] The generation AI can use its emotion estimation function to consider the user's emotions when generating ad creatives, generating ads that elicit positive emotions. For example, the generation AI can use its emotion estimation function to analyze the user's emotional state and generate ads that elicit positive emotions. For example, if the user is tired, it can generate ads that have a refreshing effect. The generation AI also takes the user's emotions into consideration when generating ad creatives, incorporating positive messages and designs. For example, it can use encouraging messages and bright colors. The generation AI also generates ads that change the user's emotions to positive ones based on the emotion estimation data. For example, if the user is feeling down, it can generate ads that incorporate uplifting messages and humor. This makes it possible to generate ad creatives that take the user's emotions into consideration and elicit positive emotions.

[0059] The advertisement display unit can analyze a user's smartphone usage patterns and display advertisements at the optimal timing. For example, an MDM system analyzes a user's smartphone usage patterns and displays advertisements at the optimal timing based on usage frequency and time of day. For example, advertisements are displayed during times when the user frequently uses their smartphone. The advertisement display unit also displays advertisements that are highly relevant to the use of a specific app based on the user's app usage history. For example, when the user is using a game app, game-related advertisements are displayed. The advertisement display unit also analyzes smartphone usage patterns and displays advertisements at times when the user is most likely to respond to advertisements. For example, advertisements are displayed during times when the user is relaxing. This allows advertisements to be displayed at the optimal timing based on the user's smartphone usage patterns.

[0060] The advertisement display unit can display highly relevant advertisements while the user is using a specific app based on the user's app usage history. For example, an MDM system analyzes the user's app usage history and displays highly relevant advertisements while the user is using a specific app. For example, when the user is using a shopping app, advertisements for related products are displayed. The advertisement display unit also displays advertisements tailored to the user's interests based on the app usage history. For example, when the user is using a fitness app, fitness-related advertisements are displayed. The advertisement display unit also displays advertisements to which the user is most likely to respond while using a specific app. For example, when the user is using a news app, advertisements related to the latest news are displayed. This makes it possible to display highly relevant advertisements based on the user's app usage history.

[0061] The advertisement display unit uses the emotion estimation function to display advertisements at a timing that corresponds to the user's emotional state, thereby maximizing the effectiveness of the advertisements. The advertisement display unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and display advertisements at optimal timing. For example, the advertisement display unit displays advertisements when the user is relaxed. The advertisement display unit also adjusts the timing of displaying advertisements according to the user's emotional state. For example, when the user is feeling stressed, the advertisement display unit displays advertisements related to relaxation. The advertisement display unit also displays advertisements at a timing that is most appropriate for the user's emotions based on the emotion estimation data. For example, when the user is happy, the advertisement display unit displays advertisements containing positive messages. This allows advertisements to be displayed at a timing that corresponds to the user's emotional state, thereby maximizing the effectiveness of the advertisements.

[0062] The advertisement display unit can cooperate with other smartphone functions to provide a more interactive advertising experience. For example, the MDM system cooperates with the smartphone's camera function to display relevant advertisements when the user is using the camera. For example, an advertisement for a photo printing service is displayed while the user is taking a photo. The advertisement display unit also cooperates with the GPS function to display advertisements based on the user's current location. For example, if the user is near a specific store, sales information for that store is displayed. The advertisement display unit also cooperates with other smartphone functions to provide an interactive advertising experience. For example, when the user is listening to music, an advertisement for a related music event is displayed. This cooperates with other smartphone functions to provide an interactive advertising experience.

[0063] The ad display unit is compatible with different devices and can display ads across multiple devices. For example, the ad display unit's MDM system can also support tablets and smartwatches, displaying ads across multiple devices. For example, the same ad can be displayed when the user is using a tablet. The ad display unit also links ads across different devices to provide a seamless advertising experience. For example, when a user taps on an ad displayed on a smartwatch, detailed information can be displayed on a smartphone. The ad display unit also builds an ad display system compatible with multiple devices, providing a consistent advertising experience regardless of which device the user is using. For example, the same ad campaign can be deployed on smartphones, tablets, and smartwatches. This allows the ad display unit to support different devices and display ads across multiple devices.

[0064] The advertisement display unit uses the emotion estimation function to adjust the frequency and content of advertisement display based on the user's emotions, thereby reducing the user's stress. The advertisement display unit, for example, uses the emotion estimation function to analyze the user's emotional state and adjust the frequency of advertisement display. For example, when the user is feeling stressed, the advertisement display unit reduces the frequency of advertisements. The advertisement display unit also adjusts the content of advertisements based on the user's emotions. For example, when the user is relaxed, it displays advertisements related to relaxation. The advertisement display unit also builds an advertisement display system for reducing the user's stress based on the emotion estimation data. For example, when the user is tired, it displays advertisements that have a refreshing effect. In this way, the frequency and content of advertisement display can be adjusted based on the user's emotions, thereby reducing the user's stress.

[0065] The reward granting unit can analyze CPC click data and dynamically adjust the optimal reward for each user. For example, the reward granting unit analyzes CPC click data and builds a system that dynamically adjusts the optimal reward for each user. For example, additional data capacity is provided to users with a high click rate. The reward granting unit also customizes the type and amount of reward based on the user's click data. For example, points or gift cards are provided to users who click many times on a specific advertisement. The reward granting unit also analyzes click data in real time and dynamically adjusts rewards according to user behavior. For example, a user who clicks many times during a specific time period is provided with a special benefit for that time period. This makes it possible to dynamically adjust the optimal reward for each user based on the CPC click data.

[0066] The reward granting unit can provide rewards tailored to a user's purchasing habits based on their PayPay usage history. For example, the reward granting unit analyzes PayPay usage history and builds a system that provides rewards tailored to a user's purchasing habits. For example, a user who frequently purchases products in a specific category is provided with rewards related to that category. The reward granting unit also provides personalized rewards based on the user's PayPay usage history. For example, a user who frequently uses PayPay at a specific store is provided with coupons that can be used at that store. The reward granting unit also analyzes PayPay usage data and customizes rewards based on the user's purchasing patterns. For example, a user who frequently uses PayPay on weekends is provided with weekend-only rewards. This makes it possible to provide rewards tailored to a user's purchasing habits based on their PayPay usage history.

[0067] The reward granting unit uses the emotion estimation function to provide a reward according to the user's emotional state, thereby improving user satisfaction. The reward granting unit, for example, uses the emotion estimation function to analyze the user's emotional state and provide a reward according to that state. For example, when the user is feeling stressed, a relaxation-related benefit is provided. The reward granting unit also adjusts the type and amount of reward based on the user's emotional state. For example, when the user is happy, additional data capacity or points are provided. The reward granting unit also builds a system for providing rewards to improve user satisfaction based on the emotion estimation data. For example, when the user is tired, a benefit with a refreshing effect is provided. In this way, rewards according to the user's emotional state can be provided, thereby improving user satisfaction.

[0068] The reward granting unit can also make CPC rewards compatible with other digital currencies and point systems. For example, the reward granting unit builds a system that makes CPC rewards compatible with other digital currencies and point systems. For example, it allows users to earn Rakuten points or Amazon gift cards by clicking. The reward granting unit also provides multiple reward options that users can choose from. For example, it allows users to choose from PayPay points, Rakuten points, and Amazon gift cards as click rewards. The reward granting unit also develops a system that works with other digital currencies and point systems and allows users to freely select rewards. For example, it allows users to customize rewards to suit their preferences. This makes it possible to make CPC rewards compatible with other digital currencies and point systems.

[0069] The reward granting unit can provide CPC rewards as multiple options that users can choose from. For example, the reward granting unit provides CPC rewards as multiple options and builds a system that allows users to choose from. For example, the reward granting unit allows users to choose from data capacity, music streaming service subscriptions, movie tickets, etc. The reward granting unit also allows users to select rewards that suit their needs. For example, the reward granting unit provides additional data capacity to users who need data capacity, and music streaming service subscriptions to users who want to enjoy entertainment. The reward granting unit also develops a system that provides multiple reward options and allows users to freely choose. For example, the reward granting unit allows users to choose from data capacity, music streaming services, and movie tickets as click rewards. This makes it possible to provide CPC rewards as multiple options that users can choose from.

[0070] The reward granting unit uses the emotion estimation function to adjust the type and amount of reward based on the user's emotion, thereby increasing user engagement. The reward granting unit, for example, uses the emotion estimation function to analyze the user's emotional state and adjust the type and amount of reward according to that state. For example, when the user is happy, additional data capacity or points are provided. The reward granting unit also builds a system that customizes the type and amount of reward based on the user's emotional state. For example, when the user is feeling stressed, relaxation-related rewards are provided. The reward granting unit also provides rewards to increase user engagement based on the emotion estimation data. For example, when the user has positive emotions, engagement is increased by increasing rewards. In this way, the type and amount of reward can be adjusted based on the user's emotion, thereby increasing user engagement.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The advertisement display system may further include a health management unit that monitors the user's health status. For example, the health management unit collects data from a smartwatch or fitness tracker and analyzes the user's heart rate, number of steps, and sleep patterns. This allows health-related advertisements to be displayed based on the user's health status. For example, if the user is not getting enough exercise, advertisements for fitness gyms may be displayed. Also, if the user is not getting enough sleep, advertisements for sleep-improvement products and services may be displayed. Furthermore, the health management unit may provide personalized health advice based on the user's health data. For example, if the user is feeling stressed, advertisements suggesting relaxation methods or stress-relief products may be displayed.

[0073] The advertisement display system may further include a purchase history analysis unit that analyzes a user's purchase history. For example, the purchase history analysis unit collects data on products purchased by the user in the past and analyzes the user's purchasing trends. This makes it possible to display highly relevant advertisements based on the user's purchase history. For example, advertisements related to product categories frequently purchased by the user may be displayed. The purchase history analysis unit may also learn the user's purchasing patterns and display advertisements tailored to specific times or events. For example, if a user purchases a specific product every Christmas, Christmas-related advertisements may be displayed to coincide with that time of year. Furthermore, the purchase history analysis unit may provide special offers and discount information based on the user's purchase history. For example, an advertisement offering a discount coupon for a product purchased by the user in the past may be displayed.

[0074] The advertisement display system may further include a social media analysis unit that analyzes the user's social media activity. For example, the social media analysis unit may analyze the user's posts and the accounts the user follows to identify the user's interests. This allows highly relevant advertisements to be displayed based on the user's social media activity. For example, if the user frequently posts about a particular brand or product, advertisements for that brand or product may be displayed. The social media analysis unit may also analyze the activities of the user's followers and friends to display advertisements that will influence the user. For example, if a user's friend purchases a particular product, an advertisement related to that product may be displayed. Furthermore, the social media analysis unit may provide information about special offers and campaigns based on the user's social media activity. For example, if a user posts using a particular hashtag, an advertisement for a campaign related to that hashtag may be displayed.

[0075] The advertisement display system can further use a user emotion estimation function to dynamically change the design of an advertisement based on the user's emotions. For example, using the emotion estimation function, if the user is happy, an advertisement can be generated that includes bright colors and a positive message. Alternatively, if the user is sad, an advertisement can be generated that includes calm colors and an encouraging message. Furthermore, using the emotion estimation function, the format of an advertisement can be changed based on the user's emotions. For example, if the user is relaxed, an advertisement can be generated that includes quiet music and smooth animations. Alternatively, if the user is excited, an advertisement can be generated that includes energetic music and dynamic animations. In this way, the design and format of an advertisement can be dynamically changed based on the user's emotions, allowing for more effective advertisement display.

[0076] The advertisement display system can further use a user emotion estimation function to adjust the frequency of advertisement display based on the user's emotions. For example, using the emotion estimation function, if the user is feeling stressed, the frequency of advertisement display can be reduced. Also, if the user is relaxed, the frequency of advertisement display can be increased. Furthermore, using the emotion estimation function, the timing of advertisement display can be adjusted based on the user's emotions. For example, if the user is concentrating, advertisement display can be refrained from. Also, if the user is taking a break, advertisements can be displayed. In this way, the frequency and timing of advertisement display can be adjusted based on the user's emotions, and effective advertisements can be displayed while reducing the user's stress.

[0077] The advertising display system can further use a user emotion estimation function to provide rewards based on the user's emotions. For example, if the user is happy, additional data capacity or points can be provided using the emotion estimation function. Also, if the user is stressed, a relaxation-related reward can be provided. Furthermore, the emotion estimation function can be used to adjust the type and amount of reward based on the user's emotions. For example, if the user is tired, a reward with a refreshing effect can be provided. Also, if the user is excited, an entertainment-related reward can be provided. In this way, rewards can be provided based on the user's emotions, and user satisfaction can be improved.

[0078] The advertisement display system can further use a user emotion estimation function to dynamically change the content of advertisements based on the user's emotions. For example, using the emotion estimation function, if the user is relaxed, advertisements for relaxation-related products and services can be displayed. Also, if the user is stressed, advertisements for stress-relief-related products and services can be displayed. Furthermore, using the emotion estimation function, the advertisement message can be adjusted based on the user's emotions. For example, if the user is happy, an advertisement containing a positive message can be displayed. Also, if the user is sad, an advertisement containing an encouraging message can be displayed. In this way, the advertisement content can be dynamically changed based on the user's emotions, and more effective advertisements can be displayed.

[0079] The advertisement display system may further include a location information analysis unit that analyzes the user's location information. For example, the location information analysis unit may analyze the user's current location and display advertisements related to that location. For example, if the user is in a shopping mall, sale information for stores in the mall may be displayed. Also, if the user is in a tourist spot, advertisements related to the tourist spot may be displayed. Furthermore, the location information analysis unit may analyze the user's movement patterns and display advertisements that are optimal for the user's location. For example, if the user is commuting, advertisements related to the user's commuting route may be displayed. Also, if the user is at home, advertisements for products and services that can be used at home may be displayed. In this way, optimal advertisements may be displayed based on the user's location information.

[0080] The advertisement display system may further include a device usage history analysis unit that analyzes a user's device usage history. For example, the device usage history analysis unit collects data on devices used by the user in the past and analyzes the user's device usage patterns. This allows for displaying highly relevant advertisements based on the user's device usage history. For example, advertisements related to devices frequently used by the user may be displayed. The device usage history analysis unit may also learn the user's device usage patterns and display advertisements that are optimal for the use of a specific device. For example, when the user is using a tablet, advertisements for accessories related to the tablet may be displayed. Furthermore, the device usage history analysis unit may provide special offers and discount information based on the user's device usage history. For example, an advertisement offering a discount coupon for a device previously purchased by the user may be displayed.

[0081] The advertisement display system may further include an app usage history analysis unit that analyzes a user's app usage history. For example, the app usage history analysis unit collects data on apps used by the user in the past and analyzes the user's app usage patterns. This makes it possible to display highly relevant advertisements based on the user's app usage history. For example, advertisements related to apps frequently used by the user may be displayed. The app usage history analysis unit may also learn the user's app usage patterns and display advertisements that are optimal for specific app usage. For example, when a user uses a fitness app, advertisements for fitness-related products and services may be displayed. Furthermore, the app usage history analysis unit may provide rewards and discount information based on the user's app usage history. For example, advertisements offering rewards for apps used by the user in the past may be displayed.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: Generative AI automatically generates image and video ad creatives. For example, generative AI receives prompts containing instructions from users as input and generates appropriate ad creatives. Generative AI can also generate ads using technologies such as deep learning and GAN (generative adversarial networks). Furthermore, generative AI can adapt the content and format of ad creatives to images, videos, text ads, etc. Step 2: The ad display unit displays the ads generated by the generation AI on smartphones with MDM installed. For example, the ads generated for each hour of use are automatically displayed on smartphones with MDM installed. The ad display unit can also analyze smartphone usage patterns and display ads at optimal times. Furthermore, the ad display unit can display highly relevant ads while using a specific app based on the user's app usage history. Step 3: The reward granting unit grants rewards based on the advertisements posted by the advertisement posting unit. For example, the reward granting unit grants a basic fee of 990 yen for 3GB of data in PayPay based on an advertisement with a CPC (cost per click) of 990 yen. The reward granting unit can also analyze user click data and dynamically adjust the optimal reward. Furthermore, the reward granting unit can provide benefits tailored to the user's purchasing habits based on their PayPay usage history.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. Generative AI automatically generates image and video advertising creatives using generative AI, an advertisement display unit that displays the advertisement generated by the generation AI on an MDM-installed smartphone; a reward granting unit that grants a reward based on the advertisement posted by the advertisement posting unit. A system characterized by:

2. The generated AI is Analyzing the user's past ad click history and generating the ad creative optimized for the user 2. The system of claim 1.

3. The generated AI is Analyzing the user's current context in real time and generating the advertisement accordingly 2. The system of claim 1.

4. The generated AI is Estimate the user's current emotional state and generate the advertising creative that best suits that emotion.

2. The system of claim 1.

5. The generated AI is Adapting to different media formats and generating said advertisements according to user preferences 2. The system of claim 1.

Images