System

Abstract

An object of a system according to an embodiment is to appropriately generate and distribute an advertisement according to a preference of a receiver.SOLUTION: A system includes a preference analysis part, an advertisement generation part, and an advertisement distribution part. The preference analysis unit analyzes a preference of a receiver. The advertisement generation unit generates an advertisement based on a result of the analysis by the preference analysis unit. The advertisement distribution unit distributes the advertisement generated by the advertisement generation unit to a receiver.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, it is difficult to generate and distribute advertisements that suit the preferences of recipients, which can lead to a negative image of the company or even a boycott.

[0005] The system according to the embodiment aims to appropriately generate and distribute advertisements that match the preferences of recipients. [Means for solving the problem]

[0006] A system according to an embodiment includes a preference analysis unit, an advertisement generation unit, and an advertisement distribution unit. The preference analysis unit analyzes the preferences of a recipient. The advertisement generation unit generates an advertisement based on the analysis results obtained by the preference analysis unit. The advertisement distribution unit distributes the advertisement generated by the advertisement generation unit to the recipient. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately generate and distribute advertisements according to the preferences of the recipient. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The advertisement distribution system according to the embodiment of the present invention utilizes AI to distribute advertisements that are likely to be accepted by recipients. As a result, the advertisement distribution system can efficiently provide advertisements that are well-received by recipients and improve the image of a company.

[0029] An advertisement distribution system according to an embodiment includes a preference analysis unit, an advertisement generation unit, and an advertisement distribution unit. The preference analysis unit analyzes the preferences of a recipient. For example, data such as the recipient's past browsing history, purchase history, and social media activity is collected, and a generation AI analyzes this data. The advertisement generation unit generates advertisements based on the results of the analysis by the preference analysis unit. For example, the advertisement generation unit generates advertisements including people and content that are likely to be liked by the recipient. The advertisement distribution unit distributes the advertisements generated by the advertisement generation unit to the recipient. For example, while the recipient is browsing a website, the generation AI displays an advertisement that is optimal for the recipient in real time. This allows the advertisement distribution system according to an embodiment to efficiently distribute advertisements that are likely to be accepted by the recipient. For example, by displaying advertisements including people and content that the recipient likes, the effectiveness of advertisements can be maximized and a company's image can be improved. Furthermore, by avoiding advertisements featuring unpleasant people, the recipient's discomfort can be reduced and a company's image can be prevented from being damaged.

[0030] The preference analysis unit collects data on the recipient's past browsing history or purchase history, and activity on social media, and the generation AI can analyze this data. For example, the preference analysis unit collects the recipient's past browsing history or purchase history, and the generation AI analyzes this data. For example, the preference analysis unit identifies the recipient's preferred product category based on the emotional score when purchasing a specific product. The preference analysis unit also collects social media activity data, and the generation AI analyzes this data. For example, the recipient's interests are identified based on the people the recipient follows and their like history. This makes it possible to analyze the recipient's preferences in detail.

[0031] The advertisement generation unit can generate advertisements that include people or content that are likely to be liked by the recipient. The advertisement generation unit identifies people or content that are likely to be liked based on, for example, the recipient's past behavioral data, and generates advertisements that include them. For example, the advertisement content is personalized based on advertisements that the recipient has liked in the past or information about people the recipient follows. The advertisement generation unit also analyzes real-time behavioral data and instantly estimates preferences to generate advertisements. For example, it analyzes the recipient's mouse movements and click patterns while browsing a website to identify preferences. This makes it possible to generate advertisements that are likely to be liked by the recipient.

[0032] The ad delivery unit allows the generation AI to display the optimal ad for the recipient in real time while the recipient is browsing a website. For example, while the recipient is browsing a website, the ad delivery unit allows the generation AI to display the optimal ad in real time based on the recipient's browsing status and context. For example, when the recipient is browsing a specific page, the generation AI displays an ad related to the content of that page. The ad delivery unit also analyzes the recipient's voice data, infers preferences from the tone of voice and speaking style, and displays ads. For example, it identifies interests based on the tone of voice when the recipient speaks about a specific topic. This makes it possible to display the optimal ad for the recipient in real time.

[0033] The preference analysis unit analyzes real-time behavioral data of the recipient and can instantly estimate their preferences. The preference analysis unit, for example, analyzes mouse movements and click patterns in real time while the recipient is browsing a website to estimate their preferences. For example, the interest of the recipient can be identified based on the click frequency for a particular advertisement or the mouse movement pattern. The preference analysis unit also instantly estimates their preferences based on real-time behavioral data. For example, it analyzes scrolling behavior and page visit time to identify their preferences. This makes it possible to instantly estimate their preferences based on real-time behavioral data of the recipient.

[0034] The preference analysis unit can analyze the recipient's voice data and infer preferences from the tone of voice or speaking style. The preference analysis unit, for example, collects the recipient's voice data and analyzes the tone of voice or speaking style to infer preferences. For example, the recipient's interests are identified based on the tone of voice when talking about a specific topic. The preference analysis unit also infers the recipient's preferences based on the voice data. For example, the preference analysis unit analyzes the pitch and speed of the voice to identify preferences. This makes it possible to infer preferences based on the recipient's voice data.

[0035] The preference analysis unit can integrate data from different devices and identify comprehensive preferences. The preference analysis unit, for example, integrates data from different devices of the recipient and identifies comprehensive preferences. For example, it unifies browsing histories and purchase histories from smartphones, tablets, and PCs. The preference analysis unit also identifies the recipient's preferences based on data from different devices. For example, it analyzes behavioral patterns across devices and estimates preferences. This makes it possible to integrate data from different devices and identify comprehensive preferences.

[0036] The preference analysis unit can compare data from different cultural regions or areas to identify region-specific preferences. For example, the preference analysis unit collects data from different cultural regions or areas, and the generation AI analyzes it to identify region-specific preferences. For example, it compares and analyzes purchasing histories from Asia and Europe. The preference analysis unit also personalizes advertisements based on region-specific preferences. For example, it generates advertisements that include products and services that are popular in a particular region. This makes it possible to compare data from different cultural regions or areas to identify region-specific preferences.

[0037] The advertisement generation unit can generate advertisements that combine related products or services based on the recipient's past purchasing history. The advertisement generation unit, for example, analyzes the recipient's past purchasing history and generates advertisements that combine related products and services. For example, it displays advertisements that combine products from the same category. The advertisement generation unit also identifies products and services that are likely to interest the recipient based on the purchasing history. For example, it generates advertisements that include products that are highly related to products purchased in the past. This makes it possible to generate advertisements that combine related products and services based on the recipient's past purchasing history.

[0038] The ad generation unit can analyze the recipient's social media activity and generate an ad that reflects the people they follow or topics of interest. The ad generation unit, for example, analyzes the recipient's social media activity and generates an ad that reflects the people they follow or topics of interest. For example, the ad generation unit displays an ad related to an influencer the recipient follows. The ad generation unit also identifies the recipient's interests based on social media data. For example, the ad generation unit analyzes the like history and post content to identify topics of interest. This makes it possible to generate an ad based on the recipient's social media activity.

[0039] The advertisement generation unit can generate advertisements incorporating a theme according to a season or an event. The advertisement generation unit generates advertisements incorporating a theme according to a season or an event, for example. For example, advertisements related to Christmas or Halloween are automatically generated. The advertisement generation unit also personalizes the content of advertisements to suit specific events. For example, advertisements including products or services related to summer vacation or New Year are generated. In this way, advertisements incorporating a theme according to a season or an event can be generated.

[0040] The advertisement distribution unit can dynamically adjust the display position or size of an advertisement based on the viewing status or context of the recipient. The advertisement distribution unit, for example, analyzes the viewing status or context of the recipient and dynamically adjusts the display position or size of an advertisement. For example, the advertisement is displayed in a position that does not interfere with content on which the recipient is concentrating. The advertisement distribution unit also adjusts the size of the advertisement based on the context. For example, the advertisement size is changed to match the screen size and resolution. This makes it possible to dynamically adjust the display position or size of an advertisement based on the viewing status or context of the recipient.

[0041] The ad delivery unit can optimize the display speed or resolution of the advertisement according to the device or network environment of the recipient. The ad delivery unit, for example, analyzes the device or network environment of the recipient and optimizes the display speed or resolution of the advertisement. For example, a lightweight advertisement is displayed in a slow network environment. The ad delivery unit also adjusts the resolution of the advertisement according to the characteristics of the device. For example, a high-quality advertisement is displayed on a high-resolution display. This makes it possible to optimize the display speed or resolution of the advertisement according to the device or network environment of the recipient.

[0042] The ad distribution unit can realize seamless ad display across different platforms. The ad distribution unit realizes seamless ad display across different platforms, for example. For example, the same ad is consistently displayed on a website, app, and social media. The ad distribution unit also strengthens ad coordination across platforms. For example, the ad display history across different platforms is shared to provide recipients with a consistent ad experience. This makes it possible to realize seamless ad display across different platforms.

[0043] The advertisement distribution unit can utilize the receiver's location information to distribute advertisements specific to a region. The advertisement distribution unit, for example, analyzes the receiver's location information and distributes advertisements specific to a region. For example, it displays advertisements for events held in a specific region. The advertisement distribution unit also generates advertisements for products and services related to the region based on the location information. For example, it displays advertisements for local specialties and local stores. In this way, it is possible to distribute advertisements specific to a region by utilizing the receiver's location information.

[0044] The ad delivery unit can track long-term behavioral changes of recipients when measuring the effectiveness of advertisements. The ad delivery unit, for example, tracks long-term behavioral changes of recipients when measuring the effectiveness of advertisements. For example, it measures repeat purchases and brand loyalty. The ad delivery unit also evaluates the effectiveness of advertisements based on long-term behavioral changes. For example, it analyzes purchasing behavior and brand loyalty after viewing an advertisement. This makes it possible to track long-term behavioral changes of recipients.

[0045] The ad distribution unit can A / B test different ad variations to measure the effectiveness of ads and identify the most effective ad. The ad distribution unit, for example, A / B tests different ad variations to measure the effectiveness of ads. For example, it compares ads with different designs or messages. The ad distribution unit also identifies the most effective ad based on the results of the A / B test. For example, it compares and evaluates click rates and conversion rates. This makes it possible to A / B test different ad variations and identify the most effective ad.

[0046] The ad delivery unit can compare the effectiveness of ads between different devices and platforms when measuring their effectiveness, and formulate an optimal ad delivery strategy. For example, the ad delivery unit compares the effectiveness of ads between different devices and platforms when measuring their effectiveness. For example, it compares the effectiveness of ads on smartphones and PCs. The ad delivery unit also formulates an optimal ad delivery strategy based on the effectiveness of each device and platform. For example, it creates an ad delivery plan tailored to a specific device or platform. This makes it possible to compare the effectiveness between different devices and platforms, and formulate an optimal ad delivery strategy.

[0047] The ad delivery unit can collect feedback from recipients and identify areas for improvement in the advertisement when measuring the effectiveness of the advertisement. The ad delivery unit, for example, collects feedback from recipients when measuring the effectiveness of the advertisement. For example, it uses an online survey or comment function. The ad delivery unit also identifies areas for improvement in the advertisement based on the feedback. For example, it analyzes the opinions and impressions of recipients and improves the content and design of the advertisement. In this way, it is possible to collect feedback from recipients and identify areas for improvement in the advertisement.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The advertising distribution system can further include a health analysis unit that analyzes the user's health data. For example, the system can analyze data obtained from the user's fitness tracker or smartwatch to generate advertisements tailored to the user's health condition. Specifically, the system can generate advertisements suggesting health foods and fitness-related products based on the user's heart rate and exercise volume. The system can also analyze the user's sleep data and display advertisements for products and services that improve sleep quality. This allows the system to provide personalized advertisements tailored to the user's health condition.

[0050] The advertisement distribution system can further include a location analysis unit that analyzes the user's location information. For example, the system analyzes data on the user's current location and places the user has visited in the past to generate advertisements related to that area. Specifically, if the user is in a specific area, advertisements for restaurants and events in that area can be displayed. Also, if the user is traveling, advertisements for tourist spots and hotels at the user's travel destination can be displayed. This makes it possible to provide personalized advertisements based on the user's location information.

[0051] The advertisement distribution system can further include a purchase analysis unit that analyzes a user's purchase history. For example, the system can analyze data on products and services purchased in the past by the user and generate advertisements for related products and services. Specifically, the system can display advertisements that suggest products that are highly related to products purchased in the past by the user. Furthermore, if a user has a preference for a particular brand, the system can display advertisements for new products and sales information for that brand. This makes it possible to provide personalized advertisements based on the user's purchase history.

[0052] The advertising distribution system may further include a social analysis unit that analyzes a user's social media activity. For example, the system may analyze the people the user follows and their like history to generate advertisements based on their interests. Specifically, the system may display advertisements for products and services related to influencers the user follows. It may also display relevant advertisements based on the content of posts that the user has liked. This makes it possible to provide personalized advertisements based on the user's social media activity.

[0053] The ad distribution system can further include a device analysis unit that analyzes the user's device usage. For example, the system can analyze the user's usage of different devices, such as a smartphone, tablet, or PC, and generate optimal ads. Specifically, if the user is using a smartphone, it can display mobile-friendly ads. Also, if the user is using a PC, it can display ads that include detailed information. This makes it possible to provide personalized ads based on the user's device usage.

[0054] The advertisement distribution system can further include a purchase intention estimation unit that estimates the user's purchasing intention. For example, the system analyzes the user's past purchase history and browsing history to generate advertisements for products and services that the user is likely to purchase. Specifically, the system can display advertisements that suggest products that are highly related to products that the user has previously purchased. Also, if the user frequently browses a particular product, it can display advertisements for sale information or coupons related to that product. This makes it possible to provide personalized advertisements based on the user's purchasing intention.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The preference analysis unit analyzes the recipient's preferences. For example, it collects data such as the recipient's browsing history, purchase history, and social media activity, and the generation AI analyzes this data. Step 2: The advertisement generation unit generates an advertisement based on the results of the analysis by the preference analysis unit. For example, an advertisement is generated that includes people and content that are likely to be liked by the recipient. Step 3: The ad distribution unit distributes the ads generated by the ad generation unit to the recipient. For example, when the recipient is browsing a website, the generation AI displays the most suitable ads for the recipient in real time.

[0057] (Example 2) The advertisement distribution system according to the embodiment of the present invention utilizes AI to distribute advertisements that are likely to be accepted by recipients. As a result, the advertisement distribution system can efficiently provide advertisements that are well-received by recipients and improve the image of a company.

[0058] An advertisement distribution system according to an embodiment includes a preference analysis unit, an advertisement generation unit, and an advertisement distribution unit. The preference analysis unit analyzes the preferences of a recipient. For example, data such as the recipient's past browsing history, purchase history, and social media activity is collected, and a generation AI analyzes this data. The advertisement generation unit generates advertisements based on the results of the analysis by the preference analysis unit. For example, the advertisement generation unit generates advertisements including people and content that are likely to be liked by the recipient. The advertisement distribution unit distributes the advertisements generated by the advertisement generation unit to the recipient. For example, while the recipient is browsing a website, the generation AI displays an advertisement that is optimal for the recipient in real time. This allows the advertisement distribution system according to an embodiment to efficiently distribute advertisements that are likely to be accepted by the recipient. For example, by displaying advertisements including people and content that the recipient likes, the effectiveness of advertisements can be maximized and a company's image can be improved. Furthermore, by avoiding advertisements featuring unpleasant people, the recipient's discomfort can be reduced and a company's image can be prevented from being damaged.

[0059] The preference analysis unit collects data on the recipient's past browsing history or purchase history, and activity on social media, and the generation AI can analyze this data. For example, the preference analysis unit collects the recipient's past browsing history or purchase history, and the generation AI analyzes this data. For example, the preference analysis unit identifies the recipient's preferred product category based on the emotional score when purchasing a specific product. The preference analysis unit also collects social media activity data, and the generation AI analyzes this data. For example, the recipient's interests are identified based on the people the recipient follows and their like history. This makes it possible to analyze the recipient's preferences in detail.

[0060] The advertisement generation unit can generate advertisements that include people or content that are likely to be liked by the recipient. The advertisement generation unit identifies people or content that are likely to be liked based on, for example, the recipient's past behavioral data, and generates advertisements that include them. For example, the advertisement content is personalized based on advertisements that the recipient has liked in the past or information about people the recipient follows. The advertisement generation unit also analyzes real-time behavioral data and instantly estimates preferences to generate advertisements. For example, it analyzes the recipient's mouse movements and click patterns while browsing a website to identify preferences. This makes it possible to generate advertisements that are likely to be liked by the recipient.

[0061] The ad delivery unit allows the generation AI to display the optimal ad for the recipient in real time while the recipient is browsing a website. For example, while the recipient is browsing a website, the ad delivery unit allows the generation AI to display the optimal ad in real time based on the recipient's browsing status and context. For example, when the recipient is browsing a specific page, the generation AI displays an ad related to the content of that page. The ad delivery unit also analyzes the recipient's voice data, infers preferences from the tone of voice and speaking style, and displays ads. For example, it identifies interests based on the tone of voice when the recipient speaks about a specific topic. This makes it possible to display the optimal ad for the recipient in real time.

[0062] The preference analysis unit can use the emotion estimation function to analyze emotional fluctuations from the recipient's past browsing history and purchasing history, and identify preferences based on the emotional fluctuation patterns. The preference analysis unit, for example, collects the recipient's past browsing history and purchasing history, and analyzes emotional fluctuations using the emotion estimation function. For example, it identifies the recipient's preferred product category based on the emotion score when purchasing a specific product. The preference analysis unit also analyzes the emotional fluctuation patterns to identify the recipient's preferences. For example, it estimates preferences based on temporal fluctuations and reactions to specific events. This makes it possible to identify preferences based on the recipient's emotional fluctuations.

[0063] The preference analysis unit analyzes real-time behavioral data of the recipient and can instantly estimate their preferences. The preference analysis unit, for example, analyzes mouse movements and click patterns in real time while the recipient is browsing a website to estimate their preferences. For example, the interest of the recipient can be identified based on the click frequency for a particular advertisement or the mouse movement pattern. The preference analysis unit also instantly estimates their preferences based on real-time behavioral data. For example, it analyzes scrolling behavior and page visit time to identify their preferences. This makes it possible to instantly estimate their preferences based on real-time behavioral data of the recipient.

[0064] The preference analysis unit can analyze the recipient's voice data and infer preferences from the tone of voice or speaking style. The preference analysis unit, for example, collects the recipient's voice data and analyzes the tone of voice or speaking style to infer preferences. For example, the recipient's interests are identified based on the tone of voice when talking about a specific topic. The preference analysis unit also infers the recipient's preferences based on the voice data. For example, the preference analysis unit analyzes the pitch and speed of the voice to identify preferences. This makes it possible to infer preferences based on the recipient's voice data.

[0065] The preference analysis unit can integrate data from different devices and identify comprehensive preferences. The preference analysis unit, for example, integrates data from different devices of the recipient and identifies comprehensive preferences. For example, it unifies browsing histories and purchase histories from smartphones, tablets, and PCs. The preference analysis unit also identifies the recipient's preferences based on data from different devices. For example, it analyzes behavioral patterns across devices and estimates preferences. This makes it possible to integrate data from different devices and identify comprehensive preferences.

[0066] The preference analysis unit can compare data from different cultural regions or areas to identify region-specific preferences. For example, the preference analysis unit collects data from different cultural regions or areas, and the generation AI analyzes it to identify region-specific preferences. For example, it compares and analyzes purchasing histories from Asia and Europe. The preference analysis unit also personalizes advertisements based on region-specific preferences. For example, it generates advertisements that include products and services that are popular in a particular region. This makes it possible to compare data from different cultural regions or areas to identify region-specific preferences.

[0067] The preference analysis unit uses the emotion estimation function to analyze the emotional response of the recipient when viewing an advertisement in real time, and can instantly reflect changes in preferences. The preference analysis unit, for example, uses the emotion estimation function to analyze the emotional response of the recipient when viewing an advertisement in real time. For example, it analyzes the recipient's facial expressions and voice using a camera or microphone. The preference analysis unit also instantly reflects changes in preferences based on the emotional response. For example, if positive emotions are strong, the preferences are updated. This makes it possible to instantly reflect changes in preferences based on the recipient's emotional response.

[0068] The advertisement generation unit can use the emotion estimation function to adjust the content and design of the advertisement in real time and generate an advertisement that elicits the most positive emotions. The advertisement generation unit, for example, uses the recipient's emotion estimation function to adjust the content and design of the advertisement in real time. For example, it automatically selects colors and fonts that elicit positive emotions. The advertisement generation unit also personalizes the content of the advertisement based on the emotion estimation function. For example, it generates an advertisement that includes themes and topics that the recipient prefers. This makes it possible to adjust the content and design of the advertisement in real time based on the recipient's emotions.

[0069] The advertisement generation unit can generate advertisements that combine related products or services based on the recipient's past purchasing history. The advertisement generation unit, for example, analyzes the recipient's past purchasing history and generates advertisements that combine related products and services. For example, it displays advertisements that combine products from the same category. The advertisement generation unit also identifies products and services that are likely to interest the recipient based on the purchasing history. For example, it generates advertisements that include products that are highly related to products purchased in the past. This makes it possible to generate advertisements that combine related products and services based on the recipient's past purchasing history.

[0070] The ad generation unit can analyze the recipient's social media activity and generate an ad that reflects the people they follow or topics of interest. The ad generation unit, for example, analyzes the recipient's social media activity and generates an ad that reflects the people they follow or topics of interest. For example, the ad generation unit displays an ad related to an influencer the recipient follows. The ad generation unit also identifies the recipient's interests based on social media data. For example, the ad generation unit analyzes the like history and post content to identify topics of interest. This makes it possible to generate an ad based on the recipient's social media activity.

[0071] The advertisement generation unit can generate advertisements incorporating a theme according to a season or an event. The advertisement generation unit generates advertisements incorporating a theme according to a season or an event, for example. For example, advertisements related to Christmas or Halloween are automatically generated. The advertisement generation unit also personalizes the content of advertisements to suit specific events. For example, advertisements including products or services related to summer vacation or New Year are generated. In this way, advertisements incorporating a theme according to a season or an event can be generated.

[0072] The advertisement generation unit uses the emotion estimation function to adjust the color or font of the advertisement according to the recipient's emotion, thereby generating a visually appealing advertisement. The advertisement generation unit, for example, uses the emotion estimation function to adjust the color or font of the advertisement according to the recipient's emotion. For example, it automatically selects colors and fonts that evoke positive emotions. The advertisement generation unit also personalizes the design of the advertisement based on the emotion estimation function. For example, it generates an advertisement based on colors and fonts that the recipient prefers. This makes it possible to adjust the color or font of the advertisement according to the recipient's emotion.

[0073] The advertisement delivery unit uses the emotion estimation function to optimize the timing of advertisement delivery, and can deliver advertisements at times that elicit the most positive emotions. The advertisement delivery unit, for example, uses the recipient's emotion estimation function to optimize the timing of advertisement delivery. For example, the advertisement is delivered during a time period when the recipient has positive emotions. The advertisement delivery unit also adjusts the advertisement delivery schedule based on the emotion estimation function. For example, the advertisement is delivered according to a specific day of the week or time period. This makes it possible to optimize the timing of advertisement delivery based on the recipient's emotions.

[0074] The advertisement distribution unit can dynamically adjust the display position or size of an advertisement based on the viewing status or context of the recipient. The advertisement distribution unit, for example, analyzes the viewing status or context of the recipient and dynamically adjusts the display position or size of an advertisement. For example, the advertisement is displayed in a position that does not interfere with content on which the recipient is concentrating. The advertisement distribution unit also adjusts the size of the advertisement based on the context. For example, the advertisement size is changed to match the screen size and resolution. This makes it possible to dynamically adjust the display position or size of an advertisement based on the viewing status or context of the recipient.

[0075] The ad delivery unit can optimize the display speed or resolution of the advertisement according to the device or network environment of the recipient. The ad delivery unit, for example, analyzes the device or network environment of the recipient and optimizes the display speed or resolution of the advertisement. For example, a lightweight advertisement is displayed in a slow network environment. The ad delivery unit also adjusts the resolution of the advertisement according to the characteristics of the device. For example, a high-quality advertisement is displayed on a high-resolution display. This makes it possible to optimize the display speed or resolution of the advertisement according to the device or network environment of the recipient.

[0076] The ad distribution unit can realize seamless ad display across different platforms. The ad distribution unit realizes seamless ad display across different platforms, for example. For example, the same ad is consistently displayed on a website, app, and social media. The ad distribution unit also strengthens ad coordination across platforms. For example, the ad display history across different platforms is shared to provide recipients with a consistent ad experience. This makes it possible to realize seamless ad display across different platforms.

[0077] The advertisement distribution unit can utilize the receiver's location information to distribute advertisements specific to a region. The advertisement distribution unit, for example, analyzes the receiver's location information and distributes advertisements specific to a region. For example, it displays advertisements for events held in a specific region. The advertisement distribution unit also generates advertisements for products and services related to the region based on the location information. For example, it displays advertisements for local specialties and local stores. In this way, it is possible to distribute advertisements specific to a region by utilizing the receiver's location information.

[0078] The ad delivery unit uses the emotion estimation function to adjust the frequency of ad display according to the recipient's emotions, thereby achieving optimal engagement. The ad delivery unit, for example, uses the emotion estimation function to adjust the frequency of ad display according to the recipient's emotions. For example, the ad delivery unit increases the frequency of ad display when positive emotions are strong. The ad delivery unit also optimizes the ad display schedule based on the emotion estimation function. For example, the ad display timing is adjusted in accordance with changes in the recipient's emotions. This makes it possible to adjust the frequency of ad display according to the recipient's emotions.

[0079] The ad delivery unit uses the emotion estimation function to measure not only the click rate and conversion rate of an ad but also the emotional response, thereby enabling evaluation of the overall effectiveness. The ad delivery unit, for example, uses the emotion estimation function to measure not only the click rate and conversion rate of an ad but also the emotional response. For example, it collects an emotion score when viewing an ad. The ad delivery unit also evaluates the overall effectiveness of the ad based on the emotional response. For example, it rates an ad that evokes a strong positive emotion highly. This makes it possible to measure not only the click rate and conversion rate of an ad but also the emotional response.

[0080] The ad delivery unit can track long-term behavioral changes of recipients when measuring the effectiveness of advertisements. The ad delivery unit, for example, tracks long-term behavioral changes of recipients when measuring the effectiveness of advertisements. For example, it measures repeat purchases and brand loyalty. The ad delivery unit also evaluates the effectiveness of advertisements based on long-term behavioral changes. For example, it analyzes purchasing behavior and brand loyalty after viewing an advertisement. This makes it possible to track long-term behavioral changes of recipients.

[0081] The ad distribution unit can A / B test different ad variations to measure the effectiveness of ads and identify the most effective ad. The ad distribution unit, for example, A / B tests different ad variations to measure the effectiveness of ads. For example, it compares ads with different designs or messages. The ad distribution unit also identifies the most effective ad based on the results of the A / B test. For example, it compares and evaluates click rates and conversion rates. This makes it possible to A / B test different ad variations and identify the most effective ad.

[0082] The ad delivery unit can compare the effectiveness of ads between different devices and platforms when measuring their effectiveness, and formulate an optimal ad delivery strategy. For example, the ad delivery unit compares the effectiveness of ads between different devices and platforms when measuring their effectiveness. For example, it compares the effectiveness of ads on smartphones and PCs. The ad delivery unit also formulates an optimal ad delivery strategy based on the effectiveness of each device and platform. For example, it creates an ad delivery plan tailored to a specific device or platform. This makes it possible to compare the effectiveness between different devices and platforms, and formulate an optimal ad delivery strategy.

[0083] The ad delivery unit can collect feedback from recipients and identify areas for improvement in the advertisement when measuring the effectiveness of the advertisement. The ad delivery unit, for example, collects feedback from recipients when measuring the effectiveness of the advertisement. For example, it uses an online survey or comment function. The ad delivery unit also identifies areas for improvement in the advertisement based on the feedback. For example, it analyzes the opinions and impressions of recipients and improves the content and design of the advertisement. In this way, it is possible to collect feedback from recipients and identify areas for improvement in the advertisement.

[0084] The ad delivery unit uses the emotion estimation function to analyze the results of measuring the effectiveness of the advertisement in real time, and can immediately reflect the results in personalizing the advertisement. The ad delivery unit, for example, uses the emotion estimation function to analyze the results of measuring the effectiveness of the advertisement in real time. For example, it collects emotion scores in real time when the advertisement is viewed. The ad delivery unit also immediately reflects the personalization of the advertisement based on the results of measuring the effectiveness. For example, it adjusts the content or design of the advertisement based on the emotion score. This allows the results of measuring the effectiveness of the advertisement to be analyzed in real time, and can immediately reflect the results in personalizing the advertisement.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The advertising distribution system can further include a health analysis unit that analyzes the user's health data. For example, the system can analyze data obtained from the user's fitness tracker or smartwatch to generate advertisements tailored to the user's health condition. Specifically, the system can generate advertisements suggesting health foods and fitness-related products based on the user's heart rate and exercise volume. The system can also analyze the user's sleep data and display advertisements for products and services that improve sleep quality. This allows the system to provide personalized advertisements tailored to the user's health condition.

[0087] The advertisement distribution system can further include a location analysis unit that analyzes the user's location information. For example, the system analyzes data on the user's current location and places the user has visited in the past to generate advertisements related to that area. Specifically, if the user is in a specific area, advertisements for restaurants and events in that area can be displayed. Also, if the user is traveling, advertisements for tourist spots and hotels at the user's travel destination can be displayed. This makes it possible to provide personalized advertisements based on the user's location information.

[0088] The advertisement distribution system can further include a purchase analysis unit that analyzes a user's purchase history. For example, the system can analyze data on products and services purchased in the past by the user and generate advertisements for related products and services. Specifically, the system can display advertisements that suggest products that are highly related to products purchased in the past by the user. Furthermore, if a user has a preference for a particular brand, the system can display advertisements for new products and sales information for that brand. This makes it possible to provide personalized advertisements based on the user's purchase history.

[0089] The advertisement distribution system can further include a voice analysis unit that analyzes the user's voice data. For example, it can analyze the content of what the user is saying and the tone of voice to generate advertisements based on their interests. Specifically, if the user is talking about a specific topic, it can display advertisements for products and services related to that topic. It can also estimate the user's emotions from the tone of voice and generate advertisements that evoke positive emotions. This makes it possible to provide personalized advertisements based on the user's voice data.

[0090] The advertising distribution system can further estimate the user's emotions and optimize the timing of advertising display based on the estimated emotions. For example, advertising effectiveness can be maximized by displaying advertisements during times when the user is feeling positive. Specifically, advertisements can be displayed during times when the user is relaxing or having fun. Furthermore, if the user is feeling stressed, advertisements for products or services that have a relaxing effect can be displayed. This makes it possible to provide optimal advertising display timing based on the user's emotions.

[0091] The advertising distribution system may further include a social analysis unit that analyzes a user's social media activity. For example, the system may analyze the people the user follows and their like history to generate advertisements based on their interests. Specifically, the system may display advertisements for products and services related to influencers the user follows. It may also display relevant advertisements based on the content of posts that the user has liked. This makes it possible to provide personalized advertisements based on the user's social media activity.

[0092] The ad distribution system can also estimate the user's emotions and adjust the content of the advertisements in real time based on the estimated emotions. For example, if the user has positive emotions, advertisements with bright colors and fun content can be displayed. Specifically, if the user is smiling, humorous advertisements or advertisements for fun events can be displayed. Also, if the user has negative emotions, advertisements with a relaxing effect or soothing content can be displayed. This makes it possible to provide optimal ad content based on the user's emotions.

[0093] The ad distribution system can further include a device analysis unit that analyzes the user's device usage. For example, the system can analyze the user's usage of different devices, such as a smartphone, tablet, or PC, and generate optimal ads. Specifically, if the user is using a smartphone, it can display mobile-friendly ads. Also, if the user is using a PC, it can display ads that include detailed information. This makes it possible to provide personalized ads based on the user's device usage.

[0094] The ad distribution system can further estimate a user's emotions and adjust the frequency of ad display based on the estimated emotions. For example, if a user has positive emotions, the frequency of ad display can be increased to increase engagement. Specifically, ads can be displayed more frequently during times when the user is enjoying themselves. Also, if a user has negative emotions, the frequency of ad display can be reduced to reduce stress. This makes it possible to provide an optimal ad display frequency based on the user's emotions.

[0095] The advertisement distribution system can further include a purchase intention estimation unit that estimates the user's purchasing intention. For example, the system analyzes the user's past purchase history and browsing history to generate advertisements for products and services that the user is likely to purchase. Specifically, the system can display advertisements that suggest products that are highly related to products that the user has previously purchased. Also, if the user frequently browses a particular product, it can display advertisements for sale information or coupons related to that product. This makes it possible to provide personalized advertisements based on the user's purchasing intention.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The preference analysis unit analyzes the recipient's preferences. For example, it collects data such as the recipient's browsing history, purchase history, and social media activity, and the generation AI analyzes this data. Step 2: The advertisement generation unit generates an advertisement based on the results of the analysis by the preference analysis unit. For example, an advertisement is generated that includes people and content that are likely to be liked by the recipient. Step 3: The ad distribution unit distributes the ads generated by the ad generation unit to the recipient. For example, when the recipient is browsing a website, the generation AI displays the most suitable ads for the recipient in real time.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0134] The 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.

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

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a preference analysis unit that analyzes the preferences of a recipient; an advertisement generation unit that generates an advertisement based on the result of the analysis by the preference analysis unit; an advertisement distribution unit that distributes the advertisement generated by the advertisement generation unit to recipients; A system characterized by:

2. The preference analysis unit Integrate data from different devices to identify global preferences 2. The system of claim 1.

3. The advertisement generation unit Adjust the content and design of the ad in real time to generate the ad that elicits the most positive emotions 2. The system of claim 1.

4. The advertisement distribution unit Optimize the timing of ad delivery and deliver the ad at the time that elicits the most positive emotions 2. The system of claim 1.

5. The advertisement distribution unit Measure the ads' click-through rates, conversion rates, and emotional responses to assess their overall effectiveness.

2. The system of claim 1.

Images