System

Abstract

An object of a system according to an embodiment is to display an optimum advertisement in a Metaverse based on a behavior pattern or a history of a user.SOLUTION: A system includes a behavior data collection part, a history data collection part, a data analysis part, an advertisement selection part, and an advertisement display part. The action data collection unit collects action data of a user. The history data collection unit collects history data of a user. The data analysis unit analyzes the data collected by the action data collection unit and the history data collection unit. The advertisement selection unit selects an advertisement based on a result of the analysis by the data analysis unit. The advertisement display unit displays the advertisement selected by the advertisement selection unit in the Metaverse.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that advertisement display within the metaverse is not individually optimized based on a user's behavioral patterns or history.

[0005] The system according to the embodiment aims to display optimal advertisements in the metaverse based on the user's behavioral patterns and history. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavioral data collection unit, a history data collection unit, a data analysis unit, an advertisement selection unit, and an advertisement display unit. The behavioral data collection unit collects user behavioral data. The history data collection unit collects user history data. The data analysis unit analyzes the data collected by the behavioral data collection unit and the history data collection unit. The advertisement selection unit selects an advertisement based on the results of the analysis by the data analysis unit. The advertisement display unit displays the advertisement selected by the advertisement selection unit in the metaverse. [Effects of the Invention]

[0007] The system according to the embodiment can display optimal advertisements in the metaverse based on the user's behavioral patterns and history. [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 display system according to an embodiment of the present invention is a system that automatically selects and displays advertisements in the metaverse based on an individual's behavioral patterns and history. As a result, the advertisement display system can analyze the user's behavioral data and history data, select the most appropriate advertisement based on the results, and display it in the metaverse.

[0029] An advertisement display system according to an embodiment includes a behavioral data collection unit, a history data collection unit, a data analysis unit, an advertisement selection unit, and an advertisement display unit. The behavioral data collection unit collects user behavioral data. For example, it collects information such as which areas the user visited, which items the user purchased, and which events the user participated in. The history data collection unit collects user history data. For example, it collects information such as which advertisements the user was interested in in the past, which content the user viewed, and which friends the user interacted with. The data analysis unit analyzes the data collected by the behavioral data collection unit and the history data collection unit. For example, the generation AI analyzes the collected behavioral data and history data to select advertisements optimal for the user. The advertisement selection unit selects advertisements based on the results of the analysis by the data analysis unit. For example, if a user has purchased many sports-related items in the past, advertisements for sporting goods are preferentially displayed. The advertisement display unit displays the advertisements selected by the advertisement selection unit in the metaverse. For example, it places advertisements in appropriate locations depending on the areas the user visits and events the user participates in. As a result, the advertisement display system according to the embodiment can select an optimal advertisement based on the user's behavioral data and history data, and display the advertisement in the metaverse.

[0030] The behavioral data collection unit can collect heart rate and eye tracking data in addition to user behavioral data and analyze the psychological state behind the behavior. For example, when a user visits a specific area in the metaverse, the behavioral data collection unit simultaneously collects heart rate and eye tracking data and analyzes the data to estimate the user's psychological state. For example, the behavioral data collection unit determines the user's level of excitement from an increase in heart rate or the degree of eye concentration. When a user purchases an item, the behavioral data collection unit collects biometric information before and after the purchase to analyze the psychological state behind the purchase. For example, the behavioral data collection unit analyzes heart rate fluctuations and eye movement before the purchase to estimate the user's willingness to purchase. When a user participates in an event, the behavioral data collection unit collects biometric information during the event to analyze the impact of the event. For example, the behavioral data collection unit analyzes heart rate and eye movement during the event to estimate the level of excitement and satisfaction with the event. This allows for more accurate advertisement selection by analyzing the psychological state behind the user's behavior.

[0031] The behavioral data collection unit can collect the content of conversations that users have in the metaverse as text data, analyze it using natural language processing technology, and identify their interests and concerns. The behavioral data collection unit, for example, collects the content of conversations that users have in the metaverse as text data and analyzes it using natural language processing technology. For example, it extracts keywords that appear frequently in the conversations to identify the user's interests and concerns. The behavioral data collection unit also analyzes the content of the conversations and quantifies how much the user talks about specific topics. For example, for a user who frequently talks about sports or music, it displays advertisements related to those topics. The behavioral data collection unit also performs sentiment analysis of the content of the conversations to identify topics that the user has positive feelings about. For example, it prioritizes displaying advertisements related to topics that the user talks about with enjoyment. In this way, by analyzing the content of the user's conversations, it is possible to identify their interests and concerns and select more appropriate advertisements.

[0032] The behavioral data collection unit can collect user behavioral data in real time and instantly reflect it in advertisement display. For example, when a user visits a specific area in the metaverse, the behavioral data collection unit collects the behavioral data in real time and instantly displays related advertisements. For example, when a user visits a sports area, an advertisement for sports equipment is displayed. Furthermore, the behavioral data collection unit collects behavioral data in real time when a user purchases an item and displays related advertisements immediately after the purchase. For example, when a user purchases an electronic device, an advertisement for accessories is displayed. Furthermore, the behavioral data collection unit collects behavioral data in real time when a user participates in an event and displays related advertisements during the event. For example, when a user participates in a music event, a music-related advertisement is displayed. In this way, by collecting user behavioral data in real time and instantly reflecting it in advertisement display, more effective advertisement display is possible.

[0033] The behavioral data collection unit can integrate behavioral data across different metaverse platforms to analyze more comprehensive behavioral patterns. The behavioral data collection unit, for example, collects, integrates, and analyzes user behavioral data from different metaverse platforms. For example, it integrates visited areas and purchase histories across multiple platforms to identify comprehensive behavioral patterns. The behavioral data collection unit also compares behavioral data across different platforms to extract common behavioral patterns. For example, a user who visits the same area across multiple platforms is displayed advertisements related to that area. The behavioral data collection unit also integrates behavioral data across different platforms to more accurately identify user interests. For example, it analyzes a user's purchasing tendencies based on purchase histories across multiple platforms. In this way, by integrating behavioral data across different metaverse platforms, it is possible to analyze more comprehensive behavioral patterns.

[0034] The history data collection unit can collect online activities performed by the user outside the metaverse in addition to the user's past behavioral history. The history data collection unit, for example, collects data on social media posts made by the user outside the metaverse and integrates it with the user's past behavioral history for analysis. For example, the user's interests and concerns are identified from the content of posts on the social media, and relevant advertisements are displayed. The history data collection unit also collects the user's search history data and integrates it with the user's past behavioral history for analysis. For example, topics of interest to the user are identified from the search history, and relevant advertisements are displayed. The history data collection unit also collects the user's online shopping history and integrates it with the user's past behavioral history for analysis. For example, the user's purchasing tendencies are identified from the user's online shopping purchase history, and relevant advertisements are displayed. In this way, by collecting the user's online activities outside the metaverse, more comprehensive behavioral patterns can be analyzed.

[0035] The history data collection unit can analyze the user's history data along a time axis and identify changes in behavioral patterns. For example, the history data collection unit analyzes the user's past behavioral history data along a time axis and identifies changes in behavioral patterns. For example, it analyzes changes in visited areas and purchase history over a specific period of time. The history data collection unit also visualizes the user's history data along a time axis and visually displays changes in behavioral patterns. For example, it displays behavioral data by hour in the form of a graph or chart. The history data collection unit also analyzes the user's history data along a time axis and identifies changes in interests and concerns over a specific period of time. For example, it analyzes changes in interests by season from past data. This allows more appropriate advertisements to be selected by identifying changes in the user's behavioral patterns.

[0036] The history data collection unit can synchronize a user's history data across different devices and perform integrated analysis. The history data collection unit, for example, builds a system that synchronizes a user's history data across different devices and performs integrated analysis. For example, data is synchronized across devices such as a PC, smartphone, and VR headset. The history data collection unit also integrates history data across different devices and analyzes user behavior patterns. For example, search history on a PC and purchase history on a smartphone are integrated and analyzed. The history data collection unit also develops a system that synchronizes history data across different devices in real time and performs integrated analysis. For example, changes in data across devices are immediately reflected. This enables more comprehensive analysis of behavior patterns by integrating history data across different devices.

[0037] The history data collection unit can compare the user's history data with other users and identify user groups with similar behavioral patterns. For example, the history data collection unit compares the user's history data with other users and identifies user groups with similar behavioral patterns. For example, it identifies a user group that frequently visits the same area. The history data collection unit also clusters the user's history data and identifies user groups with similar behavioral patterns. For example, it identifies a user group that tends to purchase the same items. The history data collection unit also analyzes the user's history data and builds a system that identifies user groups with similar behavioral patterns. For example, it identifies a user group that tends to participate in the same events. By identifying user groups with similar behavioral patterns, more effective advertisements can be selected.

[0038] The data analysis unit can use the generative AI to introduce an algorithm that predicts a user's potential interests and concerns when analyzing the behavioral data and history data. For example, the data analysis unit uses the generative AI to analyze the user's behavioral data and history data and develop an algorithm that predicts potential interests and concerns. For example, it identifies interests that the user is not yet aware of from past data. The data analysis unit also analyzes the user's behavioral data and history data and builds a machine learning model to predict potential interests and concerns. For example, it predicts future interests based on the user's behavioral patterns. The data analysis unit also uses the generative AI to predict a user's potential interests and concerns and develops a system that selects advertisements based on the results. For example, it displays advertisements for items that the user has not yet purchased but may be interested in. This allows for more effective advertisements to be selected by predicting the user's potential interests and concerns.

[0039] The data analysis unit can take into account the behavioral data of friends and followers in addition to the behavioral data and history data. For example, the data analysis unit collects the behavioral data of the user's friends and followers in addition to the user's behavioral data and history data, and reflects this in advertisement selection. For example, advertisements related to items purchased by friends may be displayed. The data analysis unit may also analyze the behavioral data of the user's friends and followers to identify behavioral patterns that influence the user. For example, advertisements related to events attended by friends may be displayed. The data analysis unit may also integrate the behavioral data of friends and followers in addition to the user's behavioral data and history data to improve the accuracy of advertisement selection. For example, advertisements related to topics that interest friends may be displayed. In this way, more effective advertisements may be selected by taking into account the behavioral data of the user's friends and followers.

[0040] The data analysis unit can compare user interests across different advertising categories and identify the most effective advertising category. For example, the data analysis unit analyzes user behavioral data and history data to compare interests across different advertising categories. For example, interest in advertisements for sporting goods and electronic devices can be compared to identify the most effective category. The data analysis unit also quantifies user interests across different advertising categories and develops an algorithm to identify the most effective advertising category. For example, the data analysis unit evaluates advertising categories based on click rates and purchase rates. The data analysis unit also builds a system to compare user interests across different advertising categories and identify the most effective advertising category. For example, the data analysis unit predicts the effectiveness of advertising categories based on past data. This makes it possible to identify the most effective advertising category by comparing user interests across different advertising categories.

[0041] The data analysis unit can take into account the user's current location information in the metaverse when selecting advertisements and display advertisements according to the location. The data analysis unit, for example, collects the user's current location information in the metaverse and builds a system that displays advertisements according to that location. For example, if the user is in a shopping area, relevant advertisements are displayed. The data analysis unit also analyzes the user's location information in real time and dynamically displays advertisements according to the location. For example, advertisements are updated every time the user moves. The data analysis unit also develops an algorithm that prioritizes displaying advertisements related to a specific area based on the user's location information. For example, if the user is in a tourist area, tourism-related advertisements are displayed. This makes it possible to display advertisements according to the location by taking into account the user's current location information in the metaverse.

[0042] When displaying an advertisement, the advertisement display unit can use the user's gaze tracking data to place the advertisement in the most visible position. The advertisement display unit, for example, collects the user's gaze tracking data and builds a system for placing the advertisement in the most visible position. For example, the advertisement is displayed in an area that the user pays the most attention to. The advertisement display unit also analyzes the gaze tracking data in real time and dynamically places the advertisement in a position where the user's gaze is concentrated. For example, the advertisement position is adjusted each time the user's gaze moves. The advertisement display unit also develops an algorithm for placing the advertisement in the most visible position based on the user's gaze tracking data. For example, the advertisement is displayed in an area where the gaze is most concentrated. In this way, the advertisement can be placed in the most visible position by using the user's gaze tracking data.

[0043] The advertisement display unit can take into account the user's current activity when displaying advertisements and display advertisements that correspond to the activity. The advertisement display unit, for example, builds a system that analyzes the user's current activity in real time and displays advertisements that correspond to that activity. For example, game-related advertisements are displayed while the user is playing a game. The advertisement display unit also collects user activity data and dynamically displays advertisements that correspond to the activity. For example, advertisements for communication tools are displayed while the user is chatting. The advertisement display unit also develops an algorithm that selects the optimal advertisement based on the user's activity. For example, a relaxation-related advertisement is displayed when the user is relaxing. This makes it possible to display advertisements that correspond to the activity by taking the user's current activity into consideration.

[0044] The advertisement display unit can select the optimal display format depending on the type of device used by the user when displaying an advertisement. The advertisement display unit, for example, detects the type of device used by the user and builds a system that selects the optimal advertisement display format for that device. For example, it displays 3D advertisements on a VR headset. The advertisement display unit also develops an algorithm that dynamically changes the advertisement display format depending on the type of device. For example, it displays banner advertisements on PCs and interstitial advertisements on smartphones. The advertisement display unit also collects user device data and builds a system that selects the optimal advertisement display format. For example, it adjusts advertisements depending on the screen size and resolution of the device. This improves advertisement visibility by selecting the optimal display format depending on the type of device used by the user.

[0045] The advertisement display unit can change the design of the advertisement when displaying the advertisement according to the user's current environment in the metaverse. The advertisement display unit, for example, detects the user's current environment in the metaverse and builds a system that changes the design of the advertisement according to the environment. For example, it displays advertisements with a modern design in urban areas and natural designs in natural areas. The advertisement display unit also analyzes environmental data in the metaverse in real time and develops an algorithm that dynamically changes the design of the advertisement according to the environment. For example, it adjusts the advertisement to match the color tone or theme of the environment. The advertisement display unit also builds a system that selects the optimal advertisement design based on the user's environmental data. For example, it changes the design of the advertisement according to the characteristics of the area the user visits. In this way, the visibility of the advertisement is improved by changing the design of the advertisement according to the user's current environment in the metaverse.

[0046] The advertising effectiveness feedback unit analyzes the user's heart rate and electrodermal response when collecting feedback on the advertising effectiveness, and evaluates the physiological response to the advertisement. For example, the advertising effectiveness feedback unit collects the user's heart rate and electrodermal response when an advertisement is displayed, and analyzes the data to evaluate the physiological response to the advertisement. For example, the effectiveness of the advertisement is evaluated based on an increase in heart rate or a change in electrodermal response. The advertising effectiveness feedback unit also collects the user's biometric information in real time and builds a system that analyzes the physiological response while the advertisement is displayed. For example, it monitors changes in the heart rate and electrodermal response while the advertisement is displayed. The advertising effectiveness feedback unit also analyzes the user's biometric information when collecting feedback on the advertising effectiveness, and develops an algorithm to evaluate the physiological response to the advertisement. For example, it quantifies the effectiveness of the advertisement based on changes in the biometric information. In this way, the physiological response to the advertisement can be evaluated by analyzing the user's biometric information.

[0047] The advertising effectiveness feedback unit reanalyzes user behavioral data and history data when collecting feedback on advertising effectiveness, thereby enabling long-term evaluation of the impact of advertising. For example, the advertising effectiveness feedback unit reanalyzes user behavioral data and history data when collecting feedback on advertising effectiveness, thereby building a system for long-term evaluation of the impact of advertising. For example, it analyzes behavioral changes after an advertisement is displayed. The advertising effectiveness feedback unit also reanalyzes user behavioral data and history data to develop an algorithm for long-term evaluation of the impact of advertising. For example, it analyzes changes in purchase history and visited areas after an advertisement is displayed. The advertising effectiveness feedback unit also reanalyzes user behavioral data and history data when collecting feedback on advertising effectiveness, thereby developing a system for long-term evaluation of the impact of advertising. For example, it analyzes changes in interests and concerns after an advertisement is displayed. In this way, the impact of advertising can be evaluated long-term by reanalyzing user behavioral data and history data.

[0048] The advertising effectiveness feedback unit can compare advertising effectiveness feedback between different metaverse platforms and identify the most effective advertising display method. The advertising effectiveness feedback unit, for example, builds a system that collects and compares advertising effectiveness feedback between different metaverse platforms. For example, it compares click rates and purchase rates on each platform. The advertising effectiveness feedback unit also analyzes advertising effectiveness feedback between metaverse platforms and develops an algorithm that identifies the most effective advertising display method. For example, it quantifies advertising effectiveness for each platform. The advertising effectiveness feedback unit also develops a system that collects and compares advertising effectiveness feedback between different metaverse platforms. For example, it analyzes user responses on each platform. This makes it possible to compare advertising effectiveness between different metaverse platforms and identify the most effective advertising display method.

[0049] The advertising effectiveness feedback unit can comprehensively evaluate the impact of an advertisement by taking into account the reactions of the user's friends and followers when collecting feedback on the advertising effectiveness. For example, the advertising effectiveness feedback unit builds a system that also takes into account the reactions of the user's friends and followers when collecting feedback on the advertising effectiveness. For example, it evaluates whether friends clicked on an advertisement. The advertising effectiveness feedback unit also analyzes the reactions of the user's friends and followers and develops an algorithm that comprehensively evaluates the impact of an advertisement. For example, it quantifies the impact of friends' reactions on the advertising effectiveness. The advertising effectiveness feedback unit also considers the reactions of the user's friends and followers when collecting feedback on the advertising effectiveness and develops a system that comprehensively evaluates the impact of an advertisement. For example, it evaluates the effectiveness of an advertisement based on the reactions of friends. In this way, the impact of an advertisement can be comprehensively evaluated by taking into account the reactions of the user's friends and followers.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The advertising display system can also analyze the type of avatar a user is using in the metaverse based on user behavior data and display advertisements related to that avatar. For example, if a user is using a sports avatar, advertisements for sports equipment can be displayed. Or, if a user is using a fantasy avatar, advertisements for fantasy-related items and events can be displayed. Furthermore, data can be collected when a user customizes their avatar, and advertisements related to that customization can be displayed. For example, displaying advertisements related to the avatar's clothing and accessories can more easily attract the user's interest.

[0052] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0053] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0054] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0055] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0056] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0057] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0058] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0059] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0060] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The behavioral data collection unit collects user behavioral data, such as which areas the user visited, which items they purchased, and which events they attended. Step 2: The history data collection unit collects user history data, such as information about which advertisements the user has shown interest in, which content the user has viewed, and which friends the user has interacted with. Step 3: The data analysis unit analyzes the data collected by the behavioral data collection unit and the history data collection unit. For example, the generation AI analyzes the collected behavioral data and history data and selects the most suitable advertisement for the user. Step 4: The advertisement selection unit selects advertisements based on the results of the analysis by the data analysis unit. For example, if the user has purchased many sports-related items in the past, advertisements for sports goods will be displayed preferentially. Step 5: The advertisement display unit displays the advertisement selected by the advertisement selection unit in the metaverse. For example, the advertisement is placed in an appropriate location depending on the area the user visits or the event the user attends.

[0063] (Example 2) The advertisement display system according to an embodiment of the present invention is a system that automatically selects and displays advertisements in the metaverse based on an individual's behavioral patterns and history. As a result, the advertisement display system can analyze the user's behavioral data and history data, select the most appropriate advertisement based on the results, and display it in the metaverse.

[0064] An advertisement display system according to an embodiment includes a behavioral data collection unit, a history data collection unit, a data analysis unit, an advertisement selection unit, and an advertisement display unit. The behavioral data collection unit collects user behavioral data. For example, it collects information such as which areas the user visited, which items the user purchased, and which events the user participated in. The history data collection unit collects user history data. For example, it collects information such as which advertisements the user was interested in in the past, which content the user viewed, and which friends the user interacted with. The data analysis unit analyzes the data collected by the behavioral data collection unit and the history data collection unit. For example, the generation AI analyzes the collected behavioral data and history data to select advertisements optimal for the user. The advertisement selection unit selects advertisements based on the results of the analysis by the data analysis unit. For example, if a user has purchased many sports-related items in the past, advertisements for sporting goods are preferentially displayed. The advertisement display unit displays the advertisements selected by the advertisement selection unit in the metaverse. For example, it places advertisements in appropriate locations depending on the areas the user visits and events the user participates in. As a result, the advertisement display system according to the embodiment can select an optimal advertisement based on the user's behavioral data and history data, and display the advertisement in the metaverse.

[0065] The behavioral data collection unit can collect heart rate and eye tracking data in addition to user behavioral data and analyze the psychological state behind the behavior. For example, when a user visits a specific area in the metaverse, the behavioral data collection unit simultaneously collects heart rate and eye tracking data and analyzes the data to estimate the user's psychological state. For example, the behavioral data collection unit determines the user's level of excitement from an increase in heart rate or the degree of eye concentration. When a user purchases an item, the behavioral data collection unit collects biometric information before and after the purchase to analyze the psychological state behind the purchase. For example, the behavioral data collection unit analyzes heart rate fluctuations and eye movement before the purchase to estimate the user's willingness to purchase. When a user participates in an event, the behavioral data collection unit collects biometric information during the event to analyze the impact of the event. For example, the behavioral data collection unit analyzes heart rate and eye movement during the event to estimate the level of excitement and satisfaction with the event. This allows for more accurate advertisement selection by analyzing the psychological state behind the user's behavior.

[0066] The behavioral data collection unit can collect the content of conversations that users have in the metaverse as text data, analyze it using natural language processing technology, and identify their interests and concerns. The behavioral data collection unit, for example, collects the content of conversations that users have in the metaverse as text data and analyzes it using natural language processing technology. For example, it extracts keywords that appear frequently in the conversations to identify the user's interests and concerns. The behavioral data collection unit also analyzes the content of the conversations and quantifies how much the user talks about specific topics. For example, for a user who frequently talks about sports or music, it displays advertisements related to those topics. The behavioral data collection unit also performs sentiment analysis of the content of the conversations to identify topics that the user has positive feelings about. For example, it prioritizes displaying advertisements related to topics that the user talks about with enjoyment. In this way, by analyzing the content of the user's conversations, it is possible to identify their interests and concerns and select more appropriate advertisements.

[0067] The behavioral data collection unit can use the emotion estimation function to estimate the emotion a user has when performing a specific behavior and classify the behavioral data based on the emotion. For example, the behavioral data collection unit estimates the emotion a user has when visiting a specific area in the metaverse and classifies the behavioral data based on the emotion. For example, if the emotion in the visited area is positive, an advertisement related to that area is displayed. The behavioral data collection unit also estimates the emotion a user has when purchasing an item and analyzes the emotion behind the purchasing behavior. For example, if the emotion at the time of purchase is positive, an advertisement for a similar item is displayed. The behavioral data collection unit also estimates the emotion a user has when participating in an event and analyzes the impact of the event. For example, if the emotion during the event is positive, an advertisement for a similar event is displayed. In this way, by classifying the behavioral data based on the user's emotion, more appropriate advertisements can be selected.

[0068] The behavioral data collection unit can collect user behavioral data in real time and instantly reflect it in advertisement display. For example, when a user visits a specific area in the metaverse, the behavioral data collection unit collects the behavioral data in real time and instantly displays related advertisements. For example, when a user visits a sports area, an advertisement for sports equipment is displayed. Furthermore, the behavioral data collection unit collects behavioral data in real time when a user purchases an item and displays related advertisements immediately after the purchase. For example, when a user purchases an electronic device, an advertisement for accessories is displayed. Furthermore, the behavioral data collection unit collects behavioral data in real time when a user participates in an event and displays related advertisements during the event. For example, when a user participates in a music event, a music-related advertisement is displayed. In this way, by collecting user behavioral data in real time and instantly reflecting it in advertisement display, more effective advertisement display is possible.

[0069] The behavioral data collection unit can integrate behavioral data across different metaverse platforms to analyze more comprehensive behavioral patterns. The behavioral data collection unit, for example, collects, integrates, and analyzes user behavioral data from different metaverse platforms. For example, it integrates visited areas and purchase histories across multiple platforms to identify comprehensive behavioral patterns. The behavioral data collection unit also compares behavioral data across different platforms to extract common behavioral patterns. For example, a user who visits the same area across multiple platforms is displayed advertisements related to that area. The behavioral data collection unit also integrates behavioral data across different platforms to more accurately identify user interests. For example, it analyzes a user's purchasing tendencies based on purchase histories across multiple platforms. In this way, by integrating behavioral data across different metaverse platforms, it is possible to analyze more comprehensive behavioral patterns.

[0070] The behavioral data collection unit can use the emotion estimation function to identify behaviors that evoke the most positive emotions in the user and select advertisements based on those behaviors. For example, the behavioral data collection unit estimates the emotions felt by the user when visiting a specific area in the metaverse and displays advertisements related to the area that evokes the most positive emotions. For example, advertisements related to areas that the user enjoys are preferentially displayed. The behavioral data collection unit also estimates the emotions felt by the user when purchasing an item and displays advertisements related to items that evoke the most positive emotions in the user. For example, advertisements related to items that the user purchased with satisfaction are displayed. The behavioral data collection unit also estimates the emotions felt by the user when participating in an event and displays advertisements related to events that evoke the most positive emotions in the user. For example, advertisements related to events that the user enjoys are preferentially displayed. This maximizes the effectiveness of advertisements by selecting advertisements based on behaviors that evoke the most positive emotions in the user.

[0071] The history data collection unit can collect online activities performed by the user outside the metaverse in addition to the user's past behavioral history. The history data collection unit, for example, collects data on social media posts made by the user outside the metaverse and integrates it with the user's past behavioral history for analysis. For example, the user's interests and concerns are identified from the content of posts on the social media, and relevant advertisements are displayed. The history data collection unit also collects the user's search history data and integrates it with the user's past behavioral history for analysis. For example, topics of interest to the user are identified from the search history, and relevant advertisements are displayed. The history data collection unit also collects the user's online shopping history and integrates it with the user's past behavioral history for analysis. For example, the user's purchasing tendencies are identified from the user's online shopping purchase history, and relevant advertisements are displayed. In this way, by collecting the user's online activities outside the metaverse, more comprehensive behavioral patterns can be analyzed.

[0072] The history data collection unit can analyze the user's history data along a time axis and identify changes in behavioral patterns. For example, the history data collection unit analyzes the user's past behavioral history data along a time axis and identifies changes in behavioral patterns. For example, it analyzes changes in visited areas and purchase history over a specific period of time. The history data collection unit also visualizes the user's history data along a time axis and visually displays changes in behavioral patterns. For example, it displays behavioral data by hour in the form of a graph or chart. The history data collection unit also analyzes the user's history data along a time axis and identifies changes in interests and concerns over a specific period of time. For example, it analyzes changes in interests by season from past data. This allows more appropriate advertisements to be selected by identifying changes in the user's behavioral patterns.

[0073] The history data collection unit can use the emotion estimation function to analyze the user's emotions regarding their past behavioral history and select advertisements that elicit positive emotions. The history data collection unit, for example, estimates the user's emotions regarding their past behavioral history and selects advertisements that elicit positive emotions. For example, it displays advertisements related to items that the user felt positive emotions about in the past. The history data collection unit also analyzes the user's emotions regarding their past behavioral history and selects advertisements that elicit positive emotions. For example, it displays advertisements related to events that the user enjoyed in the past. The history data collection unit also analyzes the user's emotions regarding their past behavioral history and selects advertisements that elicit positive emotions. For example, it displays advertisements related to purchases that the user was satisfied with in the past. In this way, it is possible to select advertisements that elicit positive emotions by analyzing the user's emotions regarding their past behavioral history.

[0074] The history data collection unit can synchronize a user's history data across different devices and perform integrated analysis. The history data collection unit, for example, builds a system that synchronizes a user's history data across different devices and performs integrated analysis. For example, data is synchronized across devices such as a PC, smartphone, and VR headset. The history data collection unit also integrates history data across different devices and analyzes user behavior patterns. For example, search history on a PC and purchase history on a smartphone are integrated and analyzed. The history data collection unit also develops a system that synchronizes history data across different devices in real time and performs integrated analysis. For example, changes in data across devices are immediately reflected. This enables more comprehensive analysis of behavior patterns by integrating history data across different devices.

[0075] The history data collection unit can compare the user's history data with other users and identify user groups with similar behavioral patterns. For example, the history data collection unit compares the user's history data with other users and identifies user groups with similar behavioral patterns. For example, it identifies a user group that frequently visits the same area. The history data collection unit also clusters the user's history data and identifies user groups with similar behavioral patterns. For example, it identifies a user group that tends to purchase the same items. The history data collection unit also analyzes the user's history data and builds a system that identifies user groups with similar behavioral patterns. For example, it identifies a user group that tends to participate in the same events. By identifying user groups with similar behavioral patterns, more effective advertisements can be selected.

[0076] The historical data collection unit can use the emotion estimation function to identify the advertising category in which the user is most interested based on the user's historical data, and preferentially display advertisements in that category. The historical data collection unit, for example, estimates the user's emotions based on the historical data and identifies the advertising category in which the user is most interested. For example, it preferentially displays advertisements in categories in which the user has felt positive emotions in the past. The historical data collection unit also analyzes the user's historical data and uses the emotion estimation function to identify the advertising category in which the user is most interested. For example, it displays advertisements related to content that the user has enjoyed in the past. The historical data collection unit also uses the emotion estimation function to identify the advertising category in which the user is most interested based on the user's historical data, and preferentially displays advertisements in that category. For example, it displays advertisements related to a purchase history that the user has been satisfied with in the past. In this way, the effectiveness of advertising can be maximized by identifying the advertising category in which the user is most interested and preferentially displaying advertisements in that category.

[0077] The data analysis unit can use the generative AI to introduce an algorithm that predicts a user's potential interests and concerns when analyzing the behavioral data and history data. For example, the data analysis unit uses the generative AI to analyze the user's behavioral data and history data and develop an algorithm that predicts potential interests and concerns. For example, it identifies interests that the user is not yet aware of from past data. The data analysis unit also analyzes the user's behavioral data and history data and builds a machine learning model to predict potential interests and concerns. For example, it predicts future interests based on the user's behavioral patterns. The data analysis unit also uses the generative AI to predict a user's potential interests and concerns and develops a system that selects advertisements based on the results. For example, it displays advertisements for items that the user has not yet purchased but may be interested in. This allows for more effective advertisements to be selected by predicting the user's potential interests and concerns.

[0078] The data analysis unit can take into account the behavioral data of friends and followers in addition to the behavioral data and history data. For example, the data analysis unit collects the behavioral data of the user's friends and followers in addition to the user's behavioral data and history data, and reflects this in advertisement selection. For example, advertisements related to items purchased by friends may be displayed. The data analysis unit may also analyze the behavioral data of the user's friends and followers to identify behavioral patterns that influence the user. For example, advertisements related to events attended by friends may be displayed. The data analysis unit may also integrate the behavioral data of friends and followers in addition to the user's behavioral data and history data to improve the accuracy of advertisement selection. For example, advertisements related to topics that interest friends may be displayed. In this way, more effective advertisements may be selected by taking into account the behavioral data of the user's friends and followers.

[0079] The data analysis unit can use the emotion estimation function to develop an analytical method for selecting an advertisement that will evoke the most positive emotions in the user. For example, the data analysis unit uses the emotion estimation function to develop an analytical method for selecting an advertisement that will evoke the most positive emotions in the user. For example, the optimal advertisement is selected based on past emotional responses to advertisements. The data analysis unit also analyzes the user's emotional data and builds an algorithm to identify advertisements that elicit positive emotions. For example, advertisements with high emotional scores are preferentially displayed. The data analysis unit also uses the emotion estimation function to develop an analytical method for selecting an advertisement that will evoke the most positive emotions in the user, and reflects the results in advertisement selection. For example, the data analysis unit analyzes the user's emotional data in real time and displays the optimal advertisement. This allows the effectiveness of advertisements to be maximized by selecting an advertisement that will evoke the most positive emotions in the user.

[0080] The data analysis unit can compare user interests across different advertising categories and identify the most effective advertising category. For example, the data analysis unit analyzes user behavioral data and history data to compare interests across different advertising categories. For example, interest in advertisements for sporting goods and electronic devices can be compared to identify the most effective category. The data analysis unit also quantifies user interests across different advertising categories and develops an algorithm to identify the most effective advertising category. For example, the data analysis unit evaluates advertising categories based on click rates and purchase rates. The data analysis unit also builds a system to compare user interests across different advertising categories and identify the most effective advertising category. For example, the data analysis unit predicts the effectiveness of advertising categories based on past data. This makes it possible to identify the most effective advertising category by comparing user interests across different advertising categories.

[0081] The data analysis unit can take into account the user's current location information in the metaverse when selecting advertisements and display advertisements according to the location. The data analysis unit, for example, collects the user's current location information in the metaverse and builds a system that displays advertisements according to that location. For example, if the user is in a shopping area, relevant advertisements are displayed. The data analysis unit also analyzes the user's location information in real time and dynamically displays advertisements according to the location. For example, advertisements are updated every time the user moves. The data analysis unit also develops an algorithm that prioritizes displaying advertisements related to a specific area based on the user's location information. For example, if the user is in a tourist area, tourism-related advertisements are displayed. This makes it possible to display advertisements according to the location by taking into account the user's current location information in the metaverse.

[0082] The data analysis unit can use the emotion estimation function to optimize the timing of advertisement display based on the user's emotions. The data analysis unit, for example, uses the emotion estimation function to build a system that optimizes the timing of advertisement display based on the user's emotions. For example, advertisements are displayed at times when the user is feeling positive emotions. The data analysis unit also analyzes user emotion data in real time and develops an algorithm that identifies the optimal timing of advertisement display. For example, advertisements are displayed at times when the emotion score is high. The data analysis unit also uses the emotion estimation function to optimize the timing of advertisement display based on the user's emotions and reflects the results in advertisement selection. For example, the timing of advertisement display is adjusted based on the user's emotion data. In this way, the effectiveness of advertisements can be maximized by optimizing the timing of advertisement display based on the user's emotions.

[0083] When displaying an advertisement, the advertisement display unit can use the user's gaze tracking data to place the advertisement in the most visible position. The advertisement display unit, for example, collects the user's gaze tracking data and builds a system for placing the advertisement in the most visible position. For example, the advertisement is displayed in an area that the user pays the most attention to. The advertisement display unit also analyzes the gaze tracking data in real time and dynamically places the advertisement in a position where the user's gaze is concentrated. For example, the advertisement position is adjusted each time the user's gaze moves. The advertisement display unit also develops an algorithm for placing the advertisement in the most visible position based on the user's gaze tracking data. For example, the advertisement is displayed in an area where the gaze is most concentrated. In this way, the advertisement can be placed in the most visible position by using the user's gaze tracking data.

[0084] The advertisement display unit can take into account the user's current activity when displaying advertisements and display advertisements that correspond to the activity. The advertisement display unit, for example, builds a system that analyzes the user's current activity in real time and displays advertisements that correspond to that activity. For example, game-related advertisements are displayed while the user is playing a game. The advertisement display unit also collects user activity data and dynamically displays advertisements that correspond to the activity. For example, advertisements for communication tools are displayed while the user is chatting. The advertisement display unit also develops an algorithm that selects the optimal advertisement based on the user's activity. For example, a relaxation-related advertisement is displayed when the user is relaxing. This makes it possible to display advertisements that correspond to the activity by taking the user's current activity into consideration.

[0085] The advertisement display unit can use the emotion estimation function to display advertisements at the timing when the user feels the most positive emotion. For example, the advertisement display unit uses the emotion estimation function to build a system that displays advertisements at the timing when the user feels the most positive emotion. For example, the advertisement is displayed at a timing when the user is having fun. The advertisement display unit also analyzes user emotion data in real time and develops an algorithm that displays advertisements at a timing when positive emotion is strong. For example, the advertisement is displayed at a timing when the emotion score is high. The advertisement display unit also uses the emotion estimation function to display advertisements at the timing when the user feels the most positive emotion and reflects the result in advertisement selection. For example, the timing of advertisement display is adjusted based on the user emotion data. This allows the effectiveness of the advertisement to be maximized by displaying advertisements at the timing when the user feels the most positive emotion.

[0086] The advertisement display unit can select the optimal display format depending on the type of device used by the user when displaying an advertisement. The advertisement display unit, for example, detects the type of device used by the user and builds a system that selects the optimal advertisement display format for that device. For example, it displays 3D advertisements on a VR headset. The advertisement display unit also develops an algorithm that dynamically changes the advertisement display format depending on the type of device. For example, it displays banner advertisements on PCs and interstitial advertisements on smartphones. The advertisement display unit also collects user device data and builds a system that selects the optimal advertisement display format. For example, it adjusts advertisements depending on the screen size and resolution of the device. This improves advertisement visibility by selecting the optimal display format depending on the type of device used by the user.

[0087] The advertisement display unit can change the design of the advertisement when displaying the advertisement according to the user's current environment in the metaverse. The advertisement display unit, for example, detects the user's current environment in the metaverse and builds a system that changes the design of the advertisement according to the environment. For example, it displays advertisements with a modern design in urban areas and natural designs in natural areas. The advertisement display unit also analyzes environmental data in the metaverse in real time and develops an algorithm that dynamically changes the design of the advertisement according to the environment. For example, it adjusts the advertisement to match the color tone or theme of the environment. The advertisement display unit also builds a system that selects the optimal advertisement design based on the user's environmental data. For example, it changes the design of the advertisement according to the characteristics of the area the user visits. In this way, the visibility of the advertisement is improved by changing the design of the advertisement according to the user's current environment in the metaverse.

[0088] The advertisement display unit can use the emotion estimation function to adjust the advertisement display frequency based on the user's emotions. The advertisement display unit, for example, uses the emotion estimation function to build a system that adjusts the advertisement display frequency based on the user's emotions. For example, the advertisement display frequency is increased when the user has positive emotions. The advertisement display unit also analyzes the user's emotion data in real time and develops an algorithm that dynamically adjusts the advertisement display frequency according to the emotion. For example, the advertisement display frequency is increased when the emotion score is high. The advertisement display unit also uses the emotion estimation function to adjust the advertisement display frequency based on the user's emotions and reflects the result in advertisement selection. For example, the advertisement display frequency is adjusted based on the user's emotion data. In this way, the effectiveness of the advertisement can be maximized by adjusting the advertisement display frequency based on the user's emotions.

[0089] The advertising effectiveness feedback unit analyzes the user's heart rate and electrodermal response when collecting feedback on the advertising effectiveness, and evaluates the physiological response to the advertisement. For example, the advertising effectiveness feedback unit collects the user's heart rate and electrodermal response when an advertisement is displayed, and analyzes the data to evaluate the physiological response to the advertisement. For example, the effectiveness of the advertisement is evaluated based on an increase in heart rate or a change in electrodermal response. The advertising effectiveness feedback unit also collects the user's biometric information in real time and builds a system that analyzes the physiological response while the advertisement is displayed. For example, it monitors changes in the heart rate and electrodermal response while the advertisement is displayed. The advertising effectiveness feedback unit also analyzes the user's biometric information when collecting feedback on the advertising effectiveness, and develops an algorithm to evaluate the physiological response to the advertisement. For example, it quantifies the effectiveness of the advertisement based on changes in the biometric information. In this way, the physiological response to the advertisement can be evaluated by analyzing the user's biometric information.

[0090] The advertising effectiveness feedback unit reanalyzes user behavioral data and history data when collecting feedback on advertising effectiveness, thereby enabling long-term evaluation of the impact of advertising. For example, the advertising effectiveness feedback unit reanalyzes user behavioral data and history data when collecting feedback on advertising effectiveness, thereby building a system for long-term evaluation of the impact of advertising. For example, it analyzes behavioral changes after an advertisement is displayed. The advertising effectiveness feedback unit also reanalyzes user behavioral data and history data to develop an algorithm for long-term evaluation of the impact of advertising. For example, it analyzes changes in purchase history and visited areas after an advertisement is displayed. The advertising effectiveness feedback unit also reanalyzes user behavioral data and history data when collecting feedback on advertising effectiveness, thereby developing a system for long-term evaluation of the impact of advertising. For example, it analyzes changes in interests and concerns after an advertisement is displayed. In this way, the impact of advertising can be evaluated long-term by reanalyzing user behavioral data and history data.

[0091] The advertising effectiveness feedback unit can use the emotion estimation function to analyze a user's emotional response to an advertisement and identify advertisements that elicit positive emotions. The advertising effectiveness feedback unit, for example, uses the emotion estimation function to analyze a user's emotional response to an advertisement and build a system that identifies advertisements that elicit positive emotions. For example, the emotion score is analyzed while the advertisement is being displayed. The advertising effectiveness feedback unit also collects user emotion data in real time and develops an algorithm that analyzes the emotional response to an advertisement. For example, it identifies advertisements that elicit a large number of positive emotional responses. The advertising effectiveness feedback unit also uses the emotion estimation function to analyze a user's emotional response to an advertisement and develops a system that identifies advertisements that elicit positive emotions. For example, it evaluates the effectiveness of the advertisement based on the emotion data. In this way, it is possible to identify advertisements that elicit positive emotions by analyzing the user's emotional response.

[0092] The advertising effectiveness feedback unit can compare advertising effectiveness feedback between different metaverse platforms and identify the most effective advertising display method. The advertising effectiveness feedback unit, for example, builds a system that collects and compares advertising effectiveness feedback between different metaverse platforms. For example, it compares click rates and purchase rates on each platform. The advertising effectiveness feedback unit also analyzes advertising effectiveness feedback between metaverse platforms and develops an algorithm that identifies the most effective advertising display method. For example, it quantifies advertising effectiveness for each platform. The advertising effectiveness feedback unit also develops a system that collects and compares advertising effectiveness feedback between different metaverse platforms. For example, it analyzes user responses on each platform. This makes it possible to compare advertising effectiveness between different metaverse platforms and identify the most effective advertising display method.

[0093] The advertising effectiveness feedback unit can comprehensively evaluate the impact of an advertisement by taking into account the reactions of the user's friends and followers when collecting feedback on the advertising effectiveness. For example, the advertising effectiveness feedback unit builds a system that also takes into account the reactions of the user's friends and followers when collecting feedback on the advertising effectiveness. For example, it evaluates whether friends clicked on an advertisement. The advertising effectiveness feedback unit also analyzes the reactions of the user's friends and followers and develops an algorithm that comprehensively evaluates the impact of an advertisement. For example, it quantifies the impact of friends' reactions on the advertising effectiveness. The advertising effectiveness feedback unit also considers the reactions of the user's friends and followers when collecting feedback on the advertising effectiveness and develops a system that comprehensively evaluates the impact of an advertisement. For example, it evaluates the effectiveness of an advertisement based on the reactions of friends. In this way, the impact of an advertisement can be comprehensively evaluated by taking into account the reactions of the user's friends and followers.

[0094] The advertising effectiveness feedback unit uses an emotion estimation function to collect feedback on advertising effectiveness in real time and immediately reflect it in the next advertisement selection. The advertising effectiveness feedback unit, for example, uses the emotion estimation function to build a system that collects advertising effectiveness feedback in real time. For example, it collects emotional data of users in real time while an advertisement is being displayed. The advertising effectiveness feedback unit also develops an algorithm that collects advertising effectiveness feedback in real time and immediately reflects the results in the next advertisement selection. For example, it selects an advertisement based on the real-time emotional data. The advertising effectiveness feedback unit also uses the emotion estimation function to develop a system that collects advertising effectiveness feedback in real time and immediately reflects it in the next advertisement selection. For example, it adjusts the advertisement based on the real-time feedback. In this way, advertising effectiveness can be maximized by collecting advertising effectiveness feedback in real time and immediately reflecting it in the next advertisement selection.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The advertising display system can also analyze the type of avatar a user is using in the metaverse based on user behavior data and display advertisements related to that avatar. For example, if a user is using a sports avatar, advertisements for sports equipment can be displayed. Or, if a user is using a fantasy avatar, advertisements for fantasy-related items and events can be displayed. Furthermore, data can be collected when a user customizes their avatar, and advertisements related to that customization can be displayed. For example, displaying advertisements related to the avatar's clothing and accessories can more easily attract the user's interest.

[0097] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0098] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0099] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0100] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0101] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0102] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0103] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0104] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0105] The behavioral data collection unit can also collect data on other users' reactions to actions taken by the user in the metaverse and select advertisements based on those reactions. For example, if a user's actions in a specific area receive many likes and comments, advertisements related to that area can be displayed. Also, if an item purchased by the user receives high ratings from other users, advertisements related to that item can be displayed. Furthermore, if an event the user participated in receives positive reactions from other users, advertisements related to that event can be displayed.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The behavioral data collection unit collects user behavioral data, such as which areas the user visited, which items they purchased, and which events they attended. Step 2: The history data collection unit collects user history data, such as information about which advertisements the user has shown interest in, which content the user has viewed, and which friends the user has interacted with. Step 3: The data analysis unit analyzes the data collected by the behavioral data collection unit and the history data collection unit. For example, the generation AI analyzes the collected behavioral data and history data and selects the most suitable advertisement for the user. Step 4: The advertisement selection unit selects advertisements based on the results of the analysis by the data analysis unit. For example, if the user has purchased many sports-related items in the past, advertisements for sports goods will be displayed preferentially. Step 5: The advertisement display unit displays the advertisement selected by the advertisement selection unit in the metaverse. For example, the advertisement is placed in an appropriate location depending on the area the user visits or the event the user attends.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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]

[0175] 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 behavioral data collection unit that collects user behavioral data; a history data collection unit that collects user history data; a data analysis unit that analyzes the data collected by the behavioral data collection unit and the history data collection unit; an advertisement selection unit that selects an advertisement based on the result of the analysis by the data analysis unit; an advertisement display unit that displays the advertisement selected by the advertisement selection unit in the metaverse; A system characterized by:

2. The behavioral data collection unit Collect the behavioral data in real time and instantly reflect it in advertising displays 2. The system of claim 1.

3. The history data collection unit Collecting users' past behavioral history as well as online activities they undertake outside the metaverse 2. The system of claim 1.

4. The data analysis unit Using generative AI, an algorithm is introduced to predict the user's potential interests and concerns when analyzing the behavioral data and history data.

2. The system of claim 1.

5. The advertisement display unit When displaying ads, the system uses user eye tracking data to place the ads in the most visible positions.

2. The system of claim 1.

6. The behavioral data collection unit Estimating the emotion of a user when performing a specific action and classifying the action data based on the emotion 2. The system of claim 1.

Images