System

Abstract

An object of the system according to the embodiment is to exclude an advertisement from online content and regenerate the content from which the advertisement has been removed in a natural and attractive form.SOLUTION: A system includes an advertisement identification unit, an advertisement exclusion unit, and a content regeneration unit. The advertisement identifier identifies an advertisement from the online content. The advertisement exclusion unit excludes the advertisement identified by the advertisement identification unit. The content regeneration unit regenerates the content after the exclusion by the advertisement exclusion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques for removing advertisements from online content can result in insufficient content regeneration, resulting in a poor user experience.

[0005] The system of the embodiment aims to remove advertisements from online content and recreate the content in a natural and engaging manner after the advertisements have been removed. [Means for solving the problem]

[0006] The system according to the embodiment includes an advertisement identification unit, an advertisement removal unit, and a content regeneration unit. The advertisement identification unit identifies advertisements from online content. The advertisement removal unit removes advertisements identified by the advertisement identification unit. The content regeneration unit regenerates content after being removed by the advertisement removal unit. [Effects of the Invention]

[0007] Embodiments of the system can remove advertisements from online content and recreate the content in a natural and engaging manner after the advertisements have been removed. [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 AI ​​AdBlocker system according to an embodiment of the present invention is a system that provides a user with a comfortable browsing experience without being interrupted by advertisements when obtaining information online. As a result, the AI ​​AdBlocker system can provide a user with a comfortable browsing experience without being interrupted by advertisements.

[0029] An AI AdBlocker system according to an embodiment includes an ad identification unit, an ad removal unit, and a content regeneration unit. The ad identification unit identifies advertisements from online content. For example, the generation AI identifies banner and pop-up ads on web pages, in-stream ads in video content, and sponsored posts on social media. The generation AI identifies advertisements by analyzing data on content viewed by users based on pre-learned ad characteristics. The ad removal unit automatically removes identified advertisements using the generation AI. For example, if a banner ad is detected on a web page, it is removed and the surrounding content is rearranged. Ads in video content are skipped during playback or completely removed. Sponsored posts on social media are similarly removed. The generation AI is designed to maintain the integrity of the content when removing advertisements. The content regeneration unit regenerates the content after the ads have been removed. This regeneration process accurately preserves the information required by the user while ensuring that the content after the ads have been removed appears natural and appealing. For example, the layout of the web page is adjusted, and text and images are rearranged to fill the space where the ads were previously located. In video content, the system smoothly connects the section where the advertisement was removed. In social media feeds, the system rearranges other posts to make the feed look natural after the sponsored post is removed. As a result, the AI ​​AdBlocker system according to the embodiment can provide users with a comfortable browsing experience without being interrupted by advertisements.

[0030] The ad identification unit can identify the type of ad and propose the optimal removal method for each. For example, using generation AI, the ad identification unit can identify banner ads, pop-up ads, in-stream ads within video content, etc. on a web page and propose the optimal removal method for each. For example, banner ads can be deleted, pop-up ads can be blocked, and in-stream ads can be skipped. This makes it possible to propose the optimal removal method depending on the type of ad.

[0031] The advertisement identification unit can analyze the user's past browsing history and prioritize identifying particularly annoying advertisements. For example, the advertisement identification unit uses a generation AI to analyze the user's past browsing history and prioritize identifying advertisements that the user finds particularly annoying. For example, it prioritizes eliminating frequently displayed banner advertisements and pop-up advertisements. This allows advertisements that are particularly annoying to the user to be prioritized.

[0032] In addition to identifying advertisements, the advertisement identification unit can also simultaneously identify malicious content including phishing sites and malware using generation AI. In addition to identifying advertisements, the advertisement identification unit can also simultaneously identify malicious content including phishing sites and malware using generation AI, for example. For example, it can detect malicious links and downloads and display a warning. This makes it possible to simultaneously identify malicious content including phishing sites and malware.

[0033] The advertisement identification unit can identify audio advertisements or advertisements in podcasts using audio analysis technology. The advertisement identification unit can identify audio advertisements or advertisements in podcasts using audio analysis technology, for example, by using generation AI. For example, the advertisement identification unit can analyze audio data and identify advertisements. This allows the advertisements in audio advertisements and podcasts to be identified.

[0034] When removing an advertisement, the advertisement removal unit automatically generates an optimal layout depending on the position or size of the advertisement, thereby maintaining the consistency of the content. When removing an advertisement, the advertisement removal unit automatically generates an optimal layout depending on the position or size of the advertisement, for example, by using a generation AI, thereby maintaining the consistency of the content. For example, the advertisement removal unit rearranges text and images to fill in the space where the advertisement was located. This makes it possible to maintain the consistency of the content when removing an advertisement.

[0035] The ad removal unit can select the optimal removal method depending on the type of device or browser of the user when removing ads. The ad removal unit can use, for example, a generation AI to select the optimal removal method depending on the type of device or browser of the user when removing ads. For example, different removal methods can be applied to smartphones and desktops. This makes it possible to select the optimal removal method depending on the type of device or browser of the user.

[0036] When removing an advertisement, the advertisement removal unit can use a generation AI to automatically insert related content that the user is interested in instead of the advertisement. For example, the advertisement removal unit uses a generation AI to automatically insert related content that the user is interested in instead of the advertisement when removing an advertisement. For example, related articles or videos are displayed. This allows related content that the user is interested in to be automatically inserted instead of the advertisement.

[0037] When removing an advertisement, the advertisement removal unit can use the generation AI to display educational information or public service announcements instead of the advertisement. For example, when removing an advertisement, the advertisement removal unit uses the generation AI to display educational information or public service announcements instead of the advertisement. For example, it displays information on learning resources or public services. This makes it possible to display educational information or public service announcements instead of advertisements.

[0038] The content regeneration unit can develop an algorithm that faithfully reproduces the original design and layout when regenerating content after advertisements have been removed. The content regeneration unit, for example, uses a generation AI to develop an algorithm that faithfully reproduces the original design and layout when regenerating content after advertisements have been removed. For example, the content regeneration unit restores the layout of a web page to its original state. This allows the development of an algorithm that faithfully reproduces the original design and layout.

[0039] The content regeneration unit can optimize the regenerated content so that it looks consistent and natural across different devices and screen sizes. The content regeneration unit can, for example, use generation AI to optimize the regenerated content so that it looks consistent and natural across different devices and screen sizes. For example, it can adjust the display on smartphones, tablets, and desktops. This allows the regenerated content to be optimized so that it looks consistent and natural across different devices and screen sizes.

[0040] The content regeneration unit can incorporate elements of content that the user has previously given a high rating into the regenerated content, thereby providing a personalized experience. The content regeneration unit can, for example, use a generation AI to incorporate elements of content that the user has previously given a high rating into the regenerated content, thereby providing a personalized experience. For example, the content regeneration unit can reflect the user's preferred design or layout. This allows the regenerated content to incorporate elements that the user has previously given a high rating, thereby providing a personalized experience.

[0041] The content regeneration unit can insert additional information and related links based on the user's interests and concerns into the regenerated content. The content regeneration unit, for example, uses a generation AI to insert additional information and related links based on the user's interests and concerns into the regenerated content. For example, related articles and videos are displayed. This allows additional information and related links based on the user's interests and concerns to be inserted into the regenerated content.

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

[0043] The AI ​​AdBlocker system can also learn users' browsing patterns and dynamically change ad blocking methods depending on specific times and situations. For example, it can strictly block all ads when a user is browsing at work, but allow some ads during relaxation times. It can also apply different ad blocking policies to specific websites and applications. This allows for flexible ad blocking based on the user's situation and time of day.

[0044] The AI ​​AdBlocker system can also analyze a user's past purchase history and prioritize the removal of related ads. For example, it can remove ads related to products the user has already purchased, reducing unnecessary ad exposure. It can also display ads related to products the user is interested in but has not yet purchased. This allows for ad removal based on the user's purchase history.

[0045] The AI ​​AdBlocker system can also analyze a user's browsing history and prioritize the removal of ads related to specific genres or categories. For example, it can remove ads related to news sites or blogs that the user frequently visits, reducing the number of unnecessary ads displayed to the user. It can also display ads related to genres or categories that the user is interested in. This allows for the removal of ads based on the user's browsing history.

[0046] The AI ​​AdBlocker system also monitors the remaining battery level of the user's device and can reduce the display of ads when the battery is low. For example, if the battery is below 20%, all ads will be removed to conserve battery life. On the other hand, if the battery is sufficient, normal ads will be displayed. This allows the display of ads to be adjusted according to the battery status of the user's device.

[0047] The AI ​​AdBlocker system can also monitor a user's internet connection speed and reduce the number of ads displayed if the connection speed is slow. For example, if the connection speed falls below a certain threshold, it can remove all ads and improve page loading speed. Alternatively, if the connection speed is fast enough, it can display regular ads. This allows ad display to be adjusted according to the user's internet connection speed.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The ad identification unit identifies advertisements from online content. For example, the generation AI identifies banner and pop-up ads on web pages, in-stream ads in video content, and sponsored posts on social media. Based on the characteristics of advertisements it has previously learned, the generation AI analyzes data on the content viewed by users to identify advertisements. Step 2: The ad removal unit automatically removes identified ads using the generation AI. For example, if a banner ad is detected on a webpage, it is removed and the surrounding content is rearranged. Ads within video content are either skipped during playback or removed entirely. Sponsored posts on social media are also removed. The generation AI is designed to maintain the integrity of the content when removing ads. Step 3: In the content regeneration section, the AI ​​regenerates the content after the ads have been removed. This regeneration process ensures that the content looks natural and engaging after the ads have been removed, while still accurately preserving the information users need. For example, it adjusts the layout of a webpage and rearranges text and images to fill in the gaps where the ads were. For video content, it creates a seamless transition from the section where the ads were removed. For social media feeds, it rearranges other posts to make the feed look natural after the sponsored posts have been removed.

[0050] (Example 2) The AI ​​AdBlocker system according to an embodiment of the present invention is a system that provides a user with a comfortable browsing experience without being interrupted by advertisements when obtaining information online. As a result, the AI ​​AdBlocker system can provide a user with a comfortable browsing experience without being interrupted by advertisements.

[0051] An AI AdBlocker system according to an embodiment includes an ad identification unit, an ad removal unit, and a content regeneration unit. The ad identification unit identifies advertisements from online content. For example, the generation AI identifies banner and pop-up ads on web pages, in-stream ads in video content, and sponsored posts on social media. The generation AI identifies advertisements by analyzing data on content viewed by users based on pre-learned ad characteristics. The ad removal unit automatically removes identified advertisements using the generation AI. For example, if a banner ad is detected on a web page, it is removed and the surrounding content is rearranged. Ads in video content are skipped during playback or completely removed. Sponsored posts on social media are similarly removed. The generation AI is designed to maintain the integrity of the content when removing advertisements. The content regeneration unit regenerates the content after the ads have been removed. This regeneration process accurately preserves the information required by the user while ensuring that the content after the ads have been removed appears natural and appealing. For example, the layout of the web page is adjusted, and text and images are rearranged to fill the space where the ads were previously located. In video content, the system smoothly connects the section where the advertisement was removed. In social media feeds, the system rearranges other posts to make the feed look natural after the sponsored post is removed. As a result, the AI ​​AdBlocker system according to the embodiment can provide users with a comfortable browsing experience without being interrupted by advertisements.

[0052] The ad identification unit can identify the type of ad and propose the optimal removal method for each. For example, using generation AI, the ad identification unit can identify banner ads, pop-up ads, in-stream ads within video content, etc. on a web page and propose the optimal removal method for each. For example, banner ads can be deleted, pop-up ads can be blocked, and in-stream ads can be skipped. This makes it possible to propose the optimal removal method depending on the type of ad.

[0053] The advertisement identification unit can analyze the user's past browsing history and prioritize identifying particularly annoying advertisements. For example, the advertisement identification unit uses a generation AI to analyze the user's past browsing history and prioritize identifying advertisements that the user finds particularly annoying. For example, it prioritizes eliminating frequently displayed banner advertisements and pop-up advertisements. This allows advertisements that are particularly annoying to the user to be prioritized.

[0054] The advertisement identification unit can use the emotion estimation function to analyze the emotions that users have toward advertisements in real time and prioritize identifying advertisements that evoke particularly negative emotions. The advertisement identification unit can, for example, use the emotion estimation function to analyze the emotions that users have toward advertisements in real time and prioritize identifying advertisements that evoke particularly negative emotions. For example, advertisements that evoke anger or annoyance can be prioritized and eliminated. This makes it possible to prioritize and eliminate advertisements that evoke particularly negative emotions in the user.

[0055] In addition to identifying advertisements, the advertisement identification unit can also simultaneously identify malicious content including phishing sites and malware using generation AI. In addition to identifying advertisements, the advertisement identification unit can also simultaneously identify malicious content including phishing sites and malware using generation AI, for example. For example, it can detect malicious links and downloads and display a warning. This makes it possible to simultaneously identify malicious content including phishing sites and malware.

[0056] The advertisement identification unit can identify audio advertisements or advertisements in podcasts using audio analysis technology. The advertisement identification unit can identify audio advertisements or advertisements in podcasts using audio analysis technology, for example, by using generation AI. For example, the advertisement identification unit can analyze audio data and identify advertisements. This allows the advertisements in audio advertisements and podcasts to be identified.

[0057] The advertisement identification unit uses the emotion estimation function to preferentially display content that is particularly interesting to the user, thereby improving the user experience while identifying advertisements. The advertisement identification unit, for example, uses the emotion estimation function to preferentially display content that is particularly interesting to the user, thereby improving the user experience while identifying advertisements. For example, content that evokes positive emotions is preferentially displayed. This allows content that is particularly interesting to the user to be preferentially displayed, improving the user experience.

[0058] When removing an advertisement, the advertisement removal unit automatically generates an optimal layout depending on the position or size of the advertisement, thereby maintaining the consistency of the content. When removing an advertisement, the advertisement removal unit automatically generates an optimal layout depending on the position or size of the advertisement, for example, by using a generation AI, thereby maintaining the consistency of the content. For example, the advertisement removal unit rearranges text and images to fill in the space where the advertisement was located. This makes it possible to maintain the consistency of the content when removing an advertisement.

[0059] The ad removal unit can select the optimal removal method depending on the type of device or browser of the user when removing ads. The ad removal unit can use, for example, a generation AI to select the optimal removal method depending on the type of device or browser of the user when removing ads. For example, different removal methods can be applied to smartphones and desktops. This makes it possible to select the optimal removal method depending on the type of device or browser of the user.

[0060] The ad removal unit uses the emotion estimation function to evaluate how much satisfaction the content has for the user after the ads have been removed, and can continuously improve the optimal removal method. The ad removal unit uses, for example, the emotion estimation function to evaluate how much satisfaction the content has for the user after the ads have been removed, and can continuously improve the optimal removal method. For example, the ad removal unit analyzes the user's facial expressions and voice to measure satisfaction. This allows the unit to evaluate how much satisfaction the content has for the user after the ads have been removed, and can continuously improve the optimal removal method.

[0061] When removing an advertisement, the advertisement removal unit can use a generation AI to automatically insert related content that the user is interested in instead of the advertisement. For example, the advertisement removal unit uses a generation AI to automatically insert related content that the user is interested in instead of the advertisement when removing an advertisement. For example, related articles or videos are displayed. This allows related content that the user is interested in to be automatically inserted instead of the advertisement.

[0062] When removing an advertisement, the advertisement removal unit can use the generation AI to display educational information or public service announcements instead of the advertisement. For example, when removing an advertisement, the advertisement removal unit uses the generation AI to display educational information or public service announcements instead of the advertisement. For example, it displays information on learning resources or public services. This makes it possible to display educational information or public service announcements instead of advertisements.

[0063] The ad removal unit uses the emotion estimation function to evaluate how relaxing the content after ad removal is for the user, and can continuously improve the optimal removal method. The ad removal unit, for example, uses the emotion estimation function to evaluate how relaxing the content after ad removal is for the user, and can continuously improve the optimal removal method. For example, the ad removal unit analyzes the user's facial expressions and voice to measure the level of relaxation. This allows the unit to evaluate the level of relaxation of the content after ad removal, and can continuously improve the optimal removal method.

[0064] The content regeneration unit can develop an algorithm that faithfully reproduces the original design and layout when regenerating content after advertisements have been removed. The content regeneration unit, for example, uses a generation AI to develop an algorithm that faithfully reproduces the original design and layout when regenerating content after advertisements have been removed. For example, the content regeneration unit restores the layout of a web page to its original state. This allows the development of an algorithm that faithfully reproduces the original design and layout.

[0065] The content regeneration unit can optimize the regenerated content so that it looks consistent and natural across different devices and screen sizes. The content regeneration unit can, for example, use generation AI to optimize the regenerated content so that it looks consistent and natural across different devices and screen sizes. For example, it can adjust the display on smartphones, tablets, and desktops. This allows the regenerated content to be optimized so that it looks consistent and natural across different devices and screen sizes.

[0066] The content regeneration unit can incorporate elements of content that the user has previously given a high rating into the regenerated content, thereby providing a personalized experience. The content regeneration unit can, for example, use a generation AI to incorporate elements of content that the user has previously given a high rating into the regenerated content, thereby providing a personalized experience. For example, the content regeneration unit can reflect the user's preferred design or layout. This allows the regenerated content to incorporate elements that the user has previously given a high rating, thereby providing a personalized experience.

[0067] The content regeneration unit can insert additional information and related links based on the user's interests and concerns into the regenerated content. The content regeneration unit, for example, uses a generation AI to insert additional information and related links based on the user's interests and concerns into the regenerated content. For example, related articles and videos are displayed. This allows additional information and related links based on the user's interests and concerns to be inserted into the regenerated content.

[0068] The content regeneration unit uses the emotion estimation function to evaluate how relaxing the regenerated content is for the user and can continuously improve the optimal regeneration method. The content regeneration unit, for example, uses the emotion estimation function to evaluate how relaxing the regenerated content is for the user and continuously improves the optimal regeneration method. For example, the content regeneration unit analyzes the user's facial expressions and voice to measure the level of relaxation. This allows the level of relaxation of the regenerated content to be evaluated and the optimal regeneration method to be continuously improved.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The AI ​​AdBlocker system can also learn users' browsing patterns and dynamically change ad blocking methods depending on specific times and situations. For example, it can strictly block all ads when a user is browsing at work, but allow some ads during relaxation times. It can also apply different ad blocking policies to specific websites and applications. This allows for flexible ad blocking based on the user's situation and time of day.

[0071] The AI ​​AdBlocker system can also estimate a user's emotions and adjust the ad blocking method based on the estimated emotions. For example, if a user is feeling stressed, it can strictly block all ads, but if the user is feeling relaxed, it can allow some ads. It can also display a specific ad if the user expresses positive emotions toward that ad. This allows for flexible ad blocking based on the user's emotions.

[0072] The AI ​​AdBlocker system can also analyze a user's past purchase history and prioritize the removal of related ads. For example, it can remove ads related to products the user has already purchased, reducing unnecessary ad exposure. It can also display ads related to products the user is interested in but has not yet purchased. This allows for ad removal based on the user's purchase history.

[0073] The AI ​​AdBlocker system can also estimate a user's emotions and adjust the frequency of ad display based on the estimated emotions. For example, it can reduce the frequency of ad display if the user is tired and increase the frequency of ad display if the user is energetic. It can also increase the frequency of ad display if the user expresses positive emotions toward a particular ad. This allows for adjustment of ad display frequency according to the user's emotions.

[0074] The AI ​​AdBlocker system can also analyze a user's browsing history and prioritize the removal of ads related to specific genres or categories. For example, it can remove ads related to news sites or blogs that the user frequently visits, reducing the number of unnecessary ads displayed to the user. It can also display ads related to genres or categories that the user is interested in. This allows for the removal of ads based on the user's browsing history.

[0075] The AI ​​AdBlocker system can also estimate a user's emotions and customize the content of ads based on the estimated emotions. For example, if a user is relaxed, it can display ads with relaxing content, and if the user is excited, it can display ads with exciting content. It can also emphasize the content of a particular ad if the user expresses positive emotions toward that ad. This allows for the customization of ad content according to the user's emotions.

[0076] The AI ​​AdBlocker system also monitors the remaining battery level of the user's device and can reduce the display of ads when the battery is low. For example, if the battery is below 20%, all ads will be removed to conserve battery life. On the other hand, if the battery is sufficient, normal ads will be displayed. This allows the display of ads to be adjusted according to the battery status of the user's device.

[0077] The AI ​​AdBlocker system can also estimate a user's emotions and adjust the timing of ad display based on the estimated emotions. For example, it can refrain from displaying ads when the user is concentrating and display ads when the user is relaxed. It can also adjust the timing of displaying ads if the user expresses positive emotions toward a particular ad. This allows for the timing of ad display to be adjusted according to the user's emotions.

[0078] The AI ​​AdBlocker system can also monitor a user's internet connection speed and reduce the number of ads displayed if the connection speed is slow. For example, if the connection speed falls below a certain threshold, it can remove all ads and improve page loading speed. Alternatively, if the connection speed is fast enough, it can display regular ads. This allows ad display to be adjusted according to the user's internet connection speed.

[0079] The AI ​​AdBlocker system can also estimate a user's emotions and select the type of ad based on the estimated emotions. For example, if a user is relaxed, it can display ads with relaxing content, and if the user is excited, it can display ads with exciting content. It can also highlight the type of ad if the user expresses positive emotions toward that particular ad. This allows for the selection of ad types according to the user's emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The ad identification unit identifies advertisements from online content. For example, the generation AI identifies banner and pop-up ads on web pages, in-stream ads in video content, and sponsored posts on social media. Based on the characteristics of advertisements it has previously learned, the generation AI analyzes data on the content viewed by users to identify advertisements. Step 2: The ad removal unit automatically removes identified ads using the generation AI. For example, if a banner ad is detected on a webpage, it is removed and the surrounding content is rearranged. Ads within video content are either skipped during playback or removed entirely. Sponsored posts on social media are also removed. The generation AI is designed to maintain the integrity of the content when removing ads. Step 3: In the content regeneration section, the AI ​​regenerates the content after the ads have been removed. This regeneration process ensures that the content looks natural and engaging after the ads have been removed, while still accurately preserving the information users need. For example, it adjusts the layout of a webpage and rearranges text and images to fill in the gaps where the ads were. For video content, it creates a seamless transition from the section where the ads were removed. For social media feeds, it rearranges other posts to make the feed look natural after the sponsored posts have been removed.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 AI 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 AI 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0126] In the 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.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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. an advertisement identification unit that identifies advertisements from online content; an advertisement removal unit that removes advertisements identified by the advertisement identification unit; a content regeneration unit that regenerates the content after it has been removed by the advertisement removal unit. A system characterized by:

2. The advertisement identification unit Identify the types of ads and suggest the best ways to remove them 2. The system of claim 1.

3. The advertisement identification unit In addition to identifying ads, generative AI is used to simultaneously identify malicious content, including phishing sites and malware.

2. The system of claim 1.

4. The advertisement removal unit When removing the advertisement, an optimal layout is automatically generated depending on the position or size of the advertisement, thereby maintaining consistency of the content.

2. The system of claim 1.

5. The content regeneration unit Develop an algorithm that faithfully recreates the original design and layout when regenerating content after the ads have been removed.

2. The system of claim 1.

6. The advertisement identification unit Analyzing users' emotions toward ads in real time and prioritizing the identification of ads that evoke particularly negative emotions 2. The system of claim 1.

7. The advertisement removal unit Evaluate how satisfied users are with the content after removing ads, and continually improve the optimal removal method.

2. The system of claim 1.

8. The content regeneration unit Evaluating how attractive the regenerated content is to users and continually improving the optimal regeneration method.

2. The system of claim 1.

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