system

Abstract

The system according to this embodiment aims to provide users with content and advertisements tailored to their needs without collecting personal information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and an advertising optimization unit. The collection unit collects the user's browsing history. The analysis unit analyzes the data collected by the collection unit. The provision unit provides content based on the analysis results obtained by the analysis unit. The advertising optimization unit optimizes advertisements based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide a personalized service due to a decrease in the user's stay time, a decrease in the advertising effect, and a problem of personal information protection.

[0005] The system according to the embodiment aims to provide content and advertisements suitable for users without collecting personal information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a content provision unit, and an advertising optimization unit. The data collection unit collects the user's browsing history. The analysis unit analyzes the data collected by the data collection unit. The content provision unit provides content based on the analysis results obtained by the analysis unit. The advertising optimization unit optimizes advertisements based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide users with content and advertisements tailored to their needs without collecting personal information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a mechanism for increasing the user's time spent on a website and improving advertising effectiveness. This system introduces a lightweight machine learning model (LLM) into the browser, learns the user's web activity using generative AI, and provides the user with the most suitable content and advertisements based on the results. This allows for the provision of personalized services without collecting personal information, improving the user experience while protecting user privacy. For example, it identifies categories of interest based on the user's browsing history. For example, it analyzes websites and search keywords that the user frequently visits to understand the user's interests. Next, it adjusts the content of web pages in real time according to the user's interests. For example, on a news site, it prioritizes displaying news articles that the user is interested in. Advertisements are also optimized based on the user's interests. This improves the click-through rate and conversion rate of advertisements. Furthermore, since personalization is achieved without transmitting personal information externally, the entire learning process is completed within the browser. This allows for the improvement of the user experience while protecting user privacy. For example, by optimizing the content of websites visited by users to suit each individual, the user's time spent on the site increases, and advertising effectiveness improves. This mechanism increases the user's time spent on the site and improves advertising effectiveness. Furthermore, by providing personalized services without collecting personal information, it is possible to improve the user experience while protecting user privacy. For example, on a news site, prioritizing the display of news articles that users are interested in increases user engagement time and improves advertising effectiveness. Also, since advertisements are optimized based on user interests, click-through rates and conversion rates for ads improve. In this way, the system can increase the time users spend on the website and improve advertising effectiveness.

[0029] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an advertising optimization unit. The collection unit collects the user's browsing history. For example, the collection unit collects data such as the URL of the website the user visited, the time spent on the website, and the time spent on the website. For example, the collection unit can analyze browser history data to collect the user's browsing history. The collection unit can also collect the user's browsing history using cookies. Furthermore, the collection unit can monitor the user's browsing behavior in real time and collect the browsing history. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses data mining techniques to identify the user's interests and preferences. For example, the analysis unit can use statistical analysis methods to analyze the user's browsing history. Furthermore, the analysis unit can use machine learning algorithms to predict the user's interests and preferences. Furthermore, the analysis unit can use natural language processing techniques to analyze the user's search keywords and identify categories of interest. The provision unit provides content based on the analysis results obtained by the analysis unit. The content delivery unit, for example, prioritizes displaying news articles that the user is interested in. The content delivery unit can provide content such as text, images, and videos based on the user's interests. The content delivery unit can also dynamically adjust the layout of web pages according to the user's interests. Furthermore, the content delivery unit can optimize the display order of content based on the user's interests. The ad optimization unit optimizes ads based on the analysis results obtained by the analysis unit. The ad optimization unit selects the most suitable ad for the user using, for example, a targeting algorithm. The ad optimization unit can display ads based on the user's interests using, for example, personalization techniques. The ad optimization unit can also adjust how ads are displayed to improve click-through rates and conversion rates. Furthermore, the ad optimization unit can optimize the timing of ad display based on the user's interests. As a result, the system according to the embodiment can improve the user experience by collecting the user's browsing history and optimizing content and ads based on the analysis results.

[0030] The data collection unit collects user browsing history. For example, it collects data such as the URLs of websites visited by the user, the time of visit, and the time spent on each site. Specifically, the data collection unit can analyze browser history data to collect user browsing history. Browser history data includes the URLs of websites accessed by the user, the date and time of visit, the time spent on each site, and the order of page transitions. This data is important for understanding user browsing behavior in detail. The data collection unit can also collect user browsing history using cookies. Cookies are small text files stored in the user's browser that record information about a user's visit to a particular website. This allows the data collection unit to track user revisits and behavioral patterns. Furthermore, the data collection unit can monitor user browsing behavior in real time and collect browsing history. A common method for real-time monitoring is to embed script code into web pages, collecting data each time a user views a page. This allows the data collection unit to instantly obtain the latest browsing history, enabling rapid analysis and provision. The collected data is stored on secure servers and encrypted for privacy protection. This allows the data collection unit to efficiently collect detailed visit history data while protecting user privacy.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses data mining techniques to identify user interests and preferences. Data mining techniques include clustering, association rules, and decision trees, which are used to extract user behavior patterns and topics of interest. The analysis unit can also analyze user visit history using statistical analysis methods. Statistical analysis methods include regression analysis, analysis of variance, and time series analysis, which are used to quantitatively evaluate user behavior data. Furthermore, the analysis unit can predict user interests and preferences using machine learning algorithms. Machine learning algorithms include support vector machines, random forests, and neural networks, which are used to predict categories and topics of interest from user behavior data. In addition, the analysis unit can analyze user search keywords and identify categories of interest using natural language processing techniques. Natural language processing techniques include morphological analysis, topic modeling, and sentiment analysis, which are used to extract meaning from user search queries and identify relevant categories. This allows the analysis unit to analyze the collected data from multiple perspectives and identify user interests and preferences with high accuracy. The analysis results are fed back to the service delivery unit and the advertising optimization unit, contributing to an improved user experience.

[0032] The content delivery unit provides content based on the analysis results obtained by the analysis unit. For example, the delivery unit prioritizes displaying news articles that the user is interested in. Specifically, it can provide content such as text, images, and videos based on the user's interests. For example, if the user is interested in sports, it will prioritize displaying sports-related news articles and videos. The delivery unit can also dynamically adjust the layout of web pages according to the user's interests. This allows it to place the content that the user is most interested in in a prominent position, thereby increasing user engagement. Furthermore, the delivery unit can optimize the display order of content based on the user's interests. For example, if the user is interested in entertainment, it will display entertainment-related content at the top and content from other categories at the bottom. To realize these functions, the delivery unit works in conjunction with a database that is updated in real time and provides content based on the user's latest interests. This allows the delivery unit to always provide the user with the most optimal content and improve the user experience.

[0033] The Ad Optimization Department optimizes ads based on the analysis results obtained by the Analytics Department. For example, the Ad Optimization Department uses targeting algorithms to select the most suitable ads for users. Targeting algorithms include methods for selecting ads based on user interests, preferences, and past behavioral data. The Ad Optimization Department can also display ads based on user interests using personalization methods. Personalization methods include methods for customizing ads based on user profile information and behavioral history. The Ad Optimization Department can also adjust how ads are displayed to improve click-through rates and conversion rates. For example, it can adjust the display position, size, and color of ads to make them more likely to attract user attention. Furthermore, the Ad Optimization Department can optimize the timing of ad display based on user interests. For example, if a user tends to view content in a specific category at a particular time of day, it will display ads relevant to that time slot. This allows the Ad Optimization Department to provide users with the most suitable ads and maximize advertising effectiveness. To achieve these functions, the Ad Optimization Department works in conjunction with a real-time updated database and optimizes ads based on the latest analysis results. This allows the Ad Optimization Department to improve the user experience and provide high effectiveness to advertisers.

[0034] The data collection unit can analyze a user's past browsing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting websites that the user frequently visits. For example, the data collection unit can analyze websites that a user visits at specific times and concentrate data collection during those times. Furthermore, if the data collection unit finds that a user is interested in a particular category based on their browsing history, it can prioritize collecting websites related to that category. This allows for efficient data collection by selecting the optimal collection method through analysis of past browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past browsing history data into a generating AI and have the generating AI select the optimal data collection method.

[0035] The data collection unit can filter the collected visit history based on the user's current browsing status and areas of interest. For example, the data collection unit can collect relevant visit history based on the category of the website the user is currently viewing. For example, if the user is searching for a specific keyword, the data collection unit can prioritize collecting visit history related to that keyword. The data collection unit can also filter and collect visit history related to a specific topic if the user has shown interest in that topic. This allows for the collection of highly relevant data by filtering based on the current browsing status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current browsing data into a generating AI and have the generating AI perform the filtering.

[0036] The data collection unit can prioritize the collection of highly relevant history by considering the user's geographical location when collecting visit history. For example, if the user is in a specific region, the data collection unit can prioritize the collection of visit history related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of visit history related to the travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of visit history related to local news and events. In this way, highly relevant data can be prioritized by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the priority collection of highly relevant history.

[0037] The collection unit can analyze a user's social media activity and collect relevant history when collecting visit history. For example, the collection unit can collect relevant visit history based on content shared by the user on social media. For example, the collection unit can collect relevant visit history based on accounts followed by the user on social media. The collection unit can also collect relevant visit history based on posts liked by the user on social media. In this way, relevant visit history can be collected by analyzing social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant history.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a concise analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, the analysis can be performed efficiently. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a topic modeling algorithm to news data. For example, the analysis unit can apply a sentiment analysis algorithm to social media data. Furthermore, the analysis unit can apply an opinion extraction algorithm to product reviews. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data to identify the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. The analysis unit may also prioritize the analysis of data collected during a specific period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input data for evaluating the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The content delivery unit can adjust the level of detail provided based on the importance of the content. For example, it can provide detailed information for highly important content, and concise information for less important content. It can also provide information of moderate importance for moderately important content. By adjusting the level of detail based on the importance of the content, content can be delivered efficiently. Some or all of the above processing in the content delivery unit may be performed using AI, for example, or without AI. For example, the content delivery unit can input data for evaluating the importance of the content into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0043] The content delivery unit can apply different delivery algorithms depending on the content category at the time of delivery. For example, the delivery unit can apply a topic modeling algorithm to news content. For example, the delivery unit can apply a sentiment analysis algorithm to social media content. Furthermore, the delivery unit can apply an opinion extraction algorithm to product review content. By applying the appropriate delivery algorithm according to the content category, the accuracy of delivery is improved. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input data for identifying the content category into a generating AI and have the generating AI execute the application of different delivery algorithms.

[0044] The content delivery unit can determine the priority of content delivery based on when the content was collected. For example, the delivery unit can prioritize the delivery of the latest content. For example, the delivery unit can provide the latest content while referring to past content. The delivery unit can also prioritize the delivery of content collected during a specific period. In this way, by determining the priority of content delivery based on when the content was collected, the latest content can be provided preferentially. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input data for evaluating the timing of content collection into a generating AI and have the generating AI perform the determination of the priority of content delivery.

[0045] The content delivery unit can adjust the order of delivery based on the relevance of the content. For example, the delivery unit can prioritize the delivery of highly relevant content. For example, the delivery unit can postpone the delivery of less relevant content. The delivery unit can also dynamically adjust the order of delivery based on the relevance of the content. This allows the delivery unit to prioritize the delivery of the most relevant content to the user by adjusting the order of delivery based on the relevance of the content. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input data for evaluating the relevance of content into a generating AI and have the generating AI perform the adjustment of the order of delivery.

[0046] The ad optimization unit can analyze a user's past ad click history to select the most suitable ad during ad optimization. For example, the ad optimization unit can provide relevant ads based on ads the user has clicked in the past. For example, the ad optimization unit can prioritize providing ads in categories of interest based on the user's past click history. The ad optimization unit can also analyze a user's past click history to select the most effective ad. This allows the ad optimization unit to provide the most relevant ads to the user by analyzing past ad click history. Some or all of the above processes in the ad optimization unit may be performed using AI, for example, or without AI. For example, the ad optimization unit can input the user's ad click history data into a generating AI and have the generating AI select the most suitable ad.

[0047] The ad optimization unit can customize how ads are displayed based on the user's current browsing status during ad optimization. For example, the ad optimization unit can display relevant ads based on the category of the website the user is currently viewing. For example, if the user is searching for a specific keyword, the ad optimization unit can display ads related to that keyword. Furthermore, if the user has shown interest in a specific topic, the ad optimization unit can customize and display ads related to that topic. By customizing how ads are displayed based on the user's current browsing status, the ad optimization unit can provide the user with the most relevant ads. Some or all of the above processes in the ad optimization unit may be performed using AI, for example, or not. For example, the ad optimization unit can input the user's browsing data into a generating AI and have the generating AI perform the customization of how ads are displayed.

[0048] The ad optimization unit can select the most relevant ads by considering the user's geographical location during ad optimization. For example, if the user is in a specific region, the ad optimization unit can prioritize displaying ads related to that region. For example, if the user is traveling, the ad optimization unit can prioritize displaying ads related to the travel destination. Furthermore, if the user is at home, the ad optimization unit can prioritize displaying ads related to local services and events. In this way, by considering geographical location, the ad optimization unit can provide the most relevant ads to the user. Some or all of the above processing in the ad optimization unit may be performed using AI, for example, or without AI. For example, the ad optimization unit can input the user's geographical location data into a generating AI and have the generating AI select the most relevant ads.

[0049] The advertising optimization unit can analyze a user's social media activity and propose ways to display ads during the ad optimization process. For example, the advertising optimization unit can display relevant ads based on content shared by the user on social media. For example, the advertising optimization unit can display relevant ads based on accounts followed by the user on social media. It can also display relevant ads based on posts that the user has "liked" on social media. This allows the advertising optimization unit to provide the most relevant ads to the user by analyzing social media activity. Some or all of the above processes in the advertising optimization unit may be performed using AI, for example, or without AI. For example, the advertising optimization unit can input user social media activity data into a generating AI and have the generating AI propose ways to display ads.

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

[0051] The data collection unit can monitor the user's device usage and determine the optimal collection timing. For example, if a user is using the device for an extended period, the collection timing can be delayed to reduce the user's burden. Conversely, if a user is only using the device for a short time, the collection timing can be brought forward to efficiently collect data. Furthermore, if a user is using a specific application, data related to that application can be prioritized for collection. By adjusting the collection timing based on device usage, the system can reduce the user's burden and collect data efficiently.

[0052] The content delivery system can analyze a user's past content viewing history and select the optimal delivery method. For example, it can prioritize providing content categories that a user frequently views. It can also analyze the content a user views at specific times and provide content that is most relevant to those times. Furthermore, if a user's past viewing history indicates an interest in a particular topic, it can prioritize providing content related to that topic. In this way, by analyzing past viewing history, the system can select the optimal delivery method and deliver content efficiently.

[0053] The data collection unit can monitor the battery level of the user's device and determine the optimal collection timing. For example, if the battery level is low, the collection timing can be delayed to conserve battery power. Conversely, if the battery level is sufficient, the collection timing can be advanced to efficiently collect data. Furthermore, if the battery level is moderate, the collection timing can be adjusted to optimize battery consumption. By adjusting the collection timing based on the device's battery level, the burden on the user is reduced, and data can be collected efficiently.

[0054] The content delivery system can adjust how content is displayed based on the user's device screen size. For example, if the screen size is large, detailed content can be displayed. If the screen size is small, concise content can be displayed. Furthermore, if the screen size is medium, content with an appropriate level of detail can be displayed. By adjusting how content is displayed based on the device's screen size, the system can provide the user with the most suitable content.

[0055] The data collection unit can monitor the network connection status of the user's device and determine the optimal collection timing. For example, if the network connection is stable, the collection timing can be advanced to efficiently collect data. Conversely, if the network connection is unstable, the collection timing can be delayed to prevent data loss. Furthermore, if the network connection is moderate, the collection timing can be adjusted to optimize data collection. In this way, data can be collected efficiently by adjusting the collection timing based on the network connection status.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects the user's browsing history. For example, it collects data such as the URLs of websites the user has visited, the time spent on each site, and the duration of visits. The collection unit can analyze browser history data and use cookies to collect the user's browsing history. It can also monitor the user's browsing behavior in real time and collect browsing history. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can use data mining techniques and statistical analysis methods to identify user interests and preferences, and use machine learning algorithms and natural language processing techniques to analyze user search keywords and identify categories of interest. Step 3: The content delivery unit provides content based on the analysis results obtained by the analysis unit. For example, it prioritizes displaying news articles that the user is interested in and provides content such as text, images, and videos based on the user's interests. It can also dynamically adjust the layout of the web page and optimize the display order of content. Step 4: The ad optimization department optimizes ads based on the analysis results obtained by the analysis department. For example, it can select the most suitable ad for the user using targeting algorithms and personalization methods, and adjust the way and timing of ad display to improve the click-through rate and conversion rate of ads.

[0058] (Example of form 2) The system according to an embodiment of the present invention is a mechanism for increasing the user's time spent on a website and improving advertising effectiveness. This system introduces a lightweight machine learning model (LLM) into the browser, learns the user's web activity using generative AI, and provides the user with the most suitable content and advertisements based on the results. This allows for the provision of personalized services without collecting personal information, improving the user experience while protecting user privacy. For example, it identifies categories of interest based on the user's browsing history. For example, it analyzes websites and search keywords that the user frequently visits to understand the user's interests. Next, it adjusts the content of web pages in real time according to the user's interests. For example, on a news site, it prioritizes displaying news articles that the user is interested in. Advertisements are also optimized based on the user's interests. This improves the click-through rate and conversion rate of advertisements. Furthermore, since personalization is achieved without transmitting personal information externally, the entire learning process is completed within the browser. This allows for the improvement of the user experience while protecting user privacy. For example, by optimizing the content of websites visited by users to suit each individual, the user's time spent on the site increases, and advertising effectiveness improves. This mechanism increases the user's time spent on the site and improves advertising effectiveness. Furthermore, by providing personalized services without collecting personal information, it is possible to improve the user experience while protecting user privacy. For example, on a news site, prioritizing the display of news articles that users are interested in increases user engagement time and improves advertising effectiveness. Also, since advertisements are optimized based on user interests, click-through rates and conversion rates for ads improve. In this way, the system can increase the time users spend on the website and improve advertising effectiveness.

[0059] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an advertising optimization unit. The collection unit collects the user's browsing history. For example, the collection unit collects data such as the URL of the website the user visited, the time spent on the website, and the time spent on the website. For example, the collection unit can analyze browser history data to collect the user's browsing history. The collection unit can also collect the user's browsing history using cookies. Furthermore, the collection unit can monitor the user's browsing behavior in real time and collect the browsing history. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses data mining techniques to identify the user's interests and preferences. For example, the analysis unit can use statistical analysis methods to analyze the user's browsing history. Furthermore, the analysis unit can use machine learning algorithms to predict the user's interests and preferences. Furthermore, the analysis unit can use natural language processing techniques to analyze the user's search keywords and identify categories of interest. The provision unit provides content based on the analysis results obtained by the analysis unit. The content delivery unit, for example, prioritizes displaying news articles that the user is interested in. The content delivery unit can provide content such as text, images, and videos based on the user's interests. The content delivery unit can also dynamically adjust the layout of web pages according to the user's interests. Furthermore, the content delivery unit can optimize the display order of content based on the user's interests. The ad optimization unit optimizes ads based on the analysis results obtained by the analysis unit. The ad optimization unit selects the most suitable ad for the user using, for example, a targeting algorithm. The ad optimization unit can display ads based on the user's interests using, for example, personalization techniques. The ad optimization unit can also adjust how ads are displayed to improve click-through rates and conversion rates. Furthermore, the ad optimization unit can optimize the timing of ad display based on the user's interests. As a result, the system according to the embodiment can improve the user experience by collecting the user's browsing history and optimizing content and ads based on the analysis results.

[0060] The data collection unit collects user browsing history. For example, it collects data such as the URLs of websites visited by the user, the time of visit, and the time spent on each site. Specifically, the data collection unit can analyze browser history data to collect user browsing history. Browser history data includes the URLs of websites accessed by the user, the date and time of visit, the time spent on each site, and the order of page transitions. This data is important for understanding user browsing behavior in detail. The data collection unit can also collect user browsing history using cookies. Cookies are small text files stored in the user's browser that record information about a user's visit to a particular website. This allows the data collection unit to track user revisits and behavioral patterns. Furthermore, the data collection unit can monitor user browsing behavior in real time and collect browsing history. A common method for real-time monitoring is to embed script code into web pages, collecting data each time a user views a page. This allows the data collection unit to instantly obtain the latest browsing history, enabling rapid analysis and provision. The collected data is stored on secure servers and encrypted for privacy protection. This allows the data collection unit to efficiently collect detailed visit history data while protecting user privacy.

[0061] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses data mining techniques to identify user interests and preferences. Data mining techniques include clustering, association rules, and decision trees, which are used to extract user behavior patterns and topics of interest. The analysis unit can also analyze user visit history using statistical analysis methods. Statistical analysis methods include regression analysis, analysis of variance, and time series analysis, which are used to quantitatively evaluate user behavior data. Furthermore, the analysis unit can predict user interests and preferences using machine learning algorithms. Machine learning algorithms include support vector machines, random forests, and neural networks, which are used to predict categories and topics of interest from user behavior data. In addition, the analysis unit can analyze user search keywords and identify categories of interest using natural language processing techniques. Natural language processing techniques include morphological analysis, topic modeling, and sentiment analysis, which are used to extract meaning from user search queries and identify relevant categories. This allows the analysis unit to analyze the collected data from multiple perspectives and identify user interests and preferences with high accuracy. The analysis results are fed back to the service delivery unit and the advertising optimization unit, contributing to an improved user experience.

[0062] The content delivery unit provides content based on the analysis results obtained by the analysis unit. For example, the delivery unit prioritizes displaying news articles that the user is interested in. Specifically, it can provide content such as text, images, and videos based on the user's interests. For example, if the user is interested in sports, it will prioritize displaying sports-related news articles and videos. The delivery unit can also dynamically adjust the layout of web pages according to the user's interests. This allows it to place the content that the user is most interested in in a prominent position, thereby increasing user engagement. Furthermore, the delivery unit can optimize the display order of content based on the user's interests. For example, if the user is interested in entertainment, it will display entertainment-related content at the top and content from other categories at the bottom. To realize these functions, the delivery unit works in conjunction with a database that is updated in real time and provides content based on the user's latest interests. This allows the delivery unit to always provide the user with the most optimal content and improve the user experience.

[0063] The Ad Optimization Department optimizes ads based on the analysis results obtained by the Analytics Department. For example, the Ad Optimization Department uses targeting algorithms to select the most suitable ads for users. Targeting algorithms include methods for selecting ads based on user interests, preferences, and past behavioral data. The Ad Optimization Department can also display ads based on user interests using personalization methods. Personalization methods include methods for customizing ads based on user profile information and behavioral history. The Ad Optimization Department can also adjust how ads are displayed to improve click-through rates and conversion rates. For example, it can adjust the display position, size, and color of ads to make them more likely to attract user attention. Furthermore, the Ad Optimization Department can optimize the timing of ad display based on user interests. For example, if a user tends to view content in a specific category at a particular time of day, it will display ads relevant to that time slot. This allows the Ad Optimization Department to provide users with the most suitable ads and maximize advertising effectiveness. To achieve these functions, the Ad Optimization Department works in conjunction with a real-time updated database and optimizes ads based on the latest analysis results. This allows the Ad Optimization Department to improve the user experience and provide high effectiveness to advertisers.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of collecting visit history based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, if the user is relaxed, the data collection unit can advance the collection timing to collect more data. The data collection unit can also adjust the collection timing if the user is excited to collect data when the user's interest is at its highest. By adjusting the collection timing according to the user's emotions, the user's burden can be reduced and more data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0065] The data collection unit can analyze a user's past browsing history and select the optimal data collection method. For example, the data collection unit can prioritize collecting websites that the user frequently visits. For example, the data collection unit can analyze websites that a user visits at specific times and concentrate data collection during those times. Furthermore, if the data collection unit finds that a user is interested in a particular category based on their browsing history, it can prioritize collecting websites related to that category. This allows for efficient data collection by selecting the optimal collection method through analysis of past browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past browsing history data into a generating AI and have the generating AI select the optimal data collection method.

[0066] The data collection unit can filter the collected visit history based on the user's current browsing status and areas of interest. For example, the data collection unit can collect relevant visit history based on the category of the website the user is currently viewing. For example, if the user is searching for a specific keyword, the data collection unit can prioritize collecting visit history related to that keyword. The data collection unit can also filter and collect visit history related to a specific topic if the user has shown interest in that topic. This allows for the collection of highly relevant data by filtering based on the current browsing status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current browsing data into a generating AI and have the generating AI perform the filtering.

[0067] The data collection unit can estimate the user's emotions and determine the priority of the browsing history to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting browsing history of relaxing content. For example, if the user is excited, the data collection unit may prioritize collecting browsing history of entertainment-related content. Also, if the user is focused, the data collection unit may prioritize collecting browsing history related to learning or work. This allows for the collection of more appropriate data by prioritizing browsing history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit may input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0068] The data collection unit can prioritize the collection of highly relevant history by considering the user's geographical location when collecting visit history. For example, if the user is in a specific region, the data collection unit can prioritize the collection of visit history related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of visit history related to the travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of visit history related to local news and events. In this way, highly relevant data can be prioritized by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the priority collection of highly relevant history.

[0069] The collection unit can analyze a user's social media activity and collect relevant history when collecting visit history. For example, the collection unit can collect relevant visit history based on content shared by the user on social media. For example, the collection unit can collect relevant visit history based on accounts followed by the user on social media. The collection unit can also collect relevant visit history based on posts liked by the user on social media. In this way, relevant visit history can be collected by analyzing social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant history.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a concise analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, the analysis can be performed efficiently. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a topic modeling algorithm to news data. For example, the analysis unit can apply a sentiment analysis algorithm to social media data. Furthermore, the analysis unit can apply an opinion extraction algorithm to product reviews. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data to identify the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, for example, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0074] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. The analysis unit may also prioritize the analysis of data collected during a specific period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input data for evaluating the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0076] The service provider can estimate the user's emotions and adjust the way the content is presented based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed content. If the user is in a hurry, the service provider can provide concise content. If the user is excited, the service provider can also provide visually appealing content. By adjusting the way the content is presented according to the user's emotions, the service provider can provide content that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0077] The content delivery unit can adjust the level of detail provided based on the importance of the content. For example, it can provide detailed information for highly important content, and concise information for less important content. It can also provide information of moderate importance for moderately important content. By adjusting the level of detail based on the importance of the content, content can be delivered efficiently. Some or all of the above processing in the content delivery unit may be performed using AI, for example, or without AI. For example, the content delivery unit can input data for evaluating the importance of the content into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0078] The content delivery unit can apply different delivery algorithms depending on the content category at the time of delivery. For example, the delivery unit can apply a topic modeling algorithm to news content. For example, the delivery unit can apply a sentiment analysis algorithm to social media content. Furthermore, the delivery unit can apply an opinion extraction algorithm to product review content. By applying the appropriate delivery algorithm according to the content category, the accuracy of delivery is improved. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input data for identifying the content category into a generating AI and have the generating AI execute the application of different delivery algorithms.

[0079] The service provider can estimate the user's emotions and adjust the length of the content provided based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise content. If the user is relaxed, the service provider can provide detailed content. If the user is excited, the service provider can also provide visually appealing content. By adjusting the length of the content according to the user's emotions, the service provider can provide content of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0080] The content delivery unit can determine the priority of content delivery based on when the content was collected. For example, the delivery unit can prioritize the delivery of the latest content. For example, the delivery unit can provide the latest content while referring to past content. The delivery unit can also prioritize the delivery of content collected during a specific period. In this way, by determining the priority of content delivery based on when the content was collected, the latest content can be provided preferentially. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input data for evaluating the timing of content collection into a generating AI and have the generating AI perform the determination of the priority of content delivery.

[0081] The content delivery unit can adjust the order of delivery based on the relevance of the content. For example, the delivery unit can prioritize the delivery of highly relevant content. For example, the delivery unit can postpone the delivery of less relevant content. The delivery unit can also dynamically adjust the order of delivery based on the relevance of the content. This allows the delivery unit to prioritize the delivery of the most relevant content to the user by adjusting the order of delivery based on the relevance of the content. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input data for evaluating the relevance of content into a generating AI and have the generating AI perform the adjustment of the order of delivery.

[0082] The ad optimization unit can estimate the user's emotions and adjust the ad optimization method based on the estimated user emotions. For example, if the user is relaxed, the ad optimization unit can provide detailed ads. If the user is in a hurry, the ad optimization unit can provide concise ads. Furthermore, if the user is excited, the ad optimization unit can provide visually appealing ads. In this way, by adjusting the ad optimization method according to the user's emotions, the system can provide the most suitable ads for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ad optimization unit may be performed using AI or not using AI. For example, the ad optimization unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0083] The ad optimization unit can analyze a user's past ad click history to select the most suitable ad during ad optimization. For example, the ad optimization unit can provide relevant ads based on ads the user has clicked in the past. For example, the ad optimization unit can prioritize providing ads in categories of interest based on the user's past click history. The ad optimization unit can also analyze a user's past click history to select the most effective ad. This allows the ad optimization unit to provide the most relevant ads to the user by analyzing past ad click history. Some or all of the above processes in the ad optimization unit may be performed using AI, for example, or without AI. For example, the ad optimization unit can input the user's ad click history data into a generating AI and have the generating AI select the most suitable ad.

[0084] The ad optimization unit can customize how ads are displayed based on the user's current browsing status during ad optimization. For example, the ad optimization unit can display relevant ads based on the category of the website the user is currently viewing. For example, if the user is searching for a specific keyword, the ad optimization unit can display ads related to that keyword. Furthermore, if the user has shown interest in a specific topic, the ad optimization unit can customize and display ads related to that topic. By customizing how ads are displayed based on the user's current browsing status, the ad optimization unit can provide the user with the most relevant ads. Some or all of the above processes in the ad optimization unit may be performed using AI, for example, or not. For example, the ad optimization unit can input the user's browsing data into a generating AI and have the generating AI perform the customization of how ads are displayed.

[0085] The ad optimization unit can estimate the user's emotions and determine ad priorities based on those emotions. For example, if the user is relaxed, the ad optimization unit can prioritize displaying detailed ads. If the user is in a hurry, the ad optimization unit can prioritize displaying concise ads. Furthermore, if the user is excited, the ad optimization unit can prioritize displaying visually appealing ads. This allows the system to provide the most appropriate ads to the user by prioritizing them according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the ad optimization unit may be performed using AI or not. For example, the ad optimization unit can input user facial expression data into a generative AI and have the generative AI perform the user emotion estimation.

[0086] The ad optimization unit can select the most relevant ads by considering the user's geographical location during ad optimization. For example, if the user is in a specific region, the ad optimization unit can prioritize displaying ads related to that region. For example, if the user is traveling, the ad optimization unit can prioritize displaying ads related to the travel destination. Furthermore, if the user is at home, the ad optimization unit can prioritize displaying ads related to local services and events. In this way, by considering geographical location, the ad optimization unit can provide the most relevant ads to the user. Some or all of the above processing in the ad optimization unit may be performed using AI, for example, or without AI. For example, the ad optimization unit can input the user's geographical location data into a generating AI and have the generating AI select the most relevant ads.

[0087] The advertising optimization unit can analyze a user's social media activity and propose ways to display ads during the ad optimization process. For example, the advertising optimization unit can display relevant ads based on content shared by the user on social media. For example, the advertising optimization unit can display relevant ads based on accounts followed by the user on social media. It can also display relevant ads based on posts that the user has "liked" on social media. This allows the advertising optimization unit to provide the most relevant ads to the user by analyzing social media activity. Some or all of the above processes in the advertising optimization unit may be performed using AI, for example, or without AI. For example, the advertising optimization unit can input user social media activity data into a generating AI and have the generating AI propose ways to display ads.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit can monitor the user's device usage and determine the optimal collection timing. For example, if a user is using the device for an extended period, the collection timing can be delayed to reduce the user's burden. Conversely, if a user is only using the device for a short time, the collection timing can be brought forward to efficiently collect data. Furthermore, if a user is using a specific application, data related to that application can be prioritized for collection. By adjusting the collection timing based on device usage, the system can reduce the user's burden and collect data efficiently.

[0090] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, it can prioritize analyzing relaxing content. If the user is excited, it can prioritize analyzing entertainment-related content. Furthermore, if the user is focused, it can prioritize analyzing content related to learning or work. By prioritizing analysis according to the user's emotions, it can provide more appropriate analysis results.

[0091] The content delivery system can analyze a user's past content viewing history and select the optimal delivery method. For example, it can prioritize providing content categories that a user frequently views. It can also analyze the content a user views at specific times and provide content that is most relevant to those times. Furthermore, if a user's past viewing history indicates an interest in a particular topic, it can prioritize providing content related to that topic. In this way, by analyzing past viewing history, the system can select the optimal delivery method and deliver content efficiently.

[0092] The ad optimization department can estimate a user's emotions and adjust how ads are displayed based on those emotions. For example, if a user is relaxed, a detailed ad can be displayed. If a user is in a hurry, a concise ad can be displayed. Furthermore, if a user is excited, a visually appealing ad can be displayed. In this way, by adjusting how ads are displayed according to the user's emotions, the most suitable ads can be provided to the user.

[0093] The data collection unit can monitor the battery level of the user's device and determine the optimal collection timing. For example, if the battery level is low, the collection timing can be delayed to conserve battery power. Conversely, if the battery level is sufficient, the collection timing can be advanced to efficiently collect data. Furthermore, if the battery level is moderate, the collection timing can be adjusted to optimize battery consumption. By adjusting the collection timing based on the device's battery level, the burden on the user is reduced, and data can be collected efficiently.

[0094] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. Furthermore, if the user is excited, it can provide visually appealing analysis results. In this way, by adjusting the level of detail in the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0095] The content delivery system can adjust how content is displayed based on the user's device screen size. For example, if the screen size is large, detailed content can be displayed. If the screen size is small, concise content can be displayed. Furthermore, if the screen size is medium, content with an appropriate level of detail can be displayed. By adjusting how content is displayed based on the device's screen size, the system can provide the user with the most suitable content.

[0096] The ad optimization department can estimate a user's emotions and adjust the timing of ad display based on those emotions. For example, if a user is relaxed, the ad display timing can be delayed to give the user more time to enjoy the content. If a user is in a hurry, the ad display timing can be advanced so that the user can see the ad immediately. Furthermore, if a user is excited, the ad display timing can be adjusted to show the ad when the user's interest is at its highest. In this way, by adjusting the timing of ad display according to the user's emotions, the most optimal ads can be delivered to the user.

[0097] The data collection unit can monitor the network connection status of the user's device and determine the optimal collection timing. For example, if the network connection is stable, the collection timing can be advanced to efficiently collect data. Conversely, if the network connection is unstable, the collection timing can be delayed to prevent data loss. Furthermore, if the network connection is moderate, the collection timing can be adjusted to optimize data collection. In this way, data can be collected efficiently by adjusting the collection timing based on the network connection status.

[0098] The content delivery system can estimate the user's emotions and adjust the type of content provided based on those estimates. For example, if the user is relaxed, it can provide entertainment-related content. If the user is in a hurry, it can provide news or information-related content. Furthermore, if the user is excited, it can provide visually appealing content. By adjusting the type of content provided according to the user's emotions, the system can deliver the most suitable content for the user.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The collection unit collects the user's browsing history. For example, it collects data such as the URLs of websites the user has visited, the time spent on each site, and the duration of visits. The collection unit can analyze browser history data and use cookies to collect the user's browsing history. It can also monitor the user's browsing behavior in real time and collect browsing history. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can use data mining techniques and statistical analysis methods to identify user interests and preferences, and use machine learning algorithms and natural language processing techniques to analyze user search keywords and identify categories of interest. Step 3: The content delivery unit provides content based on the analysis results obtained by the analysis unit. For example, it prioritizes displaying news articles that the user is interested in and provides content such as text, images, and videos based on the user's interests. It can also dynamically adjust the layout of the web page and optimize the display order of content. Step 4: The ad optimization department optimizes ads based on the analysis results obtained by the analysis department. For example, it can select the most suitable ad for the user using targeting algorithms and personalization methods, and adjust the way and timing of ad display to improve the click-through rate and conversion rate of ads.

[0101] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0104] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and advertising optimization unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's browsing history. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is implemented by the control unit 46A of the smart device 14 and provides content based on the analysis results. The advertising optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes advertisements based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 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.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and advertising optimization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's browsing history. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides content based on the analysis results. The advertising optimization unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and optimizes advertisements based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and advertising optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's browsing history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides content based on the analysis results. The advertising optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes advertisements based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and advertising optimization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the user's visit history. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides content based on the analysis results. The advertising optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes advertisements based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A collection unit that collects user visit history, An analysis unit analyzes the data collected by the aforementioned collection unit, A content provision unit provides content based on the analysis results obtained by the aforementioned analysis unit, The system includes an advertising optimization unit that optimizes advertisements based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of visit history collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past visit history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting browsing history, filtering is performed based on the user's current browsing status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the visit history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting visit history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting visit history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing content, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing content, different delivery algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the content provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing content, we will prioritize its delivery based on when the content was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When delivering content, the order of delivery will be adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advertising optimization unit, We estimate user sentiment and adjust ad optimization methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advertising optimization unit, During ad optimization, the system analyzes users' past ad click history to select the most suitable ads. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advertising optimization unit, When optimizing ads, customize how ads are displayed based on the user's current browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advertising optimization unit, It estimates user sentiment and prioritizes ads based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advertising optimization unit, When optimizing ads, the system selects the most suitable ads by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advertising optimization unit, When optimizing ads, we analyze users' social media activity and suggest how to display ads. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user visit history, An analysis unit analyzes the data collected by the aforementioned collection unit, A content provision unit provides content based on the analysis results obtained by the aforementioned analysis unit, The system includes an advertising optimization unit that optimizes advertisements based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of visit history collection based on the estimated user emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past visit history and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting browsing history, filtering is performed based on the user's current browsing status and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and determines the priority of the visit history to collect based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting visit history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting visit history, the system analyzes the user's social media activity and collects relevant history. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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