Distribution management system and distribution management method

Abstract

The present invention determines an appropriate presentation destination medium for content. Provided is a server comprising: a content acquisition unit that acquires a media page including information to be distributed, and information related to presentation content that is information to be presented on the media page; an index value calculation unit that selects a plurality of different evaluation axes, acquires a plurality of scores corresponding to the plurality of evaluation axes, respectively on the basis of the similarity between the information related to the presentation content and the media page, and calculates an index value that is an average value based on the plurality of scores and bias between the plurality of evaluation axes; a presentation destination determination unit that determines the media page corresponding to the index value exceeding a predetermined value as a presentation destination for the presentation content; and a presentation destination output unit that outputs information indicating the media page determined as the presentation destination for the presentation content.

Description

Distribution Management System, Distribution Management Method

[0001] [Related Application] This application claims the priority of Japanese Patent Application No. 2024-193116 titled "Distribution Management System, Distribution Management Method" filed on November 1, 2024, and the disclosure thereof is incorporated herein by reference in its entirety. This technology relates to a distribution management system and a distribution management method.

[0002] As a background art in this technical field, there is Japanese Patent Application Laid-Open No. 2021-149212 (Patent Document 1). This publication describes that "when the first content is requested from the first terminal by the advertising distribution system, it determines whether to provide an advertisement for the first content based on a first profile that proves the provider of the first content" (see the abstract).

[0003] Japanese Patent Application Laid-Open No. 2021-149212

[0004] Patent Document 1 describes a mechanism for reducing the provision of advertisements to media such as websites and SNSs that provide inappropriate content, such as fake news, content that infringes on the copyright of others, and content containing personal information. However, in Patent Document 1, no consideration has been given to determining the presentation destination of the generated content based on the relationship between the information related to the generated content and the distribution target media. Therefore, this technology provides a system for determining the media of the presentation destination of content based on the relationship between the content and the media that is the distribution target.

[0005] To solve the above problems, for example, the configuration described in the claims is adopted.

[0006] According to this technology, a system for determining the media of the presentation destination of content information based on the relationship between the content information and the distribution target media can be provided. Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

[0007] Figure 1 shows an example configuration of the distribution management system 10. Figure 2 shows an example hardware configuration of the management server 102. Figure 3 shows an example hardware configuration of the distribution media server 103. Figure 4 shows an example hardware configuration of the user terminal 104. Figure 5 shows an example hardware configuration of the advertising distribution server 105. Figure 6 shows an example of keyword tag information 600. Figure 7 shows an example of media metric value information 700. Figure 8 shows an example of distribution result information 800. Figure 9 shows an example of the destination output flow 900. Figure 10 shows an example of the analysis flow 1000 of media pages and advertising-related information. Figure 11 shows an example of the metric value output flow 1100. Figure 12 shows an example of the adjusted metric value output flow 1200. Figure 13 shows an example of the destination media determination flow 1300. Figure 14 shows an example of the generated content generation flow 1400. Figure 15 shows an example of the new keyword extraction flow 1500. Figure 16 is an example of the generated content evaluation flow 1600. Figure 17 is an example of the content-related information input screen 1700. Figure 18 is an example of the media URL input screen 1800. Figure 19 is an example of the metric value calculation screen 1900. Figure 20(A) is an example of the user metric value screen 2010 before update. Figure 20(B) is an example of the user metric value screen 2020 after update. Figure 21 is an example of the extraction condition input screen 2100. Figure 22 is an example of the keyword information output screen 2200. Figure 23 is an example of the keyword information output screen 2300. Figure 24 is an example of the parallel display screen 2400.

[0008] 1. Examples The following examples will be described with reference to the drawings. 1-1. Overview First, an overview of this example will be given. In advertising distribution, presenting the right advertisement on the right media is important for increasing product awareness and acquiring customers. However, for example, in media delivered to communication terminals, there are cases where the matching between the user viewing the media and the advertisement displayed on the media is not appropriate.

[0009] Furthermore, such mismatches can not only reduce the effectiveness of advertising, but also damage the branding of products, services, and the advertiser's company. Therefore, for example, in advertisements delivered to communication devices (such as rich media ads), there is a strong desire for appropriate matching between the advertiser and the viewer.

[0010] Furthermore, advertisers want systems that, for example, maintain the branding of advertiser companies and products while leading to user purchases, and they also want systems that enable the creation of reporting materials on the effectiveness of ad delivery.

[0011] In this embodiment, for example, the following can be carried out: (1) Presenting advertising content on appropriate media (media pages); (2) Determining where to present advertising content according to the viewing user; (3) Improving advertising content according to the advertising delivery results; (4) Creating reporting materials on the effectiveness of advertising delivery.

[0012] This embodiment makes it possible to provide appropriate advertising content to media and user terminals. Furthermore, it makes it possible to provide an advertising delivery system that leads to user purchases while maintaining the branding of advertiser companies and products.

[0013] 1-2. Configuration Diagram 1 shows an example of the configuration of the distribution management system 10. The distribution management system 10 shown in the diagram comprises a management server 102, a distribution media server 103, a user terminal 104, and an advertising distribution server 105, all of which are connected to each other via a network 101 that enables communication. The network 101 can be wired or wireless, and each terminal can send and receive information via the network 101.

[0014] Of these components, the management server 102 is a server for managing the destinations of content information (presented content), which is information scheduled to be distributed. The presented content is data that can be distributed via a network, such as advertising content. The destinations for the advertised content are, for example, information media that include content information and are distributed, specifically media pages such as web pages.

[0015] The distribution media server 103 is a server that distributes media pages, which are information media. For example, the distribution media server 103 is a web server, and the media pages are, for example, web pages, websites, story pages of social networking services (SNS), etc., that are included in media (distribution media) distributed by the web server or in the distribution site.

[0016] The distribution media server 103 distributes media pages in response to requests sent from external sources, for example. A media page is an information medium that can serve as a destination for content information such as advertising content. A media page contains content information such as news articles, videos, images, and text data, and the content information included in a media page is called site content. Furthermore, a media page includes, for example, display frames for site content and advertising content.

[0017] The user terminal 104 is a computer terminal device used by a user, such as a mobile terminal. The user terminal 104 outputs and displays information distributed from a web server or the like on its display.

[0018] The ad delivery server 105 is a server that delivers advertisements. The ad delivery server 105 is, for example, a group of server devices located on the cloud.

[0019] Each device constituting the above-described distribution management system 10 may be, for example, a mobile device such as a smartphone, tablet, cell phone, or personal digital assistant (PDA), or a wearable device such as glasses, a wristwatch, or clothing. It may also be a stationary or portable computer, or a server located on the cloud or network. Functionally, it may also be a VR (Virtual Reality) device, an AR (Augmented Reality) device, or an MR (Mixed Reality) device. Alternatively, it may be a combination of multiple such devices. For example, a combination of one smartphone and one wearable device can logically function as a single device. Other information processing terminals may also be used.

[0020] Each component of the distribution management system 10 comprises a processor that executes an operating system, applications, programs, etc., a main memory such as RAM (Random Access Memory), an auxiliary memory such as an IC card, a hard disk drive, an SSD (Solid State Drive), or flash memory, a communication control unit such as a network card, a wireless communication module, or a mobile communication module, input devices such as a touch panel, keyboard, mouse, voice input, or motion detection input from a camera, and output devices such as a monitor or display. The output devices may also be devices or terminals that transmit information for output to an external monitor, display, printer, or other device.

[0021] The main memory stores various programs and applications (modules), and the processor executes these programs and applications to realize each functional element of the overall system. These modules may be implemented in hardware, such as through integration. Furthermore, each module may be an independent program or application, or it may be implemented as a subprogram or function within a single integrated program or application.

[0022] In this specification, each module is described as the entity (subject) that performs the processing; however, in reality, the processor that processes various programs and applications (modules) executes the processing. Various databases (DBs) are stored in the auxiliary storage device. A "database" is a functional element (storage unit) that stores a data set so that it can handle any data manipulation (e.g., extraction, addition, deletion, overwriting, etc.) from the processor or an external computer. The implementation method of the database is not limited; for example, it may be a database management system, spreadsheet software, or text files such as XML or JSON. The following describes each device in detail.

[0023] 1-2-1. Management Server 102 Figure 2 shows an example of the hardware configuration of the management server 102. The management server 102 is composed of, for example, a server located on the cloud.

[0024] The management server 102 includes a main memory 201, an auxiliary memory 202, a processor 203, an input device 204, an output device 205, and a communication control unit 206.

[0025] Of these, the main memory 201 stores programs and applications such as the content acquisition module 211, the index value calculation module 212, the destination determination module 213, the destination output module 214, the user information acquisition module 215, the content generation module 216, the distribution result acquisition module 217, and the content evaluation module 218. The processor 203 executes these programs and applications to realize each functional element of the management server 102. Each module will be described below.

[0026] The content acquisition module 211 (content acquisition unit) acquires, for example, advertising content, information related to advertising content (also called advertising-related information), and media pages, etc.

[0027] Advertising content (sometimes referred to as "advertising content information") is, for example, information used to advertise products or services. Advertising content includes, for example, images, text, videos, and other information, or information generated using these. For example, advertising content consists of information provided by advertisers about products or services, or information generated based on the advertiser's corporate concept, such as videos, images, text data, and audio data.

[0028] Information related to advertising content includes, for example, keyword information, images, videos, etc., contained within the advertising content, as well as conceptual information about the advertised product, keywords, images, videos, and corporate messages from the advertiser company related to the advertised product. Therefore, information related to advertising content includes not only the information contained in the advertising content itself, but also peripheral information about the advertised product or service, and peripheral information about the advertising content.

[0029] Site content (sometimes referred to as "site content information") includes, for example, information contained on media pages, such as articles, images, and videos on news sites, and content such as posts, images, videos, and text information submitted by service users on blog services and social networking services (SNS).

[0030] The metric calculation module 212 (metric calculation unit) obtains, for example, a score (similarity score) indicating the similarity between advertising content-related information and media pages. The metric calculation module 212 calculates and obtains the similarity (similarity score) based, for example, on keyword information contained in the advertising content-related information and media pages.

[0031] For example, the metric calculation module 212 calculates similarity based on vector information of advertising content-related information (first vector information) and vector information of media pages (second vector information).

[0032] Furthermore, the metric calculation module 212 selects multiple different evaluation axes and calculates a metric value based on a value and score indicating the bias among these multiple evaluation axes. The metric value is, for example, an average value based on a value and score indicating the bias among multiple evaluation axes, and it shows the relationship between the media page and the advertising content-related information. The process for calculating the metric value will be described later.

[0033] The destination determination module 213 (destination determination unit) determines the media page corresponding to the calculated metric value as the destination for the advertising content if the calculated metric value exceeds a predetermined value. For example, if the destination determination module 213 is higher than the predetermined value, it may determine that the advertising content and media page corresponding to this metric value have a high relationship, and select the media page determined to have a high relationship with the advertising content as the destination.

[0034] The destination output module 214 outputs information to the ad delivery server 105 indicating the destination media, such as a media page, that has been determined or selected as the destination.

[0035] The user information acquisition module 215 acquires user response information from the user terminal 104. User response information includes, for example, the browsing history of media pages on the user terminal 104, information entered by the user, information such as conversions for media pages and advertising content displayed on the user terminal 104, and information indicating user access to delivered advertising content.

[0036] In other words, the user information acquisition module 215 acquires user response information, which is information about the user's reaction to the delivered information, and the presentation destination determination module 213 selects other media pages in accordance with the adjustment of weight values ​​by the index value calculation module 212 based on the user response information.

[0037] The content generation module 216 generates new content information (generated content) by updating the advertising content based on the extracted keyword information.

[0038] The distribution result acquisition module 217 requests the server to import the distribution result including the advertisement ID, and acquires the distribution result-related information corresponding to the advertisement content ID. Also, the distribution result acquisition module 217 registers the acquired distribution result information. As a result, for example, it becomes possible to estimate the characteristics of an advertisement page with many conversions, accesses, or clicks.

[0039] The content evaluation module 218 evaluates the similarity between the generated content, which is the newly generated content information, and the advertisement content-related information.

[0040] The auxiliary storage device 202 stores content information 221, user response information 222, keyword tag information 600, media metric value information 700, distribution result information 800, etc. Each type of information will be described later.

[0041] 1-2-2. Distribution Media Server 103 Next, the distribution media server 103 will be described. FIG. 3 shows a configuration example of the distribution media server 103. The distribution media server 103 is composed of, for example, a plurality of servers arranged on the cloud. Each distribution media server is, for example, a web server and distributes media pages (media page information).

[0042] The distribution media server 103 includes a main storage device 301, an auxiliary storage device 302, a processor 303, an input device 304, an output device 305, and a communication control unit 306.

[0043] Among these, the main storage device 301 stores programs and applications such as a content management module 311 and a content distribution module 312. By the processor 303 executing these programs and applications, each functional element of the distribution media server 103 is realized.

[0044] The content management module 311 manages the registration and reading of media pages. The content distribution module 312 distributes, for example, the media pages stored in the auxiliary storage device 302. The auxiliary storage device 302 stores media page information 321. The media page information 321 is, for example, a group of media pages to be distributed.

[0045] 1-2-3. User Terminal 104 Next, the configuration of the user terminal 104 will be described. FIG. 4 shows a configuration example of the user terminal 104. The user terminal 104 is, for example, a portable computer, a stationary or portable computer device used by a user, and the portable computer device is, for example, a communication terminal such as a tablet terminal or a smartphone.

[0046] The user terminal 104 includes a main storage device 401, an auxiliary storage device 402, a processor 403, an input device 404, an output device 405, a camera 406, and a communication control unit 407.

[0047] Among these, programs and applications such as a content acquisition module 411 and a UI module 412 are stored in the main storage device 401, and various functional elements of the user terminal 104 are realized by the processor 403 executing these programs and applications.

[0048] The content acquisition module 411, for example, downloads and acquires media pages via a network. The UI module 412, for example, displays various screens on a display (e.g., the output device 405) and accepts user operations via the input device 404. The input device 404 is, for example, a touch panel, a keyboard, a mouse, etc.

[0049] The UI module 412, for example, displays a browser and a media page on this browser. The UI module 412 accepts user operations on the displayed browser and media page.

[0050] The auxiliary storage device 402 stores, for example, user response information 421. The user response information 421 includes, for example, browsing history information (access history information) indicating media pages displayed on the display based on user operations, user behavior history information such as purchase history using websites, media pages, and apps (applications) accessed by user operations, input or transmission information entered or sent by the user, and login history information indicating the user's login history for website services.

[0051] Camera 406 captures images and videos based on user input. UI module 412 may store information such as images and videos captured by camera 406 as user response information in user response information 421.

[0052] The images and videos captured by camera 406 may include, for example, images and videos displayed on a display outside the user terminal 104, or images and videos of people or scenery around the user terminal 104 captured by camera 406. Furthermore, the images and videos captured by camera 406 may also show, for example, the gaze, movements, or gestures of the user operating the user terminal 104.

[0053] 1-2-4. Ad Delivery Server 105 Next, the configuration of the ad delivery server 105 will be described. Figure 5 shows an example of the hardware configuration of the ad delivery server 105.

[0054] The ad delivery server 105 is, for example, a group of server devices composed of computers located on the cloud. Each server device delivers ad content in response to requests sent from, for example, the user terminal 104.

[0055] The advertising distribution server 105 includes a main memory 501, an auxiliary memory 502, a processor 503, an input device 504, an output device 505, a communication control unit 506, and the like.

[0056] Of these, the main memory 501 stores programs and applications such as the destination reception module 511 and the advertising content distribution module 512. The processor 503 executes these programs and applications to realize the various functional elements of the advertising distribution server 105.

[0057] The destination reception module 511 receives information indicating the destination of the advertising content, which is output from the management server 102. The advertising content distribution module 512 distributes the advertising content information based on the destination received from the management server 102. For example, the advertising content distribution module 512 distributes the content to the media page that is the destination in response to a request sent from the browser of the user terminal.

[0058] The auxiliary storage device 502 stores advertising content information 521. The advertising content information 521 includes, for example, pre-generated advertising content to be distributed, as well as video data, image data, text data, and audio data that constitute the advertising content.

[0059] In this embodiment, the configuration for determining the destination of advertising content is described, but other content or information may be used instead of advertising content. Also, in this embodiment, the advertising distribution server 105 is configured to distribute advertising content, but this is not the only configuration. For example, the management server 102 may hold advertising content or other content information and transmit the content information to the determined distribution destination.

[0060] In this embodiment, the advertising content is described in a configuration in which it is delivered to the determined target media by the advertising delivery server 105, but the embodiment is not limited to this. For example, the management server 102 may store the advertising content and deliver it to the target media.

[0061] 2-1. Operation Flow Next, we will explain the flow executed by the management server 102 to output the destination for displaying the advertising content. Figure 9 is an example of the destination output flow 900.

[0062] First, the content acquisition module 211 acquires media pages, including websites and web pages, delivered by a web server, etc., and advertising-related information (S910).

[0063] Next, the metric calculation module 212 selects several different evaluation axes and calculates and obtains, for example, several scores corresponding to each of the multiple evaluation axes based on the similarity between the advertising content-related information and the media page. The metric calculation module 212 then calculates a metric value based on the calculated scores and the bias between the multiple evaluation axes (S920).

[0064] Next, the destination determination module 213 determines the media pages corresponding to the metric values ​​that exceed a predetermined value as the media to which the advertising content will be displayed (S930).

[0065] Next, the destination output module 214 outputs information indicating the destination media (S940). At this time, the destination output module 214 transmits, for example, information indicating the destination media to the ad distribution server 105.

[0066] As described above, the destination output flow 900 calculates an index value that shows the relationship between the advertising content and the media page, determines the media to which the advertising content will be displayed based on the value of the index value, and outputs information indicating that destination.

[0067] Specifically, the content acquisition module 211 acquires a media page containing the information to be distributed and information related to the presented content (advertising content) which is the information presented on the media page. The metric value calculation module 212 selects multiple different evaluation axes and acquires multiple scores corresponding to each of the multiple evaluation axes based on the similarity between the information related to the presented content and the media page. It then calculates a metric value based on the multiple scores and the bias between the multiple evaluation axes. The presentation destination determination module 213 determines the media page corresponding to the metric value exceeding a predetermined value as the presentation destination for the presented content, and the presentation destination output module 214 outputs information indicating the media page that has been determined as the presentation destination for the presented content.

[0068] Next, we will explain the process of acquiring media pages and advertising-related information (referred to as content information). Figure 10 shows an example of the analysis flow 1000 for media pages and advertising-related information.

[0069] First, the content acquisition module 211 acquires the delivered media page and stores it in the content information 221 (S1010). The content acquisition module 211 may, for example, continuously perform the process of acquiring media pages in parallel with various processes performed by other modules.

[0070] Next, the index value calculation module 212 analyzes the media pages stored in the content information 221 (S1020).

[0071] Here, the index value calculation module 212 extracts, for example, keyword information, images, videos, etc., contained in each media page, and performs processing (analysis processing) such as classifying the extracted information and classifying the media pages based on the extracted information.

[0072] Next, the content acquisition module 211 acquires related information about the advertising content (for example, advertising content A) input to the management server 102 and stores it in the content information 221 (S1030).

[0073] Here, the information related to advertising content A includes, for example, advertising materials such as images, videos, and advertising copy sent by the advertiser, as well as advertising creative data and advertising material data related to the advertising creative. The information related to advertising content A may also include advertising content A itself.

[0074] Next, the metric calculation module 212 analyzes the relevant information of the acquired advertising content A (S1040).

[0075] Here, the metric calculation module 212 may, for example, extract keyword information, images, videos, etc., included in the related information of advertising content A, similar to the analysis process for media pages.

[0076] By performing analysis processing on advertising-related information and media pages, it becomes possible to quickly start the calculation process of the metric values ​​described later (described later). Note that each of the above analysis processes may be executed by the content acquisition module 211. In addition, after acquiring advertising-related information, the content acquisition module 211 may search for and acquire media pages based on said advertising-related information.

[0077] Furthermore, the content acquisition module 211 may display an advertising content-related information input screen, which is a screen for inputting information related to advertising content A, on the output device 205, and acquire the information entered on this screen.

[0078] Figure 17 shows an example of a content-related information input screen 1700. The content-related information input screen 1700 shown in the figure is a screen for inputting information related to advertising content A, such as product information and attribute information, and is displayed on the output device 205 and accepts input from users of the management server 102.

[0079] The content-related information input screen 1700 includes input fields for major category information 1720, detailed category information 1730, and category trend information 1740, which are input fields for product information of the advertised product. Each of these input fields accepts text information, for example, about the product category and product of the product that is the target of the advertised content A.

[0080] Of these elements, the major category input field 1720, the detailed category input field 1730, and the category trend input field 1740 are input fields for entering classification and attribute information of the products, goods, and services advertised in advertising content A, for example, and users of the management server 102 can add related information to advertising content A.

[0081] Furthermore, the content-related information input screen 1700 includes an emotional keyword input field 1750, a functional keyword input field 1760, and a registration button 1770, which are advertising information related to advertising content A.

[0082] The emotional keyword input field 1750 and the functional keyword input field 1760 are input fields for information used to classify keyword information. The content acquisition module 211 may, for example, register the words and keywords entered in the emotional keyword input field 1750 and the functional keyword input field 1760 as tag information (evaluation axis).

[0083] Furthermore, the content acquisition module 211 can acquire information entered into the content-related information input screen 1700, for example, as input for product category information or keyword information related to product branding promotion.

[0084] When the user selects the registration button 1770, the content acquisition module 211 saves the text information entered in each of the above input fields, associating it with the relevant information of the advertising content A.

[0085] Furthermore, the content acquisition module 211 may acquire and register media pages that are entered and specified by users of the management server 102. For example, the content acquisition module 211 may display a media URL input screen on the output device 205 and acquire the information entered on the media URL input screen.

[0086] Figure 18 shows an example of the media URL input screen 1800. The media URL input screen 1800 includes media URL information 1810, which is a list of media page URLs obtained by the content acquisition module 211, and a registration button 1820.

[0087] The media URL information 1810 includes a media ID indicating the ID of each media page, a distribution eligibility record indicating whether or not advertising content can be displayed, information such as the site name of the distribution media or the name of the media page, and URL information indicating the URL of each media page.

[0088] The content acquisition module 211 registers the media page information entered in the media URL information 1810 in response to the operation of the registration button 1820 by a user of the management server 102.

[0089] On the media URL input screen 1800, for example, a pull-down menu is displayed in response to the operation of selecting each record, and registration can be performed by selecting information indicating each media page or attribute information for each media page.

[0090] Next, we will explain the process for calculating the index value. Figure 11 shows an example of the index value output flow 1100.

[0091] First, the metric calculation module 212 selects several different evaluation axes (also referred to as "evaluation tags") (S1110). For example, from the multiple evaluation axes, the metric calculation module 212 selects emotional tags related to awareness of the advertised product and functional tags related to sales acquisition of the product or service.

[0092] In other words, the multiple evaluation axes include, at a minimum, a recognition axis that shows evaluation axes related to increasing awareness or improving understanding of the product or service, and an acquisition axis that shows evaluation axes related to sales of the product or service.

[0093] Next, the index value calculation module 212 classifies the keyword information according to the selected evaluation axis (S1120). For example, the index value calculation module 212 extracts keyword information that is classified as an emotional tag. The extracted keyword information is called an emotional tag. The emotional tag includes, for example, keyword information such as sustainability, inclusive design, and innovation in driver assistance technology. In other words, the multiple evaluation axes include at least an emotional axis that shows an evaluation axis related to human emotions and a functional axis that shows an evaluation axis related to the functionality of the product.

[0094] Furthermore, the index value calculation module 212 extracts keyword information classified as functional tags from advertising content-related information and media pages. The extracted keyword information is referred to as functional tags. Functional tags include, for example, keyword information such as special edition vehicles, 20th anniversary models, standard comfort features, and high fuel efficiency.

[0095] Figure 6 shows an example of keyword tag information 600. Keyword tag information 600 is information composed of keyword item numbers, emotional tags, and functional tag records. The keyword information of keyword tag information 600 is, for example, a word, collocation, sentence, or sentence containing a catchphrase classified as an emotional tag or functional tag.

[0096] The keyword information in keyword tag information 600 is, for example, a set of keyword information obtained from corporate messages on a company website, which are advertising content-related information, or from media pages. The set of keyword information is extracted, for example, from text data contained in advertising content-related information and media pages. In addition, the keyword information may be extracted based on image data and video data contained in the advertising creative itself or advertising content-related information, or image data and video data contained in media pages such as landing pages and web pages.

[0097] The metric calculation module 212, for example, classifies keyword information extracted from advertising content-related information using two different concepts as evaluation axes.

[0098] The index value calculation module 212 classifies keyword information such as sustainability, inclusive design, innovation in driver assistance technology, comfort and safety, future-oriented, innovation, safety, comfort, savings, and family-friendly, as shown in the keyword tag information 600, into "emotional tags" which are concepts close to human emotions.

[0099] Furthermore, the index value calculation module 212 classifies keyword information such as special edition vehicles, 20th-anniversary models, standard comfort features, one-touch automatic sliding doors, fully automatic dual air conditioning, and high fuel efficiency into "function tags" as concepts related to quantifiable functionality. This makes it possible for the index value calculation module 212 to calculate index values ​​that, for example, emphasize emotionally-oriented keywords, or index values ​​that emphasize functionally-oriented keywords. The index value calculation process will be described later.

[0100] Furthermore, the maximum number of keyword information items stored in the keyword tag information 600 may be limited, for example, to 10 keywords and 5 key phrases.

[0101] Next, the metric calculation module 212 obtains, for example, the similarity score between the advertising content and the media page (S1130).

[0102] Here, we will explain the process for obtaining the similarity score. The metric calculation module 212 calculates, for example, first vector information that indicates the direction of keywords included in the relevant information of the advertising content.

[0103] The first vector information is, for example, an embedded vector of keyword information extracted from related information of advertising content A. Alternatively, the first vector information may also be an embedded vector of keyword information extracted from related information of advertising content A, such as information on a site related to the advertising campaign being advertised. The keyword information may include not only text and string keywords, but also image information, video information, 3D data information, etc.

[0104] Furthermore, the metric calculation module 212 calculates, for example, second vector information indicating the direction of the media page. The metric calculation module 212 calculates the second vector information by, for example, performing embedded vectorization on the media page using an embedded vector model. The second vector information is, for example, an embedded vector of text data contained in the media page that has been acquired in advance.

[0105] Next, the index value calculation module 212 obtains, for example, the similarity score of the first vector information and the second vector information. The similarity score is a value that indicates the degree of similarity between the first vector information and the second vector information, and is expressed as a value in the range of 0.0 to 1.0.

[0106] Returning to Figure 11, the index value calculation module 212 calculates multiple scores corresponding to each evaluation axis based on the acquired similarity scores and the classified keyword information group (S1140). The multiple scores are, for example, score (a) and score (b). Score (a) represents, for example, a value that emphasizes the emotional tag (referred to as an emotionally oriented value), and score (b) represents a value that emphasizes the functional tag (referred to as a functionally oriented value).

[0107] For example, multiple evaluation axes include at least a brand score axis that shows evaluation axes related to product branding and a general score axis that shows evaluation axes related to attributes other than product branding. Score (a) may be, for example, one of awareness, product branding, or emotion. Score (b) may be one of acquisition, an evaluation axis other than product branding (general), or function.

[0108] Next, the index value calculation module 212 calculates an index value, which is the average value based on multiple scores corresponding to each tag and values ​​indicating biases between multiple evaluation axes (S1150). The index value is, for example, a weighted harmonic mean. Furthermore, the index value calculation module 212 adjusts the calculated index value and displays it on the output device 205 (S1160).

[0109] The metric value is expressed as a value between 0 and 1.0, for example. A value closer to 1.0 indicates a higher degree of relationship and matching between the media page and the advertising content-related information.

[0110] [Explanation of the Index Value Calculation Process] Here, we will explain in detail the process for calculating index values. The index value calculation module 212 calculates index values ​​based on keyword information classified as emotional tags and functional tags. Emotional tags are evaluation axes related to information concerning human emotions, and functional tags are evaluation axes related to the functionality of a product.

[0111] The metric calculation module 212 calculates an emotional score (e) for the keyword information group of emotional tags based on the similarity between the advertising content-related information and the media page. The metric calculation module 212 also calculates a functional score (f) for the keyword information group of functional tags based on the similarity between the advertising content-related information and the media page.

[0112] The affect score (e) is, for example, the similarity between the embedded vector (Vs) obtained by embedding a media page and the embedded vector (Ve) of the keyword group of the affect tag, and can be calculated based on an arbitrary distance, such as cosine distance or squared distance (Euclidean distance). The keyword group of the affect tag is obtained, for example, by analyzing advertising campaign information related to advertising content (advertising creative). The functionality score (f) can be calculated in a similar manner.

[0113] Below is Equation 1 for calculating the cosine distance. [Equation 1] Cosine distance (e) = Vs * Ve / |Vs| × |Ve| (* represents the dot product)

[0114] In the embedded vectorization process, a general embedding model may be used, for example, but to calculate the similarity between the delivered media page or media site and the advertising content-related information, the same embedding model must be used for both. On the advertising content-related information side, keywords, phrases, and sentences such as sentiment tags are arranged and embedded vectorized as a single sentence (series of sentences). For example, OpenAI "text-embedding-3-small" generates a 1536-dimensional vector.

[0115] When using cosine distance (cos), the value indicating similarity ranges from -1 to 1, with values ​​closer to 1 indicating higher similarity. On the other hand, in some vector databases, subtracting the cos value from 1 transforms the similarity so that values ​​closer to 0, i.e., smaller values, indicate higher similarity. In the following explanation, we will assume that calculations are performed using the cos value before this transformation is applied.

[0116] The metric calculation module 212 calculates metric values ​​based on weighting values, which are values ​​that weight the bias. For example, the metric calculation module 212 calculates the overall score (s), which is a metric value that shows the relationship between advertising content-related information and media pages, as a weighted harmonic mean of multiple scores (a, b) and a weighting value (β) using the following [Equation 2].

[0117] [Equation 2] Weighted F-value (weighted harmonic mean): F = (1 + β) ２ ) × a × b / (β ２ × a + b)

[0118] For example, the cognitive-oriented score (b) of the consumer funnel may be taken as the affective score (e), and the acquisition-oriented score (a) of the consumer funnel may be taken as the functional score (f). Based on the weighted values ​​(β), the overall score (s) may be calculated using the weighted harmonic mean (F).

[0119] By setting the weighting value (β) to a value greater than 1, a value that emphasizes the emotional score e is calculated. For example, β = 2 indicates that the emotional score e is given four times more weight than the functional score f. A weighted F value of 1.0 represents 100%. For example, if the range of the overall score (s) is from 0 to 10, the weighted F value should be multiplied by 10.

[0120] For example, if the weighting value (β) is β = 1, and the emotional score e and functional score f are (emotional score e: functional score f) = (3:8) or (8:3), the weighted F-value will be 2 × 0.3 × 0.8 / (0.3 + 0.8) = 0.436. For example, if the weighting value (β) is β = 1, and the emotional score e and functional score f are (emotional score e: functional score f) = (8:8), the weighted F-value will be 0.8.

[0121] For example, if the weighting value (β) is β = 2, and the emotional score e and functional score f are (e:f) = (3:8) or (8:3), the weighted F-value will be 0.343 for (3:8), 0.6 for (8:3), and 0.8 for (8:8).

[0122] [About the Metric Calculation Screen] The metric calculation module 212 may, for example, calculate the bias between evaluation axes for each media page as the value of the rotation angle from the intermediate vector (standard vector) of score (a) and score (b). The metric calculation module 212 displays, for example, a metric calculation screen on the output device 205, which is a screen showing the inter-evaluation axis vectors for calculating the score.

[0123] Figure 19 shows an example of an index value calculation screen 1900. The index value calculation screen 1900 includes, for example, a graph 1910 having a horizontal axis as the function axis, a vertical axis as the emotion axis, and an intermediate vector (1,1) between the function axis and the emotion axis, and an index value calculation button 1920. The index value calculation module 212 calculates various scores, bias values, and index values ​​depending on whether the index value calculation button 1920 is selected.

[0124] The index value calculation module 212 calculates the rotation angle and distance values ​​relative to the intermediate vector for the score (a, b).

[0125] The index value calculation module 212, in response to the selection of the index value calculation button 1920, calculates and displays, for example, |Vm| = 8.5444 for the vector (let's call it Vm) of score (3,8) for media page m by measuring the distance from the origin. Similarly, the index value calculation module 212 calculates and displays |Vn| = 8.5444 for the vector (let's call it Vn) of score (8,3) for another media page y in the same manner. These values ​​represent the magnitude of each vector.

[0126] Furthermore, the index value calculation module 212 calculates the bias between evaluation axes as the difference from the intermediate axis of multiple evaluation axes. For example, if the index value calculation module 212 considers the scores (8,3) and (3,8) as rotation angles from a standard vector, it may calculate and display Rn = 14.44, which is calculated as a rotation angle of 14.44 degrees on the emotional axis side, and Rm = 14.44, which is calculated as the magnitude of the rotation angle on the functional axis side.

[0127] Next, we will explain the process of outputting the adjusted index value (S1160). Figure 12 shows an example of the output flow 1200 for the adjusted index value.

[0128] The index value calculation module 212 detects user stage information based on user response information previously acquired by the user information acquisition module 215 (S1210).

[0129] User response information includes, for example, user behavior history information captured by cookies or advertising tags, and includes, for example, browsing history of media pages, web pages, websites, etc. on the user terminal 104, user input information on the user terminal 104, and information such as conversions to media pages and advertising content.

[0130] User stage information is information that indicates the stages a user goes through from product awareness to product purchase (sometimes referred to as user position or user consumer funnel), detected or estimated based on user response information. User stage information also indicates stages such as the user's awareness, level of understanding / interest, and purchase intent regarding products or services related to advertising content on a scale of 0 to 10. For example, 0 could represent the stage where the user is not aware of the product, 1 the stage where they are aware, and 10 the stage where they have decided to purchase.

[0131] The metric calculation module 212 detects the user's position (the user's consumer funnel) based, for example, on the media pages in the user's browsing history of user response information. The metric calculation module 212 may also set the user's position (for example, stages 1 to 9) in association with the stages from awareness to purchase, based on the content of the media pages viewed, that is, based on which media pages were viewed.

[0132] Furthermore, the index value calculation module 212 may detect the user's position by using, for example, images and videos captured by the camera 406 of the user terminal 104, or information indicating the gaze of the user operating the user terminal 104, as user response information. For example, the user's position may be set according to the type of content information such as media pages, videos, and images that are in the user's line of sight, or the amount of time the user's gaze is directed towards the content information (viewing time).

[0133] The index value calculation module 212 adjusts the weighting value (β) to a larger value for users detected as being in the awareness stage of the consumer funnel (user stage), and conversely, adjusts the β value to a smaller value for users determined to be in the acquisition stage.

[0134] Next, the index value calculation module 212 adjusts the weighting value (β) based on the user stage of the user stage information (S1220). For example, the index value calculation module 212 changes the weighting value (β) in accordance with the change in the user stage. At this time, the index value (overall score (s)) is changed in accordance with the change in the weighting value (β). The index value calculation module 212 outputs the changed weighting value (β) and index value (overall score (s)) to the output device 205 (S1230).

[0135] In other words, the index value calculation module 212 detects user stage information, which indicates the stages from product awareness to product purchase, based on user response information, and changes the weighting value according to the user stage information.

[0136] The index value calculation module 212 may display a user index value screen showing the weighted value (β) and index value (overall score (s)) that are changed in accordance with changes in user stage information.

[0137] Figure 20(A) shows an example of the user metrics screen 2010 before update. The user metrics screen 2010 before update includes user stage metrics information 2012 and an update button 2014. The user stage metrics information 2012 consists of records for, for example, user ID, media page ID, user stage information, weighting value (β), and metrics value (overall score (s)).

[0138] The indicator value calculation module 212, for example, displays the updated user indicator value screen 2020 when there is an input operation to the update button 2014 on the pre-update user indicator value screen 2010.

[0139] Figure 20(B) is an example of the updated user metrics screen 2020. The updated user metrics screen 2020 includes user stage metrics information 2022 and an update button 2024. User stage metrics information 2022 is information in which some or all of the information (values ​​or names) in the user stage metrics information 2012 of Figure 20(A) has been updated.

[0140] Here, for example, the user stage metric information 2012 in Figure 20(A) and the user stage metric information 2022 in Figure 20(B) each contain the record for the media page with the highest metric value, i.e., the one with the highest relationship to advertising content A.

[0141] Figure 20(A) shows, for example, that user A011 has a user stage of "2," which is the awareness stage. Here, for example, the awareness stage is in the range of 1 to 3. Also in Figure 20(A), it is shown that the weighting value (β) corresponding to user A011 is set to "9," which is close to the maximum value, and an index value of 0.78 is calculated. In this case, for user A011, who is in user stage "2," pages abc, which are geared towards awareness, are selected as media pages with a high degree of matching, and their index value is 0.78.

[0142] Next, after the user stage is updated, for example, if it is detected that user A011's user stage is "6", the index value calculation module 212 sets the weighting value (β) to "3", as shown in the user stage index value information 2022 in Figure 20(B). In other words, the index value calculation module 212 adjusts the weighting value (β) from 9 to 3 in response to user A011's user stage rising from 2 to 6.

[0143] At this time, the index value corresponding to pages abc decreased from 0.78 (maximum value) before the user stage update to 0.57, as shown in Figures 20(A) and (B), while the index value corresponding to page yyy increased from 0.35 before the user stage update to 0.67 (maximum value). In other words, Figure 20(A) shows that the relationship and matching degree between user A011, whose user stage was "2", and pages abc is high (index value 0.78), and Figure 20(B) shows that the relationship and matching degree between user A011, whose user stage was "6", and page yyy is high (index value 0.67).

[0144] On the other hand, for example, in Figures 20(A) and (B), the index value for page aaa for user C033 increased from 0.39 when user stage "7" to 0.56 when user stage "9". In other words, the index value for page aaa for user C033 increased even when the user stage increased, which indicates that page aaa was a media page that was relatively closer to the purchase stage for user C033.

[0145] As described above, the destination determination module 213 changes the selection of media pages to which the advertising content will be displayed in accordance with the change in the metric value due to the adjustment of the weighting value (β).

[0146] For example, for user A011 in the awareness stage, page abc would be selected as the most relevant media page, and for user A011 in the purchase stage, page yyy would be selected.

[0147] Therefore, even when the user stage changes for the same user, for example, when the same ad creative is presented to the same user before and after the change in user stage, the presentation destination determination module 213 can select a media page (web page) that is appropriate for the user's consumer funnel as the destination for the ad creative.

[0148] Furthermore, the metric calculation module 212 may generate and output reporting materials on the effectiveness of ad delivery based, for example, on the user stage metric information 2012 and 2022 shown in Figure 20. That is, the metric calculation module 212 may output and display information such as weighted values, biases between evaluation axes, multiple scores, or at least one of the metric values.

[0149] Reporting materials may include, for example, information indicating the degree to which emotional or functional aspects are prioritized, a weighting value (β) on a 10-point scale, the degree to which emotional scores and functional scores are enhanced (emphasized), or graphs and tables showing these. This makes it possible to clearly demonstrate the compatibility and degree of matching between the advertising content and the media to which it is presented, and to clearly show the basis and objectives for selecting and deciding on the media to which it is presented.

[0150] Figure 7 shows an example of media metric information 700. Media metric information 700 consists of records comprising media ID, text information of media articles, brand (b), general (a), emotional metric value Fβ2.0, and functional metric value Fβ0.5. Note that the "emotional metric value" in media metric information 700 indicates an emotionally oriented metric value. Of the information that makes up each record, the text information of media articles refers to the text information contained in each media page, and Figure 6 shows the first part of it. The brand (b) record shows the score for the emotional tag in Figure 6. The general (a) record shows the score for the functional tag in Figure 6. The functional metric value Fβ0.5 record shows the weighted F value when β = 0.5. The emotional metric value Fβ2.0 record shows the weighted F value when β = 2.0. Note that the F values ​​here have been multiplied by 10 for readability.

[0151] For example, the emotional Fβ2.0 index value of 4.43 in cell 710 of media ID "B0021" is the highest value among all emotional Fβ2.0 records. For instance, when implementing awareness strategies for advertising content distribution, media ID "B0021" is shown to match the brand message that indicates awareness (emotion). Therefore, in the case of awareness strategies, this list indicates that the optimal target for advertising content is media ID "B0021".

[0152] Furthermore, in media metrics information 700, for functional strategies, the acquisition metric value of 4.24 in cell 720 for media ID "A0011" and 4.26 in cell 730 for media ID "B0021" both show high values. In addition, media metrics information 700 indicates that when an acquisition strategy is implemented for the distribution of advertising content, the media pages for media ID "A0011" or media ID "B0021" match the general message indicating acquisition (functionality). Therefore, for acquisition strategies, it is effective to present advertising content to media ID "A0011" or media ID "B0021".

[0153] Furthermore, the metric calculation module 212 may output and display at least one of the following pieces of information: the metric value (overall value (s)), the weighted value (β), information indicating the bias between the evaluation axes of the score vector (a, b), and the magnitude of the score vector (a, b). The output and displayed data can be used, for example, in reports on the effectiveness of advertising content delivery, or as source data or materials for reports.

[0154] Next, we will explain the process for determining the target media. Figure 13 shows an example of the target media determination flow 1300.

[0155] First, the destination determination module 213 displays the extraction condition input screen on the output device 205 and obtains the extraction conditions entered by the user of the management server 102 (S1310). Next, the destination determination module 213 determines whether there are any media pages that satisfy the condition, for example, "the index value exceeds a predetermined value" (S1320). If there are no media pages that satisfy the condition (N: S1320), the destination media determination flow is terminated (to end).

[0156] On the other hand, if the destination determination module 213 finds a media page corresponding to an index value exceeding a predetermined value (Y: S1320), it determines this media page to be the destination media for advertising content A (S1330).

[0157] Figure 21 shows an example of the extraction condition input screen 2100. The extraction condition input screen 2100 is a display screen output to the output device 205 and includes an extraction condition input field 2110, a "greater than" selection button 2120, a "less than" selection button 2130, an emotional keyword input field 2140, and a linking button 2150.

[0158] The extraction criteria input screen 2100 accepts numerical input in the extraction criteria input field 2110. For example, a user of the management server 102 has entered the value "5.3". The "Greater Than" button 2120 and the "Less Than" button 2130 are radio buttons, and when either selection is accepted, the extraction criteria can be set to be greater than or equal to the numerical value entered in the extraction criteria input field 2110, or less than or equal to it.

[0159] For example, if the extraction condition is set to have an index value of 5.3 or higher, and the link button 1950 is selected, the destination determination module 213 will determine media pages with an index value of 5.3 or higher as destinations for advertising content A. In this case, the destination determination module 213 will determine, for example, page yyy1970 and page abc1980 as destination media for advertising content A.

[0160] Furthermore, the display destination determination module 213 may, when the indicator value exceeds a predetermined value, select the advertising content corresponding to that indicator value as the target for display on the media page corresponding to that indicator value.

[0161] Once the media to which the advertising content will be displayed is determined, the display destination output module 214 outputs information indicating each display destination to the advertising distribution server 105. This information is used to identify the media page and its advertising slot, and is, for example, the URL of the media page.

[0162] As a result, the ad delivery server 105, which has ad content A, can deliver ad content A in response to a request from, for example, the browser of the user terminal 104. In this embodiment, it is possible to select a media page that has a high relationship with the information related to the ad content and display the ad content in the ad space on this media page.

[0163] Next, we will explain the flow for generating new content, which is generated content. Figure 14 shows an example of the generated content generation flow 1400. Note that the operations of S1410 to S1440 of the generated content generation flow 1400 are the same as S910 to S940 of the destination output flow 900 in Figure 9. Therefore, we will explain S1450 to S1470 of the generated content generation flow 1400 below.

[0164] In step S1450 of the generated content flow, the content acquisition module 211 acquires delivery result information. The content acquisition module 211 may, for example, acquire the delivery result information of the advertising content from an external server by sending a request message to the external server that includes the advertising ID, which is the ID of the delivered advertising content.

[0165] Delivery results information shows information about user actions and reactions to delivered advertising content. For example, it shows the number of accesses to the delivered advertising content, the number of conversions such as taps and clicks on the advertising content, and the number of times the advertising content information was displayed and viewed.

[0166] Figure 8 shows an example of distribution result information 800. Distribution result information 800 consists of records such as media page ID, URL, impressions, CT, and CV.

[0167] Among the information that makes up each record, the Media ID indicates an information ID for identifying the media page, and the URL indicates the URL of each media page where the advertising content was delivered, i.e., the URL of the web page or website. Impressions indicate the number of times the advertising content was displayed. CT (Click Through) indicates the number of times the advertising content was clicked. CV (Conversion) indicates the number of conversions.

[0168] In the distribution results information 800, for example, it is shown that the media page at URL (https: / / www.aaa.com) had 1000 impressions, 50 clicks, and 14 conversions.

[0169] Therefore, the management server 102 can be configured to generate and output information showing the relationship between the indicator value (overall value (s)), weighted value, the bias value between the evaluation axes of the similarity score, and the number of CV (Conversion) conversions of the advertising content, for example, by linking with the distribution result information 800.

[0170] Furthermore, the metrics in this embodiment may be applied to a system that charges for the delivery of advertising content. For example, if the number of ad accesses, such as the number of conversions or impressions of the advertising content, improves, and the metrics between the advertising content and the media page also improve, the amount charged for ad delivery may be increased according to the degree of improvement in the metrics.

[0171] Returning to Figure 14, the content generation module 216 extracts new keyword information from the media page (S1460).

[0172] Next, the content generation module 216 generates generated content based on the newly extracted keyword information (S1470). The generated content may be, for example, advertising content in which a portion of the delivered advertising content has been updated.

[0173] In other words, the metric calculation module 212 obtains new keyword information from advertising content-related information or media pages, and the content generation module 216 generates new content information by updating the advertising content based on the new keyword information.

[0174] Furthermore, the content generation module 216 may update the advertising content (presented content) based on weighted values ​​or values ​​indicating biases between multiple evaluation axes, which are adjusted based on user response information.

[0175] The content generation module 216 may also be implemented by using, for example, a generation AI. In this case, the content generation module 216 may extract, for example, words, phrases, text data estimated or detected from image information contained in the media page as keyword information.

[0176] Next, we will explain the process of extracting new keyword information. Figure 15 shows an example of a new keyword extraction flow 1500.

[0177] The target selection module 213 updates the selection of media pages based on the content of the acquired delivery result information (S1510). The delivery result information includes, for example, the results of offline surveys such as brand lift surveys. Based on the delivery result information, such as the results of offline surveys, the target selection module 213 newly selects media pages that have shown improved advertising performance, such as the number of accesses and impressions to the advertising content.

[0178] For example, if the distribution results information detects an increase in the number of impressions of a webpage related to "travel," the destination determination module 213 selects a new media page related to "travel" as the site from which to retrieve the media page.

[0179] Next, the content generation module 216 determines, for example, whether or not new keyword information is included in the media page based on the similarity between the newly selected media page and the advertising content-related information (S1530).

[0180] If the content generation module 216 contains new keyword information (Y: S1530), it extracts this keyword information (S1540).

[0181] Next, we will describe the flow for evaluating the generated content. Figure 16 shows an example of the generated content evaluation flow 1600.

[0182] The indicator value calculation module 212 acquires new keyword information (S1610). At this time, the indicator value calculation module 212 may, for example, display the acquired keyword information on the screen.

[0183] Figure 22 shows an example of the keyword information output screen 2200. The keyword information output screen 2200 includes a relevance emotion radio button 2210 and a relevance function radio button 2220 for selecting relevance keywords, a difference emotion radio button 2230 and a difference function radio button 2240 for selecting difference keywords, as well as a relevance keyword display area 2250, a content generation button 2260, and a difference keyword display area 2270.

[0184] The relevance keyword display area 2250 displays, for example, a group of keywords that have been determined to be highly relevant to the advertising content-related information. On the other hand, the difference keyword display area 2270 displays a group of keywords that are less relevant to the advertising content-related information but have been extracted from the advertising content-related information or media page.

[0185] On the keyword information output screen 2200, for example, on the related keyword display area 2250 side, the related emotion radio button 2210 is selected, so keywords related to the emotion tag are displayed in the related keyword display area 2250.

[0186] Furthermore, since the difference keyword display area 2270 also has the difference emotion radio button 2230 selected, keywords related to the emotion tag are also displayed in the difference keyword display area 2270.

[0187] If the user selects the difference function radio button 2240, the screen of the difference keyword display area 2270 transitions to the screen of the function keyword group display area 2370 shown in Figure 23.

[0188] Figure 23 shows an example of the keyword information output screen 2300. The keyword information output screen 2300 includes related emotion buttons 2310 and related function buttons 2320 for selecting related keywords, a differential emotion button 2330 and differential function radio buttons 2340 for selecting differential keywords, an emotion keyword display area 2350, a function keyword and content generation button 2360, and a display area 2370. In the display area 2370, the newly extracted keyword (in this case, "travel") 2380 is highlighted.

[0189] The content generation module 216 launches the creative advisor application (hereinafter simply referred to as "advisor") in response to the selection of the content generation button 2360.

[0190] The activated advisor generates new content (generated content) by updating the original advertising content based on newly extracted keywords, for example, displayed in display area 2350 or 2370 (S1620).

[0191] In other words, the content acquisition module 211 acquires, for example, delivery result information that shows information about the user's response to the delivered advertising content, the presentation destination determination module 213 selects a new media page based on the content of the delivery result information, and the content generation module 216 generates new content information based on the information contained in the selected media page.

[0192] Next, the content evaluation module 218 evaluates the generated content by calculating the similarity between the generated content and the relevant information of the original advertising content (S1630). For example, if the calculated similarity is higher than that of the original advertising content's relevant information, it indicates that the generated content is more relevant to the original advertising content's relevant information.

[0193] In this embodiment, an example of extracting keyword information such as text information, words, collocations, and sentences from a media page is described, but the embodiment is not limited to this. The information extracted from the media page may also be images, videos, color information, audio information, etc., and the content generation module 216 may generate new content (generated content) by updating the original advertising content based on this information.

[0194] The content generation module 216 may display the original advertising content and the generated content based on it in parallel on the output device 205. The original advertising content and generated content will be described below. Figure 24 is an example of a parallel display screen 2400.

[0195] In the parallel display screen 2400, for example, the original advertising content 2410 displayed on the left side of the screen and the generated content 2450 displayed on the right side of the screen are displayed side by side.

[0196] The original advertising content 2410 and the generated content 2450 are, for example, advertising images for banner ads on a web page. For example, the original advertising content 2410 consists of a person image area 2420, which is an area where a person image is displayed; a text message area 2430, which is an area where the text message of the advertisement is displayed; and a product name area 2440, which is an area where text containing the product name is displayed.

[0197] In the original advertising content 2410, for example, a woman is displayed in the person image area 2420, the message "Take care of your health and enjoy every day" is displayed in the text message area 2430, and the text including the product name "XXXX drink for nutritional supplementation" is displayed in the product name area 2440.

[0198] On the other hand, the generated content 2450 includes a person image area 2460, a text message area 2470, and a product name area 2480. For example, in the generated content 2450, the person image area 2460 displays three women, the text message area 2470 displays the advertising message "Taking care of your health will make your trip more enjoyable," and the product name area 2480 displays the text including the product name, "XXXX Drink for a Fun Trip."

[0199] In other words, in the generated content 2450, the image in the person image area 2460 is changed to three women traveling, the text in the text message area 2470 is changed to the message "Taking care of your health will make your trip more enjoyable", and the text in the product name area 2480 is changed to "XXXX drink for a fun trip".

[0200] As described above, the content generation module 216 updates the original advertising content 2410 in Figure 24 based on the keyword "travel," thereby generating generated content 2450 in which, for example, the advertising element "travel" is added to or replaced with the original advertising content 2410.

[0201] In other words, the content generation module 216 can generate generated content with a higher degree of similarity to, for example, advertising-related information by updating the original advertising content using new keyword information. Therefore, in this case, the newly generated content (generated content) may have a higher degree of similarity and relationship to, for example, the advertiser's conceptual message.

[0202] Furthermore, this technology is not limited to the embodiments described above, and includes various modifications. For example, the embodiments described above are explained in detail to make the technology easier to understand, and are not necessarily limited to those having all the configurations described. Also, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0203] Furthermore, each of the above configurations, functions, processing units, processing means, etc., may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. Alternatively, each of the above configurations, functions, etc., may be implemented in software by having the processor interpret and execute programs that implement each function. Information such as programs, tables, and files that implement each function can be stored in memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0204] Furthermore, the control lines and information lines shown are those deemed necessary for explanatory purposes, and not all control lines and information lines are necessarily shown in the actual product. In reality, it is safe to assume that almost all components are interconnected.

[0205] Furthermore, the above-described embodiments disclose at least the configurations described in the claims.

[0206] Furthermore, this technology includes at least the following embodiments (1) to (15). This technology further includes configurations that combine the following embodiments (1) to (15).

[0207] Example (1) A server comprising: a content acquisition unit that acquires a media page containing information to be distributed and information related to the presented content which is information presented on the media page; an index value calculation unit that selects a plurality of different evaluation axes, acquires a plurality of scores corresponding to each of the plurality of evaluation axes based on the similarity between the information related to the presented content and the media page, and calculates an index value based on the plurality of scores and the bias between the plurality of evaluation axes; a presentation destination determination unit that determines the media page corresponding to the index value exceeding a predetermined value as the presentation destination for the presented content; and a presentation destination output unit that outputs information indicating the media page determined as the presentation destination for the presented content. Example (2) The server according to Example (1), wherein the index value calculation unit calculates the index value based on a weighting value which is a value that weights the bias. Example (3) The server according to Example (2), further comprising: a user information acquisition unit that acquires user response information which is information about the user's response to the distributed information, wherein the presentation destination determination unit selects another media page in response to the index value calculation unit adjusting the weighting value based on the user response information. Example (4) The server according to Example (3), wherein the index value calculation unit detects user stage information indicating the stages from product awareness to product purchase based on the user reaction information, and changes the weighting value according to the user stage information. Example (5) The server according to Example (2), wherein the content acquisition unit acquires delivery result information indicating information regarding the user's reaction to the delivered presented content, and the index value calculation unit changes the weighting value based on the delivery result information. Example (6) The server according to Example (2), wherein the index value calculation unit outputs and displays at least one of the following information: the weighting value, the bias between the evaluation axes, the multiple scores, or the index value. Example (7) The server according to Example (1), wherein the multiple evaluation axes include at least a recognition axis indicating the evaluation axis related to expanding awareness or improving understanding of a product or service, and an acquisition axis indicating the evaluation axis related to sales of the product or service.Example (8) The server according to Example (1), wherein the plurality of evaluation axes include at least an emotional axis indicating an evaluation axis related to human emotions and a functional axis indicating an evaluation axis related to the functionality of a product.

[0208] Example (9) The server according to Example (1), wherein the plurality of evaluation axes include at least a brand score axis that shows the evaluation axis relating to product branding and a general score axis that shows the evaluation axis relating to attributes other than product branding.

[0209] Example (10) The server according to Example (1), wherein the index value calculation unit calculates the bias between the evaluation axes as the difference from the intermediate axis of the plurality of evaluation axes.

[0210] Example (11) The server according to Example (1), wherein the presentation destination determination unit selects the presentation content corresponding to the index value as the target for presentation to the media page when the index value exceeds a predetermined value.

[0211] Example (12) The server according to Example (1), wherein the index value calculation unit further includes a content generation unit that obtains new keyword information from information related to the presented content or from the media page, and generates new content information by updating the presented content based on the new keyword information.

[0212] Example (13) The server according to Example (12), wherein the content acquisition unit acquires delivery result information indicating the user's response to the delivered presented content, the presentation destination determination unit selects a new media page based on the content of the delivery result information, and the content generation unit generates new content information based on the information contained in the new media page.

[0213] Example (14) A distribution management method characterized by: acquiring a media page containing information to be distributed and information related to the presented content which is information presented on the media page; selecting a plurality of different evaluation axes; acquiring a plurality of scores corresponding to each of the plurality of evaluation axes based on the similarity between the information related to the presented content and the media page; calculating an index value which is the average value based on the plurality of scores and the bias between the plurality of evaluation axes; determining the media page corresponding to the index value which exceeds a predetermined value as the presentation destination for the presented content; and outputting information indicating the media page determined as the presentation destination for the presented content.

[0214] Example (15) A program for causing a computer to execute each step of the distribution method described in Example (14).

[0215] 10...Distribution management system, 101...Network, 102...Management server, 103...Distribution media server, 104...User terminal, 105...Ad distribution server

Claims

1. A server comprising: a content acquisition unit that acquires a media page containing information to be distributed and information related to the presented content which is the information presented on the media page; an index value calculation unit that selects a plurality of different evaluation axes, acquires a plurality of scores corresponding to each of the plurality of evaluation axes based on the similarity between the information related to the presented content and the media page, and calculates an index value based on the plurality of scores and the bias between the plurality of evaluation axes; a presentation destination determination unit that determines the media page corresponding to the index value exceeding a predetermined value as the presentation destination for the presented content; and a presentation destination output unit that outputs information indicating the media page determined as the presentation destination for the presented content.

2. The server according to claim 1, wherein the index value calculation unit calculates the index value based on a weighting value which is a value that weights the bias.

3. The server according to claim 2, further comprising a user information acquisition unit that acquires user response information, which is information relating to the user's response to the distributed information, wherein the destination determination unit selects other media pages in accordance with the adjustment of the weighting values ​​based on the user response information by the index value calculation unit.

4. The server according to claim 3, wherein the index value calculation unit detects user stage information indicating the stages from product awareness to product purchase based on the user response information, and changes the weighting value according to the user stage information.

5. The server according to claim 2, wherein the content acquisition unit acquires delivery result information indicating information regarding the user's response to the delivered presented content, and the index value calculation unit changes the weighting value based on the delivery result information.

6. The server according to claim 2, wherein the index value calculation unit outputs and displays at least one of the following pieces of information: the weighted value, the bias between the evaluation axes, the plurality of scores, or the index value.

7. The server according to claim 1, wherein the plurality of evaluation axes include at least a recognition axis indicating the evaluation axis related to increasing awareness or improving understanding of a product or service, and an acquisition axis indicating the evaluation axis related to sales of the product or service.

8. The server according to claim 1, wherein the plurality of evaluation axes include at least an emotional axis indicating an evaluation axis related to a person's emotions and a functional axis indicating an evaluation axis related to the functionality of a product.

9. The server according to claim 1, wherein the plurality of evaluation axes include at least a brand score axis representing the evaluation axis relating to the branding of the product and a general score axis representing the evaluation axis relating to attributes other than the branding of the product.

10. The server according to claim 1, wherein the index value calculation unit calculates the bias between the evaluation axes as the difference from the intermediate axis of the plurality of evaluation axes.

11. The server according to claim 1, wherein the presentation destination determination unit selects the presentation content corresponding to the index value as the target for presentation to the media page when the index value exceeds a predetermined value.

12. The server according to claim 1, further comprising a content generation unit which obtains new keyword information from information related to the presented content or from the media page, and generates new content information by updating the presented content based on the new keyword information.

13. The server according to claim 12, wherein the content acquisition unit acquires delivery result information indicating the user's response to the delivered presented content, the presentation destination determination unit selects a new media page based on the content of the delivery result information, and the content generation unit generates new content information based on the information contained in the new media page.

14. A distribution management method characterized by: acquiring a media page containing the information to be distributed and information related to the presented content which is the information presented on the media page; selecting multiple different evaluation axes; acquiring multiple scores corresponding to each of the multiple evaluation axes based on the similarity between the information related to the presented content and the media page; calculating an index value which is the average value based on the multiple scores and the bias between the multiple evaluation axes; determining the media page corresponding to the index value that exceeds a predetermined value as the presentation destination for the presented content; and outputting information indicating the media page determined as the presentation destination for the presented content.

15. A program for causing a computer to perform each step of the distribution method described in claim 14.

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