Information processing device, information processing program, information processing method

Abstract

This system hierarchically manages the relationship between content creators and users who engage in transactions, enabling effective analysis of user transaction trends. [Solution] In an information processing system in which a server device is connected to user terminals and business terminals via a network, the server device, which is an information processing device that manages content transmitted by users, comprises: a content acquisition unit that acquires user information and content from multiple users; a user identification unit that hierarchically identifies derived users, including the user who transmitted the content and the user who performed a transaction based on the content; a related content management unit that hierarchically associates and manages the content transmitted by derived users as related content; a user analysis unit that analyzes trends in users' transaction activities based on the relationships of at least the related content; and an advice presentation unit that presents advice to users regarding content based on the trends.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing program, and an information processing method for managing content transmitted by a user.

Background Art

[0002] In social media and e-commerce platforms, technologies for managing and analyzing content transmitted by users are known.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional technologies have not been able to sufficiently analyze the relationship between the content sender and the user who conducted a transaction based on that content. Therefore, it has been difficult to accurately grasp the influence of the content and the tendency of the user's transaction behavior.

[0005] The present invention has been made in view of such a background, and aims to hierarchically manage the relationship between the content sender and the user who conducted a transaction, and effectively analyze the tendency in the user's transaction behavior.

Means for Solving the Problems

[0007] According to the present invention, the relationship between content creators and users who engage in transactions can be managed hierarchically, and trends in users' transaction behavior can be effectively analyzed. [Brief explanation of the drawing]

[0008] [Figure 1] This figure shows an example of the overall configuration of an information processing system according to one embodiment of the present invention. [Figure 2] This figure shows an example of the hardware configuration of the server device 1 according to the same embodiment. [Figure 3] This figure shows an example of the functional configuration of the server device 1 according to the same embodiment. [Figure 4] This figure shows an example of user information stored in the user information storage unit 1 according to the same embodiment. [Figure 5] This figure shows an example of content stored in the content storage unit according to the same embodiment. [Figure 6] This figure shows an example of related content stored in the related content storage unit according to the same embodiment. [Figure 7] This figure shows an example of user analysis results stored in the user analysis result storage unit 134 according to the same embodiment. [Figure 8] This is a flowchart showing the processing flow of the server device 1 according to the same embodiment. [Modes for carrying out the invention]

[0009] <Summary of the Invention> The embodiments of the present invention will be described by listing them. The present invention has, for example, the following configuration. [Item 1] An information processing device that manages user-generated content, A content acquisition unit that acquires the content from multiple users, A user analysis unit that hierarchically identifies derived users, including the user who transmitted the content and the user who performed a transaction based on the content, and analyzes the trends of the user's transaction activities. An advice presentation unit that provides the user with advice regarding the content based on the aforementioned trends, An information processing device equipped with the following features. [Item 2] The information processing device described in item 1, Related content management unit manages the content transmitted by the aforementioned derived users by hierarchically associating them as related content, An information processing device equipped with the following features. [Item 3] The information processing device described in item 2, The user analysis unit is an information processing device that analyzes the user's trends in transactional activities based at least on the relevance of the related content. [Item 4] An information processing device as described in claim 2 or 3, A reward granting unit grants rewards to each user who has transmitted the related content in accordance with the transaction activity of the derivative user, An information processing device that further includes these features. [Item 5] An information processing program that manages user-generated content, In the processor, A content acquisition step of acquiring the content from multiple users, Hierarchically identify derived users including the user who transmitted the content and the user who conducted a transaction based on the content, and perform a user analysis step of analyzing the tendencies in the transaction behavior of the user. Based on the tendencies, perform an advice presentation step of presenting advice on the content to the user. An information processing program for causing the above to be executed. [Item 6] An information processing method for managing content transmitted by a user, comprising: a processor perform a content acquisition step of acquiring the content from a plurality of the users; Hierarchically identify derived users including the user who transmitted the content and the user who conducted a transaction based on the content, and perform a user analysis step of analyzing the tendencies in the transaction behavior of the user. Based on the tendencies, perform an advice presentation step of presenting advice on the content to the user. An information processing method for causing the above to be executed.

[0010] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the present specification and the drawings, components having substantially the same functional configuration are denoted by the same reference numerals, and redundant description is omitted.

[0011] ==Overview== FIG. 1 is a diagram showing the overall configuration of an information processing system. As shown in FIG. 1, the information processing system includes a server device 1, a user terminal 3, and a business operator terminal 4. The server device 1 is connected to the user terminal 3 and the business operator terminal 4 via a network 2. Although only one server device 1, user terminal 3, and business operator terminal 4 are shown, it is needless to say that there may be more than one.

[0012] ==Server Device 1== Server device 1 may be a general-purpose computer such as a workstation or personal computer, or it may be logically implemented through cloud computing.

[0013] ==User Terminal 3== User terminal 3 is a computer used by users to upload content to the information processing system or to conduct transactions. User terminal 3 can be, for example, a smartphone, tablet computer, or personal computer. Users can access server device 1, for example, through applications or web browsers running on user terminal 3.

[0014] ==Carrier Terminal 4== The business terminal 4 is a computer used by a business that conducts marketing and other activities for users through an information processing system. The business terminal 4 can be, for example, a smartphone, tablet computer, or personal computer. Users can access the server device 1, for example, through applications or web browsers running on the business terminal 4.

[0015] ==Hardware Configuration== Figure 2 shows an example of the hardware configuration of server device 1. Note that the illustrated configuration is just one example, and other configurations are also possible. Server device 1 includes a CPU 101, memory 102, storage device 103, communication interface 104, input device 105, and output device 106. The storage device 103 stores various data and programs, such as a hard disk drive, solid-state drive, or flash memory. The communication interface 104 is an interface for connecting to the communication network 2, such as an adapter for connecting to Ethernet®, a modem for connecting to a public telephone network, a wireless communication device for wireless communication, or a USB (Universal Serial Bus) connector or RS232C connector for serial communication. The input device 105 is for inputting data, such as a keyboard, mouse, touch panel, button, or microphone. The output device 106 is for outputting data, such as a display, printer, or speaker. Furthermore, each functional unit of the server device 1, as described later, is realized by the CPU 101 reading a program stored in the storage device 103 into the memory 102 and executing it, and each storage unit of the server device 1 is realized as part of the storage area provided by the memory 102 and the storage device 103.

[0016] ==Functions of Server Device 1== As shown in Figure 3 as an example, the server device 1 includes processing units such as a content acquisition unit 111, a content presentation unit 112, a user identification unit 113, a related content management unit 114, a user analysis unit 115, a content nature analysis unit 116, an advice presentation unit 117, and a reward granting unit 118, as well as storage units such as a user information storage unit 131, a content storage unit 132, a related content storage unit 133, and a user analysis result storage unit 134.

[0017] ==Explanation of the Memory Unit== Hereinafter, the storage unit of an embodiment of the present invention will be described in detail with reference to the drawings.

[0018] The user information storage unit 131 stores the user information acquired by the content acquisition unit 111. As shown in Figure 4 as an example, the user information includes basic information such as user ID, name, account name, age, gender, address, email address, and number of SNS followers, as well as transaction-related information such as the type of transaction performed by the user, transaction execution date and time, transaction amount, success or failure of the transaction, transaction history, and interest categories.

[0019] The content storage unit 132 stores the content acquired by the content acquisition unit 111. The content includes various forms of data such as the user ID of the user who posted or distributed the content, the content ID, text, images, videos, audio, and distribution information, as shown in an example in Figure 5. The content may also include information about the subject matter (product name, cost, etc.) and information about the provider of the subject matter (company name, location, contact information, etc.). The content storage unit 132 may also store user ratings and comments entered on the content, which have been acquired by the content presentation unit 112, linked to the content.

[0020] The related content storage unit 133 stores information about related content managed by the related content management unit 114. Related content information includes hierarchical relationships, relevance levels, and interaction information between content, as shown in an example in Figure 6.

[0021] The user analysis result storage unit 134 stores the analysis results of user transaction behavior trends analyzed by the user analysis unit 115. The analysis results include information such as transaction patterns, preferences, and influence, as shown in Figure 7 as an example.

[0022] ==Explanation of the processing unit== Embodiments of the present invention will be described in detail below with reference to the drawings.

[0023] The content acquisition unit 111 has the function of acquiring user information and content from the user. The content acquisition unit 111 acquires this information, for example, by receiving user information and content from the user terminal 3 via the network 2. The content acquisition unit 111 stores the acquired user information in the user information storage unit 131 and the content in the content storage unit 132.

[0024] The content acquired by the content acquisition unit 111 may include, for example, content related to a user's transaction of an item, receiving and experiencing the item, and including information on its usage, effects, and impressions.

[0025] The content presentation unit 112 has the function of presenting the content acquired by the content acquisition unit 111 to the user terminal 3.

[0026] The content presentation unit 112 may be equipped with a function to accept various evaluations of the content. The types of evaluations may include empathy evaluations such as "likes," content quality evaluations (accuracy, clarity, usefulness, etc.), and value evaluations of the offerings (cost-effectiveness, satisfaction, etc.), or they may simply be quantitative indicators such as the number of views, the number of content shares, the number of favorites, and the number of comments written. In particular, the content presentation unit 112 may provide a variety of evaluation buttons that allow users to select the intention and type of evaluation, such as "helpful," "I agree," and "I want to try," in addition to a simple "like." Furthermore, the content presentation unit 112 may also allow users to cancel "likes" or add reasons for evaluation, or it may simply be equipped with a function to input comments. The content presentation unit 112 may store these evaluations in the content storage unit 132, linked to the content.

[0027] The content display unit 112 also has the function of placing transaction buttons and other elements on the content for performing transactional actions. Transaction buttons are implemented as interactive elements that allow users to perform various transactional actions, such as reserving, applying for, or purchasing offerings, as well as adding items to favorites or making inquiries. The content display unit 112 can automatically select and place transaction buttons that allow users to perform appropriate transactional actions depending on the type of content and the nature of the offerings. In addition, the content display unit 112 can accept selection operations from the user's user terminal 3 and customize the design, size, and placement of the transaction buttons.

[0028] In this embodiment, the offerings broadly include items that are the subject of the transaction, such as products and services.

[0029] The term "product" includes all physical goods. Specifically, it refers to tangible goods such as cosmetics, pharmaceuticals, health foods, clothing, electrical appliances, and books.

[0030] Services include intangible services such as hairdressing, beauty treatments, massage, counseling, consulting, educational services, and travel services.

[0031] If the service is a medical procedure, it includes all services provided by medical institutions, such as medical examinations, tests, treatments, surgeries, medications, cosmetic procedures, and massages.

[0032] Furthermore, transaction activities encompass a wide range of activities, including reservations, applications, and purchases of offerings. Reservations include, for example, appointments for medical consultations, beauty treatments, and restaurant visits. Applications include requests for information, quotes, membership registration, and service usage applications. Purchases include immediate purchases of products and services, subscription contracts, and license purchases. The content presentation unit 112 should provide an appropriate transaction flow according to the type of transaction activity through the transaction buttons.

[0033] The content presentation unit 112 acquires information about the transaction performed via the transaction button and records it in the user information storage unit 131. The acquired information includes the type of transaction, the date and time of execution, the transaction amount, and whether the transaction was successful or not. This allows the user identification unit 113 to accurately identify the user who performed the transaction via specific content.

[0034] The content display unit 112 can also link the transaction button to external payment systems and reservation systems via APIs and SDKs. This allows users to complete transactions directly within the content. Furthermore, the content display unit 112 can set display conditions and usage restrictions for the transaction button, and by referring to user information, it can, for example, restrict usage in specific regions, ages, or time zones, or require age verification.

[0035] The user identification unit 113 has the function of hierarchically identifying derived users, including the user who posted the content and the users who conducted transactions based on that content. The user identification unit 113 identifies the content poster as the first layer and the users who conducted transactions based on that content as the second layer. Furthermore, it identifies users who conducted transactions based on content posted by the second-layer users as the third layer, and so on, identifying users hierarchically thereafter.

[0036] The related content management unit 114 has the function of hierarchically associating and managing content posted by derived users as related content. Based on the hierarchical structure identified by the user identification unit 113, the related content management unit 114 manages the relationships between content posted by each user. The related content management unit 114 also manages relationships including interaction information such as replies, quotes, reactions, and comments to a given piece of content.

[0037] The Related Content Management Unit 114 manages the relationships between content from multiple perspectives. Types of relationships may include: relationships through transactional activity (e.g., content posted by users who have viewed content and engaged in transactional activity); relationships based on explicit references such as citations and shares within content; relationships based on content similarities such as offerings and target audiences analyzed by the Content Nature Analysis Unit 116; and chronological relationships such as posting and update history within a specific period. The Related Content Management Unit 114 may also use quantitative indicators such as the number of shares and favorites of content as indicators of relevance. Furthermore, the Related Content Management Unit 114 may also use evaluations of other content cited within the content (e.g., number of likes, number of comments, evaluation score) as indicators of relevance. The Related Content Management Unit 114 may calculate the degree of relevance between content by evaluating these relationships individually or comprehensively, and may also enable the selection of effective content combinations for recommending offerings and generating advertisements by grouping content based on relevance.

[0038] The User Analysis Unit 115, for example, has a function to analyze trends in the trading activities of users or user groups (hereinafter collectively referred to as "users") based on the relevance of related content. Based on information such as the user's past trading history and the content they viewed, rated, or pressed the trading button on during transactions, the User Analysis Unit 115 analyzes the user's trading patterns and preferences, and assigns them to user groups with similar trading patterns and preferences. The User Analysis Unit 115 may also assign users who have conducted trading activities through related content to the same user group.

[0039] The user trends analyzed by the User Analysis Department 115 may include, but are not limited to, offers suitable for the user, offers preferred by the user, and advertisements and influencers that users are likely to respond to. The User Analysis Department 115 can identify offers suitable for the user as offers that match the user's needs and challenges, based on the user's past content, transaction history, content viewing history, and content and user search history. The User Analysis Department 115 can identify offers preferred by the user as offers that match the user's preferences and values, based on the nature of the user's content, content evaluation behavior, comments on content, and time spent on content. The User Analysis Department 115 can identify advertisements and influencers that users are likely to respond to as advertising forms, influencers, and influencer segments that can effectively influence users, based on metrics such as the number and rate of transaction button clicks, ad clicks and rates, conversion numbers and rates, and engagement numbers and rates for the content that the advertisement or influencer publishes or appears in.

[0040] The user analysis unit 115 may also analyze these trends over time to understand changes in users' interests and behavioral patterns. For example, the user analysis unit 115 may analyze changes in trends due to seasonality or time of day, changes in preferences associated with life events, and changes in the degree of influence from specific influencers. In addition, as described above, the user analysis unit 115 may group multiple users that have some commonalities, such as users with similar trends or users who have conducted transactions through related content, and perform trend analysis on a group basis. This makes it possible for the server device 1 to recommend appropriate content, offerings, advertisements, etc., to new users based on the trends of similar users.

[0041] The content nature analysis unit 116 has the function of analyzing the nature of the content. For example, the content nature analysis unit 116 uses natural language processing technology and image recognition technology to extract keywords, topics, emotional expressions, etc. contained in the content and analyze the characteristics of the content qualitatively and quantitatively. The content nature analysis unit 116 may also analyze temporal characteristics such as the time of posting and frequency of posting of the content.

[0042] The properties analyzed by the content property analysis unit 116 may include the reliability of the content, its content characteristics, temporal characteristics, and taste. The results of the property analysis may also be used as input information for trend analysis by the user analysis unit 115, which enables the user analysis unit 115 to perform more detailed trend analysis, such as identifying users who are highly influenced by highly reliable content or users who are more likely to respond to content of a particular taste.

[0043] The Content Nature Analysis Unit 116 may consider, in its reliability analysis, the number of related contents hierarchically associated with the content, the attributes of the content creator (verified account, past transaction history, number of SNS followers, etc.), the quality of the content (accuracy of the writing, supporting evidence, reliability of citations, etc.), and evaluations from other users (user evaluations, expert evaluations, number of reports, etc.). The Content Nature Analysis Unit 116 evaluates the content based on at least one of these elements and calculates a reliability score for the content.

[0044] The content nature analysis unit 116 analyzes the elements contained in the content in detail when analyzing its content characteristics. Specifically, the content nature analysis unit 116 may analyze the types of goods or services dealt with in the content, the people or items that appear, the ratio of text to images, and, in the case of videos, the methods of presentation. The content nature analysis unit 116 may also use image recognition technology and natural language processing technology to identify goods, detect people, analyze text density, and analyze image composition.

[0045] The content characteristics analysis unit 116 analyzes the length of the content, posting timing, update frequency, and seasonality in the temporal characteristics analysis. For video and audio content, the content characteristics analysis unit 116 calculates the estimated reading time from the playback time, and for text content, from the number of characters. It also analyzes the relationship between the posting time, the freshness of the content, the update pattern, and seasonality. As a result, the content characteristics analysis unit 116 can predict the optimal distribution timing and the lifespan of the content.

[0046] The Content Nature Analysis Unit 116 analyzes the impression and atmosphere emanating from the content in its taste analysis. For example, the Content Nature Analysis Unit 116 may use a model generated by machine learning or other methods to evaluate the taste from various perspectives, such as luxury, casualness, formality, approachability, and innovation. The Content Nature Analysis Unit 116 may also quantify the taste by comprehensively analyzing the fonts used, color schemes, image tones, and linguistic styles. Furthermore, the Content Nature Analysis Unit 116 may set appropriate taste evaluation criteria for each industry and target audience to determine the suitability of the content.

[0047] The content nature analysis unit 116 may accumulate these analysis results in chronological order and grasp trends in changes in nature. For example, it may analyze changes in the taste of a particular sender or changes in content trends across the entire industry. The content nature analysis unit 116 may also analyze correlations between natures and identify, for example, common features of highly reliable content or combinations of tastes that tend to attract high engagement.

[0048] The content nature analysis unit 116 may analyze the nature using the method described above and then identify natures common to related content. The content nature analysis unit 116 may also analyze the reactions and evaluations of users who uploaded lower-level content to higher-level related content. Furthermore, the content nature analysis unit 116 also analyzes the propagation patterns of nature between hierarchical levels. For example, it analyzes whether, if the reliability of first-level content is high, second-level content that references it tends to have similarly high reliability, or how the expression style of first-level content influences second- and third-level content. The content nature analysis unit 116 also tracks changes in nature within the hierarchical structure. For example, it analyzes whether certain natures tend to be emphasized or diminished as one descends the hierarchy, or whether new natures tend to be added. This makes it possible to understand how the nature of content propagates and changes among users.

[0049] The content nature analysis unit 116 can use these analysis results to more accurately evaluate the reliability of the content. For example, content with a large amount of reliable related content can be evaluated as having higher reliability. Also, content that is supported by users from various levels can be evaluated as having more universal value. The advice provision unit 117 has the function of providing users with content-related advice based on the trends analyzed by the user analysis unit 115. The advice provision unit 117 provides advice from various perspectives so that the user's content can promote more transactions and receive high ratings from other users.

[0050] The advice-providing unit 117 provides advice on the structure of the content. For example, the advice-providing unit 117 suggests the order in which information is likely to lead to a transaction, the optimal placement of transaction buttons, how to create effective headings, and how to structure persuasive sentences. Specifically, the advice-providing unit 117 advises on things like an introductory sentence that concisely shows the features of the offering, a main body that clearly shows the user's problem and solution, the presentation of performance data that enhances credibility, and the placement of effective calls to action that encourage transactions.

[0051] The advice-providing unit 117 also provides advice on the use of media elements. Regarding images and videos, the advice-providing unit 117 suggests shooting angles that effectively convey the characteristics of the offering, arrangement methods that easily attract attention, and effective expression methods that increase the willingness to make a purchase. For example, the advice-providing unit 117 suggests effective visual expressions according to the type of offering, such as before-and-after comparison images of the product, videos showing actual usage scenes, and close-up photos that show the details of the product.

[0052] The advice-providing unit 117 also provides advice on expression methods tailored to the target user. For example, based on attributes such as age group, gender, and interests, the advice-providing unit 117 suggests expressions that are likely to resonate with users and persuasive selling points. Specifically, the advice-providing unit 117 suggests the most appropriate expression method for each target group, such as using a casual tone and emphasizing visual elements for younger audiences, and using detailed data and technical explanations for experts.

[0053] The advisory department 117 also provides advice on elements that enhance reliability. The advisory department 117 proposes effective ways to utilize information that supports reliability, such as third-party reviews, expert recommendations, certification and qualification information, and performance data. In addition, the advisory department 117 also advises on how to present information that enhances confidence, such as product manufacturing processes, quality control systems, and service provision systems.

[0054] The advice and guidance unit 117 also proposes effective appeal methods tailored to the season and time of year. The advice and guidance unit 117 provides effective promotional methods appropriate to the time of year, such as the timing of advance announcements for seasonal products, methods for announcing special offers timed to coincide with events, and selling points for limited-time products.

[0055] The advice provision unit 117 also provides specific improvement suggestions based on past success stories. The advice provision unit 117 provides practical reference information such as characteristic analyses of content that have achieved high transaction results, specific examples of effective expression methods, and examples of cases where results have improved through improvements.

[0056] The advice-providing unit 117 also makes suggestions to enhance the synergistic effect with related content. The advice-providing unit 117 proposes effective appeal methods that utilize multiple content, such as effective ways to cite past related content, ways to present complementary information, and ways to develop information in a narrative style.

[0057] The advice presentation unit 117 presents these suggestions in an easy-to-understand format, such as text, images, videos, and infographics. The advice presentation unit 117 also has a function to measure the effectiveness of the suggestions and continuously provide improvement suggestions.

[0058] Hierarchy starts here The advice provision unit 117 provides advice on creating effective content that utilizes a hierarchical structure. For example, based on information about the characteristics of content posted by users (second-tier users) who have made transactions by referring to first-tier content, which has been analyzed by the content nature analysis unit 116, the advice provision unit 117 proposes expressions and content structures that are likely to lead to transactions. Specifically, based on information such as the characteristics of products and services that second-tier users valued, the information that was the deciding factor, and the points of satisfaction after the transaction, which has been analyzed by the content nature analysis unit 116, the advice provision unit 117 presents methods for creating content that effectively appeals to these elements.

[0059] The advice-providing unit 117 also provides advice based on interaction patterns between layers. For example, based on information analyzed by the content nature analysis unit 116, such as comments and questions from second-tier users on first-tier content, and reactions from third-tier users to content posted by second-tier users, the advice-providing unit 117 proposes ways to proactively explain common questions and concerns.

[0060] The advice provision unit 117 also proposes story development that spans multiple hierarchical levels. For example, based on information such as the results of analysis by the content nature analysis unit 116, which shows that the content consists of product introduction content at the first level, user experience content at the second level, and derivative use case content at the third level, the advice provision unit 117 proposes content that should be linked and what should be included in the linked content in order to organically link content at different levels and develop information that allows users to understand the value of the offering more deeply.

[0061] The advice provision unit 117 analyzes the characteristics of influential users at each tier and provides advice based on those insights. For example, based on information analyzed by the content nature analysis unit 116, such as the content characteristics of first-tier users that generated many transactions from second-tier users, and the content characteristics of second-tier users that acquired even more third-tier users, the advice provision unit 117 proposes effective methods of expression and selling points.

[0062] The advice-providing unit 117 also provides advice that utilizes the citation and reference relationships of content between hierarchical levels. For example, the advice-providing unit 117 suggests methods for effectively citing higher-level content, methods for guiding users to related content, and methods for presenting persuasive information by combining content from multiple levels.

[0063] The advice provision unit 117 also provides advice based on trend analysis of transaction activities at each level. For example, based on information about the properties of content in patterns where transaction activities propagate from the first level to the second level, and from the second level to the third level, which has been analyzed by the content nature analysis unit 116, the advice provision unit 117 proposes a content creation method to generate more derivative transactions.

[0064] The advice suggestion unit 117 can be implemented by the following technical means. The advice suggestion unit 117 can be implemented using a rule-based engine. Based on a predefined set of conditions and actions, it generates and presents content improvement suggestions using specific rules such as "suggest adding images if there are fewer than three images" and "suggest the location of text division if necessary." The advice suggestion unit 117 can also be implemented using a machine learning model. Using past content data and transaction history as training data, and taking text features, image features, and structural features as input, a model is constructed that predicts transaction history and makes improvement suggestions. The advice suggestion unit 117 also provides an implementation that utilizes a generative AI model. Using a large-scale language model, it generates improvement suggestions based on understanding the context of the content. In addition, it generates samples of effective image composition and layout using image generation AI. The advice suggestion unit 117 can also be implemented in a hybrid form that combines these technologies. For example, it provides an implementation that leverages the strengths of each technology, such as using rule-based methods for basic checks, machine learning for detailed analysis, and generative AI for generating specific improvement suggestions. Furthermore, it is possible to implement features such as hierarchical structure analysis using graph databases and real-time feedback generation.

[0065] The reward distribution unit 118 includes, for example, a function to award rewards to users who have published content in response to transactional activities based on that content. The reward distribution unit 118 awards rewards to users who have published content based on their contribution, for example, on the association of related content.

[0066] The reward granting unit 118 provides a variety of reward granting methods based on a hierarchical structure. It grants rewards not only for direct transaction activities but also for derivative transaction activities at the second and third tiers. For example, the reward granting unit 118 sets rewards hierarchically, such as a basic reward when a user who viewed its content (second tier) performs a transaction, and a derivative reward when another user (third tier) who viewed content posted by that user performs a transaction. The reward granting unit 118 can also grant bonus rewards by multiplying the basic reward by an addition rate according to the transaction performance at each tier.

[0067] The reward distribution unit 118 also distributes rewards according to the scope of the content's impact. For example, if a chain reaction of transactions occurs across multiple levels, it will award a special reward to the creator of the content that initiated that chain. In addition, the reward distribution unit 118 provides additional rewards to content that generates many levels within a specific period or content that has an impact on a wide range of fields.

[0068] The reward distribution unit 118 also provides rewards based on the qualitative evaluation of the content. For example, the reward distribution unit 118 provides rewards as quality bonuses to content that has a high reliability score analyzed by the content nature analysis unit 116, content that has received high ratings from many users, and content that has been recommended by experts.

[0069] The rewards distribution unit 118 also distributes rewards that take time into consideration. For example, the rewards distribution unit 118 sets up a variety of rewards according to the time axis, such as long-term retention bonuses for content that has a continuous influence, increased bonuses during the season for highly seasonal content, and early-bird bonuses for highly novel content.

[0070] The reward distribution unit 118 also provides rewards for the synergistic effects of multiple pieces of content. For example, if a group of highly relevant pieces of content is created and these pieces of content collectively contribute to promoting trading activity, the reward distribution unit 118 will provide a reward as a group bonus. In addition, if a user effectively quotes and references other users' content and contributes to promoting trading activity, the reward distribution unit 118 will provide a reward as a collaboration bonus.

[0071] The rewards distribution unit 118 distributes rewards in various forms. In addition to money, points, and digital tokens with monetary value, the rewards distribution unit 118 provides a variety of benefits that will motivate users to be active, such as special privileges within the platform, unlocking customization functions, opportunities for expert consultation, priority access to events, exclusive badges and status displays, and the right to try new products from businesses.

[0072] The reward distribution unit 118 also provides a ranking system that utilizes a hierarchical structure. It creates rankings based on multiple evaluation axes, such as influence score, contribution score, and reliability score, and awards special rewards to top-ranking users. Furthermore, the reward distribution unit 118 provides rankings from various perspectives, such as by category, period, and region, enabling diverse forms of evaluation and reward distribution.

[0073] ==Processing Flow== The processing flow of the server device 1 in this embodiment will be explained using Figure 8. First, the content acquisition unit 111 acquires user information and content, and the content presentation unit 112 presents the content (S101). Next, the user identification unit 113 hierarchically identifies derived users (S102). The related content management unit 114 manages the content transmitted by derived users in association (S103). The content nature analysis unit 116 analyzes the nature of the content (S104), and the user analysis unit 115 analyzes the user's transaction behavior trends (S105). Based on the analysis results, the advice presentation unit 117 generates and presents advice to the user (S106), and the reward granting unit 118 grants rewards to the user (S107).

[0074] ==Other Embodiments==

[0075] This section describes a characteristic implementation of a hierarchical structure in the field of cosmetic medicine. For example, each level has a different role, such as the first level consisting of content introducing procedures by clinics and doctors, the second level consisting of progress reports from actual patients who have undergone procedures, and the third level consisting of content detailing the consideration process by users with similar concerns. Based on this hierarchical structure, the advice provision unit 117 presents users with the most suitable information dissemination method for each level, for example, "providing expert explanations and safety information for procedures at the first level," "recording specific progress and experiences at the second level," and "sharing concerns and deciding factors during consideration at the third level." The reward provision unit 118 provides bonus rewards to both first- and second-level users if the progress reports at the second level influence the decisions at the third level.

[0076] In the travel sector, the advice-providing unit 117 forms a hierarchical structure, for example, with first-tier tourist spot introduction content, second-tier actual visit reports, and third-tier derivative tourist route suggestions. The advice-providing unit 117 presents users with information dissemination methods tailored to the role of each tier, such as "basic information and comprehensive coverage of highlights in the first tier," "concrete on-site experiences and practical tips in the second tier," and "suggestions for new ways to enjoy the area in the third tier." In particular, the advice-providing unit 117 presents methods for adding new value while appropriately referencing content from higher tiers. When a popular tourist route is formed across multiple tiers, the reward-granting unit 118 grants a route establishment bonus to users at all tiers who contributed to the formation of that route.

[0077] In the restaurant sector, the advice and presentation unit 117 forms a hierarchical structure consisting of first-tier store and menu introduction content, second-tier actual user reviews, and third-tier suggested menu variations and usage scenarios. The advice and presentation unit 117 proposes the optimal way to present information according to each tier, such as "presenting basic menu information and store atmosphere in the first tier," "detailed reviews of the actual appearance and taste of the dishes in the second tier," and "discovering hidden charms and new ways to enjoy the restaurant in the third tier." The reward distribution unit 118 grants market development bonuses to users at all relevant tiers, especially if derivative usage suggestions from the second to the third tier lead to the development of a new customer segment.

[0078] In these areas, the content nature analysis unit 116 analyzes the propagation and transformation of information between hierarchical levels. For example, it analyzes the process by which a specialized explanation at the first level changes into a more easily understandable expression at the second level, and the process by which a concrete experience at the second level is reconstructed as a new perspective at the third level. Based on the results of this analysis, the advice provision unit 117 proposes more effective methods of information dissemination, and the reward provision unit 118 designs rewards to promote the dissemination of valuable information.

[0079] Furthermore, mutually complementary relationships in reliability between layers are also important in each field. When expert and official information is included at some layer of each related content, content based on real-world experience and content from diverse perspectives at other layers complement each other, resulting in a more reliable group of content. The advice provision unit 117 presents users with information dissemination methods that strengthen this mutually complementary relationship, and the reward provision unit 118 provides group synergy bonuses to user groups that have built effective complementary relationships.

[0080] In this embodiment, the hierarchical structure may form a fan-shaped structure in which numerous second-tier content pieces are linked to first-tier content pieces. For example, many users may engage in transactions related to popular first-tier content pieces and then share their experiences and impressions as second-tier content pieces.

[0081] In this fan-shaped hierarchical structure, the content nature analysis unit 116 analyzes the similarities and differences in nature between the first-tier content and the numerous second-tier content. For example, it analyzes how specific expressions or selling points of the first-tier content are perceived and changed in the second-tier content. It also extracts features that appear commonly across the numerous second-tier content, as well as features that appear individually, to analyze the characteristics of the influence of the first-tier content.

[0082] The advice-providing unit 117 provides advice based on the analysis results of this fan-shaped hierarchical structure to encourage the generation of more second-tier content. For example, it suggests expression methods that easily elicit diverse experiences and impressions, and the placement of elements that increase users' motivation to share. It also suggests expression methods that can be expected to have similar effects, referencing the characteristics of content for which a large amount of second-tier content has already been generated.

[0083] The reward distribution unit 118 grants special rewards to the creators of first-tier content in a fan-shaped hierarchical structure, depending on the number and quality of second-tier content derived from first-tier content. For example, it sets up a volume bonus when a certain number of second-tier content pieces are generated, or a quality bonus when many high-quality second-tier content pieces are generated.

[0084] The related content management unit 114, within a fan-shaped hierarchical structure, prioritizes and manages related content from among numerous second-tier content pieces, focusing on those with particularly high influence or distinctive perspectives. This creates a group of related content that more effectively complements and enhances the value of first-tier content.

[0085] ==Implementations in the field of treatment and therapy== The following describes embodiments of the present invention and their applications in the fields of medical treatment, aesthetics, and beauty. In these fields, it is important to confirm the effects of treatments and therapies by comparing the state before and after the treatment. This embodiment supports the visualization and evaluation of such effects.

[0086] ==Implementations in the field of treatment and therapy== This embodiment is particularly effective in fields of treatment and therapy such as medicine, aesthetics, and beauty. In these fields, it is important to confirm the effects of treatments and therapies by comparing the condition before and after the treatment.

[0087] When acquiring experience content from a user, the content acquisition unit 111 presents the user with predetermined requirements. These requirements include, for example, technical requirements such as the orientation and size of the subject to be captured, such as faces, in photos and videos included in the experience content, lighting conditions, and shooting angles; requirements for recording numerical data such as weight, body fat percentage, and subjective symptoms (physical condition, degree of pain, etc.); and requirements for including text information such as the details and impressions of treatments and subjective symptoms (physical condition, degree of pain, etc.). The content acquisition unit 111 automatically evaluates the degree to which these requirements are met for the experience content entered by the user and acquires it only if it exceeds the standard value.

[0088] For example, requirements for a face in a photograph may include that the entire face fits within a specified frame when photographed from the front, and that the face's tilt is within a certain angle. The content acquisition unit 111 uses image recognition technology to quantify and evaluate the degree to which these requirements are met.

[0089] The content display unit 112 displays information about the patient's condition before and after treatment side by side, allowing the user to easily compare them. This condition information includes records of appearance through photographs and videos, numerical data such as weight and body fat percentage, and records of subjective symptoms and physical condition. In particular, the condition information at multiple points in time after treatment can be changed to information at a point specified by the user, allowing the user to check changes over time. At this time, the content display unit may fix the image before treatment and replace it with the image after treatment at the user's selection, or it may fix the image at the first point in time after treatment and replace it with the image at the second point in time after treatment at the user's selection.

[0090] The content display unit 112 has a function to correct images and other content to match the state information of one image or other content if the shooting conditions, resolution, ISO, etc., of the images and other content included in the experience content differ before and after the procedure or treatment. For example, when comparing photographs of images and other content included in the experience content that have different shooting distances, angles, and shooting conditions (brightness, resolution, ISO, etc.), the content display unit 112 corrects the size and orientation of faces, skin brightness, etc., through image processing so that they can be compared under similar conditions.

[0091] A new evaluation support unit 119 may be provided. The evaluation support unit 119 receives evaluations from users who have experienced the treatment and presents the results to other users. As an evaluation method, the evaluation support unit 119 presents the user terminal 3 with two options, such as "effective / not effective" or "satisfied / dissatisfied," and aggregates and displays the selection results obtained over a predetermined period in chronological order. This makes it possible to visualize the persistence of the effect and the changes in satisfaction levels.

[0092] ==Coordination with each processing unit== The User Analysis Department 115 combines changes in the state information of the experience content with evaluation results to analyze user improvement patterns and conditions under which effects are likely to be achieved. For example, it identifies the characteristics of users with a large difference between the initial state and the final state, and patterns of state changes common to highly-rated experience content.

[0093] The content nature analysis unit 116 quantitatively analyzes changes in state information within the experiential content. For images, it analyzes the rate of change in contours and changes in color tone; for numerical data, it analyzes improvement rates and fluctuation patterns. In addition, it extracts the type and degree of effect from textual descriptions and impressions using natural language processing.

[0094] The advice-providing unit 117 proposes methods for creating more effective experiential content based on the analysis results. For example, it provides specific advice on how to photograph state information, how to present it in an easy-to-compare manner, and how to structure persuasive explanations. It also offers suggestions on how to select time points to effectively show changes over time.

[0095] The reward distribution unit 118 designs rewards based on the quality and impact of the experience content. For example, it provides additional rewards for experience content with comprehensive status information, experience content with clearly demonstrated effects, and experience content that has been helpful to many users.

[0096] While preferred embodiments of the present disclosure have been described in detail above with reference to the attached drawings, the technical scope of the present disclosure is not limited to such examples. It is clear to any person with ordinary skill in the art of the present disclosure that various modifications or alterations may be conceived within the scope of the technical idea set forth in the claims, and these will naturally also fall within the technical scope of the present disclosure.

[0097] The devices described herein may be implemented as a single device, or as a group of devices (e.g., cloud servers) that are partially or entirely connected by a network. For example, the CPU and storage device of server device 1 may be implemented by different servers that are connected to each other by a network.

[0098] The series of processes performed by the apparatus described herein may be implemented using software, hardware, or a combination of software and hardware. Computer programs for implementing each function of the server device 1 according to this embodiment can be created and implemented on a PC or the like. Furthermore, a computer-readable recording medium containing such a computer program can also be provided. Examples of recording media include magnetic disks, optical disks, magneto-optical disks, and flash memory. Alternatively, the computer program may be distributed without using a recording medium, for example, via a network.

[0099] Furthermore, the processes described herein do not necessarily have to be performed in the order described. Some processing steps may be performed in parallel. Additional processing steps may be employed, and some processing steps may be omitted.

[0100] Furthermore, the effects described herein are merely descriptive or illustrative and not limiting. In other words, the technology relating to this disclosure may produce other effects that are obvious to those skilled in the art from the description herein, in addition to or in lieu of the effects described herein.

[0101] 1 Server device 2 Network 3. User terminals 101 CPU 102 memory 103 Storage device 104 Communication Interface 105 Input device 106 Output device 111 Content Acquisition Section 112 Content Presentation Section 113 User Identification Section 114 Related Content Management Department 115 User Analysis Department 116 Content Nature Analysis Department 117 Advisory Section 118 Reward Granting Section 131 User Information Storage Unit 132 Content Storage Unit 133 Related Content Storage Unit 134 User Analysis Result Storage Unit

Claims

1. An information processing device that manages user-generated content, A content acquisition unit that acquires the content from multiple users, A user analysis unit that hierarchically identifies derived users, including the user who transmitted the content and the user who performed a transaction based on the content, and analyzes the trends of the user's transaction activities. An advice presentation unit that provides the user with advice regarding the content based on the aforementioned trends, An information processing device equipped with the following features.

2. An information processing apparatus according to claim 1, Related content management unit manages the content transmitted by the aforementioned derived users by hierarchically associating them as related content, An information processing device equipped with the following features.

3. An information processing apparatus according to claim 2, The user analysis unit is an information processing device that analyzes the user's trends in transactional activities based at least on the relevance of the related content.

4. An information processing apparatus according to claim 2 or 3, A reward granting unit grants rewards to each user who has transmitted the related content in accordance with the transaction activity of the derivative user, An information processing device that further includes these features.

5. An information processing program that manages user-generated content, In the processor, A content acquisition step of acquiring the content from multiple users, A user analysis step to hierarchically identify derived users, including the user who transmitted the content and the user who performed a transaction based on the content, and to analyze the trends of the user in the transaction. An advice presentation step is provided to the user based on the aforementioned trends, An information processing program that executes [something].

6. An information processing method for managing user-generated content, The processor, A content acquisition step of acquiring the content from multiple users, A user analysis step to hierarchically identify derived users, including the user who transmitted the content and the user who performed a transaction based on the content, and to analyze the trends of the user in the transaction. An advice presentation step is provided to the user based on the aforementioned trends, An information processing method that performs [this action].

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