Information providing device, information providing method, and information providing program

Abstract

To provide a metaverse space that is useful for asset building.SOLUTION: An information provision device according to the present application comprises a classification unit for classifying users into predetermined groups in accordance with the users' financial trends estimated from behavior of the users in a metaverse space, and a proposal unit for proposing financial information to the users according to classification results from the classification unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information providing device, an information providing method, and an information providing program. [Background technology]

[0002] Conventionally, there is a conversation system that provides counseling to a user in a virtual space. For example, in such a conversation technology, a counselor provides counseling to a user through an avatar in a virtual space (for example, see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2011-39860 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the conventional technology did not consider providing a metaverse space that is useful for asset formation. For example, in recent years, the number of users who start investing in stocks and other products is increasing, but for users who have little knowledge about finance, it is still difficult to take the first step to start investing.

[0005] The present invention has been made in consideration of the above, and aims to provide an information providing device, an information providing method, and an information providing program that can provide a metaverse space that is useful for asset formation. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objectives, the information providing device of the present invention comprises a classification unit that classifies the user into a predetermined group based on the user's financial tendencies estimated from the user's behavior in the metaverse space, and a proposal unit that proposes financial-related information to the user based on the classification result by the classification unit. Effect of the Invention

[0007] According to the present invention, a metaverse space useful for asset formation can be provided. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. [Diagram 2] FIG. 2 is a block diagram illustrating an example of the configuration of the information providing device according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating an example of a user information database according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a community database according to the embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of a financial product information database according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of a financial topic database according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of a conversation in the metaverse space according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of finance-related information according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of finance-related information according to the embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of finance-related information according to the embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of finance-related information according to the embodiment. [Figure 12]FIG. 12 is a flowchart illustrating an example of a processing procedure executed by the information providing device according to the embodiment. [Figure 13] FIG. 13 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information providing device according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the information providing device, the information providing method, and the information providing program according to the present application (hereinafter, referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information providing device, the information providing method, and the information providing program according to the present application are not limited to these embodiments. In addition, each embodiment can be appropriately combined as long as the processing contents are not contradictory. In addition, the same parts in each of the following embodiments are given the same reference numerals, and duplicated explanations will be omitted.

[0010] [1. Overview of Information Processing] FIG. 1 is a diagram showing an example of information processing according to the embodiment, and the information providing method according to the embodiment is executed by an information providing device 1 shown in FIG.

[0011] The information providing device 1 shown in Fig. 1 is a device that provides a metaverse space to each user, etc. The metaverse space is a virtual space set on the Internet, and for example, each user can move within the metaverse space and interact with other users through an avatar.

[0012] As will be described later, the information providing device 1 can also provide various services to the user U in cooperation with, for example, an electronic settlement service or a securities company.

[0013] The user terminal 10 is a terminal device owned by a user U, and for example, the user U can experience the metaverse space provided by the information providing device 1 through the user terminal 10. A typical example of the user terminal 10 is a smartphone, but the user terminal 10 may also be realized by a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. In the example of FIG. 1, a smartphone is shown as the user terminal 10. In the following description, the user terminal 10 may be referred to as the user U. In other words, the user U can be read as the user terminal 10.

[0014] In recent years, attention has been focused on asset formation due to concerns about asset problems after retirement and the collapse of the mandatory retirement system, etc. For this reason, for example, various media outlets are overflowing with information on asset formation, but since the information to be referred to varies depending on the user's asset situation, income situation, knowledge about asset formation, etc., it is difficult for users to select information on asset formation.

[0015] It is also possible to receive counseling from a financial professional such as a banker or financial planner, but in reality, this is often a high hurdle for users.

[0016] For example, when a user with no investment experience starts investing, it is preferable that they have someone they can consult with, such as family or friends, but many users hesitate to invest because they do not have anyone they can consult with.

[0017] Therefore, in the embodiment, the information providing device 1 provides a metaverse space related to asset formation (step S1). For example, in the metaverse space, there are communities C1 to Cn (n is a natural number). In the following, when there is no need to distinguish between the communities C1 to Cn, they will also be simply referred to as community C.

[0018] For example, as shown in Fig. 1, the information providing device 1 accepts a membership registration of a user U1 through a user terminal 10 (step S2). For example, the membership registration may be a registration for creating an account in the metaverse space, or may be a registration for creating an account for another service (e.g., an electronic payment service, etc.) linked to the information providing device 1. For example, when guiding users from another service to the metaverse space, it is possible to guide a variety of users, particularly users who have not yet started asset management, etc., to the metaverse space.

[0019] Furthermore, when registering as a member, the information providing device 1 may accept input of items such as the user U1's occupation, income, asset status, and investment experience. Upon accepting the member registration, the information providing device 1 invites the user U1 to the metaverse space (step S3). As shown in FIG. 1, the user U1 who newly registers as a member is assumed to be inexperienced in investing.

[0020] For example, each community C may have avatars that include existing members who have already started investing (e.g., user U2 who has investment experience), experts such as bankers and financial planners, or chatbots, and users may converse or chat with existing members through the avatars.

[0021] For example, in community C, user U1 can consult with existing members (e.g., members with little investment experience) who have started investing through the metaverse space about various aspects of asset formation. For example, user U1 can resolve questions about investment through conversations with existing members, or share concerns about asset formation.

[0022] At this time, the information providing device 1 analyzes logs related to various actions of the user U1, including conversations of the user U1 within the community C (step S5). For example, the information providing device 1 analyzes the content of the conversation of the user U1 by a predetermined natural language processing, and analyzes the financial tendencies of the user U1 from the content of the conversation.

[0023] For example, finance-related tendencies include investment interest, financial knowledge, risk tolerance, knowledge level, etc. Finance-related tendencies may also include the balance between monthly income and expenditure, surplus funds available for investment, future plans, investment objectives, whether income gains or capital is emphasized, etc.

[0024] Then, the information providing device 1 divides the users into groups according to their tendency regarding finance (step S6). The group here is, for example, a community C, and the grouping is performed so that users with similar tendency regarding finance join the same community C.

[0025] In this way, by dividing the community C according to financial trends, it is possible to provide a place for users participating in the same community C to share and consult with each other about their concerns. This allows users to ask other users participating in community C for advice or consultation regarding asset formation, for example.

[0026] In particular, users can share their financial concerns and worries through avatars in the metaverse, lowering the psychological barrier compared to asking for advice in the real world. Also, for example, even if a user does not have any acquaintances around them who are knowledgeable about finance, they can easily find someone to consult with through Community C in the metaverse.

[0027] Each community C may be a community in which users with similar attributes participate. For example, the attributes may be classified according to age, income, occupation, personality, hobbies, family structure, etc., or may be classified in consideration of the similarity of avatars.

[0028] In other words, the groups classified according to finance-related tendencies do not have to have a one-to-one relationship with the community C, and users from multiple groups classified according to finance-related tendencies may participate in one community C classified according to attributes. In other words, users with different backgrounds may participate in the same community C classified according to attributes.

[0029] Then, the information providing device 1 proposes investment methods and information on financial products to the user U1 according to the grouping result (step S7). For example, the investment methods include types of investments, methods for opening a securities account, investment trusts, or methods for purchasing stocks, and the information on financial products includes types of financial products, expected returns and risks for each financial product, and the like.

[0030] For example, the information providing device 1 will propose investment methods and financial products to the user U1 according to the grouping results. The financial products proposed to the user U1 who is inexperienced in investing are, for example, widely diversified investment trusts recommended by the Financial Services Agency, but other financial products according to the investment objectives and risk tolerance of the user U1 may also be proposed.

[0031] In addition, for example, the information providing device 1 may also suggest to a user U1 who is inexperienced in investing to open a tax-exempt account, such as a NISA account or an ideco account.

[0032] In addition, the information providing device 1 may suggest investment methods and financial products through avatars in the metaverse space, or may suggest investment methods and financial products to the user terminal 10 by push-type notifications, for example using messages.

[0033] In particular, when an investment method or financial product is proposed through an avatar, the investment method or financial product can be proposed in a conversational format, making it possible to provide information according to the user U1's level of understanding.

[0034] User U1 can deepen his / her understanding of investment through experiences in these metaverse spaces. In particular, by linking the user U1 to an experience of actually opening a securities account and purchasing a financial product, the investment experience of user U1 can be promoted. For example, the user purchases a proposed financial product through a securities company with which the information providing device 1 is linked. A series of processes from the proposal of the financial product to the purchase may be completed within the metaverse space, or the proposal of the financial product may be performed in the metaverse space, and the purchase of the financial product may be performed on the website of the linked securities company. For example, the information providing device 1 proposes information on financial products handled by the linked securities company to the user, so that beginner investors can purchase financial products with confidence.

[0035] After that, user U1 will join the metaverse space as an existing member and can provide advice to new users who are new to investing, and can also share information with existing members who have a lot of experience and knowledge about investing. In this way, user U1 who has started investing can share information and concerns about asset formation through the metaverse space.

[0036] The information providing device 1 then proposes information on investment methods and financial products in accordance with the investment experience and investment knowledge of the user U1. For example, the information providing device 1 may propose individual stocks as financial products in addition to investment trusts, or may propose buying and selling of FX and crypto assets, depending on the user's investment experience, etc.

[0037] In this way, by providing a metaverse space related to asset formation, the information providing device 1 can solve problems that a user U1 who is a beginner investor faces when starting to invest, such as not knowing who to consult or being unable to choose because there are so many investment options.

[0038] As described above, the information providing device 1 according to the embodiment classifies users into groups according to the user's tendency regarding finances estimated from the user's behavior in the metaverse space. Then, the information providing device 1 according to the embodiment proposes finance-related information according to the grouping result.

[0039] Therefore, according to the information providing device 1 of the embodiment, it is possible to provide a metaverse space that is useful for asset formation. It is expected that fraudulent acts such as the sale of expensive information materials and the introduction of investment fraud will occur in such a metaverse space. Therefore, the information providing device 1 may monitor the behavior of each user in the metaverse space and take measures such as excluding users who engage in fraudulent acts from the metaverse space.

[0040] In addition, at this time, the information providing device 1 may provide each user with information on the method of fraudulent acts in the metaverse space to alert them. That is, in this case, information on the method of fraudulent acts committed in the metaverse space may be provided as financial-related information.

[0041] [2. Example of the configuration of the information providing device] Next, a configuration example of the information providing device 1 according to the embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing a configuration example of the information providing device 1 according to the embodiment. As shown in Fig. 2, the information providing device 1 according to the embodiment includes a communication unit 2, a storage unit 3, and a control unit 4.

[0042] The communication unit 2 is realized by, for example, a network interface card (NIC) etc. The communication unit 2 transmits and receives information to and from an external device via a network such as various wireless communication networks, such as 4G (Generation), 5G, LTE (Long Term Evolution), WiFi (registered trademark), or wireless LAN (Local Area Network), or various wired communication networks.

[0043] The storage unit 3 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 3 also has a user information database 31, a community database 32, a financial product information database 33, and a financial topic database .

[0044] The user information database 31 is a database that stores user information related to users. Fig. 3 is a diagram showing an example of the user information database 31 according to the embodiment.

[0045] As shown in Figure 3, the user information database 31 of the embodiment stores information on items such as "user ID," "account information," "behavioral history," "asset information," "risk tolerance," "knowledge level," and "investment history" in association with each other.

[0046] The "user ID" is an identifier for identifying each user U. The "account information" is the account information of the user U identified by the corresponding user ID. For example, the account information includes demographic attributes such as the name, age, occupation, and address of the user U, and psychographic attributes such as hobbies and preferences.

[0047] "Behavior history" is the behavior history of a user identified by a corresponding user ID in the metaverse space. For example, the behavior history includes conversation history such as when, with whom, and what kind of conversation was held. Note that the conversation may be voice or text (e.g., chat).

[0048] "Asset information" is asset information of a user identified by a corresponding user ID. For example, the asset information is information about a user's bank account or securities account, and is obtained, for example, through a bank or securities company associated with the user.

[0049] "Risk tolerance" is the risk tolerance regarding asset management of a user identified by a corresponding user ID. "Knowledge level" is the knowledge level regarding financial literacy of a user identified by a corresponding user ID. Note that risk tolerance and knowledge level are also estimated, for example, from the user's behavior in the metaverse space. Risk tolerance and knowledge level may be estimated, for example, including the user's investment history.

[0050] For example, the risk tolerance is a quantification of the risk tolerance in asset management, and the higher the risk tolerance, the more one desires high-risk, high-return investments. The knowledge level is a quantification of knowledge about finance. For example, the knowledge level is estimated from the contents of conversations about finance between users in the metaverse space, and is ranked according to the financial terms that are understood and financial worries. The knowledge level may also be ranked according to the investment history to date. In the example shown in FIG. 3, the "risk tolerance" and "knowledge level" are each shown on a ten-level scale from level 1 to level 10, but are not limited to this.

[0051] "Investment history" is the investment history of a user identified by the corresponding user ID. For example, the investment history may include information such as when and for how much a financial product was purchased, as well as information on total profits and losses and current profits and losses.

[0052] Returning to the explanation of Fig. 2, a description will be given of the community database 32. The community database 32 is a database that stores information related to communities within the metaverse space.

[0053] Fig. 4 is a diagram showing an example of the community database 32 according to the embodiment. As shown in Fig. 4, the community database 32 stores information items such as "community ID", "members", and "characteristics" in association with each other. The "community ID" is an identifier for identifying each community C existing in the metaverse space.

[0054] A "member" is a member (each user) who participates in a community C identified by a corresponding community ID. A "characteristic" is a characteristic of a community C identified by a corresponding community ID. The characteristic of a community C is, for example, an n-dimensional vector and is a characteristic related to the attributes of the participating members, but may also be a characteristic of a conversation within the community C. The characteristic of a conversation indicates, for example, the relationship between a topic and the liveliness of the conversation. For example, the suggestion unit 47 described later can adjust the degree of liveliness of each community C by providing finance-related information according to the characteristics of the conversation.

[0055] Returning to the explanation of Fig. 2, the financial product information database 33 will be explained. The financial product information database 33 is a database that stores information on financial products. Fig. 5 is a diagram showing an example of the financial product information database 33 according to the embodiment.

[0056] As shown in FIG. 5, the financial product information database 33 stores information on items such as "product ID," "product type," "features," and "purchase recommender" in association with each other. "Product ID" is an identifier for identifying each financial product. For example, financial products are financial products that the information providing device 1 proposes to each user, and are financial products that can be purchased through securities companies, such as stocks, bonds, investment trusts, exchange traded funds (ETFs), and REITs, but may also include various virtual currencies, NFT (Non-Fungible Token) art, and the like.

[0057] "Product type" is the type of the financial product identified by the corresponding product ID. For example, product types can be stocks, bonds, mutual funds, exchange traded funds (ETFs), REITs, etc., but in the case of mutual funds and exchange traded funds, it includes information on the index that is linked and information on the sector or theme of the mutual fund.

[0058] "Characteristics" are characteristics of the financial product identified by the corresponding product ID, such as information on a rating by a specified rating agency, expected yield, risk, etc. "Purchase recommender" is a user who recommends the purchase of the financial product identified by the corresponding product ID. For example, purchase recommenders are set according to assets, risk tolerance, etc.

[0059] Returning to the explanation of FIG. 2, the financial topic database 34 will be explained. The financial topic database 34 is a database that stores information related to financial topics. Financial topics include topics that affect stock prices and the like, topics related to revisions to various financial systems (e.g., the NISA system, etc.). Furthermore, for example, financial topics may include topics related to the stocks of each company. Topics related to the stocks of each company include, for example, information published in the Nikkei Financial Yearbook and stock price trends.

[0060] Fig. 6 is a diagram showing an example of the financial topic database 34 according to the embodiment. As shown in Fig. 6, the financial topic database 34 stores information on items such as "topic ID", "provider", "content", and "proposed condition" in association with each other.

[0061] "Topic ID" is an identifier for identifying each financial topic. "Provider" is the provider of the financial topic identified by the corresponding topic ID, and "Content" is content related to the financial topic identified by the corresponding topic ID.

[0062] The "proposal condition" is a condition for proposing a financial topic identified by a corresponding topic ID to a user in the metaverse space. For example, the proposal condition may be a condition related to a user proposing a financial topic, or a condition related to a conversation when proposing the corresponding financial topic.

[0063] Returning to the explanation of Fig. 2, the control unit 4 will be described. The control unit 4 is, for example, a controller, and is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like, executing various programs stored in a storage device inside the information providing device 1 using a RAM as a working area.

[0064] As shown in FIG. 2, the control unit 4 includes a providing unit 41, a receiving unit 42, an acquiring unit 43, a selecting unit 44, a learning unit 45, a classifying unit 46, and a proposing unit 47.

[0065] The providing unit 41 provides the metaverse space to each user, etc. The providing unit 41 controls a corresponding avatar in response to a command or voice input from each user terminal 10 via the communication unit 2. The providing unit 41 also registers the actions of each user in the metaverse space in the action history of the user information database 31. As will be described later, the providing unit 41 can set up an exhibition space in the metaverse space and display information on the user's financial tendencies for each avatar.

[0066] The reception unit 42 receives registration (account creation) for the metaverse space from each user via the communication unit 2. For example, the reception unit 42 receives input of information on demographic attributes such as the age, sex, occupation, and address of each user during registration, and also receives settings for an avatar to be used in the metaverse space. Note that the avatar is, for example, a combination of parts such as a face and clothes prepared by the information providing device 1, but may also be an NFT owned by the user.

[0067] Furthermore, when registering as a member, the reception unit 42 may ask questions about asset management such as income, assets, investment history, etc. After completing the member registration, the reception unit 42 issues a user ID to the user and registers various information about the user in the user information database 31.

[0068] The acquisition unit 43 acquires various information related to financial products and various information related to financial topics via the communication unit 2. For example, the acquisition unit 43 acquires various information related to financial products and various information related to financial topics from a predetermined website.

[0069] In addition, the acquisition unit 43 registers the acquired various information related to financial products in the financial product information database 33, and registers the acquired various information related to financial topics in the financial topic database .

[0070] The selection unit 44 selects the community C in the metaverse space to which the user is invited according to the group classified by the classification unit 46. For example, for existing members, the selection unit 44 selects the community C to which the user is invited such that the group classified by the classification unit 46 and the community C match.

[0071] For example, each user will participate in the community C selected by the selection unit 44. Furthermore, when a change occurs to the group classified by the classification unit 46, the selection unit 44 will invite the user to the community C corresponding to the changed group.

[0072] In addition, for users who have completed membership registration, i.e., users who will soon join the metaverse space, their behavioral history within the metaverse space is not accumulated, so it is not possible to infer financial-related tendencies.

[0073] Therefore, for example, for a user before being classified into a group by the classification unit 46, the selection unit 44 selects a group to invite the user based on the learning result by the learning unit 45 described later.

[0074] As a result, for example, the selection unit 44 selects a community C that contains other users whose real-world attributes are similar to those of the user who will join the metaverse space. The attributes here include age, sex, annual income, occupation, family structure, address, hobbies, and the like.

[0075] For example, the selection unit 44 selects a community C in which there are other users with attributes similar to those of the user by matching the characteristics of the user estimated from the information entered by the user when registering as a member or an avatar, etc., with the characteristics of each community C.

[0076] For example, the selection unit 44 selects a community C having attributes similar to those of the user, can smoothly participate in the community C. The selection unit 44 may also re-invite the user to another community C depending on, for example, the amount of conversation of the user in the community C. For example, when a condition is met such as the amount of conversation of the user is small in the initially selected (invited) community C, the selection unit 44 invites the user to the other community.

[0077] The learning unit 45 learns the attribute information of users for each group classified by the classification unit 46. For example, the learning unit 45 refers to the community database 32 and learns the characteristics of the attribute information of the members participating in each community C for each community C. Note that the attribute information here may be age, sex, annual income, occupation, family structure, address, hobbies, etc., and may include information on financial assets, etc.

[0078] The learning unit 45 may also learn the relationship between topics of conversations that are popular within the community C as a feature of each community C. Note that the topic may be, for example, a finance-related topic or a topic other than finance.

[0079] For example, the learning unit 45 may learn the characteristics of each community C by linking a topic related to finance with a financial topic reported on television, in newspapers, online news, and the like.

[0080] For example, when a certain financial topic (such as news about the depreciation of the yen) is reported, it is possible to learn between a community C that engages in conversations about the depreciation of the yen and a community C that is not interested in the depreciation of the yen and does not engage in conversations about the depreciation of the yen.

[0081] The classification unit 46 classifies users into predetermined groups according to the user's tendency regarding finances estimated from the user's behavior in the metaverse space. First, the classification unit 46 estimates the user's tendency regarding finances from the user's behavior in the metaverse space.

[0082] 7 is a diagram showing an example of a conversation in the metaverse space according to the embodiment. As shown in FIG. 7, a scene is shown in which an avatar Ab1 of a user who has no investment experience and an avatar Ab2 of a user who has already started investing exist in a community C in the metaverse space, and the avatar Ab1 is consulting the avatar Ab2 about "how to start investing," and the avatar Ab2 is explaining his own investment experience (purchasing the investment trust "AA").

[0083] For example, through these conversations, information such as what concerns a user who is inexperienced in investing consulted with whom, and whether the concern was resolved or not, will be accumulated in the information providing device 1.

[0084] For example, the classification unit 46 estimates the user's tendency regarding finance based on the content of the conversation, the attributes of the user who spoke, the investment tendency of the user who spoke, and the evaluation (positive or negative) of the user regarding the conversation.

[0085] For example, the classification unit 46 estimates the user's tendency regarding finance by extracting the corresponding user's behavioral history from the user information database 31 and inputting it into a learning model. For example, the learning model is a model that learns the relationship between the user's behavioral history (conversation content, etc.) and the tendency regarding finance. For example, the tendency regarding finance includes the user's investment history, the user's tendency of interest in investment, risk tolerance, knowledge level, etc.

[0086] The learning model may also be a model that further learns the relationships between financial products purchased by the user, financial products sold by the user, the price difference at the time of buying and selling, the reason for selling, etc. in the user's investment history. In other words, the learning model may be a model that learns the investment tendency of the user, such as what financial products the user purchased and sold under what circumstances.

[0087] The learning model may be a model that analyzes, for example, a user's future plans, investment objectives, etc., from the user's behavioral history in the metaverse space. The future plans include future family composition, whether or not to purchase a home, the target amount of financial assets, etc.

[0088] The classification unit 46 inputs the behavioral history of each user and classifies the users into groups according to the financial tendencies of each user output from the learning model. For example, the classification of groups by the classification unit 46 can be realized by a predetermined clustering process. For example, in this case, each group includes users with similar financial tendencies.

[0089] Returning to the explanation of FIG. 2, the suggestion unit 47 will be explained. The suggestion unit 47 suggests finance-related information to the user according to the classification result by the classification unit 46. For example, the suggestion unit 47 suggests investment methods, financial products, etc. according to the group to which each user belongs. The suggestion unit 47 may also provide finance-related information according to the real-world attributes of the user. The real-world attributes include gender, assets, age, income, expenditure, etc.

[0090] Here, specific examples of finance-related information proposed by the proposal unit 47 will be described with reference to Figures 8 to 11. Figures 8 to 11 are diagrams showing examples of finance-related information according to the embodiment.

[0091] The example shown in Fig. 8 illustrates a scene in which users who have actually started investing in community C are having a conversation about investment through avatars Ab1 and Ab3. For example, the example shown in Fig. 8 illustrates a scene in which the conversation between avatars Ab1 and Ab3 has not resolved a question about investment losses.

[0092] For example, if the suggestion unit 47 determines that the above question is not resolved from a subsequent conversation between the avatar Ab1 and the avatar Ab3, the suggestion unit 47 participates in the conversation between the avatar Ab1 and the avatar Ab3 as the avatar Ab11, and answers the question of the avatar Ab1 and the avatar Ab3 through the avatar Ab11.

[0093] That is, in this case, avatar Ab11 will participate in community C as a so-called chatbot and respond to the doubts and questions of each user. Note that avatar Ab11 may be an avatar operated by a financial expert. That is, the financial expert may provide financial advice in each community C. In this case, it is advisable to create a dedicated account for notifying the financial expert of financial worries in each community C.

[0094] In addition to the above examples, for example, there are cases where users solve their questions among themselves. That is, it is also assumed that the question of avatar Ab1 is solved by avatar Ab3 answering the question of avatar Ab1. In that case, the information providing device 1 may pay a reward to the user of avatar Ab3. That is, in this case, it is expected that a metaverse space will be formed in which each user actively supports other users.

[0095] 9 shows a scene in which avatar Ab1 and avatar Ab4 are having a conversation in community C. In the example shown in Fig. 9, avatar Ab1, a user who has become accustomed to investing, is consulting with avatar Ab4, another user, about an investment product with a higher yield than the current one.

[0096] 9, the avatar Ab4 of the other user who received the consultation suggests to the avatar Ab1 that the proportion of stocks in financial assets be increased. In the subsequent conversation, if the user of the avatar Ab1 agrees with the suggestion of the avatar Ab4, the suggestion unit 47 suggests to the user of the avatar Ab1 a financial product according to the risk tolerance of the user of the avatar Ab1.

[0097] That is, the suggestion unit 47 suggests financial products, etc. to the user when the user is actually convinced of the investment method or financial product in the conversation within the community C. For example, the example in Fig. 9 shows a case where the user of avatar Ab1 is convinced of the proposal by avatar Ab4 and the subsequent explanation and indicates an intention to newly purchase stocks, and the suggestion unit 47 selects a financial product to be recommended to the user of avatar Ab1 and suggests it to the user.

[0098] This allows the proposal unit 47 to propose investment methods and financial products that match the user's financial tendencies after the user has acquired knowledge about financial products and the like.

[0099] 10 and 11 show examples in which the suggestion unit 47 suggests finance-related information to promote a conversation about finance. For example, FIG. 10 shows a scene in which users are having a conversation about a specific game through avatars Ab1 and Ab5.

[0100] For example, in such a case, the suggestion unit 47 displays a financial topic D corresponding to the conversation content in the metaverse space. In the example shown in Fig. 10, the financial topic D includes information on the market capitalization, current stock price, most recent dividend, etc. of the game maker that provides the game, but is not limited thereto.

[0101] In this case, both users of avatar Ab1 and avatar Ab5 can obtain knowledge about the asset status, stocks, etc. of the game maker through the financial topic D. In other words, the suggestion unit 47 can stimulate each user's interest in finance by displaying financial topics according to the conversation content in the metaverse space.

[0102] In particular, the suggestion unit 47 can further stimulate each user's interest in finance by, for example, suggesting a financial topic related to a field of interest of the user from among the contents of the user's conversation. Note that, for example, when proposing a financial topic D according to the contents of the conversation as shown in Fig. 10, it is also assumed that, for example, each user may become interested in another game maker in the subsequent conversation. In that case, the suggestion unit 47 may add and display a new financial topic D related to the other game maker while continuing to display the current financial topic D as a comparison target.

[0103] In addition, the suggestion unit 47 may select, depending on the content of the conversation, the items to be displayed as the financial topic D. For example, in response to the content of the conversation regarding the trend of stock prices, a graph showing the most recent trend of stock prices may be displayed.

[0104] Also, as shown in FIG. 11, an exhibition space SP for financial products may be provided in the metaverse space, and the proposal unit 47 may propose finance-related information through the exhibition space SP.

[0105] For example, the example shown in Figure 11 shows a scene in which avatar Ab1 and avatar Ab5 exist in the metaverse space, and the suggestion unit 47 is proposing a recommended portfolio to avatar Ab1 and avatar Ab5 as finance-related information through the exhibition space SP.

[0106] The portfolio here may be information on rough composition ratios at the granularity of domestic stocks, foreign stocks, domestic bonds, foreign bonds, etc., or may be information on more specific stock composition ratios.

[0107] For example, the suggestion unit 47 creates a portfolio based on the financial tendencies of both users, avatar Ab1 and avatar Ab5, and suggests it to the avatar Ab1 and avatar Ab5 through the exhibition space SP.

[0108] This makes it possible to efficiently encourage conversations about finance between the users of both avatars Ab1 and Ab5. As a result, the number of conversations between the users increases, and the financial tendencies of both users can be estimated with greater accuracy.

[0109] The exhibition space SP may display information about specific stocks that are not dependent on the user's financial tendencies, and may also display financial news. For example, financial news includes news that may affect stock prices, exchange rates, commodity prices, etc.

[0110] From the viewpoint of promoting conversation among users in the metaverse space, information regarding each user's investment experience, attributes, etc. may be displayed on the avatar. The investment experience here includes, for example, the number of years since the user started investing, the performance of the investment so far, the financial products and assets held, etc.

[0111] Also, for example, a user interface (UI) may be provided within the metaverse space that allows users to search for users to consult with based on their investment experience or attributes. Users can easily find users to consult with based on the information on the user's investment experience and attributes displayed on the avatar and the UI that allows users to be searched for.

[0112] [3. Processing flow] Next, a process procedure executed by the information providing device 1 according to the embodiment will be described with reference to Fig. 12. Fig. 12 is a flowchart showing an example of a process procedure executed by the information providing device 1 according to the embodiment.

[0113] 12, first, the information providing device 1 selects a community C in the metaverse space for a new user (step S101). Next, the information providing device 1 invites the new user to the selected community C (step S102).

[0114] Next, the information providing device 1 analyzes the behavior of users in the community C set in the metaverse space (step S103). Next, the information providing device 1 divides the users into groups according to financial trends estimated from the behavior of the users in the community C (step S104).

[0115] Then, the information providing device 1 proposes finance-related information to the user according to the grouping result (step S105), and ends the process.

[0116] 4. Modifications In the above embodiment, the information providing device 1 provides financial information related to investment to a user who is inexperienced in investing, but the present invention is not limited to this. For example, the financial information provided may include information for supporting a side job, information for reviewing a household budget (e.g., information for reducing fixed expenses (e.g., reviewing insurance), and information for acquiring skills that lead to increased income.

[0117] Furthermore, the information providing device 1 may propose, as the finance-related information, information on video content or books that correspond to the user's tendency regarding finance.

[0118] 6. Effects The information providing device 1 according to the above-described embodiment includes a classification unit 46 that classifies users into predetermined groups based on the users' financial tendencies estimated from their behavior in the metaverse space, and a proposal unit 47 that proposes financial information to the users based on the classification results by the classification unit 46.

[0119] Moreover, the information providing device 1 according to the embodiment includes a selection unit 44 that selects a community C in the metaverse space in which a user can participate, according to the group classified by the classification unit 46. Moreover, for a user before being classified into a group by the classification unit 46, the selection unit 44 selects a community in which other users who have similar attributes in the real world to the user can participate.

[0120] In addition, the information providing device 1 according to the embodiment includes a learning unit 45 that learns the attributes of users for each group classified by the classification unit 46, and the selection unit 44 selects a community C for the user before the user is classified into a group by the classification unit 46 based on the learning results by the learning unit 45.

[0121] The classification unit 46 also classifies users into predetermined groups according to the users' financial tendencies estimated from the communication between users in the metaverse space. The classification unit 46 also classifies users into the groups according to the users' investment experience estimated from the users' actions in the metaverse space, and the proposal unit 47 provides information on financial products according to the investment experience as financial-related information.

[0122] The classification unit 46 classifies users into predetermined groups based on the user's risk tolerance for investment estimated from the user's behavior in the metaverse space, and the suggestion unit 47 suggests, as finance-related information, information on financial products according to the user's risk tolerance for investment. The suggestion unit 47 further suggests information on financial products based on the user's attributes in the real world. The suggestion unit 47 also suggests finance-related information to the user through an avatar existing in the metaverse space.

[0123] The classification unit 46 classifies users into groups based on their avatars in the metaverse space. The information providing device 1 according to the embodiment includes a provision unit 41 that provides the metaverse space to users, and the provision unit 41 displays the user's financial tendencies for the user's avatar in the metaverse space.

[0124] The providing unit 41 also sets up an exhibition space for financial-related information in the metaverse space. The suggesting unit 47 also suggests financial topics related to the contents of conversations by users in the metaverse space as financial-related information. The suggesting unit 47 also suggests information on financial products handled by affiliated securities companies as financial-related information.

[0125] By using any one of the above-mentioned processes or a combination thereof, the information providing device according to the present application can provide a metaverse space that is useful for asset formation. The information providing device according to the present application can provide a metaverse space that is particularly useful in the initial stage of asset formation.

[0126] [7. Hardware Configuration] The information providing device 1 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in Fig. 13. Fig. 13 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the embodiment. The computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, a HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0127] The CPU 1100 operates and controls each unit based on a program stored in the ROM 1300 or the HDD 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, programs that depend on the hardware of the computer 1000, and the like.

[0128] The HDD 1400 stores programs executed by the CPU 1100, data used by such programs, etc. The communication interface 1500 receives data from other devices via a network (communication network) N and sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the network N.

[0129] The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse (in FIG. 13, the output devices and the input devices are collectively referred to as "input / output devices") via an input / output interface 1600. The CPU 1100 acquires data from the input devices via the input / output interface 1600. The CPU 1100 also outputs generated data to the output devices via the input / output interface 1600.

[0130] The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0131] For example, when the computer 1000 functions as the user terminal 10 according to the embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 4. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via the network N.

[0132] Although some of the embodiments of the present application have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be embodied in other forms that incorporate various modifications and improvements based on the knowledge of those skilled in the art, including the forms described in the Disclosure of the Invention section.

[0133] [8. Other] Furthermore, among the processes described in the above embodiments and modifications, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

[0134] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc.

[0135] Furthermore, the above-described embodiments and modifications can be appropriately combined as long as the processing contents are not contradictory.

[0136] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit", etc. For example, an acquisition section can be read as an acquisition means or an acquisition circuit. [Explanation of symbols]

[0137] 1 Information provision device 2. Communications Department 3 Storage section 4. Control section 10 User terminal 31 User information database 32 Community Database 33 Financial product information database 34 Financial Topics Database 41 Providing Department 42 Reception 43 Acquisition Department 44 Selection section 45 Learning Department 46 Classification Department 47 Proposal Department U User

Claims

1. a classification unit that estimates the knowledge level of a person to be classified from the content of the conversation related to finance in the metaverse space of the person to be classified, using a learning model that has learned the relationship between the content of the conversation related to finance of the user in the metaverse space and the knowledge level indicating the level of financial knowledge, which is the tendency of the user regarding finance, and classifies the person to be classified into a predetermined group according to the knowledge level of the person to be classified; a suggestion unit that suggests financial information to the classification target person according to a classification result by the classification unit; Equipped with The classification unit is classifying the classification target person into the group formed by users having similar knowledge levels of the users; The suggestion unit, Proposing the financial information according to the knowledge level of the classification target person. An information providing device comprising:

2. A selection unit that selects a community in the metaverse space in which other users who have real-world attributes similar to the classification target person before being classified into the group by the classification unit participate.

2. The information providing device according to claim 1, further comprising:

3. The selection unit is For the classification target person after being classified into the group by the classification unit, the community corresponding to the group is selected.

3. The information providing device according to claim 2, wherein:

4. A learning unit that learns attributes of the users for each group classified by the classification unit. Equipped with The selection unit is selecting the community for the classification target person before being classified into the group by the classification unit based on a learning result by the learning unit; 3. The information providing device according to claim 2, wherein:

5. The classification unit is Using a learning model that has learned the relationship between the user's finance-related conversations in the metaverse space and the user's investment experience, classify the subject into a predetermined group based on the investment experience of the subject estimated from the finance-related conversations of the subject; The suggestion unit, As the financial-related information, to provide information on financial products according to the investment experience 2. The information providing device according to claim 1,

6. The classification unit is Using a learning model that has learned the relationship between the user's finance-related conversation in the metaverse space and the user's risk tolerance for investment, classifying the user into a predetermined group based on the risk tolerance for investment of the classification target person estimated from the finance-related conversation of the classification target person; The suggestion unit, As the financial-related information, the user is provided with information on financial products according to the user's investment risk tolerance.

2. The information providing device according to claim 1,

7. The suggestion unit, Furthermore, the present invention provides a method for providing information related to financial products based on the user's attributes in the real world.

7. The information providing device according to claim 6,

8. The suggestion unit, Providing the financial information to the user through an avatar present in the metaverse space.

2. The information providing device according to claim 1,

9. The classification unit is Classifying the users into groups based on their avatars in the metaverse space.

2. The information providing device according to claim 1,

10. A provision unit that provides the metaverse space to the user Equipped with The providing unit is Displaying the knowledge level of the user on an avatar of the user in the metaverse space.

2. The information providing device according to claim 1,

11. The providing unit is Setting up an exhibition space for said financial-related information within said metaverse space.

11. The information providing device according to claim 10,

12. The suggestion unit, Suggesting financial topics related to the content of conversations by the users in the metaverse space as the financial-related information.

2. The information providing device according to claim 1,

13. The suggestion unit, Proposing information on financial products handled by affiliated securities companies as the aforementioned financial-related information 2. The information providing device according to claim 1,

14. 1. A computer-implemented information providing method, comprising: a classification process in which, using a learning model that has learned the relationship between the content of a user's finance-related conversation in the metaverse space and a knowledge level that indicates the level of financial knowledge, which is the user's tendency regarding finance, an estimation of the knowledge level of a person to be classified from the content of the finance-related conversation in the metaverse space of the person to be classified, and classifying the person to be classified into a predetermined group according to the knowledge level of the person to be classified; a proposal step of proposing financial information to the classification target person according to a classification result by the classification step; Including, The classification step includes: classifying the classification target person into the group formed by users having similar knowledge levels of the users; The proposed process comprises: Proposing the financial information according to the knowledge level of the classification target person.

1. A method for providing information, comprising:

15. a classification step of estimating the knowledge level of a person to be classified from the content of the conversation related to finance in the metaverse space of the person to be classified, using a learning model that has learned the relationship between the content of the conversation related to finance of the user in the metaverse space and the knowledge level indicating the level of financial knowledge, which is the tendency of the user regarding finance, and classifying the person to be classified into a predetermined group according to the knowledge level of the person to be classified; a proposal step of proposing financial information to the classification target person according to a classification result by the classification step; on the computer, The classification procedure includes: classifying the classification target person into the group formed by users having similar knowledge levels of the users; The proposed procedure comprises: Proposing the financial information according to the knowledge level of the classification target person. An information program characterized by:

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