Device and method

Abstract

This invention generates and provides an appropriate tutorial in a virtual space.　A virtual space provision server 100 comprises an attribute information storage unit 105 that functions as a history information storage unit for storing behavior history information of a plurality of users in a virtual space. A virtual space information provision unit 101 provides the virtual space to user terminals 200. A tutorial model acquisition unit 102 functions as a guidance location acquisition unit, and specifies at least one spot area in the virtual space on the basis of a tutorial model (including a spot area corresponding to a guidance location) generated on the basis of the plurality of users' behavior history information. The spot area is a location corresponding to a user attribute that is a guidance condition. The virtual space information provision unit 101 also functions as a guidance unit, and guides one user in the virtual space to the spot area of the tutorial model. The guidance is performed by superimposing, onto the virtual space, guidance information such as an arrow within the virtual space.

Description

Device and Method

[0008] ,

[0007] ,

[0001] The present invention relates to a device and method for generating a tutorial.

[0002] Patent Document 1 describes an information processing system that enables a user to smoothly receive the provision of a plurality of contents while sequentially moving to each position in virtual reality. In this Patent Document 1, there is a description of viewing the content of an entrance tutorial in a virtual space.

[0003] Japanese Unexamined Patent Application Publication No. 2022 - 87631

[0004] However, in Patent Document 1, there is a problem that it is a stereotyped tutorial limited to a specific situation, such as an entrance tutorial, and is not suitable for other situations. In particular, when a user is in a metaverse with an unclear purpose, conversely, when there are multiple purposes of the user in the metaverse and anything can be done, there is a problem that it is difficult to provide a general-purpose tutorial.

[0005] Therefore, an object of the present disclosure is to provide a device and method capable of generating and providing an appropriate tutorial in a virtual space.

[0006] The device of the present disclosure includes a history information storage unit that stores the action history information of a plurality of users in a virtual space, a guidance location acquisition unit that acquires at least one guidance location in the virtual space according to guidance conditions from at least one guidance location generated based on the action history information, and a guidance unit that guides a user in the virtual space to the guidance location.

[0007] According to the present invention, an appropriate tutorial can be generated and provided in a virtual space.

[0008] Figure 1 is a diagram showing the system configuration including the virtual space provision server 100 of this disclosure. Figure 2 is a block diagram showing the functional configuration of the virtual space provision server 100 of this disclosure. Figure 3 is a diagram showing a specific example of its attribute information. Figure 4 is a diagram showing a specific example of a tutorial model stored in the tutorial model storage unit 107. Figure 5 is a flowchart showing the operation of the virtual space provision server 100. Figure 6 is a flowchart showing the details of the tutorial provision process in the virtual space provision server 100. Figure 7 is a flowchart showing the tutorial model generation process in the virtual space provision server 100. Figure 8 is a flowchart showing the detailed processing of processes S203 and S204 in tutorial generation. Figure 9 is a diagram schematically showing a tutorial (spot area and its guidance order) in a virtual space attraction. Figure 10 is a diagram showing an actual tutorial. Figure 11 is a diagram showing a specific example of a questionnaire. Figure 12 is a diagram showing the processing progress of obtaining the relationship between the spot area and user attributes from the visiting user for each spot area of ​​this disclosure. Figure 13 is a diagram showing a method with a different approach from Figure 12. Figure 14 is a diagram explaining the relationship between user attributes and the spot area to be guided. Figure 15 shows an example of the hardware configuration of a virtual space provisioning server 100 according to one embodiment of the present disclosure.

[0009] Embodiments of this disclosure will be described with reference to the attached drawings. Where possible, the same parts will be denoted by the same reference numerals, and redundant descriptions will be omitted.

[0010] Figure 1 is a diagram showing the system configuration including the virtual space provision server 100 of this disclosure. As shown in the figure, when the virtual space provision server 100 receives access from a user terminal 200, it provides a virtual space to the user terminal 200. The user terminal 200 presents the virtual space to the user. The user can move around and engage in activities within the virtual space by operating the user terminal 200.

[0011] The virtual space provider server 100 in this disclosure provides tutorials according to the user's login frequency in the virtual space. In this disclosure, a tutorial is guidance information that guides beginners or inexperienced users on how to behave in the virtual space, the areas they can move around in, and so on.

[0012] In this disclosure, the virtual space provider server 100 provides an appropriate tutorial according to the user's attribute information. The virtual space provider server 100 also generates the appropriate tutorial.

[0013] Figure 2 is a block diagram showing the functional configuration of the virtual space provision server 100 of this disclosure. As shown in the figure, the virtual space provision server 100 is configured to include a virtual space information provision unit 101, a tutorial model acquisition unit 102, a guidance unit 103, a virtual space information storage unit 104, an attribute information storage unit 105, a tutorial generation unit 106, and a tutorial model storage unit 107.

[0014] The virtual space information provision unit 101 is the part that, upon receiving a login request from the user terminal 200 using a user ID, provides the user terminal 200 with virtual space information corresponding to that user ID.

[0015] The tutorial model acquisition unit 102 is responsible for acquiring a tutorial model (spot area and its guidance sequence) based on the user attributes of the user indicated by the user ID, by referring to the tutorial model storage unit 107.

[0016] The guidance unit 103 is the part that adds guidance information to the virtual space to guide the user to the designated spot areas based on the acquired tutorial model. The virtual space information provision unit 101 provides the user with the virtual space to which the guidance information has been added. A spot area is a predefined area in the virtual space, such as an event venue, a store, or any other area where a user can linger.

[0017] The virtual space information storage unit 104 is the part that stores virtual space information to be provided to the user terminal 200. The virtual space information is information that constitutes the virtual space (buildings, ground, roads, and NPCs (Non-Player Characters, etc.)). In addition to this information, the virtual space information also includes guidance information for the tutorial. The guidance information is arrow information indicating the direction the user (avatar) should go. This information may be other information, or information other than arrow information that guides the user (text information, other symbols, etc.). Information for the tutorial framework is also stored.

[0018] The attribute information storage unit 105 is the part that stores the user attributes (attribute information) and user behavior history information of all users in the virtual space, including the user being guided. Figure 3 is a diagram showing a specific example of this attribute information. As shown in the figure, it includes user ID, login time and date, login frequency, login duration, virtual space movement history, chat history, item possession information, event participation information, hobbies and preferences, friend registration information, and tutorial progress.

[0019] The User ID is an ID used to uniquely identify a user. The Login Time and Date are the date and time the user logged in. This may include the login start and end times. The Login Frequency is the cumulative number of logins and the number of logins in the most recent specified period. The Login Time is the total time the user was logged in. This may include the cumulative time in the most recent specified period. The Virtual Space Movement History is a log of the coordinates of the virtual spaces the user has moved to. In addition, the Virtual Space Movement History includes the visit history (visit frequency) of areas with a set Area ID (a unique ID for each area). Furthermore, it may include the visit frequency and stay time of specific areas (such as tutorial areas or specific landmark areas) among all areas.

[0020] Chat history includes the frequency of chats (number of messages sent, number of messages received), and keywords or emotions expressed in those chats. Chat history may also include the chat partner and the relationship (friend status).

[0021] Item ownership information refers to items acquired or purchased within the virtual world. This includes clothing and accessories worn by the avatar, as well as tools used by the avatar. Items purchased for use in the real world may be excluded. Event participation information is a record of past events, including the event name, date and time of participation, actions taken during the event, and results. Results include participation in competitions and awards received.

[0022] Hobbies and preferences refer to the user's favorite activities, events, or items. This information can be inferred from chat content, item ownership history, and events attended, but may also be obtained through surveys. Friend registration information is a list of users registered as friends. It may also include the frequency of mutual communication. Tutorial progress includes the stage of the tutorial the user has progressed through, the speed of progress, and actions taken during the tutorial (completion time, number of attempts, etc.).

[0023] In addition, attribute information may include the time and duration of stay for each spot area.

[0024] The tutorial generation unit 106 is the part that generates a tutorial model based on virtual space information (spot area) and attribute information. As a prerequisite, a basic tutorial framework is generated according to the tutorial framework, and the tutorial generation unit 106 generates a tutorial model accordingly.

[0025] For example, the tutorial generation unit 106 determines at least one spot area (guidance location) to guide the user based on user attributes and stores a tutorial model (information defining the spot areas and their order) in the tutorial model storage unit 107, which determines the order of the guidance. The tutorial generation unit 106 determines the guidance order of the spot areas based on the user's location. In this disclosure, the guidance order of the spot areas is determined starting from the user's initial location when they log in, so as to shorten the travel path.

[0026] The tutorial model storage unit 107 is the part that stores the tutorial model generated by the tutorial generation unit 106. As described above, the tutorial model is information that defines the spot areas, which are the locations to be guided, and the order in which they should be guided.

[0027] Figure 4 shows a specific example of a tutorial model stored in the tutorial model storage unit 107. As shown in the figure, each tutorial model is associated with user attributes. These user attributes are abstract information, such as "likes new things." As will be described later, these user attributes are generated based on the user's actions (movement and speech / comments, etc.). In this disclosure, an AI such as a natural language generation (NLG) model can generate abstract information of those user attributes (for example, generate the name or description of those user attributes) based on the historical information it has collected and analyzed.

[0028] The tutorial model acquisition unit 102 then refers to the tutorial model storage unit 107 to acquire a tutorial model corresponding to the user attributes. The tutorial model storage unit 107 also defines the spot areas to be guided and the order in which they will be guided for each tutorial model. The tutorial model acquisition unit 102 then refers to the tutorial model storage unit 107 to acquire the guided spot areas and their guidance order information corresponding to the acquired tutorial model.

[0029] The operation of the virtual space provision server 100 configured in this way will now be explained. Figure 5 is a flowchart showing the operation of the virtual space provision server 100. The virtual space information provision unit 101 accepts a login from the user terminal 200 (S101). During this login, the user ID is transmitted from the user terminal (or user).

[0030] The virtual space information provision unit 101 refers to the attribute information storage unit 105 to determine whether the user with the user ID has logged in less than N times (S102). If the virtual space information provision unit 101 determines that the user has logged in N times or more (S102: NO), it transmits the virtual space information stored in the virtual space information storage unit 104 to the user terminal 200 (S106).

[0031] If the virtual space information provision unit 101 determines that the user has logged in less than N times (S102: YES), it determines whether the user logged in after a long period of time since the last login (S103). If the virtual space information provision unit 101 determines that a long period of time has not passed (S103: NO), it provides the virtual space (S106).

[0032] When the virtual space information provision unit 101 determines that a long period of time has elapsed (S103: YES), it collects questionnaire information (S104). This questionnaire information is information previously filled out by the user and is stored in a questionnaire information storage unit (not shown). This questionnaire information storage unit stores information such as the user's preferences and tendencies in virtual space behavior for each user ID. Figure 11 shows a specific example of the questionnaire. As shown in the figure, the user fills in information such as their favorite genre and whether or not they want to interact more frequently. Abstracted user attributes are generated from this questionnaire.

[0033] The virtual space information provision unit 101 then provides the user with a tutorial based on the questionnaire (S105). Specifically, the tutorial model acquisition unit 102 generates abstracted user attributes of the user to be guided from the user's questionnaire and acquires a tutorial model based on them. The virtual space information provision unit 101 then provides a virtual space according to that tutorial model (guiding the user through spot areas in the order of guidance).

[0034] Figure 6 is a flowchart detailing the tutorial provision process on the virtual space provision server 100. The tutorial model acquisition unit 102 analyzes questionnaires and other user actions to acquire user attributes of the user to be guided (S105a). User actions include, for example, a beginner user setting up an avatar or other settings for the virtual space. Based on the details of these settings and the time taken for the settings, user attributes such as the user's personality can be grasped. The user attributes acquired by the tutorial model acquisition unit 102 are abstracted information (textual information such as names or descriptions, see Figure 14(a)). This is acquired by AI analysis such as generation AI.

[0035] Then, the tutorial model acquisition unit 102 refers to the tutorial model storage unit 107 and selects a tutorial model corresponding to the acquired user attributes (S105b). Since the user attributes of the user to be guided and the user attributes stored in the tutorial model storage unit 107 are abstract information, they do not perfectly match. Therefore, correspondence information is generated in advance by associating these pieces of information, or the correspondence is made using natural language processing such as generation AI.

[0036] The guidance unit 103 generates guidance information (arrow information, etc.) by superimposing it onto the virtual space based on the location and guidance sequence of the spot areas included in the tutorial model, the location of the virtual space where the user has logged in, and virtual space information (for example, routes that the user can move along, such as roads and sidewalks in the virtual space) (S105c).

[0037] The virtual space information provision unit 101 provides the user with a virtual space that includes guidance information generated by the guidance unit 103 (S105d). In this disclosure, for example, the guidance information is arrow information, and the user can easily move in the direction indicated by the arrow in the virtual space.

[0038] In processing S105a, there are cases where it is not possible to determine the appropriate user attribute. For example, if user behavior and survey results contradict each other, several user attributes can be determined from the survey results, and it may be difficult to decide which to prioritize. For example, suppose the following user attributes can be obtained from the survey: - Likes music events - Likes the latest One way to prioritize is to determine priority based on the intensity of the responses to the survey items. For example, if there are three questions in the survey about interest in music events, and the answers are requested on a 5-point scale, the average value of those answers will be used as the average intensity of the answers (for example, assuming an average intensity of 4). Also, if there are three questions in the survey about liking the latest, and the average intensity of those answers is 5, then the user attribute "likes the latest" will be prioritized because it has a higher average intensity, and the user attribute will be determined to be "likes the latest". If there is no difference in the average intensity of the answers (or if the difference is slight), the user attribute will be determined randomly.

[0039] Furthermore, user attributes can be determined based on either survey data, user behavior such as initial setup, or both, but using only one of them is also acceptable. Additionally, it may be possible to prioritize user attributes based on user behavior, for example.

[0040] Figure 9 is a schematic diagram illustrating a tutorial (spot areas and their guidance order) in a virtual space attraction. Figure 9(a) is a schematic diagram showing guidance to locations that were popular spots at time x. As shown in the figure, the starting point is the location where the user logged in. Spot area A and spot area B are spot areas (recommended areas) suitable for the user's user attributes. These spot areas and the guidance order are described in a tutorial model that corresponds to the user attributes. In this disclosure, as shown in Figure 9(a), the tutorial model describes a guidance order that first guides the user to spot area A, and then to spot area B.

[0041] Fig. 9(b) is a schematic diagram showing guiding to a place that has become a popular spot at time x+1. The popular spot changes over time. Therefore, the historical information of the user is collected as appropriate, the tutorial model is updated, and the spot area to be guided for each user attribute is changed. Here, instead of spot areas A and B, spot areas C and D are newly determined as the guiding places.

[0042] Fig. 10 is a diagram showing an actual tutorial. Fig. 10(a) is a screen view of the virtual space seen from the starting point when the user logs in to the virtual space. The user can see a guiding arrow p in the virtual space. Also, the user can see the positions of spot x and spot y. Fig. 10(b) is the screen view seen by the user when arriving at spot x. Fig. 10(b) shows a spot area where the activities of other users are active. On the other hand, Fig. 10(c) shows a spot area where other users are playing games. The tutorial model is determined according to the user attribute, and it is determined which spot area to guide to.

[0043] Next, the generation process of the tutorial model that defines the information for guiding to the spot area according to the user attribute as described above will be explained. Fig. 7 is a flowchart showing the generation process of the tutorial model in the virtual space providing server 100. When the collection timing of the historical information is reached (S201: YES), the tutorial generation unit 106 collects the historical information of the actions of some users or all users from the attribute information storage unit 105 (S202).

[0044] For example, as basic data, the tutorial generation unit 106 collects the stay data (stay time, stay duration, number of people, number of speeches) of the users in the spot area, the amount of comments of the users and their contents, the amount of messages and their contents, and the activity amount (moving or stopped). Also, as application data, the tutorial generation unit 106 collects information such as which users with what hobbies gather where (which spot area), and which gender of users has a long stay time.

[0045] As collection timing, for example, the passage of a determined time (e.g., one month), when a new area is added, or when a specific event is held can be considered.

[0046] Then, the tutorial generation unit 106 analyzes some or all of the spot areas in the virtual space and determines the relevance between the user attributes and the spot areas (S203). For example, the tutorial generation unit 106 refers to the behavior history information for each spot area to determine whether the area is an area where the user stays, an area where conversations are active, or an active area, and determines which type of user each spot area is suitable for.

[0047] Also, for example, when the tutorial generation unit 106 determines that many users are staying in a certain spot area (e.g., x or more people), it determines that that spot area is suitable for users who like communication. Also, when the tutorial generation unit 106 determines that many comments and conversations are made in a certain spot area (e.g., the chat frequency is y or more times), it determines that that spot area is suitable for users who like conversations. These analysis processes are performed by a machine learning algorithm such as AI, but other methods may also be used.

[0048] The tutorial generation unit 106 determines the spot areas suitable for users having the user attributes for each user attribute, and determines the guidance order based on the starting location of the user's login (a predetermined location in the virtual space) (S204). Then, the tutorial generation unit 106 generates a tutorial model consisting of at least one spot area and its guidance order (S205). The tutorial generation unit 106 stores the generated tutorial model in the tutorial model storage unit 107 (S206). The user attributes stored as the tutorial model are information abstracted based on the user's behavior history, etc., and are composed of, for example, the name of the user attribute, the text explaining it, etc. This is generated based on AI, etc., but the operator may also assign the name, etc.

[0049] Next, the detailed processing of steps S203 and S204 in tutorial generation will be explained. The tutorial generation unit 106 collects history information from the attribute information storage unit 105 and performs spot analysis based on the collected history information. The tutorial generation unit 106 then analyzes the trends in user attributes for each spot area. Figure 8 shows a flowchart of this specific processing.

[0050] Figure 8 is a flowchart showing the detailed processing of steps S203 and S204 in tutorial generation. The tutorial generation unit 106 refers to the attribute information storage unit 105 to collect historical information and analyzes the user attributes to be used for tutorial generation. For example, the tutorial generation unit 106 performs cluster analysis on the historical information and classifies it into several user attributes. Then, the tutorial generation unit 106 obtains historical information for multiple user attributes through the analysis and determines one of the user attributes (S301).

[0051] The tutorial generation unit 106 analyzes and determines from the classified history information which spot areas a user with a particular user attribute (a cluster) has visited. Based on this analysis, the tutorial generation unit 106 determines n spot areas (S302).

[0052] If there are other user attributes (other clusters) to be generated (S303: YES), the tutorial generation unit 106 refers to the attribute information storage unit 105 and performs a spot area determination process based on the other user attributes (S301), and these processes are repeated for all user attributes (clusters). This makes it possible to determine one or more spot areas for each user attribute. In this disclosure, abstraction processing (assignment of names, etc.) is performed on the user attributes from the attribute information of users included in several clusters.

[0053] Next, we will explain specific examples of spot area analysis. As mentioned above, one may analyze the spot areas visited by users with certain user attributes and determine the correspondence between those spot areas and user attributes. Conversely, one may analyze some or all of the spot areas in the virtual space and determine the correspondence (suitability) between user attributes and spot areas.

[0054] For example, if there is a "cutting-edge area where the latest things are gathered," then the trends of existing users who gather there can be analyzed. Conversely, another method is to track existing users who answered in a survey that they "like the latest things" and analyze what areas they visit.

[0055] These processes are implemented using machine learning algorithms such as AI. For example, K-means clustering allows users to be grouped based on their user attributes, and by analyzing which user attributes are most prevalent, it is possible to analyze specific areas. Hierarchical clustering can also be used to classify users hierarchically and analyze user attribute trends in detail. In addition, classification algorithms such as decision trees, random forests, and support vector machines (SVMs) can be used as general machine learning algorithms to predict user attributes. Furthermore, collaborative filtering can be used to group users with similar user attributes based on user behavior data and analyze trends.

[0056] It should be noted that there are also methods for analyzing each spot area without using the machine learning algorithms described above. For example, to determine the characteristics of each spot area, such as a spot area where people linger, a spot area where conversations are active, or a spot area where users are otherwise active, rules can be used to determine which areas are where people linger, or which areas have active conversations, based on user behavior. For instance, if the number of users staying at an area exceeds a threshold, it can be classified as a spot area where people linger; if the comment frequency exceeds a threshold, it can be classified as a spot area where conversations are active.

[0057] Figure 12 shows the process of obtaining the relationship between a spot area and user attributes from the users who visited each spot area in this disclosure. Figure 12(a) shows the users who visited each spot area. Here, it is shown that users a and b visited spot area a. Figure 12(b) shows the behavioral history and survey results information for each user. By analyzing this, the user attributes for each user can be determined. In this disclosure, user a tends to comment frequently and like new things. User b, on the other hand, does not comment very frequently and does not tend to like new things. User c, like user a, tends to comment frequently and like new things. From this, it can be determined that spot area a is an area suitable for users who like new things and enjoy interacting with others. In this disclosure, an AI such as a Natural Language Generation (NLG) model can generate abstract information of the user attributes (for example, generate the name or description of the user attributes) based on the collected and analyzed historical information.

[0058] Figure 12(c) shows the user attributes of spot area a. As shown in the figure, user attributes are obtained by NLG through analysis of information obtained from the behavioral history and questionnaires of users a and c, and these are then associated with the user attributes of spot area a.

[0059] Then, the tutorial model shown in Figure 4 is generated based on this information so that user attributes are associated with spot areas (Figure 12(c)).

[0060] Figure 13 illustrates a method that differs from the approach in Figure 12. As shown in Figure 13(a), the visited spot areas are associated with each user ID and obtained. This shows which users went to which places. Then, the users who visited each spot area are aggregated, and their activity history and questionnaires are compiled (Figure 13(b)). Based on the aggregated results, the user attributes of spot area a are determined (Figure 13(c)).

[0061] Next, we will explain the relationship between user attributes and the spot areas to be guided. Figure 14 is a diagram illustrating this relationship, showing the relationship between the user attributes from which behavioral history is collected, the user attributes from which guidance is provided, and the spot areas to be guided. The user attributes to be guided refer to the user attributes of the user who will be guided. The user attributes to be collected refer to the user attributes from which behavioral history is collected in order to generate the tutorial model, and are the user attributes shown in Figure 4. As shown in Figure 14, the tutorial model (see Figure 4) is determined according to the user attributes to be guided. This tutorial model associates the user attributes to be collected with the spot areas to be guided, and as a result, the spot areas to be guided are determined according to the user attributes to be guided.

[0062] As shown in Figure 14(a), for the target user attribute "Users who like trendy things," the user attribute to be collected in order to derive an appropriate spot area is associated with "Users who have moved to many spot areas." This association information may be stored in an association information storage unit (not shown), or the association processing may be performed by other methods. This derives the spot area to be guided, "Latest spot area x, y." Similarly in Figure 14(b), for the target user attribute "Users who spent a lot of time creating their initial avatar," the user attribute to be collected is "Users who own many avatar items and frequently purchase items."

[0063] By the way, as shown in Figure 14, the representation of the user attributes to be guided and the user attributes to be collected do not necessarily match. Therefore, it is advisable to establish a correspondence between the user attributes obtained from the survey results in advance and the user attributes obtained when generating the tutorial model, as shown in Figure 14. This correspondence may be performed by the operator of the virtual space provision server 100, or it may be generated by a generation AI. If a generation AI is used, it is necessary to generate a prompt that instructs the AI ​​to create combinations that have a relationship between multiple user attributes to be guided and multiple user attributes to be collected.

[0064] In addition to the example shown in Figure 14, the following correspondences are possible. This information may be stored in a correspondence memory unit (not shown), or an AI model including a generating AI may be used to select a target user attribute stored in the tutorial model from the guidance-responding user attributes. ・Guidance target user attribute "User who likes music, events, and interacting with people" → Target user attribute "User who mainly frequents music event spaces and often uses words that have the meaning of "support"" in their speech ・Guidance target user attribute "[Common to beginners] Asks questions or problems in places where many people gather" → Target user attribute "User who speaks frequently and converses with a large number of people" ・Guidance target user attribute "Active user who plays games regularly (e.g., answered that they are good at games in a survey)" → Target user attribute "User who owns many avatar items and frequently purchases items" As described above, guidance target user attributes and target user attributes can be associated.

[0065] Next, the effects of the virtual space provision server 100 of this disclosure will be explained. The virtual space provision server 100 includes an attribute information storage unit 105 that functions as a history information storage unit for storing the behavior history information of multiple users in the virtual space. The virtual space information provision unit 101 provides the virtual space to the user terminal 200, and the tutorial model acquisition unit 102 functions as a guidance location acquisition unit and identifies at least one spot area in the virtual space based on a tutorial model (including a spot area corresponding to a guidance location) generated based on the behavior history information of multiple users. This spot area is a location that conforms to the user attribute, which is the guidance condition. The virtual space information provision unit 101 then functions as a guidance unit and guides a user in the virtual space to the spot area of ​​the tutorial model. Guidance is provided by superimposing guidance information, such as arrows, onto the virtual space.

[0066] This configuration allows for guiding users to suitable spot areas within the virtual space. Many metaverses face the problem of first-time users leaving because they don't understand "what to do" or "what they can do where." This configuration solves that problem, thus preventing a decline in user experience and enabling continuous user acquisition.

[0067] In other words, the virtual space provider server 100 of this disclosure automatically provides tutorials to novice users, making it possible to understand them intuitively by making full use of a 3D space. This prevents new users from dropping out and encourages more users to continue using the virtual space.

[0068] The following are important points in this disclosure: ・Behavioral analysis and guidance The virtual space provider server 100 in this disclosure collects and analyzes behavioral data of existing players to identify "places visited by many users," "places where people linger," "places where communication is active," and "places where many avatars gather."

[0069] Based on this process, new users can be guided to the appropriate locations, allowing even first-time users to effectively explore the virtual space. • Chat / Comment Analysis: The virtual space provider server 100 disclosed in this document is a system that accumulates and analyzes frequently used phrases in chat or comment sections. Based on these analysis results, it can provide more helpful guides by incorporating frequently asked questions or important information into the tutorial. • 3D Space Tutorial: Unlike conventional 3D tutorials, the virtual space provider server 100 disclosed in this document is a system that guides users in real time using a 3D space.

[0070] Furthermore, by abstracting the behavior of existing players and predicting the needs of new players, it is possible to automatically generate tutorials.

[0071] The following issues are presented in this disclosure.

[0072] First, to address the first challenge—that "in the metaverse, where objectives are not as clear as in games and people have different things they want to do, it is difficult to create a general tutorial, or the tutorial will be too superficial"—we will provide a system that allows users to actually walk around in a 3D space and move to specific locations, in addition to a tutorial guide that is only available on a 2D screen. This will give users a sense of "where to go to do what they want to do."

[0073] Next, to address the second challenge, "As users create and move between communities on a daily basis, standardized tutorials quickly become outdated," we will automatically collect and analyze user behavior data to gain insights into trends, such as "what kind of users are gathering where recently." This will solve problems such as users guided by tutorials visiting sparsely populated areas, allowing us to continuously improve the user experience while keeping the drop-off rate of new users low.

[0074] Finally, to address issue 3, "Creating tutorials is time-consuming," we will implement and provide a system that automatically generates tutorials.

[0075] By creating a basic tutorial framework in advance and automatically filling it with tutorial content, we can ensure a certain level of tutorial quality while minimizing the need for manual intervention.

[0076] This disclosure can solve the above-mentioned problems.

[0077] In this disclosure, the virtual space information provision unit 101 guides a user, such as a beginner user, whose number of times entering the virtual space is below a threshold number, to a spot area suitable for that user. This enables the implementation of a so-called tutorial. In a typical virtual space, there are many things to do or do, making it difficult to implement a tutorial for beginners. However, by guiding users to an appropriate spot area according to guidance conditions such as user attributes, an appropriate tutorial can be implemented.

[0078] Furthermore, in this disclosure, the behavioral history information includes the user actions of multiple users and the locations where multiple users were. In other words, the attribute information storage unit 105 stores the user's behavioral history. As described above, the user's behavioral history includes movement history (spot area history), chat history (comment frequency, chat frequency), etc.

[0079] In this disclosure, for example, the tutorial generation unit 106 acquires at least one of the following as a guide location based on behavioral history information: a spot area where the number of users is greater than or equal to a predetermined threshold, a spot area where the number of comments is greater than or equal to a predetermined threshold, and a spot area where the stay time of multiple users is greater than or equal to a threshold.

[0080] This configuration allows for the acquisition of spot areas suitable for beginners and other users. In other words, spot areas with a large number of users or a large number of comments are popular spot areas, and depending on the user's characteristics, they are easy to recommend. Therefore, it is good to guide beginners and other users to these appropriate spot areas.

[0081] Furthermore, in this disclosure, the virtual space providing server 100 further includes a tutorial model storage unit 107 that functions as a guide location storage unit that stores spot areas for each user attribute. The tutorial model acquisition unit 102 refers to the tutorial model storage unit 107 to acquire spot areas corresponding to the user attributes of a particular user.

[0082] In this disclosure, a tutorial model is prepared in advance. When a user (for example, a beginner) logs in, the virtual space provider server 100 selects a tutorial from the pre-prepared tutorial model that is appropriate for the user's attributes. This configuration allows appropriate guidance to be provided to the user.

[0083] Furthermore, it is not necessary to prepare a tutorial model in advance. When a user (a beginner) logs in, the tutorial model can be generated, and the user can be guided to the designated spot area based on that model.

[0084] The tutorial model acquisition unit 102 functions as a derivation unit that derives a correspondence between the user attributes of a user to be guided and the user attributes stored in the tutorial model storage unit 107. Then, based on the derived correspondence, the tutorial model acquisition unit 102 acquires a spot area from the tutorial model storage unit 107.

[0085] For example, as shown in Figure 14, the corresponding user attributes collected from the behavioral history are derived from the attributes of the user being guided.

[0086] For example, the virtual space provision server 100 further includes a correspondence relationship storage unit (not shown) that stores the correspondence between the user attributes to be guided and the user attributes to be collected for behavioral history. The tutorial model acquisition unit 102 can acquire the user attributes to be collected that correspond to the user attributes of a particular user by referring to the correspondence relationship storage unit. Therefore, it can acquire a tutorial model that corresponds to these user attributes to be collected.

[0087] The user attributes of a single user obtained from surveys, etc., do not necessarily match the user attributes stored in a pre-prepared tutorial model. Therefore, the word matching process cannot retrieve spot areas using user attributes as keys. Thus, the correspondence derivation process is performed as described above. In this disclosure, as described above, the user attributes of a single user may be based on surveys and actions during initial setup after login. User attributes are determined based on this information. On the other hand, the tutorial model includes user attributes and corresponding spot areas generated based on the actions and attributes of other users.

[0088] Each user attribute consists of a name or description, which is generated, for example, by a natural language generation model. Therefore, user attribute matching cannot be done at the word level, and it is better to derive the correspondence relationship as described above. Alternatively, the names or descriptions of user attributes may be defined in advance, and user attributes may be defined within that scope.

[0089] In addition to the above, the tutorial model acquisition unit 102 may also function as an analysis unit that analyzes the meaning of each user attribute of a user and the user attributes stored in the tutorial model using natural language processing. As a result, the tutorial model acquisition unit 102 can derive a correspondence between the user attributes of the user to be guided and the user attributes stored in the tutorial model acquisition unit 102, based on the analysis results.

[0090] Furthermore, in this disclosure, when the update conditions are met, the tutorial generation unit 106 again collects behavioral history information and generates at least one guided location in the virtual space according to the guidance conditions (user attributes). In this disclosure, the update conditions are based, for example, on when a new event is held or when a new spot area is added. It may also be when a predetermined time has elapsed since a new event was held or a new spot area was added, because new history information is generated during that time. Alternatively, it may simply be when a predetermined time has elapsed.

[0091] This allows for updating the appropriate guidance location. In this disclosure, the guidance location is determined by the actions of other users, so it is best to perform the update process at an appropriate time.

[0092] In the virtual space provision server 100 of this disclosure, the virtual space information provision unit 101 displays directional information in the virtual space indicating the direction in which a user should move to reach a designated location. With this configuration, a user in the virtual space will be guided to the appropriate location by moving in that direction.

[0093] The apparatus and method of this disclosure have the following configurations.

[0094] [1] A device comprising: a history information storage unit that stores behavior history information of multiple users in a virtual space; a guidance location acquisition unit that acquires at least one guidance location in the virtual space according to guidance conditions from at least one guidance location generated based on the behavior history information; and a guidance unit that guides one user in the virtual space to the guidance location.

[0095] [2] The apparatus according to [1], wherein the guidance unit guides a user whose number of times entering the virtual space is less than or equal to a threshold number of times to the guidance location.

[0096] [3] The apparatus according to [1] or [2], wherein the behavior history information includes the user behavior of multiple users and the locations where the multiple users were, and the guidance conditions are user attributes based on at least one of the survey conducted by one user and the user behavior at the time of login.

[0097] [4] The device according to any one of [1] to [3], wherein the guidance location acquisition unit acquires as a guidance location at least one of the following based on the behavior history information: a location where the number of users is greater than or equal to a predetermined threshold, a location where the number of comments is greater than or equal to a predetermined threshold, and a location where the stay time of multiple users is greater than or equal to a threshold.

[0098] [5] The apparatus according to any one of [1] to [4], further comprising a guidance location storage unit that stores at least one guidance location generated based on the behavioral history information for each user attribute generated based on the behavioral history information, wherein the guidance location acquisition unit obtains a guidance location corresponding to the user attribute of the one user by referring to the guidance location storage unit.

[0099] [6] The apparatus according to [5], further comprising a derivation unit for deriving a correspondence between the user attributes of one user and the user attributes stored in the guidance location storage unit, wherein the guidance location acquisition unit acquires a guidance location from the guidance location storage unit based on the correspondence derived by the derivation unit.

[0100] [7] The device according to any one of [1] to [6], wherein the guidance location acquisition unit, when the update conditions are met, collects the behavior history information and acquires at least one guidance location in the virtual space in accordance with the guidance conditions.

[0101] [8] The device described in [7], wherein the update conditions are based on when a new event is held or when a new spot area is added.

[0102] [9] The device according to any one of [1] to [8], wherein the guidance unit displays directional information in the virtual space indicating the direction in which the user can move to reach the guidance location.

[0103]

[10] A method for acquiring behavioral history information from a history information storage unit that stores behavioral history information of multiple users in a virtual space, comprising: a guidance location identification step of identifying at least one guidance location in the virtual space according to guidance conditions based on the behavioral history information; and a guidance step of guiding one user in the virtual space to the guidance location.

[0104] The block diagram used in the description of the above embodiment shows functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired or wireless connections). A functional block may be realized by combining the one or more devices with software.

[0105] Functions include, but are not limited to, judgment, decision, determination, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. In all cases, as mentioned above, the method of implementation is not particularly limited.

[0106] For example, the virtual space provision server 100 in one embodiment of the present disclosure may function as a computer that processes the virtual space provision method of the present disclosure. Figure 15 is a diagram showing an example of the hardware configuration of the virtual space provision server 100 according to one embodiment of the present disclosure. The virtual space provision server 100 described above may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0107] In the following explanation, the term "device" can be replaced with "circuit," "device," "unit," etc. The hardware configuration of the virtual space provider server 100 may include one or more of the devices shown in the diagram, or it may be configured to omit some of the devices.

[0108] Each function in the virtual space provision server 100 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which allows the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of data reading and writing in the memory 1002 and storage 1003.

[0109] The processor 1001 controls the entire computer, for example, by running the operating system. The processor 1001 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control devices, arithmetic units, registers, etc. For example, the virtual space information provision unit 101, tutorial model acquisition unit 102, guidance unit 103, and tutorial generation unit 106 described above may be implemented by the processor 1001.

[0110] Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the virtual space information provision unit 101, the tutorial model acquisition unit 102, the guidance unit 103, and the tutorial generation unit 106 may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and other functional blocks may be implemented similarly. The above-described various processes have been explained as being executed by one processor 1001, but they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0111] The memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. The memory 1002 may also be called a register, cache, main memory, etc. The memory 1002 can store executable programs (program code), software modules, etc., for implementing the virtual space provision method according to one embodiment of the present disclosure.

[0112] The storage 1003 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital multipurpose disc, a Blu-ray® disc), a smart card, flash memory (e.g., a card, a stick, a key drive), a floppy® disk, a magnetic strip, etc. The storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0113] The communication device 1004 is hardware (transmitting / receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include high-frequency switches, duplexers, filters, frequency synthesizers, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the virtual space information provision unit 101 described above may be implemented by the communication device 1004. The communication device 1004 may be implemented with physically or logically separated transmitting and receiving units.

[0114] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0115] Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0116] Furthermore, the virtual space providing server 100 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0117] Information notification is not limited to the embodiments described herein and may be carried out by other means. For example, information notification may be carried out by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or combinations thereof. RRC signaling may also be called RRC messages, and may be, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc.

[0118] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be reordered, provided they do not contradict each other. For example, the methods described in this disclosure present various step elements using exemplary order and are not limited to the specific order presented.

[0119] Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be transmitted to other devices.

[0120] The determination may be made by a value represented by one bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value).

[0121] Each aspect / embodiment described in this disclosure may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0122] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Accordingly, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0123] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0124] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technologies (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technologies (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0125] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0126] In addition, terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of the channel and symbol may be a signal (signaling). Also, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, cell, frequency carrier, etc.

[0127] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a given value, or other corresponding information. For example, wireless resources may be indicated by an index.

[0128] The names used for the parameters described above are not restrictive in any way. Furthermore, the formulas and other expressions using these parameters may differ from those expressly disclosed in this disclosure. Various channels (e.g., PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name, and therefore, the various names assigned to these various channels and information elements are not restrictive in any way.

[0129] In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" may be used interchangeably.

[0130] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other appropriate term.

[0131] As used in this disclosure, the terms “determining” and “determining” may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, or inquiring (e.g., searching in a table, database, or other data structure), or ascertaining. “Determining” may also include, for example, receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, or accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0132] The terms “connected,” “coupled,” or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be reinterpreted as “access.” As used in this disclosure, two elements may be considered to be “connected” or “coupled” with each other using at least one of one or more wires, cables, and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain.

[0133] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0134] Any reference to elements using designations such as “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to first and second elements do not imply that only two elements may be employed, or that the first element must precede the second element in any way.

[0135] Where the terms “include,” “including,” and their variations are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to be exclusive OR.

[0136] In this disclosure, if articles are added by translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0137] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different."

[0138] 100...Virtual space provision server, 200...User terminal, 101...Virtual space information provision unit, 102...Tutorial model acquisition unit, 103...Guidance unit, 104...Virtual space information storage unit, 105...Attribute information storage unit, 106...Tutorial generation unit, 107...Tutorial model storage unit.

Claims

A history information storage unit that stores the behavioral history information of multiple users in a virtual space, A guidance location acquisition unit acquires at least one guidance location in the virtual space according to guidance conditions from at least one guidance location generated based on the aforementioned behavioral history information, A guidance unit that guides a user in the virtual space to the designated location, A device equipped with the following features. 　 The aforementioned guide section is The apparatus according to claim 1, which guides a user whose number of entries into the virtual space is less than or equal to a threshold number of times to the guided location. 　 The aforementioned behavioral history information includes the user actions of multiple users and the locations where those multiple users were located. The aforementioned guidance conditions are user attributes based on at least one of the user's survey and user behavior during login. The apparatus according to claim 1. 　 The aforementioned guidance location acquisition unit is: The apparatus according to claim 1, which, based on the aforementioned behavioral history information, acquires at least one of the following as a guide location: a location where the number of users is greater than or equal to a predetermined threshold, a location where the number of comments is greater than or equal to a predetermined threshold, and a location where the stay time of multiple users is greater than or equal to a threshold. 　 The system further includes a guidance location storage unit that stores at least one guidance location generated based on the behavioral history information for each user attribute generated based on the behavioral history information, The guidance location acquisition unit refers to the guidance location storage unit and acquires a guidance location corresponding to the user attributes of the first user. The apparatus according to claim 1. 　 The system further includes a derivation unit that derives a correspondence between the user attributes of the aforementioned user and the user attributes stored in the guidance location storage unit. The aforementioned guidance location acquisition unit is: Based on the correspondence derived by the derivation unit, the guide location in the guide location storage unit is obtained. The apparatus according to claim 5. 　 The aforementioned guidance location acquisition unit is: When the update conditions are met, the behavioral history information is collected and at least one guided location in the virtual space is obtained in accordance with the guidance conditions. The apparatus according to claim 1. 　 The aforementioned update conditions are based on when a new event is held or a new spot area is added. The apparatus according to claim 7. 　 The guidance unit displays directional information in the virtual space indicating the direction in which the user should move to reach the guidance location. The apparatus according to claim 1. 　 In a method for acquiring behavioral history information from a history information storage unit that stores behavioral history information of multiple users in a virtual space, A guidance location identification step, which identifies at least one guidance location in the virtual space according to the guidance conditions based on the aforementioned behavioral history information, A guidance step of guiding a user in the virtual space to the guidance location, A method for providing this.

Images