Information presentation device and method

Abstract

To propose an information presentation device and method that can selectively present necessary information (content) to a user in a metaverse space.SOLUTION: An information presentation device is configured to: calculate, based on history information on communications conducted between a user and other stakeholders, the relationship strength between the user and each stakeholder at locations or areas in the virtual space where the user has previously performed operations, for each stakeholder; calculate, based on the calculated relationship strength, the relevance between the content registered and associated with each location in the virtual space and the user for each piece of content; retrieve, based on the calculated relevance for each content, the top predetermined number or top predetermined percentage of content with the highest relevance; and selectively present the retrieved content to the user in the virtual space.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] The present invention relates to an information presentation device and method, and is suitable for application to, for example, an information extraction device that presents information to a user in a virtual space. [Background technology]

[0002] In recent years, metaverse spaces realized in various forms such as virtual reality (VR), augmented reality (AR), and mixed reality (MR) have begun to be used in games, entertainment, and corporate activities.

[0003] The term "metaverse space" here refers to spatial information that allows movement in two-dimensional or three-dimensional space. This also applies below. In such a metaverse space, users can communicate with other users and obtain content associated with each location within the metaverse space.

[0004] Patent Document 1 discloses a technology for identifying the features of content of interest to a user based on the features of main content that the user has visited while exploring a virtual space, and using the features as a selection criterion to select secondary content of a different type from the main content as related content. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-52665 Summary of the Invention [Problem to be solved by the invention]

[0006] Incidentally, in the metaverse space, which reflects information from the real world, there is a large amount of information linked to each location within the metaverse space by multiple users who use the same space, and the people involved change for each location within the metaverse space.

[0007] Therefore, if all the information associated with each location in the metaverse space were presented to a user, the user would be presented with unnecessary information that is not relevant to that user. In such cases, the user would have to sift through the presented information to extract the information they need, but this process is cumbersome and can lead to important information being overlooked.

[0008] The present invention has been made in consideration of the above points, and aims to propose an information presentation device and method that can selectively present information (content) required by a user on a metaverse space to the user. [Means for solving the problem]

[0009] In order to solve such problems, in the present invention, an information presentation device presents to a user who has logged in to a virtual space content that is registered and linked to each location in the virtual space, and is provided with: a relationship strength calculation unit that calculates, for each of the other parties, the relationship strength between the user and each of the other parties at a location or range where the user previously performed operations in the virtual space based on historical information of communication between the user and the other parties; a relevance calculation unit that calculates, for each of the pieces of content, the relevance between the user and the content that is registered and linked to each location in the virtual space based on the calculated relationship strength; and a content acquisition unit that acquires the top specified number or top specified percentage of the contents with the highest relevance based on the calculated relevance for each piece of content, and selectively presents the acquired content to the user in the virtual space.

[0010] In addition, the present invention provides an information presentation method executed by an information presentation device that presents to a user logged in to a virtual space content that is registered and associated with each location in the virtual space, the method including: a first step of calculating, for each of the other parties, the relationship strength between the user and each of the other parties at a location or range where the user previously performed operations in the virtual space based on historical information of communication between the user and the other parties; a second step of calculating, for each of the content, the relevance between the user and the content that is registered and associated with each location in the virtual space based on the calculated relationship strength; and a third step of acquiring the top specified number or top specified percentage of the content with the highest relevance based on the calculated relevance for each of the content, and selectively presenting the acquired content to the user in the virtual space. [Effects of the Invention]

[0011] According to the present invention, it is possible to selectively present to a user information (content) required by the user on the metaverse space. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a block diagram showing the overall configuration of an information presentation system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a conceptual diagram illustrating an information presentation function according to the present embodiment. [Figure 3] FIG. 2 is a conceptual diagram illustrating an information presentation function according to the present embodiment. [Figure 4] 10 is a diagram showing an example of the configuration of a login information table. [Figure 5] 10 is a diagram showing an example of the configuration of an operation history table; [Figure 6] 10 is a diagram showing an example of the configuration of an attribute information table. [Figure 7] 10 is a diagram showing an example of the configuration of a communication history table. [Figure 8]10 is a diagram showing an example of the configuration of a management information table; [Figure 9] FIG. 2 is a block diagram illustrating each program. [Figure 10] FIG. 10 is a conceptual diagram illustrating input information. [Figure 11] FIG. 10 is a diagram illustrating an example of a screen configuration of an information presentation screen. [Figure 12] 10 is a flowchart showing a processing procedure for operation point related person calculation processing. [Figure 13] 10 is a diagram showing an example of calculation of a relationship value between related parties. [Figure 14] FIG. 10 is a diagram illustrating an example of calculation of a related party network. [Figure 15] 10 is a diagram showing an example of calculation of relationship strength. [Figure 16] 10 is a flowchart showing the processing procedure of a point information assignment process. [Figure 17] 10 is a flowchart showing a processing procedure for data relevance calculation processing; [Figure 18] 10 is a diagram showing an example of calculating a weight for each section in the metaverse space. [Figure 19] 10 is a diagram illustrating data similarity γ. [Figure 20] 10 is a diagram illustrating a method for calculating data relevance; [Figure 21] 10 is a flowchart showing a processing procedure for weight / data similarity β calculation processing. [Figure 22] 10 is a diagram illustrating a weight / data similarity β calculation process. [Figure 23] 10 is a diagram showing an example of calculation of data similarity β; [Figure 24] 10 is a flowchart showing the processing procedure of a data acquisition process. [Figure 25] 10 is a diagram illustrating a data acquisition process. DETAILED DESCRIPTION OF THE INVENTION

[0013] An embodiment of the present invention will be described in detail below with reference to the drawings.

[0014] (1) Configuration of the information presentation system according to this embodiment 1, the information presentation system according to this embodiment is generally designated by reference numeral 1. This information presentation system 1 is configured to include an information presentation device 5 connected to an external server 3 and an external system 4 via a network 2.

[0015] The external server 3 and the external system 4 are server devices and systems that supply the information presentation device 5 with spatial information constituting the metaverse space 40 (Figures 2 and 3) described below, and information representing each railway facility installed along the railway tracks 41 (Figures 2 and 3) described below within the metaverse space 40 and its distance in kilometers from its reference position.

[0016] The information presentation device 5 is configured from a general-purpose computer device equipped with a CPU (Central Processing Unit) 10, a main storage device 11, an auxiliary storage device 12, an input / output unit 13, and an interface 14.

[0017] The CPU 10 is a processor that controls the overall operation of the information presentation device 5. The main memory device 11 is configured, for example, from a non-volatile semiconductor memory, and is used to temporarily store various programs and data required for processing. An operation point related person calculation unit 20, a data relevance calculation unit 21, and a data acquisition unit 22, which are part of a related data extraction program 19 described below, are also read from the auxiliary memory device 12 described below to the main memory device 11 and stored therein when the information presentation device 5 is started up or when needed.

[0018] The auxiliary storage device 12 is configured with a large-capacity nonvolatile storage device such as a hard disk drive or SSD (Sloid State Drive), and stores various programs and various data that require long-term storage. A user information database 23, a related party information database 24, and a registration information database 25, which will be described later, are also stored and maintained in this auxiliary storage device 12.

[0019] The input / output unit 13 is composed of an input unit 13A and an output unit 13B. The input unit 13A is composed of an interface device that receives input from an input device 6 such as a keyboard or a mouse. The output unit 13B is composed of a liquid crystal display or an organic EL (Electro-Luminescence) display, or a graphic board that displays information required for a display device 7 such as a VR device such as VR goggles.

[0020] The interface 14 is configured by, for example, a NIC (Network Interface Card) and performs protocol control when communicating with an external server 3 or an external system 4 via the network 2 .

[0021] (2) Information Presentation Function According to the Present Embodiment Next, an information presentation function installed in the information presentation device 5 of this embodiment will be described. This information presentation function is a function that selectively extracts only necessary content related to a target user (hereinafter referred to as a target login user) who is currently logged in to the metaverse space 40 as shown in Fig. 3 from a plurality of pieces of information (hereinafter referred to as content) that have been linked to each point by the relevant parties along the railway track 41 in the metaverse space 40 as shown in Fig. 2, and presents the extracted content to the target login user.

[0022] In reality, those in charge of maintenance work for railway stations and tracks, especially construction designers, construction supervisors, and maintenance supervisors, are usually in charge of multiple tracks and long sections. In addition, in such maintenance work, there are multiple stakeholders for each area, and the stakeholders differ for each project.

[0023] Therefore, if these parties present the target login user with a large amount of content linked to each point along the railway tracks 41 in the metaverse space 40, it will be cumbersome for the target login user to extract the content they need at that time from the presented content, and there is a risk that important information will be overlooked.

[0024] Based on the information presentation function of this embodiment, the information presentation device 5 extracts and presents information that is deemed necessary for the target login user at that time, using location information of the location where the target login user logged in (hereinafter referred to as the login location), the history of communication with other related parties related to the login location, and attribute information of the related parties (registrants) who registered registration information having location information linked to each location in the metaverse space and the users (related parties) of that registration information. Note that, hereinafter, the terms "location" and "location" are used interchangeably.

[0025] As a means for realizing the information presentation function of this embodiment, the auxiliary memory device 12 of the information presentation device 5 stores a user information database 23, a related party information database 24, and a registration information database 25, and the main memory device 11 stores a related data extraction program 19.

[0026] Of these, the user information database 23 includes a login information table 30 and an operation history table 31.

[0027] The login information table 30 is a table for managing login information entered by a user when logging in to the metaverse space 40 (FIGS. 2 and 3). As shown in FIG. 4, this login information table 30 is configured with a related party ID column 30A and a login location column 30B. In the login information table 30, one row corresponds to the login information of one target login user who is currently logged in to the metaverse space 40.

[0028] The related party ID field 30A stores an identifier (related party ID) unique to the corresponding target login user, and the login location field 30B stores the location (in kilometers from the reference location) along the railway track 41 (FIGS. 2 and 3) where the target login user logged in at that time. Therefore, the example in FIG. 4 shows that the login information registered shows that a user with a related party ID of "Electricity0001" logged in at a location "11.8 km" from the reference location.

[0029] Moreover, the operation history table 31 is a table used to store and retain, as an operation history, the operation details performed by a user on the metaverse space 40 after the user logs in to the metaverse space 40 as described above. The operation history table 31 is created for each user by collecting necessary data by the relational data extraction program 19.

[0030] 5, this operation history table 31 includes a history number column 31A, an operation time column 31B, an operation location column 31C, an operation target column 31D, and an operation content column 31E. In Fig. 5, one row corresponds to information on the operation history of one operation of the corresponding target login user (hereinafter, this will be referred to as operation history information).

[0031] The history number column 31A stores a number (history number) unique to the corresponding operation history information that is assigned to that operation history information on the operation history table 31. In Fig. 5, an example is shown in which consecutive numbers starting from 1 are applied as the history numbers.

[0032] Furthermore, the operation content field 31E stores specific contents (operation contents) of the operation performed by the corresponding target login user corresponding to the operation history information. Such operation contents include "move" for moving within the metaverse space 40, "view" for viewing information linked to any point on the railroad track 41 within the metaverse space 40, and "addition registration" for linking and registering a file to any point on the metaverse space 40.

[0033] Furthermore, the operation time field 31B stores the period (seconds) during which the target login user performed the corresponding operation in the metaverse space 40, and the operation position field 31C stores the position (in kilometers from the reference position) at which the operation was performed in the metaverse space 40. If the operation content of the operation is "movement," the start and end positions of the movement are stored in the operation position field 31C.

[0034] Furthermore, the operation target field 31D stores the target of the operation. In this case, if the operation content is "view", the viewed content corresponds to the operation target, and if the operation content is "additional registration", the additionally registered content corresponds to the operation target. If the operation content is "movement", there is no operation target, so the operation target field 31D is blank.

[0035] Therefore, the operation history information assigned the history number "1" indicates that the corresponding target login user "moved" the section from "11.8 km" to "12.0 km" in the metaverse space 40 over "10 (seconds)."

[0036] On the other hand, as shown in FIG. 1, the related party information database 24 is configured to include an attribute information table 32 and a communication history table 33.

[0037] The attribute information table 32 is a table for managing the attribute information of each related person registered in the information presentation device 5, and is configured with a related person ID column 32A, a name column 32B, and an affiliated department column 32C, as shown in Fig. 6. In the attribute information table 32, one row corresponds to the attribute information of one related person registered in the information presentation device 5.

[0038] The related party ID column 32A stores the identifier (related party ID) of the corresponding related party. The name column 32B stores the name of the related party, and the department column 32C stores the department to which the related party belongs. Therefore, in the example of Figure 6, the name of the related party assigned the related party ID "Electricity0001" is "Ito", and the department to which the related party belongs is shown to be "Electricity Section".

[0039] The communication history table 33 is a table for managing information collected by the relationship data extraction program 19 regarding communications that a related party has with other related parties in the metaverse space 40, and communications that a related party has with other related parties in the real world via email or chat.

[0040] 7, this communication history table 33 is configured to include a number column 33A, a date and time column 33B, a device column 33C, a data acquisition form column 33D, a location characteristics column 33E, a location information column 33F, a respondent column 33G, and a communication time column 33H. In the communication history table 33, one row corresponds to the history information of one communication that the corresponding party had with another party in the past (hereinafter, this will be referred to as communication history information).

[0041] The number column 33A is assigned an identification number (communication history number) unique to the communication history information, which is assigned to the corresponding communication history information on the communication history table 33. Fig. 7 shows an example in which consecutive numbers starting from 1 are applied as the communication history numbers.

[0042] The date and time column 33B stores the date and time when the corresponding communication took place, and the device column 33C stores the device that performed the communication. Such devices include the "metaverse" when the communication took place in the metaverse space 40, the "PC" when the communication took place via online conference, chat, or email using a personal computer, and the "smartphone" when the communication took place via chat, email, or the like using a smartphone.

[0043] Furthermore, the data acquisition form field 33D stores the form of communication in which the information presentation device 5 acquired the corresponding communication data, as will be described later. Such forms include "conversations" such as chats conducted in the metaverse space 40, "online meetings" conducted using a personal computer, and "chat" and "emails" conducted using a personal computer or smartphone.

[0044] The location feature column 33E stores information (hereinafter referred to as location feature) for identifying locations or objects in the metaverse space 40, such as railway facilities, construction work, or minutes that were the subject of the communication. These location features are extracted by the operation location related person calculation unit 20 (described later) from chat or email text, as will be described later, or identified by the operation location related person calculation unit 20 from the content of the conversation recognized by voice recognition processing of the voice conversation between the related parties.

[0045] Furthermore, the location information column 33F stores information indicating the specific location (such as the distance in kilometers from a reference position or the range) of the railway facility, etc. that was the subject of the corresponding communication. For example, if such communication is carried out through a conversation while moving within the metaverse space 40, the range of movement at that time is stored as location information in the location information column 33F. In this case, the location characteristics column 33E stores the information "(spatial movement)". Furthermore, if the content of the communication is about railway facilities in general and is not targeted at a specific railway facility, etc., the location information column 33F stores the information "All," which indicates railway facilities in general. In this case, no information is stored in the location characteristics column 33E.

[0046] Furthermore, the respondent column 33G stores the names and related party IDs of all parties who performed the corresponding communication, and the communication time column 33H stores the time during which the communication was performed. For example, if the communication was performed by conversation, the time of the conversation is stored in the communication time column 33H, and if the communication was performed by an online conference, the time required for the online conference is stored in the communication time column 33H.

[0047] Therefore, in the example of Figure 7, it is shown that on "2022 / 12 / 3", four people, "Ito (Electric Power 0001)", "Nakamura (Track Maintenance 0002)", "Watanabe (Track Maintenance 0001)" and "Yamamoto (Civil Engineering 0001)", "traveled (space travel)" to a point "11.8-12.4 km" within the "Metaverse" space 40 and communicated through "conversation" for only "10 minutes".

[0048] As shown in FIG. 1, the registration information database 25 includes a management information table 34, a content data table 35, and a location-related information table 36.

[0049] Management information table 34 is a table for managing each piece of content registered by related parties in association with each location on the metaverse space 40. As shown in Fig. 8, management information table 34 is configured with a data number column 34A, a registration date column 34B, a location column 34C, a data name column 34D, a data summary column 34E, a registrant column 34F, and a most frequent accessor column 34G. In management information table 34, one row corresponds to data of one piece of content (hereinafter referred to as content data) registered in association with a location on the metaverse space 40.

[0050] The data number column 34A stores a number (data number) unique to the content data that is assigned to the corresponding content data, and the registration date column 34B stores the date on which the content data was registered and linked to a location on the metaverse space 40.

[0051] Furthermore, the location column 34C stores the location or range (kilometers or range of kilometers from a reference location) in the metaverse space 40 to which the content data is linked. The location or range stored in this location column 34C is the location or range to which the relevant person linked the content data in the metaverse space 40, which is automatically acquired and stored by the information presentation device 5, but GPS (Global Positioning System) information of that location or range acquired by the relevant person in real space may also be stored in the location column 34C.

[0052] Furthermore, the data name column 34D stores the data name of the corresponding content data (the file name if the content is a file), and the data summary column 34E stores a summary of the content data. Note that in the example of Figure 8, only a short data summary is stored in the data summary column 34E of each record, but in reality, a data summary with a much longer sentence is stored.

[0053] Furthermore, the registrant column 34F stores the name and related party ID of the person involved (hereinafter also referred to as the registrant) who registered the content of the content data by linking it to that location on the metaverse space 40, and the most frequent accessor column 34G stores the name and related party ID of the person involved (hereinafter referred to as the most frequent accessor) who accessed the content data (content) the most.

[0054] Therefore, in the example of Figure 8, the content data assigned the data number "data001" is data that was registered by "Yamamoto (Civil Engineering 0001)" on "2022 / 12 / 3" linked to the location (range) "[12.0km, 12.1km]" on the metaverse space 40, and the data name is "Processed Photo.jpg", the data summary is "Slope Reinforcement", and the most frequent accessor is "Ito (Electric Power 0001)".

[0055] The content data table 35 (FIG. 1) is a table in which the content data of all content linked to any point or range in the metaverse space 40 is stored in association with the content name. As shown in the data name column 34D in FIG. 8, content data includes text data, text file data, and photo file data.

[0056] The location-related information table 36 (Figure 1) is a table that stores spatial information of the metaverse space 40 provided by the external server 3 or external system 4 as described above, and information representing each railway facility installed along the railway tracks 41 (Figures 2 and 3) within the metaverse space 40 and its distance in kilometers from its reference position.

[0057] On the other hand, the operation point related person calculation unit 20 (Figure 1) is a program that has the function of calculating the degree of strength of the relationship (hereinafter referred to as relationship strength) with other related parties at the point (hereinafter referred to as operation point) where the target login user performed an operation on the metaverse space 40, based on the login information 30 of the target login user, history information of the target login user's communication with other related parties stored in the operation history table 31, and information on the target login user's operation history on the metaverse space 40 to date stored in the communication history table 33, as shown in Figure 9.

[0058] In practice, information about the currently targeted project (e.g., construction number, construction name, construction period, construction overview, etc.) input by the target login user when logging in to the metaverse space 40, as shown in Fig. 10, is provided to the operation site related person calculation unit 20 as input information INF via the input unit 13A of the input / output unit 13 (Fig. 1). Note that the input information INF may be linked to other information such as the construction number and registered by the related person in advance, rather than at the time of login by the target login user.

[0059] As will be described later, this input information INF is an extraction condition for extracting content to be presented to the target logged-in user in the metaverse space 40. Note that the input information INF is not limited to text, and may be a numerical value indicating the distance in kilometers from a reference position or a data display period.

[0060] Then, the operation point related party calculation unit 20 calculates the relationship strength R between the target login user and other related parties for each of these related parties based on the input information INF, communication history information regarding the communication history between the target login user and other related parties, and the attributes of the other related parties who have communicated with the target login user, and outputs the calculation results to the data relevance calculation unit 21.

[0061] The operation location related person calculation unit 20 also has a function of generating AI (Artificial Intelligence), and generates answers to questions from the target login user by referring to the content data table 35 (Figure 1) and the location related information table 36 (Figure 1), and presents the generated answers to the target login user.

[0062] In addition, the data relevance calculation unit 21 is a program that has the function of calculating, for each piece of content registered and linked to each point on the metaverse space 40, the degree of strength of association (hereinafter referred to as data relevance P) between that content and the target login user (hereinafter referred to as target login user) that is the target at that time.

[0063] In practice, the data relevance calculation unit 21 calculates the data relevance P between the target login user and each piece of content based on the relationship strength R between the target login user and other related parties calculated by the operation location related party calculation unit 20, the management information of each piece of content linked to each point in the metaverse space 40 stored in the management information table 34 (FIG. 8), and the input information INF entered by the target login user when logging in to the metaverse space 40. Then, the data relevance calculation unit 21 outputs the calculated data relevance P between the target login user and each piece of content to the data acquisition unit 22.

[0064] Furthermore, the data acquisition unit 22 is a program that has the function of selecting one or more pieces of content to be displayed on the metaverse space 40 at that time based on the data relevance P between the target login user and each piece of content provided by the data relevance calculation unit 21, and acquiring the content data of the selected content from the content data table 35 (Figure 1).

[0065] The data acquisition unit 22 outputs the acquired content data and the spatial information of the metaverse space 40 to the display device 7 (FIG. 1) via the output unit 13B. As a result, the spatial information in which each piece of content selected by the data acquisition unit 22 is displayed at a corresponding position in the metaverse space 40 based on the spatial information is displayed on the display device 7.

[0066] (3) Information presentation screen 11 shows an information presentation screen 50 that is displayed on the display device 7 based on the spatial information and content data output from the data acquisition unit 22 via the output unit. As shown in this Fig. 11, the information presentation screen 50 is configured to include an information display field 51 and a metaverse space display field 52.

[0067] The information display field 51 is provided with a user information display area 53, a purpose of use setting area 54, a display content specification area 55, and a Q&A area 56. The user information area 53 displays the name and attributes (affiliation) of the target login user who has logged in to the metaverse space 40 and is detected based on the login information of the target login user.

[0068] The purpose of use setting area 54 is an area where the target login user sets the purpose of using the metaverse space 40 this time. In practice, the purpose of use setting area 54 is provided with a purpose of use display field 60 and a pull-down menu button 61. The target login user can then select a desired purpose of use from various purposes of use listed in a pull-down menu (not shown) that is displayed by clicking the pull-down menu button 61, and set that purpose of use as the current purpose of use. In this case, a mark 62 representing that purpose of use is displayed in the purpose of use field 60.

[0069] The intended use setting area 54 also has a construction number display field 63 and a pull-down menu 64. The target logged-in user can then click the construction number input setting checkbox 65 to display a check mark in the construction number input setting checkbox 65, and then click the pull-down menu button 64 to display a pull-down menu listing all construction numbers currently registered.

[0070] Furthermore, the target login user can select a corresponding construction number from among the construction numbers listed in the pull-down menu, thereby displaying that construction number in the construction number display field 63. Note that the "corresponding construction number" here refers to the construction number of the construction identified from the input information INF (FIG. 10) entered by the target login user when logging in to the metaverse space 40.

[0071] Furthermore, in the purpose of use setting area 54, a character string 66 indicating the content of the input information INF entered by the target login user when logging in to the metaverse space 40 is displayed below the construction number display field 63.

[0072] The display content specification area 55 is an area for specifying the type of content to be displayed in the metaverse space 40. In practice, in this embodiment, the types of content to be displayed in the metaverse space 40 can be one or all of the following: content related to the purpose of use specified by the target login user in the purpose of use display area 54 (the purpose of use corresponding to the mark displayed in the purpose of use display field of the purpose of use display area) (hereinafter, this will be referred to as purpose-related content); content related to the construction corresponding to the input information INF entered by the target login user when logging in to the metaverse space 40 (the construction whose construction number is displayed in the construction number display field of the purpose of use display area) (hereinafter, this will be referred to as specified construction-related content); and content registered by a related party with an attribute related to the attribute of the target login user (hereinafter, this will be referred to as attribute-related content). Furthermore, with regard to the attribute-related content, it is possible to specify either content registered by another related party belonging to the same department as the target login user (hereinafter, this will be referred to as department-registered content) or content registered by a related party of the construction identified in the purpose of use display area (hereinafter, this will be referred to as related party-registered content).

[0073] In practice, check boxes 67 are provided in the display content designation area 55 corresponding to each of the intended use-related content, the designated construction-related content, and the attribute-related content. The target logged-in user can then click on the check box 67 corresponding to the desired type of content from among these check boxes 67 to display a check mark in the check box 67, thereby designating that type of content as the type of content to be displayed in the metaverse space 40.

[0074] For attribute-related content, radio buttons 68 are displayed corresponding to the department-registered content and the related party-registered content, respectively. The target logged-in user can then click on the radio button 68 corresponding to the desired type of content (department-registered content or related party-registered content) from among these radio buttons 68 to transition the display state of the radio button 68 to the display state at the time of selection, thereby specifying that type of content as the type of content to be displayed in the metaverse space 40.

[0075] Then, on the information presentation screen 50, after specifying the type of content to be displayed on the metaverse space 40 as described above, by clicking the related information display button 69, the icons 71 (Figure 11) described below attached to each piece of content of the specified type can be displayed on the metaverse space 40.

[0076] Furthermore, the Q&A area 56 is provided with a text box 70. The target login user can input a question in text form into this text box 70 by operating the input device 7 (FIG. 1). A speech bubble containing the answer to this question is then displayed in the metaverse space display field 52. Note that this answer is generated by the operation location related person calculation unit 20 using a generation AI function based on the data summary of content already registered at the location on the metaverse space 40 where the target login user has logged in and the surrounding locations.

[0077] On the other hand, the metaverse space display field 52 displays space information around the login position specified by the target login user when logging in to the metaverse space 40.

[0078] In addition, the metaverse space display column 52 displays an icon 71 corresponding to the type of content specified by the target logged-in user in the display content specification area 55 of the information display column 51, among the content registered by any of the parties in association with a location or area on the metaverse space 40, and a line 72 connecting the icon 71 to the position or area on the metaverse space 40 to which the content is associated.

[0079] In this case, the icon 71 displays the data name of the content data of the corresponding content, the name of the registrant of that content, and the department to which they belong, so that the target login user can easily grasp the outline of the content corresponding to each icon 71 based on this displayed information.

[0080] Thus, the target login user can refer to the information displayed on each icon 71 and select the desired icon 71 by double-clicking or the like, thereby superimposing the content (photos, text, etc.) corresponding to that icon 71 on the information presentation screen 50.

[0081] Furthermore, in the metaverse space display field 52, character strings 73 indicating the distance in kilometers from the reference position are displayed at regular intervals, and a data display period designation meter 74 is displayed. The target logged-in user can then specify the period from when to when the icons 71 of the registered content are to be displayed in the metaverse space display field 52 by operating the data display period designation meter 74 in a predetermined manner.

[0082] (3) Various processes related to the information presentation function Next, the specific processing contents of various processes executed in the information presentation device in relation to the information presentation function according to the present embodiment as described above will be explained. Note that, although the processing entities of various processes will be explained below as programs ("... units"), it goes without saying that in reality, the CPU of the information presentation device executes the processes based on the programs.

[0083] (3-1) Operation point related person calculation processing Figure 12 shows the flow of a process (hereinafter referred to as the operation point related person calculation process) for calculating the relationship strength R between the target login user at the operation point and other related parties based on the location information of the login location of the target login user who logged in to the metaverse space 40 and the communication history with other related parties.

[0084] This operation point related person calculation process is executed by the operation point related person calculation unit 20 when a user logs in to the metaverse space 40 or when the user (target login user) clicks the related information display button 69 after specifying the type of content to be displayed at that time in the display content specification area 55 of the information display column 51 of the information presentation screen 50 described above with reference to Figure 11.

[0085] In practice, when a user logs in to the metaverse space 40 as described above, the operation point related person calculation unit 20 starts the operation point related person calculation process shown in Figure 12, and first calculates the minimum value x1 and maximum value x2 of the range that the user (target login user) has moved so far in the metaverse space 40 (S1).

[0086] In practice, the operation location related person calculation unit 20 calculates these minimum value x1 and maximum value x2 based on the login information of the target login user stored in the login information table 30 (Figure 4) and the operation history information of various operations performed by the target login user so far stored in the operation history table 31 (Figure 5).

[0087] For example, if the login location of the target login user is "11.8 km" as shown in Figure 4 and the operation history of the target login user on the metaverse space 40 is as shown in Figure 5, the range of the operation locations of the target login user is "11.8 to 12.2 km", which includes the login location of the target login user, "11.8 km", so the minimum value x1 is calculated to be "11.8 km" and the maximum value x2 is calculated to be "12.2 km".

[0088] In this case, even if the operation history includes an operation location of "10.0 to 11.2 km," this range does not include the target login user's login location of "11.8 km," and therefore is not used to calculate the minimum value x1 and maximum value x2.

[0089] Note that the minimum value x1 and maximum value x2 do not have to be the minimum and maximum values ​​of the distance in kilometers actually traveled by the target login user in the metaverse space 40, and the minimum value x1 may be the minimum distance in kilometers actually traveled by the target login user in the metaverse space 40 minus a certain margin, and the maximum value x2 may be the maximum distance in kilometers of that range plus a certain margin.

[0090] Next, the operation point related person calculation unit 20 determines whether or not there is a record to which point information should be added in the communication history table 33 (FIG. 7) (S2). In the example of FIG. 7, such a record corresponds to a record in which a range is stored in parentheses in the point information column 33E.

[0091] For example, in the example of Figure 7, the record assigned the communication history number "3" contains only the construction name "A Station Electric Light Power Switching Construction" and the construction number "23-0318" of the construction subject of the online conference obtained from the conversation log during the online conference, and therefore the location information field 33F was initially blank because location information could not be obtained at the time of registration of the record. Note that in Figure 7, a number in parentheses is stored in the location information field 33F, but this number is the number that will be stored later in the location information assignment process described later in Figure 16. The same applies hereinafter.

[0092] Furthermore, in the example of Figure 7, for the record assigned the communication history number "10," the only information about the subject of the online conference obtained by voice recognition processing during the online conference was the "minutes" of "Station B," and since location information could not be obtained at the time the record was registered, the location information column 33F was initially blank.

[0093] Therefore, if the operation point related person calculation unit 20 obtains a negative result in step S2, it proceeds to step S4, but if the operation point related person calculation unit 20 obtains a positive result in step S2, it executes a point information assignment process (S3) in which it extracts the distance and range from the reference point of the railway facility or the like that was the subject of communication for such a record from the point related information table 36 (FIG. 1) and stores the extracted distance and range in the point information column 33F of that record. The specific contents of this point information assignment process will be described later.

[0094] Next, the operation location related person calculation unit 20 identifies all records in the communication history table 33 in which the mileage stored in the location information column 33F of the communication history table 33 or part or all of the range stored in the location information column 33F includes the range greater than or equal to the minimum value x1 and less than or equal to the maximum value x2 calculated in step S1 (S4).

[0095] For example, if the minimum value x1 is calculated as "11.8 km" and the maximum value x2 is calculated as "12.2 km" in step S1 as described above, in the example of Figure 7, the operation point related person calculation unit 20 identifies communication history records with communication history numbers "1" to "9" other than "10" and "11".

[0096] Then, the operation location related person calculation unit 20 calculates the relationship value between each related person stored in the corresponding person column 33G of each identified record (S5). In this embodiment, the relationship value between the related people is calculated as the total value of the communication time between these related people.

[0097] For example, in the above example, the correspondent column 33G of each record of the communication history with communication history numbers "1" to "9" stores the name of one of seven people: "Ito (Electric Power 0001)," "Nakamura (Track Maintenance 0002)," "Watanabe (Track Maintenance 0001)," "Yamamoto (Civil Engineering 0001)," "Sato (Electric Power 0002)," "Suzuki (Electric Power 0003)," and "Tanaka (Track Maintenance 0003)."

[0098] Therefore, the operation point related party calculation unit 20 calculates the total communication time as the relationship value between the corresponding related parties for each combination of these related parties, based on the communication time stored in the communication time column 33H of each record of the communication history with communication history numbers "1" to "9", as shown in Figure 13.

[0099] Specifically, in the example of Figure 7, for example, for the combination of "Ito (Electric Power 0001)" and "Nakamura (Track Maintenance 0002)", only the communication history with the communication history number "1" applies, so the value "10" stored in the communication time column 33H of the record with the communication history number "1" becomes the relationship value between these two people.

[0100] Furthermore, for the combination of "Ito (Electric Power 0001)" and "Watanabe (Track Maintenance 0001)," the communication histories with communication history numbers "1" and "2" correspond, so the relationship value between these two people is "40," which is the sum of "10" stored in communication time column 33H of the record with communication history number "1" and "30" stored in communication time column 33H of the record with communication history number "2."

[0101] The number of communications between users may be used as the relationship value between the parties. The calculation method of the relationship value may be changed, for example, by weighting the relationship value according to the communication format, such as "PC," "metaverse," or "smartphone" shown in Fig. 7. Furthermore, the relationship value may be weighted using attribute information of the parties, for example, by doubling the relationship value if one of the parties belongs to the "Electricity Department."

[0102] Next, the operation location related person calculation unit 20 determines whether or not to calculate the relationship strength R between each related person (S6). Whether or not to calculate the relationship strength R between each related person is set in advance, so in step S6, the operation location related person calculation unit 20 determines whether or not such a setting has been made.

[0103] If the operation point related person calculation unit 20 obtains a positive result in this judgment, it sets the relationship value between the target login user and other related persons, among the relationship values ​​between each related person calculated in step S5, to the relationship strength R between the target login user and the other related persons (S7), and then terminates this operation point related person calculation process.

[0104] On the other hand, if the operation site related person calculation unit 20 obtains a negative result in the determination in step S6, it calculates the relationship strength R between each related person based on the relationship value between each related person calculated in step S5 (S8). Various methods can be applied to calculate such relationship strength R. Here, a method using a related person network will be described.

[0105] First, as shown in Fig. 14, a party network 80 is created in which each party, including the target login user, is treated as a node and the relationship value between parties is the length of the link connecting those parties. Note that the party network 80 created at this time may be unidirectional rather than bidirectional.

[0106] Next, in the related party network 80, one related party is selected from the above-mentioned related parties, and as shown in Figure 15, the relationship strength R is set to "1" for other related parties who exist inside a circle 81 of a predetermined size centered on the selected related party, and the relationship strength R is set to "0.3" for related parties who exist outside the circle 81. However, the size of the circle 81 may be dynamically set so that a predetermined proportion (for example, half) of the related parties are divided into those inside and those outside the circle 81.

[0107] 14 shows an example in which "Ito (Electric Power Division)" is selected. The above process is executed for all parties whose relationship values ​​with other parties have been calculated in step S5. This allows the relationship strength between each party to be calculated.

[0108] Then, the operation location related person calculation unit 20 outputs the relationship strength R between the target login user and other related persons set in step S7, or the relationship strength R between each related person calculated in step S8, to the data relevance calculation unit 21 (S9), and then terminates this operation location related person calculation process.

[0109] 16 shows specific processing details of the location information assignment process executed by the operation location related person calculation unit 20 in step S3 of the above-mentioned operation location related person calculation process. When the operation location related person calculation unit 20 proceeds to step S3 of the operation location related person calculation process, it starts the location information assignment process shown in FIG.

[0110] The operation location related person calculation unit 20 first selects one record (hereinafter referred to as the target record) from the records in the communication history table 33 (Figure 7) that does not have location information stored in the location information column 33F and that has not been processed in steps S11 to S13 (S10).

[0111] Next, the operation location related person calculation unit 20 extracts features that can identify the location from the information stored in the location feature column 33E of the target record selected in step S10 (hereinafter referred to as the selected record) (S11).

[0112] Here, "features that can identify a location" refers to feature words that are linked to some kind of location information, such as a location name, a construction name that indicates the construction location, etc. These feature words can be extracted by, for example, preparing a table in advance that stores the name and location of each railway facility, the name and location of each construction project, etc., and comparing the information stored in the location feature column 33E with the information stored in this table, or by extracting feature words from the frequency of appearance of words in a sentence, but this is not limited to these.

[0113] 7, for example, when the record numbered "3" in the communication history table 33 is the selected record, the operation location related person calculation unit 20 can extract "A station electric light power switching work" and "No. 23-0318" from the information "A station electric light power switching work (No. 23-0318)" stored in the location characteristics column 33E as "characteristics that can identify a location." Furthermore, when the record numbered "10" in the communication history table 33 is the selected record, the operation location related person calculation unit 20 can extract information "B station" as "characteristics that can identify a location."

[0114] Next, the operation location related person calculation unit 20 acquires location information corresponding to the "location-identifying characteristics" extracted in step S11 from the location-related information table 36 (Figure 1) (S12).

[0115] For example, if the "feature by which a location can be identified" extracted in step S11 is "Station A," the operation location related person calculation unit 20 acquires the location information of "Station A" from the location related information table 36. Also, if the "feature by which a location can be identified" extracted in step S11 is "Station B," the operation location related person calculation unit 20 acquires the location information of "Station B" from the location related information table 36.

[0116] Then, the operation location related person calculation unit 20 stores the location information acquired in step S12 in the location information column 33F of the selected record (S13), and then determines whether or not the processing of steps S11 to S13 has been completed for all target records (S14).

[0117] If the operation location related person calculation unit 20 obtains a negative result in this judgment, it returns to step S10, and thereafter repeats the processing of steps S10 to S14 while sequentially switching the target record selected in step S10 to other target records that have not been processed in steps S11 and onwards.

[0118] When the operation point related person calculation unit 20 obtains a positive result in step S14 by eventually storing the point information in the point information column 33F of all target records, it ends this point information assignment process and returns to the operation point related person calculation process.

[0119] (3-2) Data relevance calculation process 17 shows the flow of a series of processes (data relevance calculation process) executed by the data relevance calculation unit 21 (FIG. 1). The data relevance calculation unit 21 calculates the data relevance P between the target login user and each piece of content associated with each point in the metaverse space 40, according to the processing procedure shown in FIG.

[0120] In practice, when the data relevance calculation unit 21 is given the relationship strength R between the target login user and each related party, or the relationship strength R between each related party, from the operation location related party calculation unit 20, it starts the data relevance calculation process shown in Figure 17, and first determines whether or not to use the input information INF (Figure 10) entered by the target login user when logging in to the metaverse space 40 when calculating the data relevance P (S20).

[0121] This determination is made by determining whether or not a check mark is set in at least one of the check boxes corresponding to the above-mentioned purpose-of-use related content and designated construction related content as the content to be displayed (at least one of the purpose-of-use related content and designated construction related content is specified) in the display content designation area 55 of the information display field 51 on the information presentation screen 50 described above with reference to Fig. 11. If the data relevance calculation unit 21 obtains a negative result in this determination, it proceeds to step S22.

[0122] In response to this, if the determination in step S20 is affirmative, the data relevance calculation unit 21 acquires the purpose for which the target login user logged in to the metaverse space 40 this time and the details of the related construction work from the input information INF, and calculates the data similarity α between the acquired information and the metadata of each content (S21). Note that the "content metadata" here refers to the data summary of the content stored in the data summary column 34E of the record corresponding to that content among the records in (Fig. 8).

[0123] Therefore, for each piece of content data, the data relevance calculation unit 21 calculates the similarity between the text representing the data summary of the content stored in the data summary column 34E of the record corresponding to that content data in the management information table 34 and the text of the input information INF as data similarity α using an existing calculation method such as cosine similarity.

[0124] Next, the data relevance calculation unit 21 determines whether or not to use the operation history information registered in the operation history table 31 (FIG. 5) when calculating the data relevance P (S22).

[0125] In the present embodiment, it is set in advance whether or not to use operation history information in calculating the data relevance P. Therefore, this determination is made in step S22 by determining whether or not a setting has been made to use operation history information in calculating the data relevance P.

[0126] If the data relevance calculation unit 21 obtains a negative result in this determination, it proceeds to step S24. If the data relevance calculation unit 21 obtains a positive result in the determination in step S22, it executes a weight / data similarity β calculation process to calculate at least one of the weight W and the data similarity β (S23).

[0127] 18, the "weight W" here represents the importance of each section of a certain distance (e.g., 100 m) calculated for the range traveled by the target login user in the metaverse space 40. As will be described later, the weight W for each section is calculated based on the cumulative time that the target login user has stayed in that section.

[0128] Furthermore, the "data similarity β" here represents the degree of similarity between the content that has been selected (viewed) by the target login user or registered by the target login user among the content associated with each point in the metaverse space 40 and registered, and the content associated with each point in the metaverse space 40 and registered. The data similarity β is calculated for each piece of content associated with each point in the metaverse space 40 and registered. Details of the data similarity β will be described later.

[0129] Next, when calculating the data relevance P, the data relevance calculation unit 21 determines whether or not to use information on the access history of related parties to each content registered in association with each point in the metaverse space 40 (S24).

[0130] In this embodiment, it is set in advance whether or not to use information on the access history of related parties in calculating the data relevance P. Therefore, this determination is made in step S24 by determining whether or not a setting has been made to use information on the access history of related parties in calculating the data relevance P.

[0131] If the data relevance calculation unit 21 obtains a negative result in this judgment, it obtains the related party ID of the related party who registered the content from the management information table 34 (Figure 8) as related party information for that content for each content registered and linked to each point in the metaverse space 40 (S25).

[0132] In contrast, if the data relevance calculation unit 21 obtains a positive result in the judgment of step S24, it obtains the related party ID of the related party who has accessed the content the most (hereinafter referred to as the most frequent accessor) for each content registered and linked to each point in the metaverse space 40 from the management information table 34 as related party information for that content (S26).

[0133] However, in step S26, not only the person with the most accesses but also the related party IDs of the top few related parties with the most accesses may be obtained as related party information for that content from the management information table 34. Also, weights may be set for the related parties, and the related party whose related party ID is to be obtained may be selected based on the number of accesses of the related party and the related party weight.

[0134] After this, the data relevance calculation unit 21 calculates the data relevance P, which is the relevance between the content and the target login user, for each content based on the weight W for each section traveled by the target login user in the metaverse space 40 obtained as described above, the data similarity α and data similarity β for each content, and the relationship strength α for each related party calculated by the operation point related party calculation unit 20 (S27).

[0135] Specifically, the data relevance calculation unit 21 calculates the data relevance for each piece of content registered in the management information table 34 using the following method.

[0136] As shown in FIG. 19, the data relevance calculation unit 21 first compares, for each content, the data similarity α calculated in step S21 normalized to the range of "0" to "1" with the data similarity β calculated in step S23 normalized to the range of "0" to "1", and determines the larger one as the data similarity MAX for that content.

[0137] Next, for each piece of content, the data relevance calculation unit 21 calculates the weight W of each section in the metaverse space 40 calculated in step S23 based on the position in the metaverse space 40 to which the content is linked, using the following formula:

number

[0138] For example, in the example of FIG. 19, the content "edited photo.jpg" with the data number "data001" has a data similarity MAX of "1", and according to the management information table 34 (FIG. 8), this content is linked to the section "[12.0 km, 12.1 km]" in the metaverse space 40. Also, referring to FIG. 18, the weight W of this section is "1". Therefore, the data relevance calculation unit calculates the data similarity γ of this content using the following formula:

number

[0139] In this case, if the content is linked to the boundary between two sections, the data relevance calculation unit 21 determines the average value of the weights W of the sections on both sides of the boundary as the "weight W of the section to which the content is linked."

[0140] For example, as shown in FIG. 19, the content "Construction Documents.docx" with the data number "data003" has a data similarity MAX of "1", and according to the management information table 34, this content is linked to the position of "11.9km" which is the boundary between the section [11.8km, 11.9km] and the section [11.9km, 12.0km] in the metaverse space. Also, referring to FIG. 18, the weight W of these two sections is both "0.5". Therefore, the data relevance calculation unit 21 calculates the data similarity γ of this content using the following formula:

number

[0141] Furthermore, when the content is linked to a range consisting of two or more sections, the data relevance calculation unit 21 determines the average value of the weights W of these sections as the "weight W of the section linked to the content."

[0142] For example, as shown in FIG. 19, the content "accident information.docx" with the data number "data004" has a data similarity MAX of "1", and according to the management information table 34, the content is linked to the range of [12.0 km, 12.2 km] on the metaverse space 40, that is, both the section [12.0 km, 12.1 km] and the section [12.1 km, 12.2 km] on the metaverse space 40. Also, referring to FIG. 18, the weight W of these two sections is both "1". Therefore, the data relevance calculation unit calculates the data similarity γ of this content using the following formula:

number

[0143] 20, the data relevance calculation unit 21 calculates the data relevance P of each piece of content by multiplying the data similarity γ of the content calculated as described above by the relationship strength R between the content and the target login user notified by the operation location related person calculation unit 20 (S27). However, various other methods can be widely applied as a method for calculating the data relevance P.

[0144] Furthermore, the data relevance calculation unit 21 outputs the data relevance P for each piece of content calculated in this way to the data acquisition unit 22 (FIG. 1) (S28), and then ends this data relevance calculation process.

[0145] In the above-described data relevance calculation process, if negative results are obtained in both step S20 and step S22, none of the data similarity α, data similarity β, and weight W of each section are calculated, and therefore the above-described method cannot calculate the data relevance P. Therefore, in such a case, the data relevance calculation unit 21 outputs the relationship strength R calculated for each piece of content by the operation location related person calculation unit 20 to the data acquisition unit 22 as the data relevance P of that content.

[0146] In addition, if the data relevance calculation unit 21 calculates only the weight W for each section on the metaverse space 40 in step S23, it calculates the data relevance S by weighting the relationship strength R for each section, in the same way as when calculating the data similarity γ.

[0147] Next, the specific processing contents of the weight / data similarity β calculation processing executed by the data relevance calculation unit 21 in step S23 of the data relevance calculation processing will be described.

[0148] 21 shows specific processing contents of the weight / data similarity β calculation processing. When the data relevance calculation unit 21 proceeds to step S23 of the data relevance calculation processing, it starts the weight / data similarity β calculation processing shown in FIG.

[0149] Then, in steps S30 to S32, the data relevance calculation 21 first determines the weight W of each section on the metaverse space 40 for the target login user. Note that, in the following, each section on the metaverse space 40 is assumed to be a section every 100 m from the reference position.

[0150] Specifically, the data relevance calculation unit 21 determines whether to determine the weight W of each section using the movement information of the target logged-in user, that is, the location information stored in the location information column 33F of the record in the communication history table 33 (Figure 7) whose location feature stored in the location feature column 33E is "(spatial movement)" (S30).

[0151] In the present embodiment, it is set in advance whether or not to use the movement information of the target login user in determining the weight W of each section. Therefore, this determination is made in step S30 by determining whether or not a setting has been made to use the movement information of the target login user in determining the weight W of each section.

[0152] If the data relevance calculation unit 21 obtains a negative result in this determination, it determines the weight W of all sections within the movement range of the target login user in the metaverse space 40 to be "1" (S31). For example, if the movement range of the target login user is 11.8 km to 12.2 km, the weight W of each of the sections 11.8 km to 11.9 km, 11.9 km to 12.0 km, 12.0 km to 12.1 km, and 12.1 km to 12.2 km is all set to "1".

[0153] Furthermore, if the data relevance calculation unit 21 obtains a positive result in the judgment of step S30, it determines the weight W of each section based on the time spent by the target login user in each section within the target login user's movement range in the metaverse space 40 (S32).

[0154] Specifically, as shown in Fig. 18, for each section on the metaverse space 40, the data relevance calculation unit 21 calculates the total amount of time that the target logged-in user spent in that section ("total travel time" in Fig. 18), and sets the weight W of sections whose calculated total value is greater than a preset threshold to a first value, and sets the weight W of sections whose calculated total value is equal to or less than the preset threshold to a second value. Note that Fig. 18 shows an example in which the threshold is "100", the first value is "1", and the second value is "0.5".

[0155] Next, in steps S33 to S35, the data relevance calculation unit 21 determines the content to be displayed in the metaverse space 40 at that time from the content registered in association with each point on the metaverse space 40.

[0156] In practice, when determining the content to be displayed on the metaverse space 40, the data relevance calculation unit 21 determines whether or not to use the operation history stored in the operation history table 31 (Figure 5) corresponding to the target login user (S33).

[0157] In this embodiment, it is set in advance whether or not to use the operation history when determining the content to be displayed in the metaverse space 40. Therefore, this determination is made by judging whether or not a setting has been made to use the operation history when determining the content to be displayed in the metaverse space 40. If the data relevance calculation unit 21 obtains a positive result in this determination, it ends this weight / data similarity β calculation process and returns to the data relevance calculation process of FIG.

[0158] In response to this, if the data relevance calculation unit 21 obtains a positive result in the judgment of step S33, it calculates the relevance between the content that the target login user has previously selected (viewed) or registered by the target login user and each piece of content that has been linked to and registered at each point in the metaverse space 40 (S34).

[0159] Here, as shown in Figure 22, it is assumed that the target logged-in user has previously selected (viewed) the data number "data001", the data name "constructionphoto.jpg", and the data summary "slope reinforcement" content linked to the "12.0 km" section, and has also registered the data number "data006", the data name "comment1.json", and the data summary "cable crossing point" content linked to the "12.2 km" position in the metaverse space (see Figure 5).

[0160] In this case, as shown in the column of "Relevance (Selection 001)" in Fig. 23, the data relevance calculation unit 21 first calculates the relevance between the content that the target login user has selected (viewed) so far (hereinafter referred to as selected content) and each content (including the selected content) that has been linked to and registered at each point in the metaverse space 40. In the example of Fig. 22, the content "(data001)constructionphoto.jpg" corresponds to the selected content.

[0161] As a method for calculating the relevance, various existing calculation methods can be applied. In the present embodiment, the data relevance calculation unit 21 calculates the cosine similarity between the data summary of the selected content and the data summary of the currently targeted content as the relevance, based on the text of the data summary stored in the data summary column 34E of the management information table 34 (FIG. 8).

[0162] Furthermore, when there are multiple selected contents, the data relevance calculation unit 21 calculates, for each selected content, the relevance between each of the contents registered in association with each point in the metaverse space 40. Therefore, in this case, columns such as "Selection 002," "Selection 003," etc. corresponding to those contents are added to the table in FIG.

[0163] Next, the data relevance calculation unit 21 calculates the relevance between each piece of content (hereinafter referred to as registered content) that the target login user has previously registered in association with any point in the metaverse space 40, and each piece of content (including registered content) that has been registered in association with each point in the metaverse space, as shown in the column of "Relevance (Registration 001)" in Fig. 23. In the example of Fig. 22, the content "(data006)comment1.json" corresponds to the registered content.

[0164] Various existing calculation methods can be applied as a method for calculating the relevance. In this embodiment, the data relevance calculation unit 21 calculates the cosine similarity between the data summary of the registered content and the data summary of the currently targeted content as the relevance, based on the text of the data summary stored in the data summary column 34E of the management information table 34. Note that a calculation method other than cosine similarity may be used to calculate the relevance.

[0165] Furthermore, when there are multiple registered contents, the data relevance calculation unit 21 calculates, for each registered content, the relevance between each registered content and each content linked to each point in the metaverse space 40. Therefore, in this case, columns such as "Registration 002," "Registration 003," etc. corresponding to those contents are added to the table in FIG.

[0166] Next, as shown in FIG. 23, for each piece of content registered in the management information table 34, the data relevance calculation unit 21 calculates the largest relevance between the calculated relevance between the content and the selected content (or each selected content if there are multiple selected contents) and the calculated relevance between the content and the registered content (or each registered content if there are multiple registered contents) as the data similarity β of the content (S35).

[0167] However, the data similarity β of the content may be a value obtained by converting the average value of the relevance, the largest relevance, or the average value of the relevance using a predefined function. Also, the relevance of each content may be weighted depending on the operation details such as selection (viewing) and registration.

[0168] Then, the data relevance calculation unit 21 thereafter ends this weight / data similarity β calculation process and returns to the data relevance calculation process of FIG.

[0169] (3-3) Data acquisition process 24 shows specific processing details of the data acquisition process executed by the data acquisition unit 22 (FIG. 1) after the processing is completed by the data relevance calculation unit 21. Based on the data relevance P of each content calculated by the data relevance calculation unit 21, the data acquisition unit 22 acquires content data of each content to be displayed on the metaverse space 40 at that time, according to the processing procedure shown in FIG.

[0170] In practice, when the data acquisition unit 22 receives the data relevance P for each piece of content from the data relevance calculation unit 21, it starts the data acquisition process shown in Fig. 24, and first extracts all data numbers of content whose data relevance P is in the top m% or more from each piece of content registered in the management information table 34 (S40). In this case, "m" is a predetermined threshold. Note that an example in which the data relevance P of each piece of content is as shown in Fig. 20 and "m" is "60" is shown in Fig. 25.

[0171] However, in step S40, it is also possible to extract a predetermined number of data numbers with the highest data relevance P. It is also possible to extract data numbers with data relevance P equal to or greater than a predetermined threshold value.

[0172] Next, data acquisition unit 22 acquires content data of the content of each data number extracted in step S40 from content data table 35 (FIG. 1) (S41). Data acquisition unit 22 also outputs the acquired content data together with image data of the VR image of metaverse space 40 at the point where the target login user logged in to display device 7 (FIG. 1) via output unit 13B (FIGS. 1 and 2) (S42). Then, data acquisition unit 22 ends this data acquisition process.

[0173] As a result, based on this content data and image data of the VR image, an information presentation screen 50 (FIG. 11) showing only the icon 71 (FIG. 11) of the content related to the target logged-in user at that time is displayed on the display device 7.

[0174] (4) Effects of this embodiment As described above, the information presentation device 5 of this embodiment calculates, for each of the other related parties, the relationship strength R between the target login user and each of the other related parties at the location or range where the target login user previously performed operations within the metaverse space 40, based on historical information of communication between the related parties; calculates, for each of the other related parties, the data relevance P between the target login user and the content registered and linked to each location on the metaverse space 40, based on the calculated relationship strength R; obtains content data for a predetermined number of contents with the highest calculated data relevance P for each of the contents; and selectively presents the content for which the content data has been obtained to the target login user on the metaverse space 40.

[0175] Therefore, the information presentation device 5 can selectively present, in the metaverse space 40, only content that is highly relevant to the target login user at the point in the metaverse space 40 where the target login user is currently located. As a result, the information presentation device 5 can eliminate the need for the target login user to extract necessary content from content that is unrelated to the target login user, and can reduce the risk that the target login user will overlook important content.

[0176] (5) Other embodiments In the above embodiment, the information presentation device 5 is described as being configured by one computer device, but the present invention is not limited to this, and the information presentation device 5 may be configured by multiple computer devices that form a distributed computing system.

[0177] Furthermore, in the above embodiment, the content is displayed on a screen for the target logged-in user, but the present invention is not limited to this, and the content may be output as audio, for example. [Industrial Applicability]

[0178] The present invention can be widely applied to information presentation devices of various configurations that present content to users in virtual spaces. [Explanation of symbols]

[0179] 1...Information presentation system, 5...Information presentation device, 7...Display device, 10...CPU, 20...Operation location related person calculation unit, 21...Data relevance calculation unit, 22...Data acquisition unit, 23...User information database, 24...Related person information database, 25...Registration information database, 30...Login information table, 31...Operation history table, 32...Attribute information table, 33...Communication history table, 34...Management information table, 35...Content data table, 36...Location related information table, 40...Metaverse space, 50...Information presentation screen, INF...Input information.

Claims

1. An information presentation device presents content associated with each location in a virtual space to a user who has logged in to the virtual space, a relationship strength calculation unit that calculates, for each of the other parties, a relationship strength between the user and each of the other parties at a point or range where the user previously performed an operation in the virtual space, based on history information of communications between the user and the other parties; a relevance calculation unit that calculates, for each piece of content, a relevance between the content associated with each point in the virtual space and the user based on the calculated relationship strength; a content acquisition unit that acquires a top predetermined number or a top predetermined percentage of the content items having the highest relevance based on the calculated relevance for each of the content items, and selectively presents the acquired content items to the user in the virtual space; An information presentation device comprising:

2. The relationship strength calculation unit An extraction range of the communication history information is determined based on movement history information that represents the movement history of the user in the virtual space, and the relationship strength is calculated based on the communication history information within the determined extraction range.

2. The information presentation device according to claim 1.

3. The relevance calculation unit The relevance of the content is calculated based on the relationship strength of the other related parties who registered the content, or the relationship strength of a representative user of the content among the other related parties.

3. The information presentation device according to claim 2.

4. The relevance calculation unit The relevance of each piece of content is calculated by correcting the relationship strength of the other related person who registered the content or the relationship strength of the related person who is a representative user of the content, based on the content extraction conditions input by the user and metadata including an outline of each piece of content.

4. The information presentation device according to claim 3.

5. The relationship strength calculation unit extracting features for identifying a location in the virtual space related to the communication, the features being included in the communication history information; Identifying a location in the virtual space related to the communication based on the extracted features; The location information of the identified location is added to the history information of the communication.

2. The information presentation device according to claim 1.

6. The relationship strength calculation unit Calculating the relationship strength between each of the participants through network analysis based on communication history information of all the participants including the user.

2. The information presentation device according to claim 1.

7. The relationship strength calculation unit In response to a question from the user who is logged in to the virtual space, an answer is generated based on the content of each of the registered contents and presented to the user.

2. The information presentation device according to claim 1.

8. An information presentation method executed by an information presentation device that presents content associated with and registered at each location in a virtual space to a user who has logged in to the virtual space, the method comprising: a first step of calculating, for each of the other parties, a relationship strength between the user and each of the other parties at a point or range where the user previously performed an operation in the virtual space, based on history information of communications between the user and the other parties; a second step of calculating, for each piece of content, a degree of association between the content associated with each location in the virtual space and the user based on the calculated relationship strength; a third step of acquiring a top predetermined number or a top predetermined percentage of the content items having the highest relevance based on the calculated relevance for each of the content items, and selectively presenting the acquired content items to the user in the virtual space; An information presentation method comprising:

9. In the first step, the information presentation device An extraction range of the communication history information is determined based on movement history information that represents the movement history of the user in the virtual space, and the relationship strength is calculated based on the communication history information within the determined extraction range.

9. The information presentation method according to claim 8.

10. In the second step, the information presentation device The relevance of the content is calculated based on the relationship strength of the other related parties who registered the content, or the relationship strength of a representative user of the content among the other related parties.

10. The information presentation method according to claim 9.

11. In the third step, the information presentation device The relevance of each piece of content is calculated by correcting the relationship strength of the other related person who registered the content or the relationship strength of the related person who is a representative user of the content, based on the content extraction conditions input by the user and metadata including an outline of each piece of content.

11. The information presentation method according to claim 10.

12. In the first step, the information presentation device extracting features for identifying a location in the virtual space related to the communication, the features being included in the communication history information; Identifying a location in the virtual space related to the communication based on the extracted features; The location information of the identified location is added to the history information of the communication.

9. The information presentation method according to claim 8.

13. In the first step, the information presentation device Calculating the relationship strength between each of the participants through network analysis based on communication history information of all the participants including the user.

9. The information presentation method according to claim 8.

14. In any one of the first step to the third step, the information presentation device In response to a question from the user who is logged in to the virtual space, an answer is generated based on the content of each of the registered contents and presented to the user.

9. The information presentation method according to claim 8.

Images