Information processing device, information processing method, and program

Abstract

This information processing device comprises: a reception unit that receives content; a content processing unit that adds information specifying a physical address to the content received by the reception unit, stores the content in a content pool, and virtually classifies the content stored in the content pool; and a display control unit that displays, on a display unit, a virtual classification structure of the content virtually classified by the content processing unit.

Description

Information processing device, information processing method, and program

[0001] The present invention relates to an information processing device, an information processing method, and a program. This application claims priority based on Japanese Patent Application No. 2025-028806, filed in Japan on February 26, 2025, the contents of which are incorporated herein by reference.

[0002] The search server uses the entered keywords to retrieve documents related to those keywords from its stored documents as search results. The search server provides the search results to the user's terminal device, allowing the user to view the results on the terminal device. A technique is known for clustering the search results according to their degree of similarity (see, for example, Patent Document 1).

[0003] Japanese Patent Publication No. 2005-078245

[0004] Content is managed according to established management rules, but creating these rules requires a significant amount of effort. Furthermore, management rules, once established, can become outdated over time. Additionally, users must configure access rights and other checks when storing content, which is time-consuming.

[0005] The object of the present invention is to provide an information processing device, an information processing method, and a program that can reduce the effort required for content management.

[0006] One aspect of the present invention is an information processing device comprising: a receiving unit for receiving content; a content processing unit for assigning information to identify a physical address to the content received by the receiving unit, storing it in a content pool, and virtually classifying the content stored in the content pool; and a display control unit for causing a display unit to display the results of the virtual classification of the content by the content processing unit.

[0007] One aspect of the present invention is an information processing method performed by a computer, which includes receiving content, assigning information to the received content that identifies a physical address, storing the content to which the physical address has been assigned in a content pool, virtually classifying the content stored in the content pool, and displaying the results of the virtually classifying of the content on a display unit.

[0008] One aspect of the present invention is a program that causes a computer to receive content, assign information to the received content that identifies a physical address, store the content to which the physical address has been assigned in a content pool, virtually classify the content stored in the content pool, and display the results of the virtual classification of the content on a display unit.

[0009] According to the present invention, the effort required for content management can be reduced.

[0010] This is a diagram showing an example of the information processing device of this embodiment. This is a diagram showing an example of user attribute information. This is a diagram showing an example of content metadata. This is a diagram showing an example of creator attribute information. This is a flowchart showing an example of the operation flow of the information processing device of this embodiment. This is a flowchart showing an example of the operation flow of the information processing device of this embodiment. This is a diagram showing an example of the classification structure setting screen. This is a flowchart showing an example of the operation flow of the information processing device of this embodiment. This is a diagram showing an example of an N-dimensional vector space. This is a flowchart showing an example of the operation flow of the information processing device of this embodiment. This is a flowchart showing an example of the operation flow of the information processing device of this embodiment. This is a flowchart showing an example of the operation flow of the information processing device of this embodiment. This is a flowchart showing another example of the operation flow of the information processing device of this embodiment. This is a diagram showing an example of the content classification screen.

[0011] Hereinafter, the information processing apparatus, information processing method, and program of the embodiments will be described with reference to the drawings. The embodiments described below are merely examples, and the embodiments to which the present invention is applied are not limited to the embodiments described below. In all the drawings used to describe the embodiments, components having the same function will be given the same reference numerals, and repeated explanations will be omitted. Furthermore, in this application, "based on XX" means "based on at least XX," and includes cases where it is based on another element in addition to XX. Furthermore, "based on XX" is not limited to cases where XX is used directly, but also includes cases where it is based on XX after calculations or processing have been performed on it. "XX" is any element (for example, any information).

[0012] (Embodiment) (Information Processing Device) The information processing device 100 of this embodiment creates management rules for content such as documents and manages content based on the created management rules. Figure 1 is a diagram showing an example of the information processing device 100 of this embodiment. The information processing device 100 is implemented by a device such as a personal computer, server, smartphone, tablet computer, or industrial computer.

[0013] The information processing device 100 receives content. The information processing device 100 assigns information that identifies the physical address to the received content and stores the content with the information that identifies the physical address in the content pool. An example of information that identifies the physical address is the physical address and an ID equivalent to the physical address. The following explanation continues with the case where the physical address is applied as an example of information that identifies the physical address. The information processing device 100 virtually classifies the content stored in the content pool and displays the result of the virtual classification of the content on the display unit. An example of the result of the virtual classification of the content is a classification tree that hierarchically represents the content. The information processing device 100 also searches for content stored in the content pool based on user specifications, for example. The information processing device 100 displays the result of the content search on the display unit.

[0014] The information processing device 100 includes a user interface (UI) 10, an account registration / update processing unit 20, a reception unit 25, a content pool 30, a content processing unit 70, and a display control unit 66. The content processing unit 70 includes a virtual content pool construction processing unit 40, a hierarchical structure generation agent 50, a pseudo-metadata search agent 62, a natural language search agent 64, and a virtual content pool 80.

[0015] UI10 comprises an account registration / update UI12, a content pool UI14, a classification display UI16, and a natural language search UI18. An example of UI10 includes a display unit. The virtual content pool construction processing unit 40 constructs a virtual content pool 80. The virtual content pool construction processing unit 40 comprises a content pool crawler 42, a metadata generation agent 44, and an LLM (Large-Scale Language Model) 4.

[0016] The virtual content pool 80 comprises a user attribute database (DB: Data Base) 81, a metadata DB 82, a usage log DB 83, a vector space generation agent 84, an embedded processing unit 85, and a content vector space storage unit 86.

[0017] The account registration / update UI 12 is a UI for user U to register and update their account. The content pool UI 14 is a UI for user U to view the content included in the content pool 30 and perform predetermined processing. The classification display UI 16 is a UI for classifying and displaying content. For example, a classification display UI may be created for each organization or user, and the displayed content may be made viewable based on user U's specifications. The natural language search UI 18 is a UI that receives a search prompt from user U and allows the user U to perform a search using the received search prompt.

[0018] The account registration / update processing unit 20 acquires an account registration request from the account registration / update UI 12. The account registration / update processing unit 20 registers user attribute information in the user attribute DB 81 based on user identification information and the like included in the acquired account registration request.

[0019] FIG. 2 is a diagram showing an example of user attribute information. As shown in FIG. 2, an example of the user attribute information includes an employee number as an example of user identification information, a name, affiliation data, information indicating a post, and a job type code. An example of the affiliation data includes a business headquarters name, a division name, a department name, a section name, work content, and a department code. Returning to FIG. 1, the description will be continued.

[0020] After the user attribute information is registered in the user attribute DB 81, when the account registration / update processing unit 20 acquires an account update request from the account registration / update UI 12, the account registration / update processing unit 20 updates the user attribute information registered in the user attribute DB 81 based on user identification information and the like included in the acquired account update request.

[0021] The reception unit 25 receives content and stores the received content in the content pool 30. Here, the content may be created by the information processing device 100, or it may be created outside the information processing device 100 and input to the information processing device 100. When the content is created outside the information processing device 100 and input to the information processing device 100, the content may be transmitted to the information processing device 100 from a terminal device (not shown) connected to the information processing device 100 via a communication network. In this case, in the information processing device 100, the communication unit (not shown) receives the content transmitted by the terminal device, and the reception unit 25 acquires the content received by the communication unit. Here, the communication network includes the Internet, WAN (Wide Area Network), LAN (Local Area Network), public lines, provider equipment, dedicated lines, wireless base stations, etc. The reception unit 25 may or may not store the received content in the content pool 30. The following explanation continues, as an example, of the case where the reception unit 25 saves the received content to the content pool 30. The content pool 30 stores one or more contents in the information processing device 100. One or more contents can be displayed on the UI 10. The content pool 30 assigns a physical address to each of the one or more contents stored. Each of the one or more contents remains stored in the content pool 30 and is displayed on the UI 10. The content pool 30 notifies the virtual content pool construction processing unit 40 of the physical addresses assigned to the stored contents.

[0022] The virtual content pool construction processing unit 40 constructs a virtual content pool 80. The content pool crawler 42 receives physical addresses notified from the content pool 30. In addition, the content pool crawler 42 checks the update status of content in the content pool 30 and newly added content, and collects (crawls) physical addresses. For example, the content pool crawler 42 polls the update status of content in the content pool 30 and newly added content to collect physical addresses. The metadata generation agent 44 acquires physical addresses from the content pool crawler 42, and identifies content corresponding to the physical addresses from the content pool 30 based on the acquired physical addresses. The metadata generation agent 44 generates metadata from the identified content. For example, the metadata generation agent 44 uses an LLM (Large Language Model) 4 to generate metadata from the content. The metadata generation agent 44 stores the generated metadata in the metadata DB 82 in association with content identification information.

[0023] FIG. 3 is a diagram showing an example of content metadata. In FIG. 3, for each category, attribute items, descriptions, methods for automatic metadata extraction by AI agents, number of characters, number of embedding dimensions, subspaces, chunk embedding / categories, etc. are associated with each other. As shown in FIG. 3, an example of content metadata includes, as categories, basic information, content information, authority / access information, and tracking / management information.

[0024] Examples of basic information include title, author, creation date and time, last updater, and last update date. All of these are subject to vectorization. Examples of content information include version, summary, keywords, category, theme, project status, purpose of use, content type, content format, and number of pages. Of these, the summary, keywords, category, theme, project status, purpose of use, content type, and content format are subject to vectorization. Permission and access information includes access rights, confidentiality level, approver, and approval date. Of these, access rights and confidentiality level are subject to vectorization. Tracking and management information includes status, storage location, related content, expiration date (lifecycle), and content ID. Of these, status, expiration date (lifecycle), and content ID are subject to vectorization. The metadata generation agent 44 may query the user for missing information and obtain it. For example, the metadata generation agent 44 outputs information to the display control unit 66 for querying for missing information. The display control unit 66 obtains information for querying missing information from the metadata generation agent 44 and displays this information on the UI 10. The metadata generation agent 44 obtains the information entered by the user in response to the information for querying missing information displayed on the UI 10. This allows the metadata generation agent 44 to complete the metadata. Return to Figure 1 and continue the explanation.

[0025] The user attribute database 81 stores user attribute information registered by the account registration / update processing unit 20. The metadata database 82 stores metadata stored by the metadata generation agent 44. The usage log database 83 stores the usage log of user U. An example of a usage log is the search history, which includes information indicating the date and time of the search, the content ID (identification information), the search user's attributes, and content metadata information. If a natural language search is performed, the search history also includes information indicating the search conditions.

[0026] The vector space generation agent 84 obtains user attribute information from the user attribute DB 81, metadata from the metadata DB 82, and usage logs from the usage log DB 83. Based on the obtained user attribute information, metadata, and usage logs, the vector space generation agent 84 generates an N-dimensional vector space (where N is an integer N > 1) as metadata representing the characteristics of the content. For example, N may be the number of attribute items to be vectorized among the metadata included in the content. The N dimensions include the creator (affiliation, employee number, name, etc.) and the summary / abstract. An example of the creator is 10 dimensions, and an example of the summary / abstract is 768 × 6 dimensions (in the case of 3,000 characters), assuming that there are 125 overlapping characters in a 500-character chunk. The vector space generation agent 84 stores the generated N-dimensional vector space in the content vector space storage unit 86.

[0027] The content vector space storage unit 86 stores the N-dimensional vector space stored by the vector space generation agent 84. The embedding processing unit 85 obtains user attribute information from the vector space generation agent 84 and embeds the content into the N-dimensional vector space stored in the content vector space storage unit 86 based on the obtained user attribute information. After the embedding processing unit 85 has embedded the content into the N-dimensional vector space, the vector space generation agent 84 derives the inter-content distance between the multiple contents embedded in the N-dimensional vector space stored in the content vector space storage unit 86. Based on the derived inter-content distance, the vector space generation agent 84 generates k clusters (where k is an integer k > 1) from the multiple contents. For example, the vector space generation agent 84 generates k clusters using the k-means method based on the inter-content distance.

[0028] The hierarchical structure generation agent 50 analyzes metadata for each content item included in the cluster generated by the vector space generation agent 84, using LLM5, for the content stored in the content vector space storage unit 86. Based on the metadata analysis results, the hierarchical structure generation agent 50 recommends the optimal folder (lowest level). The hierarchical structure generation agent 50 displays a classification display UI 16 for recommending the optimal folder on UI 10. For example, the hierarchical structure generation agent 50 outputs classification information to the display control unit 66 for recommending the optimal folder. The display control unit 66 obtains the classification information from the hierarchical structure generation agent 50 and displays the classification information on UI 10. The hierarchical structure generation agent 50 obtains information entered by the user in relation to the classification information.

[0029] The natural language search agent 64 obtains user identification information and search prompts from the natural language search UI 18 and outputs them to the pseudo-metadata search agent 62. The pseudo-metadata search agent 62 obtains user identification information and search prompts from the natural language search agent 64. Based on the obtained user identification information, the pseudo-metadata search agent 62 obtains user attribute information from the user attribute DB 81. The pseudo-metadata search agent 62 generates pseudo-user attribute information using LLM 6. If the pseudo-metadata search agent 62 has all the pseudo-user attribute information necessary for the search, it stores the pseudo-user attribute information and generates pseudo-metadata based on the stored pseudo-user attribute information.

[0030] The pseudo-metadata search agent 62 places pseudo-user attribute information in an N-dimensional vector space where multiple contents stored in the content vector space storage unit 86 are embedded, based on the pseudo-metadata it has generated. The pseudo-metadata search agent 62 lists a predetermined number of contents, starting with those closest to the placed pseudo-user attribute information, and derives their likelihood.

[0031] The natural language search agent 64 obtains a predetermined number of content items and information indicating the likelihood of each of those content items from the pseudo-metadata search agent 62. The natural language search agent 64 outputs the obtained predetermined number of content items and information indicating the likelihood of each of those content items to the display control unit 66. The display control unit 66 obtains the predetermined number of content items and information indicating the likelihood of each of those content items from the natural language search agent 64 and displays them on the natural language search UI 18.

[0032] In the information processing device 100, the vector space generation agent 84 may also derive the distance between contents based on the weight vector. For example, the vector space generation agent 84 may derive the weight c of all attributes ｉ The weights may be made equal. In this case, the vector space generation agent 84 analyzes the distribution state for each attribute and uses the following method to determine the weights c ｉ This determines the weight c. This makes it possible to define the distance between content by averaging the variability between each attribute. ｉ It is assumed that it follows a normal distribution (mean μ, standard deviation σ). In this case, the mean of the variance for all attributes is given by equation (1), and the weight c for attribute i is given by ｉ σ is expressed by equation (2). ｉ This is the standard deviation of attribute i.

[0033] Furthermore, for example, the vector space generation agent 84 assigns weights c according to the importance of the attributes. ｉ You may also set this. In this case, you give more weight to the attribute whose influence on the distance between content you want to increase. An example of an attribute whose influence on the distance between content you want to increase is an important attribute. For example, if attribute j is important and the importance of the other attributes is equal, the average variance for all attributes is given by equation (3).

[0034] In the information processing device 100, the vector space generation agent 84 may optimize classification according to the creator's circumstances. For example, the vector space generation agent 84 creates the creator's attributes as M-dimensional (M is an integer M > 1) vector data. Figure 4 shows an example of creator attribute information. As shown in Figure 4, an example of creator attribute information includes an employee number as an example of creator identification information, a name, affiliation data, information indicating the job title, and a job title code. An example of affiliation data includes the name of the business division, the name of the business unit, the name of the department, the name of the section, the business content, the department code, the job title, and the job title code. For example, creator attribute information may be represented by an employee number, business content, department code, information indicating the job title, and a job title code.

[0035] The vector space generation agent 84 further adds the created M-dimensional vector data to generate an (M+N)-dimensional vector space as metadata representing the characteristics of the content. The vector space generation agent 84 stores the generated (M+N)-dimensional vector space in the content vector space storage unit 86. Returning to Figure 1, the explanation continues.

[0036] The embedding processing unit 85 embeds the content into the (N+M)-dimensional vector space stored in the content vector space storage unit 86. Here, the vector space generation agent 84 may create attributes of the creator, such as relationships between organizations (departments) and hierarchical relationships within departments. For example, the vector space generation agent 84 uses a graph neural network (GNN) to create relationships between organizations (departments) and hierarchical relationships within departments. By specifying the creator's attributes and cutting a cross-section of the N-dimensional space on the specified creator's attributes, it is possible to generate subclassifications of the classification. This method makes it possible to display the classification that is optimal for organizations and individuals.

[0037] In the information processing device 100, the vector space generation agent 84 may acquire management rules. The vector space generation agent 84 quantifies the acquired management rules and generates an R-dimensional vector space (where R is an integer R > 1). For example, management rules include confidentiality, access rights, compliance requirements, lifecycle settings, etc. The management rules may be set by user U. The vector space generation agent 84 further adds the created R-dimensional vector data and generates an (R + M + N)-dimensional vector space as metadata representing the characteristics of the content. The vector space generation agent 84 stores the generated (R + M + N)-dimensional vector space in the content vector space storage unit 86.

[0038] The embedding processing unit 85 embeds the content into a (R+N+M)-dimensional vector space stored in the content vector space storage unit 86. The embedding processing unit 85 acquires an image with the content embedded in the (R+N+M)-dimensional vector space and outputs it to the display control unit 66. The display control unit 66 acquires the image with the content embedded in the (R+N+M)-dimensional vector space from the embedding processing unit 85 and displays it on the UI 10. This makes it possible to visualize the distribution state within the (R+N+M)-dimensional, i.e., (R, N, M) vector space.

[0039] In the information processing device 100, the vector space generation agent 84 may optimize classification according to time attributes. For example, the vector space generation agent 84 creates time attributes as T-dimensional (T is an integer T > 1) vector data. An example of a time attribute is a two-dimensional (attribute, time) attribute that specifies the creation date or expiration date and a time indicating the corresponding date and time. The vector space generation agent 84 further adds the created T-dimensional vector data and generates a (T + R + M + N)-dimensional vector space as metadata that represents the characteristics of the content. The vector space generation agent 84 stores the generated (T + R + M + N)-dimensional vector space in the content vector space storage unit 86. The embedding processing unit 85 embeds the content into the (T + R + N + M)-dimensional vector space stored in the content vector space storage unit 86.

[0040] In the information processing device 100, the vector space generation agent 84 may create user attributes as U-dimensional vector data (where U is an integer U > 1). Since Figure 4 can be applied to user attributes, a detailed explanation is omitted. The vector space generation agent 84 further adds the created U-dimensional vector data to generate a (U + T + R + M + N)-dimensional vector space as metadata representing the characteristics of the content. The vector space generation agent 84 stores the generated (U + T + R + M + N)-dimensional vector space in the content vector space storage unit 86.

[0041] The embedding processing unit 85 embeds the content that user U has searched so far into a (U + T + R + N + M) dimension vector space stored in the content vector space storage unit 86. The vector space generation agent 84 calculates the distance between the content and specific content included in the search history. The pseudo-metadata search agent 62 obtains search results based on the distance calculated by the vector space generation agent 84. For example, the pseudo-metadata search agent 62 may preferentially obtain the search results starting with those with the shortest distance.

[0042] Here, the vector space generation agent 84 may calculate the distance not only to specific content included in the search history, but also to all content included in the search history or to content included in the same classification. The pseudo-metadata search agent 62 may have a first trained model that derives content features based on a combination of one or more contents included in the search results. The pseudo-metadata search agent 62 creates information for presenting the derived content features and outputs it to the display control unit 66. The display control unit 66 displays the information for presenting the content features from the pseudo-metadata search agent 62 on the UI 10.

[0043] The first trained model is created by machine learning the relationship between one or more content combinations included in the search results and the content features derived from those combinations. For example, the first trained model is created by machine learning with one or more content combinations included in the search results as explanatory variables and the content features derived from those combinations as the dependent variable.

[0044] Furthermore, the pseudo-metadata search agent 62 may have a second trained model that derives questions for narrowing down content based on the characteristics of the derived content. The pseudo-metadata search agent 62 creates questions for narrowing down the derived content and outputs them to the display control unit 66. The display control unit 66 displays the questions for narrowing down content from the pseudo-metadata search agent 62 on the UI 10. The second trained model is created by machine learning the relationship between content characteristics and questions for narrowing down content derived based on the content characteristics. For example, the second trained model is created by machine learning with content characteristics as the explanatory variable and questions for narrowing down content derived based on the content characteristics as the target variable. This makes it possible to create information to present the characteristics of the content to be presented to the user based on the search results for content, and to conduct questions for narrowing down the optimal content. Based on the answers, more optimal content can be presented.

[0045] In the information processing device 100, the embedding processing unit 85 may embed the content into a (T+R+N+M) dimension vector space stored in the content vector space storage unit 86, and present nearby content as similar documents to the user. Furthermore, the vector space generation agent 84 may present the reciprocal of the distance between contents as the similarity score to the user. In this case, if there is content with a distance of zero between contents, the vector space generation agent 84 creates information to notify the user U that there is a high possibility of it being a duplicate document and outputs it to the display control unit 66. The display control unit 66 may display the information from the vector space generation agent 84 indicating a high possibility of it being a duplicate document on the UI 10.

[0046] The vector space generation agent 84 may generate a (R+N)-dimensional vector space, a (T+N)-dimensional vector space, a (U+N)-dimensional vector space, a (T+M+N)-dimensional vector space, or a (U+M+N)-dimensional vector space as metadata representing the characteristics of the content. In other words, the vector space generation agent 84 can generate a vector space of any combination of T, R, M, and U plus N.

[0047] In the embodiments described above, the functions of the information processing device 100 may be implemented in a distributed manner by multiple devices. In this case, the multiple devices that implement the functions of the information processing device 100 in a distributed manner may be configured to be directly connected to perform information input and output, or they may be configured to be connected via a communication network to perform information transmission and reception. Furthermore, among the multiple devices that implement the functions of the information processing device 100 in a distributed manner, there may be a mix of directly connected devices and devices connected via a network. All or part of the account registration / update processing unit 20, reception unit 25, content pool crawler 42, metadata generation agent 44, hierarchical structure generation agent 50, pseudo-metadata search agent 62, natural language search agent 64, display control unit 66, vector space generation agent 84, and embedded processing unit 85 are functional units (hereinafter referred to as software functional units) that are implemented by a processor such as a CPU (Central Processing Unit) executing a program stored in a memory unit (not shown).

[0048] Furthermore, all or part of these functional units may be implemented by hardware such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), or FPGA (Field-Programmable Gate Array), or by a combination of software functional units and hardware.

[0049] The operation of the information processing device 100 will be described below with reference to Figures 5 to 15. Figure 5 is a flowchart showing an example of the operation flow of the information processing device 100 in this embodiment. The process by which the information processing device 100 stores content will be described with reference to Figures 1 and 5. (Step S1-1) The information processing device 100 performs a login process. For example, user U enters user identification information and a password on the initial screen (not shown) displayed on UI 10. The information processing device 100 performs a login process based on the entered user identification information and password.

[0050] (Step S2-1) The information processing device 100 creates content. For example, user U creates content using the information processing device 100. Content may also be input to the information processing device 100 from an external device. For example, content may be sent to the information processing device 100 from a terminal device (not shown) connected to the information processing device 100 via a communication network. In this case, the information processing device 100 receives and acquires the content sent by the terminal device. (Step S3-1) The content pool UI 14 detects that the "Save As" button (not shown) has been pressed.

[0051] (Step S4-1) The reception unit 25 receives the content and stores the received content in the content pool 30. (Step S5-1) The content pool 30 notifies the content pool crawler 42 of the physical address assigned to the stored content. (Step S6-1) The content pool crawler 42 receives the physical address notified by the content pool 30.

[0052] (Step S7-1) The metadata generation agent 44 obtains a physical address from the content pool crawler 42. Based on the obtained physical address, the metadata generation agent 44 identifies the content corresponding to the physical address from the content stored in the content pool 30. The metadata generation agent 44 generates metadata from the identified content. The metadata generation agent 44 stores the generated metadata in the metadata DB 82 in association with the content identification information.

[0053] (Step S8-1) The hierarchical structure generation agent 50 analyzes metadata for each piece of content included in the cluster generated by the vector space generation agent 84, for the content stored in the content vector space storage unit 86. Based on the metadata analysis results, the hierarchical structure generation agent 50 recommends the optimal folder (lowest level). The hierarchical structure generation agent 50 displays a classification display UI 16 for recommending the optimal folder on the UI 10.

[0054] (Step S9-1) The hierarchical structure generation agent 50 determines whether user U has performed an operation to respond to the recommendation on the classification display UI 16. (Step S10-1) If the hierarchical structure generation agent 50 determines that user U has not performed an operation to respond to the recommendation on the classification display UI 16, it displays the classification display UI 16 for recommending the next most suitable folder on UI 10. Proceed to step S9-1. (Step S11-1) If the hierarchical structure generation agent 50 determines that user U has performed an operation to respond to the recommendation on the classification display UI 16, it opens the recommended folder on the classification display UI 16 and displays the file name of the corresponding content.

[0055] In the flow of operations of the information processing apparatus 100 shown in FIG. 5, the processing may be performed as follows. For example, in step S6-1, the content pool crawler 42 is not limited to the physical address notified from the content pool 30, and may accept a physical address notified by any method. Further, in step S7-1, the metadata generation agent 44 may ask the user U a question to compensate for the metadata if there is missing information in the metadata generated from the identified content. Thereby, the metadata generation agent 44 can complete the metadata.

[0056] FIG. 6 is a flow diagram showing an example of the flow of operations of the information processing apparatus 100 according to the present embodiment. The processing for generating a classification hierarchical structure by the information processing apparatus 100 will be described with reference to FIGS. 1 and 6. (Step S1-2) The hierarchical structure generation agent 50 activates a classification setting program. An example of the classification setting program is included in the hierarchical structure generation agent 50. (Step S2-2) The hierarchical structure generation agent 50 causes all folders of the content pool 30 to be displayed.

[0057] (Step S3-2) The hierarchical structure generation agent 50 detects that a folder to be classified is selected by the user U and an "execute" button (not shown) is pressed. (Step S4-2) The hierarchical structure generation agent 50 displays a "classification structure setting screen" based on the pressed "execute" button.

[0058] FIG. 7 is a diagram showing an example of a classification structure setting screen. As shown in FIG. 7, in an example of the classification structure setting screen, the total number of contents to be classified M, the maximum number of hierarchies m, the average number of contents n contained in the lowermost folder １ , the number of folders (number of classifications) k in the lowermost layer １ (=[M / n １ +1), the number of folders n contained in an arbitrary i-th hierarchy from the lowermost layer ｉ and the number of folders k contained in the i-th layer ｉ can be set. Among these, the total number of contents to be classified M, the number of folders n in the lowermost layer１ and the number of folders n in the i-th layer ｉ This is set by the information processing device 100. User U has a maximum number of levels m and an average number of contents n in the lowest level folder. １ and the number of folders k located in any i-th level from the lowest level. ｉ This can be configured by user U.

[0059] If a major category (1 items) and a subcategory (m items) are set, the subcategory folders are created as follows: The average number of folders in the folder one level up is n. ２ The number of items is [number]. The average number of folders is entered on UI10. In this case, the number of categories is calculated using formula (4). k ２ = [k １ / n ２ ] + 1 (4) The final centroid (k elements) of the lowest folder using the k-means method, k ２ By dividing the data into individual files, sub-categorization folders are created. The user U pre-sets the number of layers m in the hierarchical structure, and the process terminates when the m layer is reached.

[0060] User U refers to the classification structure settings screen and determines the number of folders n included in each hierarchy i. ｉ (In the case of the lowest level, the number of content items is n) １ The user inputs the number of folders n in each level i entered by user U. ｉ (In the case of the lowest level, the number of content items is n) １ ) and set the maximum number of layers m. Return to Figure 6 and continue the explanation.

[0061] (Step S5-2) The hierarchical structure generation agent 50 sets the hierarchy i to 1. (Step S6-2) The hierarchical structure generation agent 50 calculates the number of folders contained in the i-th folder from the lowest level using the following formula (5). k ｉ = [k ｉ－１ / n ｉ ] + 1 (5) However, k ０ = M (total number of content items).

[0062] (Step S7-2) The hierarchical structure generation agent 50 determines whether or not hierarchical i is m. If the hierarchical structure generation agent 50 determines that hierarchical i is m, it considers the hierarchical structure of the classification to be complete. (Step S8-2) If the hierarchical structure generation agent 50 determines that each hierarchical i is not m, it sets i to i+1 and proceeds to step S6-2.

[0063] In the information processing device 100, the hierarchical structure generation agent 50 may be configured to automatically improve the hierarchical structure. Specifically, the hierarchical structure generation agent 50 may reconfigure the folders when the distance between the centroids of the lowest-level folders falls below the threshold initially set. Also, the hierarchical structure generation agent 50 may reconfigure the folders when the number of contents of the lowest-level folder falls below the threshold initially set. １ The system may configure the system to reconfigure folders when the value exceeds the initially set range. The hierarchical structure generation agent 50 may also configure the system to reconfigure folders based on the number of views. By configuring the system in this way, folders can be reconfigured when the initially set hierarchical structure is no longer optimal due to increases or decreases in the stored content.

[0064] Figure 8 is a flowchart showing an example of the operation flow of the information processing device 100 in this embodiment. Referring to Figures 1 and 8, the process by which the information processing device 100 starts generating the optimal folder (lowest level) will be described. Here, as an example, the case in which the vector space generation agent 84 generates an N-dimensional vector space will be described. (Step S1-3) The vector space generation agent 84 collects data for all files to be classified from the user attribute DB 81, metadata DB 82, and usage log DB 83.

[0065] (Step S2-3) The vector space generation agent 84 generates an N-dimensional vector space based on the acquired user attribute information, metadata, and usage log. The vector space generation agent 84 stores the generated N-dimensional vector space in the content vector space storage unit 86. The embedding processing unit 85 acquires content identification information and metadata from the vector space generation agent 84 and embeds the content into the N-dimensional vector space stored in the content vector space storage unit 86 based on the acquired content identification information and metadata.

[0066] (Step S3-3) The vector space generation agent 84 generates k from an N-dimensional space. １ Extract k points. For example, the vector space generation agent 84 extracts k points from an N-dimensional space. １ The points may be extracted according to a predetermined rule, or they may be extracted randomly. (Step S4-3) The vector space generation agent 84 generates each content and the extracted k １ The inter-content distance between individual points is calculated. For example, the vector space generation agent 84 calculates the inter-content distance according to the definition of inter-content distance shown in equation (6).

[0067]

[0068] The vector space generation agent 84 identifies the points with the closest content distances among the points extracted in step S3-3, and gathers the content close to the identified points, k １ Generate individual clusters.

[0069] (Step S5-3) The vector space generation agent 84 calculates the centroid point in each cluster based on the distance between contents. (Step S6-3) For all contents, the vector space generation agent 84 calculates k １ The system calculates the distance to each individual's centroid and moves content that is close to the centroid of a different cluster from the cluster to which it belongs to the closer cluster.

[0070] (Step S7-3) The vector space generation agent 84 moves N of the content between clusters. Ｔ Determine whether the value is 0 or not. N is the number of contents moved between clusters. Ｔ If the value is 0, the creation of the optimal folder is completed. Number of contents moved between clusters: N Ｔ If the result is not equal to 0, proceed to step S6-3.

[0071] Figure 9 shows an example of an N-dimensional vector space. As shown in Figure 9, content is embedded in this example of an N-dimensional vector space. Furthermore, Figure 9 shows clusters C1 to C6 and the centroids COG1 of cluster C1 to COG6 of cluster C6.

[0072] Figure 10 is a flowchart showing an example of the operation flow of the information processing device 100 in this embodiment. Referring to Figures 1 and 10, the process by which the information processing device 100 starts generating the optimal folder (upper layer) will be described. Here, as an example, the case in which the vector space generation agent 84 generates an N-dimensional vector space will be described. (Step S1-4) The vector space generation agent 84 generates the centroid point k of the cluster directly below it. １ From individual k ２ Extract a number of points. For example, the vector space generation agent 84 extracts the centroid k of the cluster directly below it. １ From individual k ２ The points may be extracted according to a predetermined rule, or they may be extracted randomly. (Step S2-4) The vector space generation agent 84 then extracts the k ２ The distance between each point and the centroid of the lower cluster is measured, k ２ The number of clusters is generated using the k-means method.

[0073] (Step S3-4) The vector space generation agent 84 calculates the centroid point in each cluster based on the distance between contents. (Step S4-4) For all folders, the vector space generation agent 84 calculates k ２The system calculates the distance to each individual centroid and moves folders that are close to the centroid of a different cluster (not the one they belong to) to that cluster.

[0074] (Step S5-4) The vector space generation agent 84 calculates the number of contents moved between clusters N Ｔ Determine whether the value is 0 or not. The number of content items moved between clusters is N. Ｔ If the value is 0, the creation of the optimal parent folder is completed. Number of contents moved between clusters N Ｔ If the result is not equal to 0, proceed to step S4-4.

[0075] Figure 11 is a flowchart showing an example of the operation flow of the information processing device 100 in this embodiment. The process by which the information processing device 100 performs natural language search will be explained with reference to Figures 1 and 11. Here, as an example, we will explain the case where user U is logged in and the vector space generation agent 84 is generating a (T + R + M + N) dimension vector space.

[0076] (Step S1-5) The natural language search UI 18 receives a search prompt entered by user U. The natural language search agent 64 obtains the search prompt received by the natural language search UI 18 and the logged-in user identification information. Based on the obtained user identification information, the natural language search agent 64 obtains user attribute information from the user attribute DB 81. The natural language search agent 64 creates a prompt creation request including the user identification information and user attribute information and outputs it to the pseudo-metadata search agent 62.

[0077] (Step S2-5) The pseudo-metadata search agent 62 obtains a prompt creation request from the natural language search agent 64. The pseudo-metadata search agent 62 obtains user identification information and user attribute information included in the obtained prompt creation request, and creates a prompt using the LLM6 based on the obtained user attribute information. The LLM6 includes models that have been trained for pseudo-metadata generation. (Step S3-5) The pseudo-metadata search agent 62 uses the LLM6 to check whether the user attribute information necessary for the search is complete. The LLM6 includes models that have been trained to check the completeness of the user attribute information necessary for the search and list the necessary user attribute information. For example, the pseudo-metadata search agent 62 outputs information to the display control unit 66 for querying missing user attribute information. The display control unit 66 obtains information from the pseudo-metadata search agent 62 for querying missing user attribute information and displays the information for querying missing user attribute information on the UI 10. The pseudo-metadata search agent 62 retrieves information entered by user U in order to query the missing user attribute information displayed on UI 10.

[0078] (Step S4-5) The pseudo-metadata search agent 62 determines whether the user attribute information necessary for the search is available. (Step S5-5) If the pseudo-metadata search agent 62 determines that the user attribute information necessary for the search is not available, it requests additional information from user U. For example, the pseudo-metadata search agent 62 may request additional information via chat. After that, the process proceeds to step S1-5.

[0079] (Step S6-5) If the pseudo-metadata search agent 62 determines that it has all the user attribute information necessary for the search, it stores pseudo-user attribute information and generates pseudo-metadata based on the stored pseudo-user attribute information. (Step S7-5) Based on the generated pseudo-metadata, the pseudo-metadata search agent 62 places the pseudo-metadata in a (T+R+M+N) dimension vector space in which multiple contents stored in the content vector space storage unit 86 are embedded.

[0080] (Step S8-5) The pseudo-metadata search agent 62 lists a predetermined number of content items, starting with those closest to the placed pseudo-metadata, and derives their likelihood. When P is the coordinate of a point in the content vector space generated by the pseudo-metadata, the likelihood (L) of content X is expressed, for example, by equation (7). L(x) = α(1 / D(P,X)) (7) In equation (7), α is an arbitrary constant for adjusting the value. According to equation (7), it can be seen that the closer (smaller) the distance D, the larger the likelihood L becomes.

[0081] The natural language search agent 64 obtains a predetermined number of content items and information indicating the likelihood of each of those content items from the pseudo-metadata search agent 62. The natural language search agent 64 outputs the obtained predetermined number of content items and information indicating the likelihood of each of those content items to the display control unit 66. The display control unit 66 obtains the predetermined number of content items and information indicating the likelihood of each of those content items from the natural language search agent 64 and displays them on the UI 10.

[0082] Figure 12 is a flowchart showing an example of the operation flow of the information processing device 100 in this embodiment. Referring to Figures 1 and 12, other processes performed by the information processing device 100 for natural language search will be described. Here, as an example, we will describe the case where user U is logged in and the vector space generation agent 84 is generating a (T + R + M + N) dimension vector space.

[0083] (Step S1-6) The natural language search UI 18 receives a search prompt entered by user U. The natural language search agent 64 obtains the search prompt received by the natural language search UI 18 and the logged-in user identification information. Based on the obtained user identification information, the natural language search agent 64 obtains user attribute information from the user attribute DB 81. The natural language search agent 64 creates a prompt creation request including the user identification information and user attribute information and outputs it to the pseudo-metadata search agent 62.

[0084] (Step S2-6) The pseudo-metadata search agent 62 obtains a prompt creation request from the natural language search agent 64. The pseudo-metadata search agent 62 obtains user identification information and user attribute information included in the obtained prompt creation request, and creates a prompt using the LLM6 based on the obtained user attribute information. The LLM6 includes models that have been trained for pseudo-metadata generation.

[0085] (Step S3-6) The pseudo-metadata search agent 62 uses the LLM 6 to check whether the user attribute information necessary for the search is complete. The LLM 6 has learned to check the completeness of the user attribute information necessary for the search and to list the necessary user attribute information. For example, the pseudo-metadata search agent 62 outputs information to the display control unit 66 for querying missing user attribute information. The display control unit 66 obtains the information for querying missing user attribute information from the pseudo-metadata search agent 62 and displays the information for querying missing user attribute information on the UI 10. The pseudo-metadata search agent 62 obtains the information entered by user U in response to the information for querying missing user attribute information displayed on the UI 10.

[0086] (Step S4-6) If attribute generation is not complete, the pseudo-metadata search agent 62 excludes the dimensions relating to the ungenerated attributes from the search space. (Step S5-6) After excluding the dimensions relating to the ungenerated attributes from the search space in step S4-6, or if attribute generation is complete in step S3-6, the pseudo-metadata search agent 62 stores user attribute information and generates pseudo-metadata based on the stored user attribute information.

[0087] (Step S6-6) The pseudo-metadata search agent 62 places the pseudo-metadata, based on the generated pseudo-metadata, into a (T+R+M+N)-dimensional vector space in which multiple contents stored in the content vector space storage unit 86 are embedded. The pseudo-metadata search agent 62 generates a subspace containing only the dimensions in which attribute data exists, and searches this space.

[0088] (Step S7-6) The pseudo-metadata search agent 62 lists a predetermined number of content items, starting with those closest to the placed pseudo-metadata, and derives their likelihood. The natural language search agent 64 obtains a predetermined number of content items and information indicating the likelihood of each of those content items from the pseudo-metadata search agent 62. The natural language search agent 64 outputs the obtained predetermined number of content items and information indicating the likelihood of each of those content items to the display control unit 66. The display control unit 66 obtains a predetermined number of content items and information indicating the likelihood of each of those content items from the natural language search agent 64 and displays them on the UI 10.

[0089] Figure 13 is a flowchart showing an example of the operation flow of the information processing device 100 in this embodiment. Referring to Figures 1 and 13, the process by which the information processing device 100 displays the optimal classification for user U will be described. Here, as an example, the case in which the vector space generation agent 84 generates a (T + R + M + N) dimension vector space will be described. (Step S1-7) The information processing device 100 performs login processing. For example, user U enters user identification information and a password on the initial screen (not shown) displayed on UI 10. The information processing device 100 performs login processing based on the entered user identification information and password.

[0090] (Step S2-7) The display control unit 66 displays the classification creation screen on the UI 10. The classification creation screen includes a display for selecting whether to target content created by the user (user U), content created by others other than user U, or all content.

[0091] First, we will explain the case where user U chooses to target content that he or she has created. (Step S3-7-1) The vector space generation agent 84 creates a subspace in a (T+R+M+N) dimension vector space in which multiple contents stored in the content vector space memory unit 86 are embedded, by fixing the creator attribute, which is M-dimensional vector data, to itself. (Step S4-7-1) The hierarchical structure generation agent 50 creates and completes the classification on this subspace.

[0092] Next, we will explain what happens when user U chooses to target content created by others. (Step S3-7-2) The vector space generation agent 84 creates a subspace in a (T+R+M+N) dimension vector space in which multiple contents stored in the content vector space storage unit 86 are embedded, by fixing the creator attribute, which is M-dimensional vector data, to someone other than itself.

[0093] (Step S4-7-2) The hierarchical structure generation agent 50 creates classifications on this subspace. (Step S5-7-2) Based on the content viewing history, the hierarchical structure generation agent 50 leaves the lowest-level folder containing previously viewed content as is, and creates a classification called "Other" for the other folders, moves them under this folder, and completes the process. The hierarchical structure generation agent 50 can improve visibility by classifying items with a viewing frequency below a pre-set threshold into "Other".

[0094] Next, we will explain the case where user U chooses to include all content. (Step S3-7-3) The vector space generation agent 84 creates and completes the classification in a (T+R+M+N) dimension vector space in which the multiple contents stored in the content vector space storage unit 86 are embedded.

[0095] In the information processing device 100, the vector space generation agent 84 may use LLM to extract creator attributes that are similar to the user attributes of the user themselves. The vector space generation agent 84 may also create a classification based on a subset of the content vector space based on the extracted creator attributes.

[0096] Figure 14 is a flowchart showing another example of the operation flow of the information processing device 100 in this embodiment. Referring to Figures 1 and 14, the process by which the information processing device 100 displays the optimal classification for user U will be described. Here, as an example, the case in which the vector space generation agent 84 generates a (T + R + M + N) dimension vector space will be described. (Step S1-8) The information processing device 100 performs login processing. For example, user U enters user identification information and a password on the initial screen (not shown) displayed on UI 10. The information processing device 100 performs login processing based on the entered user identification information and password.

[0097] (Step S2-8) The natural language search UI 18 accepts the conditions for classification creation entered as prompts on the Chat input screen of the LLM (not shown). The natural language search agent 64 obtains user identification information and the conditions for classification creation entered as prompts from the natural language search UI 18 and outputs them to the pseudo-metadata search agent 62. The pseudo-metadata search agent 62 obtains user identification information and the conditions for classification creation entered as prompts from the natural language search agent 64. The pseudo-metadata search agent 62 outputs the obtained user identification information and prompts to the LLM (not shown). The LLM (not shown) obtains user identification information and prompts from the pseudo-metadata search agent 62, refers to the (T+R+M+N) dimension vector space in which multiple contents stored in the content vector space storage unit 86 are embedded, and creates a content subspace that matches the conditions of user U.

[0098] (Step S3-8) The vector space generation agent 84 creates and completes a classification in the content subspace that matches the conditions of user U created by LLM (not shown). Figure 15 shows an example of a content classification screen. As shown in Figure 15, the example of a content classification screen includes a section that displays major classifications and a section that displays subclassifications. Furthermore, the example of a content classification screen includes an update button UPBU.

[0099] The section displaying major categories shows one or more folders. In the example shown in Figure 15, the folders "AAA", "BBB", "CCC", ..., "XXX", ..., and "KKK" are displayed. The section displaying subcategories shows one or more folders that are included in the folder specified by user U, from among the one or more folders displayed in the section displaying major categories. In the example shown in Figure 15, the folders "PPP", "QQQ", "RRR", ..., "SSS", and "TTT" that are included in the "XXX" folder are displayed. Furthermore, when the update button UPBU is pressed, the maximum number of levels m that can be set by user U in the classification structure setting screen shown in Figure 7, and the average number of contents n in the lowest level folder are displayed. １ and the number of folders k located in the i-th level from the bottom. ｉ This will allow for corrections.

[0100] <Configuration Example> As an example configuration, the information processing device comprises a receiving unit that receives content, a content processing unit that assigns information identifying the physical address to the content received by the receiving unit and stores it in a content pool, and virtually classifies the content stored in the content pool, and a display control unit that causes the content processing unit to display the results of the virtual classification of the content on a display unit. By configuring it in this way, the information processing device can display the results of the virtual classification of the content stored in the content pool on a display unit. This reduces the effort required for content management.

[0101] As an example configuration, the content processing unit searches for content stored in the content pool, and the display control unit displays the results of the content search by the content processing unit on the display unit. By configuring it in this way, the information processing device can display content stored in the content pool on the display unit. For example, a virtual classification display UI may be created and displayed for each organization or user. By specifying content on the virtual classification display UI, the information processing device can display the specified content on the display unit. This reduces the effort required for content management. Here, instead of a physical address, or together with a physical address, a URI (Uniform Resource Identifier) ​​may be specified so that the content corresponding to the specified URI is displayed on the display unit.

[0102] As an example configuration, the content processing unit further comprises: a content pool crawler that receives information identifying the physical address assigned to content stored in a content pool; a metadata generation unit that obtains the physical address from the content pool, obtains information identifying the content from the content pool based on the obtained physical address, and generates metadata based on the information identifying the content; a vector space generation unit that generates an N-dimensional vector space (where N is an integer N > 1); and an embedding processing unit that embeds the content into the N-dimensional vector space generated by the vector space generation unit based on the metadata. The vector space generation unit generates k clusters (where k is an integer k > 1) from the multiple content embedded in the N-dimensional vector space. With this configuration, the information processing unit can generate an N-dimensional vector space as metadata representing the characteristics of the content. Furthermore, the information processing unit can generate k clusters from the multiple content embedded in the generated N-dimensional vector space. Since multiple content can be classified into k clusters, the effort required for content management can be reduced.

[0103] As one example configuration, the vector space generation unit derives the inter-content distances between multiple contents embedded in an N-dimensional vector space, and generates k clusters from the multiple contents based on the derived inter-content distances. By configuring it in this way, the information processing device can generate k clusters based on the inter-content distances between multiple contents embedded in an N-dimensional vector space. Since multiple contents can be classified into k clusters, the effort required for content management can be reduced.

[0104] As one example configuration, the vector space generation unit derives the distance between content items based on the weight vector. By configuring it in this way, the information processing device can derive the distance between content items based on the weight vector, which allows it to average out the variability between each attribute to derive the distance between content items, or to increase the weight of attributes whose influence on the distance between content items you want to increase.

[0105] As one example configuration, the content processing unit further comprises a hierarchical structure generation unit that generates a hierarchical structure based on the distance between the centroids of each of the k clusters. By configuring it in this way, the information processing unit can classify content based on the distance between the centroids of each of the k clusters, thereby reducing the effort required for content management.

[0106] As an example configuration, the vector space generation unit creates M-dimensional vector data (where M is an integer M > 1) representing the attributes of the content creator, and generates an M-dimensional vector space. The embedding processing unit then embeds the content into the N+M-dimensional vector space. With this configuration, the information processing device can create M-dimensional vector data representing the attributes of the content creator and generate an N+M-dimensional vector space. Furthermore, the information processing device can generate multiple clusters based on the inter-content distances between multiple contents embedded in the generated N+N-dimensional vector space. Since multiple contents can be classified into multiple clusters, the effort required for content management can be reduced.

[0107] As one example configuration, the vector space generation unit creates R-dimensional (where R is an integer R > 1) vector data based on management rules and generates an R-dimensional vector space. The embedding processing unit embeds the content into the N + M + R-dimensional vector space. With this configuration, the information processing device can create R-dimensional vector data based on management rules and generate an N + M + R-dimensional vector space. Furthermore, the information processing device can generate multiple clusters based on the inter-content distances between multiple contents embedded in the generated N + N + R-dimensional vector space. Since multiple contents can be classified into multiple clusters, the effort required for content management can be reduced.

[0108] As one example configuration, the vector space generation unit creates T-dimensional vector data (where T is an integer T > 1) representing the time axis and attributes, and generates a T-dimensional vector space. The embedding processing unit embeds the content into the N + M + R + T-dimensional vector space. By configuring it in this way, the information processing device can create T-dimensional vector data representing the time axis and attributes, and generate an N + M + R + T-dimensional vector space. Furthermore, the information processing device can generate multiple clusters based on the inter-content distances between multiple contents embedded in the generated N + M + R + T-dimensional vector space. Since multiple contents can be classified into multiple clusters, the effort required for content management can be reduced.

[0109] As one example configuration, the vector space generation unit creates U-dimensional vector data (where U is an integer U > 1) representing user attributes and generates a U-dimensional vector space. The embedding unit then embeds the content into the U + N + M + R + T-dimensional vector space. By configuring it in this way, the information processing device can create U-dimensional vector data representing user attributes and generate a U + N + M + R + T-dimensional vector space. Furthermore, the information processing device can generate multiple clusters based on the inter-content distances between multiple contents embedded in the generated U + N + M + R + T-dimensional vector space. Since multiple contents can be classified into multiple clusters, the effort required for content management can be reduced.

[0110] As an example configuration, the content processing unit further includes a pseudo-metadata search unit that generates pseudo-metadata based on a search prompt and user attributes, and searches for content based on the generated pseudo-metadata. The display control unit causes the display unit to display the content searched by the pseudo-metadata search unit. With this configuration, the information processing device can generate pseudo-metadata based on a search prompt and user attributes, and search for content based on the generated pseudo-metadata.

[0111] As one example configuration, the vector space generation unit derives the similarity between content based on the distance between content, and the display control unit displays the similarity derived by the vector space generation unit on the display unit. By configuring it in this way, the information processing device can inform the user of how similar content is to a given content by notifying them of the similarity. Here, cosine similarity, Manhattan distance, Mahalanobis distance, Perceptual Distance, tree distance, etc., can also be applied as the distance between content.

[0112] As one example configuration, the vector space generation unit derives the inter-content distance between multiple content items based on the content search results obtained by the pseudo-metadata search unit. With this configuration, the information processing device can generate k clusters based on the inter-content distances between multiple content items, further based on the content search results. Since multiple content items can be classified into k clusters, the effort required for content management can be reduced. Here, the method for classifying multiple content items into k clusters does not depend on how the distance is defined. In addition to the k-nearest neighbor method, other clustering methods can also be applied.

[0113] As one example configuration, the hierarchical structure generation unit analyzes metadata for the content contained in each of the k clusters and creates classification information for recommending folders based on the metadata analysis results. By configuring it in this way, the information processing device can analyze metadata for the content contained in each of the k clusters, and therefore can create classification information for recommending folders based on the metadata analysis results.

[0114] As one example configuration, the metadata generation unit, if there is missing information in the metadata generated based on the content, creates information to query for the missing information, retrieves the information input to the created query information, and completes the metadata. By configuring it in this way, the information processing device can query the user U for missing metadata, and can then generate a vector space based on the metadata obtained through the query.

[0115] As one example configuration, the hierarchical structure generation unit generates a hierarchical structure based on the maximum number of levels, the average number of contents in the lowest-level folder, and the number of folders in any level below the lowest level. By configuring it in this way, the information processing device can generate a hierarchical structure based on the maximum number of levels, the average number of contents in the lowest-level folder, and the number of folders in any level below the lowest level, as set by user U.

[0116] As one example configuration, the pseudo-metadata search unit derives content characteristics based on the search results for content, and the display control unit displays information indicating the content characteristics derived by the pseudo-metadata search unit on the display unit. By configuring it in this way, the information processing device can derive content characteristics based on the search results for content and display them on the display unit, thereby allowing the user U to recognize the characteristics of the content.

[0117] As an example configuration, the pseudo-metadata search unit derives a question to narrow down the content based on the characteristics of the derived content, and the display control unit displays information indicating the question derived by the pseudo-metadata search unit on the display unit. By configuring it in this way, the information processing device can derive a question to narrow down the content based on the characteristics of the content and display it on the display unit, so that the optimal content can be selected based on the answers to the questions.

[0118] As one example configuration, the pseudo-metadata search unit creates information to query for missing user attributes if there is missing information in the user attributes. By configuring it in this way, the information processing device can query user U for missing user attributes, and then search for content based on the user attributes obtained through the query.

[0119] While embodiments of the present invention have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments, and design changes and the like are also included within the scope of the gist of the present invention. Furthermore, a computer program for realizing the functions of the information processing device 100 described above may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be read by a computer system and executed. The term "computer system" as used herein may include hardware such as an operating system and peripheral devices.

[0120] Furthermore, "computer-readable recording media" refers to writable non-volatile memory such as flexible disks, magneto-optical disks, ROMs, and flash memory, portable media such as DVDs (Digital Versatile Disks), and storage devices such as hard disks built into computer systems.

[0121] Furthermore, "computer-readable recording media" also includes volatile memory (such as DRAM (Dynamic Random Access Memory)) within computer systems that act as servers or clients when programs are transmitted via networks such as the Internet or communication lines such as telephone lines, which retain programs for a certain period of time.

[0122] Furthermore, the above program may be transmitted from a computer system that stores the program in a memory device or the like to another computer system via a transmission medium or by transmission waves within the transmission medium. Here, the "transmission medium" for transmitting the program refers to a medium that has the function of transmitting information, such as a network (communication network) like the Internet or a communication line (communication line) like a telephone line.

[0123] Furthermore, the above program may be intended to implement some of the functions described above. It may also be a so-called differential file (differential program) that can implement the aforementioned functions in combination with programs already recorded in the computer system.

[0124] 100... Information processing device, 10... UI, 12... Account registration / update UI, 14... Content pool UI, 16... Classification display UI, 18... Natural language search UI, 20... Account registration / update processing unit, 30... Content pool, 40... Virtual content pool construction processing unit, 42... Content pool crawler, 44... Metadata generation agent, 50... Hierarchical structure generation agent, 62... Pseudo-metadata search agent, 64... Natural language search agent, 66... ​​Display control unit, 80... Virtual content pool, 81... User attribute DB, 82... Metadata DB, 83... Usage log DB, 84... Vector space generation agent, 85... Embedded processing unit, 86... Content vector space storage unit

Claims

1. An information processing device comprising: a receiving unit for receiving content; a content processing unit for assigning information to identify a physical address to the content received by the receiving unit, storing it in a content pool, and virtually classifying the content stored in the content pool; and a display control unit for causing a display unit to display the results of the virtual classification of the content by the content processing unit.

2. The information processing apparatus according to claim 1, wherein the content processing unit searches for content stored in the content pool, and the display control unit causes the content processing unit to display the results of the content search on the display unit.

3. The information processing apparatus according to claim 2, wherein the content processing unit further comprises: a content pool crawler that receives information identifying a physical address assigned to content stored in the content pool; a metadata generation unit that obtains information identifying a physical address from the content pool crawler, obtains content from the content pool based on the obtained information identifying a physical address, and generates metadata based on the obtained content; a vector space generation unit that generates an N-dimensional vector space (where N is an integer N > 1); and an embedding processing unit that embeds content into the N-dimensional vector space generated by the vector space generation unit based on metadata, wherein the vector space generation unit generates k clusters (where k is an integer k > 1) from a plurality of content embedded in the N-dimensional vector space (where N is an integer N > 1).

4. The information processing apparatus according to claim 3, wherein the vector space generation unit derives the inter-content distances between a plurality of contents embedded in an N-dimensional vector space, and generates k clusters from the plurality of contents based on the derived inter-content distances.

5. The information processing apparatus according to claim 4, wherein the vector space generation unit further derives the distance between contents based on the weight vector.

6. The information processing apparatus according to claim 3, further comprising: a content processing unit that generates a hierarchical structure based on the distance between the centroids of each of the k clusters.

7. The information processing apparatus according to claim 3, wherein the vector space generation unit creates M-dimensional vector data (where M is an integer M > 1) indicating the attributes of the content creator, and generates an M-dimensional vector space, and the embedding processing unit embeds the content into an N + M-dimensional vector space.

8. The information processing apparatus according to claim 7, wherein the vector space generation unit creates R-dimensional (where R is an integer R > 1) vector data based on management rules and generates an R-dimensional vector space, and the embedding processing unit embeds the content into an N + M + R-dimensional vector space.

9. The information processing apparatus according to claim 8, wherein the vector space generation unit creates T-dimensional vector data (where T is an integer T > 1) representing the time axis and attributes, and generates a T-dimensional vector space, and the embedding processing unit embeds the content into an N + M + R + T-dimensional vector space.

10. The information processing apparatus according to claim 9, wherein the vector space generation unit creates U-dimensional vector data (where U is an integer U > 1) representing user attributes and generates a U-dimensional vector space, and the embedding processing unit embeds the content into a U + N + M + R + T-dimensional vector space.

11. The information processing apparatus according to claim 3, wherein the content processing unit further comprises a pseudo-metadata search unit that generates pseudo-metadata based on a search prompt and user attributes, and searches for content based on the created pseudo-metadata, and the display control unit causes the display unit to display the content searched by the pseudo-metadata search unit.

12. The information processing apparatus according to claim 4, wherein the vector space generation unit derives a similarity between contents based on the distance between contents, and the display control unit causes the display unit to display the similarity derived by the vector space generation unit.

13. The information processing apparatus according to claim 11, wherein the vector space generation unit derives the distance between multiple contents based on the search results of the pseudo-metadata search unit.

14. The information processing apparatus according to claim 6, wherein the hierarchical structure generation unit analyzes metadata for the content contained in each of the k clusters and creates classification information for recommending folders based on the metadata analysis results.

15. The information processing apparatus according to claim 3, wherein, if there is missing information in the metadata generated based on the content, the metadata generation unit creates information for querying for the missing information, obtains the information input to the created query information, and completes the metadata.

16. The information processing apparatus according to claim 6, wherein the hierarchical structure generation unit generates a hierarchical structure based on the maximum number of levels, the average number of contents in the lowest level folder, and the number of folders in any level below the lowest level.

17. The information processing apparatus according to claim 11, wherein the pseudo-metadata search unit derives the characteristics of the content based on the search results for the content, and the display control unit causes the display unit to display information indicating the characteristics of the content derived by the pseudo-metadata search unit.

18. The information processing apparatus according to claim 17, wherein the pseudo-metadata search unit derives a question for narrowing down the content based on the characteristics of the derived content, and the display control unit causes the display unit to display information indicating the question for narrowing down the content derived by the pseudo-metadata search unit.

19. The information processing apparatus according to claim 11, wherein the pseudo-metadata search unit creates information for querying for missing information if there is missing information in the user attributes.

20. An information processing method performed by a computer, comprising: receiving content; assigning information to the received content that identifies a physical address; storing the content to which the physical address has been assigned in a content pool; virtually classifying the content stored in the content pool; and displaying the results of the virtually classifying of the content on a display unit.

21. A program that enables a computer to receive content, assign information to the received content that identifies a physical address, store the content with the assigned physical address in a content pool, virtually classify the content stored in the content pool, and display the results of the virtual classification of the content on a display unit.

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