Information processing system, information processing device, and control program

Abstract

To provide an information processing system configured to annotate a keyword of input data in accordance with knowledge of a user, an information processing apparatus, and a control program.SOLUTION: An information processing system includes: acquiring keyword information and knowledge information of a specific user (S201); generating a subset (sub-network) of the knowledge information of the specific user (S202); comparing the keyword information with the subset (S203); annotating the keyword on the basis of a result of the comparison (S204); and generating output data (S205).SELECTED DRAWING: Figure 12

Description

Technical Field

[0001] The present invention relates to an information processing system, an information processing apparatus, and a control program.

Background Art

[0002] In recent years, the development of technologies for assisting users in better understanding the content of documents has been active. Such technologies include, for example, technologies related to creating summaries of documents, translation to make documents easier for readers to understand, and adding explanations (annotations) of technical terms (keywords) included in documents.

[0003] For example, Patent Document 1 below discloses a technique for providing explanations corresponding to the respective specialized fields of each listener (user) who listens to a speech for the technical terms included in the speech of a lecturer to each listener.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, while the technology described in Patent Document 1 provides explanations of technical terms based on each listener's area of ​​expertise, it does not take into account each listener's level of knowledge or individuality. Therefore, there is a possibility that necessary explanations may not be provided to listeners, or conversely, that unnecessary explanations may be provided. For example, even if a technical term belongs to a listener's area of ​​expertise, the listener may not necessarily have knowledge of that term. If a technical term belongs to a listener's area of ​​expertise, even if the listener does not have knowledge of that term, it may be determined that the listener has knowledge of the term, and no explanation of the technical term will be provided. However, in this case, listeners who do not have knowledge of the technical term will have difficulty understanding the content of the lecture.

[0006] On the other hand, even if a technical term does not belong to the listener's field of expertise, the listener may still possess knowledge of that term. In this case, because the technical term does not belong to the listener's field of expertise, it is determined that the listener does not possess knowledge of the term, and an explanation of the technical term is provided. However, this means that the listener is provided with an explanation even though they already possess knowledge of the technical term. As a result, the listener may feel annoyed because they are required to confirm more information, and they may have difficulty finding explanations of other technical terms they need.

[0007] This invention has been made in view of the above circumstances, and aims to provide an information processing system, an information processing device, and a control program that can add annotations to keywords in input data according to the user's knowledge. [Means for solving the problem]

[0008] The above objectives of the present invention are achieved by the following means.

[0009] (1) Data acquisition unit and corresponding to multiple pieces of information that constitute knowledge multiple element includingThe system includes an information storage unit that stores knowledge information for each user, an information comparison unit that compares keyword information related to keywords extracted from data acquired as input data by the data acquisition unit with knowledge information associated with the user, and an annotation unit that adds annotations to the keywords in the input data based on the comparison results by the information comparison unit, wherein the information comparison unit ,before Whether the recorded knowledge information contains the keywords from the aforementioned keyword information or not , or whether the keyword information and the knowledge information match or are similar. The annotation addition unit determines that, If, among the multiple keywords in the keyword information, the number of keywords included in the user's knowledge information, or the number of keywords that match or are similar to the elements of the knowledge information, is less than a threshold, An information processing system that adds annotations to keywords among a plurality of the aforementioned keywords that are not included in the user's knowledge information.

[0010] (2) before The keyword information listed is multiple no Ki -word including This is information, and the information comparison unit is the keyword information By calculating the similarity based on the vector of keywords contained in the knowledge information and the vector of elements contained in the knowledge information, it is determined whether the keyword information and the knowledge information match or are similar. An information processing system as described in (1) above, which determines whether or not it is present.

[0011] (3) The keyword information is a plurality of the keyword Includes multiple nodes, each representing a different element, and links that define the relationships between the nodes. network Expressed by This is information ru, the above( 1 The information processing system described in ).

[0012] (4) The knowledge information is information that networks the relationships between the elements corresponding to multiple pieces of information that constitute the knowledge. The information comparison unit determines whether the keyword information and the knowledge information match or are similar by calculating a similarity between the keyword information network and the knowledge information network based on the node match rate and the distance between nodes. the above( 3) The information processing system described.

[0013] (5) The subset extraction unit further extracts a subset of the user's knowledge information based on the keywords. ,before The subset extraction unit searches for nodes tagged with the keyword from the user's knowledge information, extracts a subset containing the nodes tagged with the keyword, and the information comparison unit compares the keyword information with the subset. 4)The described information processing system.

[0015] ( 6 ) Further comprising an output generation unit that generates output data based on the annotations added to the keyword by the annotation addition unit, the information processing system according to any one of (1) to ( 5 ).

[0016] ( 7 ) The data acquisition unit acquires the data uploaded by the user as the input data, the information processing system according to any one of (1) to ( 6 ).

[0017] ( 8 ) Further comprising a mail monitoring unit that monitors the received mail of the user, and the data acquisition unit acquires the received mail as the input data when the received mail satisfies a predetermined condition, the information processing system according to any one of (1) to ( 6 ).

[0018] ( 9 ) Further comprising a file monitoring unit that monitors files moved to a folder for storing the user's files, and a file reading unit that reads the files stored in the folder, and the data acquisition unit acquires the files in the folder or the file reading result as the input data when the files satisfy a predetermined condition, the information processing system according to any one of (1) to ( 6 ).

[0019] (10) Data server knowledge server And an information processing system having an information processing apparatus, wherein the data server includes a data acquisition unit, a keyword information generation unit that generates keyword information from the data acquired as input data by the data acquisition unit, The knowledge server has, An information storage that stores knowledge information including a plurality of elements corresponding to a plurality of information constituting knowledge for each user departmentAn information processing system comprising: a keyword information generation unit that stores the keyword information generated by the keyword information generation unit, and transmits the keyword information and the knowledge information to the information processing device, the information processing device comprising: an information comparison unit that compares the acquired keyword information with the knowledge information associated with the user, and an annotation unit that adds annotations to the keywords of the input data based on the comparison results by the information comparison unit, the information comparison unit that determines whether the keywords of the keyword information are included in the knowledge information, or whether the keyword information and the knowledge information match or are similar, and the annotation unit that adds annotations to the keywords among the multiple keywords of the keyword information that are not included in the user's knowledge information if the number of keywords among the multiple keywords of the keyword information that are included in the user's knowledge information, or the number of keywords that match or are similar to the elements of the knowledge information is less than a threshold, wherein the annotation unit adds annotations to the keywords among the multiple keywords that are not included in the user's knowledge information.

[0021] ( 11 ) The annotation unit adjusts the threshold according to the attributes of the input data, ( 10) The information processing system described above.

[0022] (12) The information storage unit is This is something that the knowledge server possesses. The aforementioned knowledge information about multiple users is stored for each user. ru , the above (1)~(9 )of An information processing system described in any one of the following.

[0025] ( 13 ) The annotation unit performs either a simple annotation that adds an annotation containing only text to the keyword, or a detailed annotation that adds an annotation containing at least a figure to the keyword, as described above (1 1) The information processing system described.

[0026] ( 14 The annotation unit calculates the difficulty level of the input data based on the threshold and the comparison result, and if the difficulty level is high, it performs the detailed annotation, while if the difficulty level is low, it performs the simplified annotation.13 The information processing system described in ).

[0027] ( 15 The information processing system described in (6) above, wherein the output data includes the input data and annotations attached to the keywords.

[0028] ( 16 ) The aforementioned knowledge information is generated based on at least one of the following: data relating to social networking services (SNS) used by the user, data relating to websites viewed by the user, and document data created by the user, as described in (1)~( 15 An information processing system described in any one of the following:

[0029] (17) A keyword information acquisition unit that acquires keyword information from data acquired as input data; a knowledge information acquisition unit that acquires knowledge information for a specific user that includes multiple elements corresponding to multiple pieces of information that constitute knowledge; an information comparison unit that compares the keyword information with the knowledge information associated with the specific user; and an annotation addition unit that adds annotations to the keywords of the input data based on the comparison results by the information comparison unit, wherein the information comparison unit determines whether the keywords of the keyword information are included in the knowledge information, or whether the keyword information and the knowledge information match or are similar, and the annotation addition unit determines whether, among the multiple keywords of the keyword information, identification An information processing device that, when the number of keywords included in the user's knowledge information, or the number of keywords that match or are similar to elements of the knowledge information, is less than a threshold, adds annotations to keywords among a plurality of keywords that are not included in the knowledge information of the specific user.

[0030] (18) A keyword information acquisition step (a) which acquires keyword information from data acquired as input data; a knowledge information acquisition step (b) which acquires knowledge information for a specific user that includes multiple elements corresponding to multiple pieces of information that constitute knowledge; a comparison step (c) which compares the keyword information with the knowledge information associated with the specific user; and an annotation addition step (d) which adds annotations to the keywords of the input data based on the comparison results in the comparison step (c). It is a process In the comparison step (c), it is determined whether the knowledge information contains the keywords of the keyword information, or whether the keyword information and the knowledge information match or are similar. In the annotation step (d), among the multiple keywords of the keyword information, identification A control program for causing a computer to perform a process of adding annotations to keywords among a plurality of keywords that are not included in the knowledge information of a particular user, if the number of keywords included in the user's knowledge information, or the number of keywords that match or are similar to elements of the knowledge information, is less than a threshold. [Effects of the Invention]

[0031] According to the present invention, annotations are added to the keywords in the input data based on the comparison result between the keyword information of the input data and the user's knowledge information, so that annotations are added to the keywords in the input data according to the knowledge the user possesses. [Brief explanation of the drawing]

[0032] [Figure 1] This is a schematic block diagram illustrating the configuration of an information processing system according to the first embodiment. [Figure 2] Figure 1 is a schematic block diagram illustrating the hardware configuration of the data server shown. [Figure 3] This is a schematic diagram illustrating the generation of keyword information in a data server. [Figure 4]Figure 1 is a network diagram illustrating a portion of the knowledge information stored in the knowledge database shown. [Figure 5] Figure 1 is a schematic block diagram illustrating the configuration of the information processing device shown. [Figure 6] Figure 5 is a functional block diagram illustrating the main functions of the information processing device shown. [Figure 7] This is a conceptual diagram illustrating the similarity between keyword information and knowledge information. [Figure 8] Figure 1 is a schematic block diagram illustrating the hardware configuration of the client terminal shown. [Figure 9] This is a sequence chart illustrating a general processing procedure for the control method of the information processing system according to the first embodiment. [Figure 10] Figure 1 is a schematic diagram showing an example of how output data from the information processing device is displayed. [Figure 11] Figure 1 is a schematic diagram showing another example of how to display the output data of the information processing device shown. [Figure 12] Figure 9 is a flowchart illustrating the general processing procedure for step S103 of the sequence chart. [Figure 13] This is a schematic diagram illustrating the comparison between keyword information and knowledge information. [Figure 14] This is a conceptual diagram illustrating a method for determining whether or not to add annotations to keywords in the examples and comparative examples. [Figure 15] This is a conceptual diagram illustrating a method for determining whether or not to add annotations to keywords in the examples and comparative examples. [Figure 16] This is a sequence chart illustrating the acquisition of input data in the second embodiment. [Modes for carrying out the invention]

[0033] Embodiments of the present invention will be described below with reference to the attached drawings. In the description of the drawings, the same elements are denoted by the same reference numerals, and redundant explanations are omitted. Also, the dimensional ratios in the drawings are exaggerated for illustrative purposes and may differ from the actual ratios.

[0034] (First Embodiment) <Information Processing System 100> Figure 1 is a schematic block diagram illustrating the configuration of an information processing system 100 according to the first embodiment. The information processing system 100 includes a data server 200, a knowledge server 300, and an information processing device 400, which are interconnected via a communication network 101, including, for example, a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet. As will be described later, the data server 200 is responsible for generating keyword information based on keywords contained in the input data and providing it to the information processing device 400, and the knowledge server 300 is responsible for providing knowledge information stored for each user to the information processing device 400. The information processing device 400 adds annotations to keywords based on the provided keyword information and knowledge information.

[0035] Furthermore, the information processing system 100 is connected to multiple client terminals A501 to C503 via a communication network 101. Note that the example shown in Figure 1 illustrates a case where three client terminals A501 to C503 are connected to the information processing system 100, but there may be more or fewer client terminals than three. Also, the information processing system 100 may include client terminals.

[0036] <Data Server 200> Figure 2 is a schematic block diagram illustrating the hardware configuration of the data server 200 shown in Figure 1, and Figure 3 is a schematic diagram illustrating the generation of keyword information in the data server 200.

[0037] The data server 200 functions as a server (computer). As shown in Figure 2, the data server 200 includes a CPU (Central Processing Unit) 210, RAM (Random Access Memory) 220, ROM (Read Only Memory) 230, auxiliary storage unit 240, and communication unit 250, etc.

[0038] The CPU 210 executes the OS (Operating System) and control programs for the data server 200, which are deployed in the RAM 220, and controls the operation of the data server 200. The control programs are pre-stored in the ROM 230 or auxiliary storage unit 240. The RAM 220 stores data and other information temporarily generated by the processing of the CPU 210. The ROM 230 stores programs executed by the CPU 210, as well as data and parameters used to execute those programs.

[0039] The auxiliary storage unit 240 includes, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive), or flash memory.

[0040] The communication unit 250 has communication devices such as a network interface card (NIC) and transmits data to the knowledge server 300 and the information processing device 400 via the communication network 101.

[0041] As shown in Figure 3, the CPU 210 controls the communication unit 250 by executing a control program, and functions as a data acquisition unit 261. In this embodiment, the data acquisition unit 261 acquires input data from, for example, a client terminal A501 used by user A. The input data may be a document containing at least one keyword (e.g., W1 to W9). This input data is also transmitted to the knowledge server 300.

[0042] Furthermore, the CPU 210 functions as a keyword information generation unit 262. The keyword information generation unit 262 extracts keywords from the input data using natural language processing and generates keyword information based on the extracted keywords. The generated keyword information is stored in the RAM 220 or the auxiliary storage unit 240.

[0043] More specifically, the keyword information generation unit 262 performs morphological analysis on the input data to divide the sentences contained in the input data into parts of speech, and extracts, for example, words that appear repeatedly as keywords. The keyword information generation unit 262 also extracts the relationships between the extracted keywords and generates structured keyword information. The keyword information includes, for example, multiple keywords and the relationships (links) between those multiple keywords. For example, the keyword information can be represented as a network. In this case, each keyword can be represented as a node (shown as a circle), and the relationships between nodes can be represented as lines (links) connecting each node.

[0044] <Knowledge Server 300> Figure 4 is a network diagram illustrating a portion of the knowledge information stored in the knowledge database 310 shown in Figure 1. Knowledge information is, for example, information that networks the relationships between elements corresponding to multiple pieces of information that constitute the knowledge possessed by a single user. The elements can be concepts represented by words, sentences, clauses, etc. In this embodiment, the knowledge information of a single user is modeled as a knowledge network (or semantic network) in which multiple concepts are linked according to their relationships. Knowledge information of multiple users is stored in the knowledge database 310. Knowledge information is generated or updated based on the usual work of a user on client terminal 501A (for example, the input data received from client terminal A501) and the usual work of another user on another client terminal, and is stored in the knowledge database 310.

[0045] The knowledge server 300 is a server (computer) that has a knowledge database 310 (see Figure 1). The knowledge server 300 has the same hardware configuration as the data server 200, so a detailed explanation is omitted. The knowledge database 310 is stored in an auxiliary storage unit (information storage unit).

[0046] Knowledge information includes multiple elements and relationships (connections) between those elements. For example, as shown in the network diagram in Figure 4, in knowledge information, each element can be represented by a node (shown as a circle) 312, and the connections between nodes 312 can be represented by lines (links) 313 connecting each node 312. Figure 4 illustrates network 311, which is part of user A's knowledge information (knowledge network). For example, nodes N00, N01, N04, N08, and N09 correspond to the elements "hypertension," "guidelines," "treatment," "side effects," and "definition," and nodes N01, N04, N08, and N09 are connected to node N00. Relationships between nodes include superordinate / subordinate concept relationships (for example, the source node is a superordinate concept and the destination node is a subordinate concept), relationships where the destination node is an attribute of the source node, where the destination node is a required or optional attribute of the source node, and where the destination node is any possible value of the source node.

[0047] Furthermore, knowledge information may include information representing the relative importance of each element within a group of interconnected elements (hereinafter also referred to as an "element group"). Elements that are more important than other elements may, for example, become the starting point or key point of the knowledge represented by the knowledge information. For example, elements that are frequently linked to other elements are considered relatively important. Therefore, the importance of an element can be judged by the degree of its connections to other elements. The knowledge database 310 may also be configured to store the importance of elements numerically.

[0048] On a network diagram, for example, nodes with many connections to other nodes are drawn larger than nodes with fewer connections. In the example shown in Figure 4, node N00, which represents "hypertension," is drawn larger than other nodes because it has many connections to other nodes, N01, N04, N08, and N09. Furthermore, node N00 is the starting point of the knowledge represented by network 311.

[0049] Furthermore, knowledge information may also include information representing the strength of the connections between elements. The strength of these connections can also be stored numerically in the knowledge database 310. On the network diagram, for example, the stronger the connection, the thicker the line is drawn.

[0050] Furthermore, knowledge information may include information about the field of knowledge. For example, each node in the knowledge information is associated with a tag that indicates the field of knowledge, etc. For instance, the knowledge database 310 or a table stores information about the node number and the tag corresponding to that node number.

[0051] The knowledge database 310 stores knowledge information for multiple users, with each user having their own set of knowledge information. This knowledge information is generated or updated based on each user's usual activities on the client terminal and stored in the knowledge database 310. User activities on the client terminal include, for example, creating various documents using word processing software or email software, and browsing various documents and web pages. Knowledge information can be generated based on at least one of the following: document data created or reviewed by the user, data related to social networking services (SNS) used by the user, or data related to documents, websites, and emails viewed by the user.

[0052] Furthermore, considering that document creation generally requires deeper knowledge than simply viewing documents or web pages, it is possible to rank the elements of knowledge information (knowledge) according to the type of work performed by the user. For example, knowledge gained through document creation could be given the highest rank, knowledge gained through document review could be given a medium rank, and knowledge gained from viewing web pages or emails could be given the lowest rank.

[0053] The knowledge server 300 reads the user's knowledge information from the auxiliary storage unit and transmits it to the information processing device 400 in response to a request from the information processing device 400.

[0054] In the example above, a knowledge network model of element sets was illustrated, but it is also possible to model it as a network (semantic network) that considers not only the presence or absence of connections between elements and the strength of those connections, but also the relationships between them (such as inclusion relationships).

[0055] <Information Processing Device 400> The information processing device 400 functions as a server (computer). As shown in Figure 5, the information processing device 400 includes a CPU 410, RAM 420, ROM 430, auxiliary storage unit 440, and communication unit 450, etc. Since the information processing device 400 has a hardware configuration similar to that of the data server 200 and the knowledge server 300, a detailed explanation is omitted. In this embodiment, the auxiliary storage unit 440 stores information processing programs executed by the CPU 410. The auxiliary storage unit 440 also stores data related to definitions and explanations of terms in various fields for adding annotations to keywords. This data includes text, diagrams, photographs, videos, audio, etc.

[0056] Figure 6 is a functional block diagram illustrating the main functions of the CPU 410 of the information processing device 400 shown in Figure 5. In this embodiment, the CPU 410 performs the functions of a subset extraction unit 411, an information comparison unit 412, an annotation addition unit 413, and an output generation unit 414 by executing a control program for the information processing device 400.

[0057] The subset extraction unit 411 extracts a subset (subnetwork) from the knowledge network of a specific user (for example, user A). More specifically, the subset extraction unit 411 obtains keyword information from the data server 200 and knowledge information (knowledge network) of a specific user from the knowledge server 300, and extracts a subset from the knowledge information based on the keywords contained in the input data. The subset extraction unit 411 functions as both a keyword information acquisition unit and a knowledge information acquisition unit. The subset extraction unit 411 determines the range of the subset to be extracted from the knowledge information based on the tag information attached to each node. For example, in the example shown in Figure 4, since the input data contains the keyword "hypertension," the subset extraction unit 411 searches for nodes in the user's knowledge information that are tagged with the medical field. As a result of the search, for example, a range including nodes tagged with the medical field is extracted as a subset.

[0058] The information comparison unit 412 compares the keyword information with the knowledge information (subset) associated with a specific user (the user who entered the input data). More specifically, the information comparison unit 412 determines whether the keywords in the keyword information are included in the knowledge information of the specific user. Alternatively, the information comparison unit 412 determines whether the keyword information and the knowledge information match (or are similar). To determine whether the keyword information and the knowledge information match (or are similar), for example, the similarity between the keyword information and the knowledge information is calculated. If the similarity is greater than or equal to a predetermined value, it is determined to be similar; if it is less than the predetermined value, it is determined to be dissimilar.

[0059] Similarity is an index that represents the degree to which keyword information and knowledge information are similar. As shown in Figure 7, similarity can be represented, for example, by the distance between the centers of two conceptual regions 800 represented by a user's knowledge information and conceptual region 801 represented by keyword information, or by the overlapping region 802 of the two regions. In other words, the smaller the distance between the centers of the two regions, the higher the similarity; the larger the distance, the lower the similarity. Also, the larger the overlapping region 802 of the two regions, the higher the similarity; and the smaller the region 802, the lower the similarity. Region 802 is defined by the keyword groups and element groups included in this region and the relationships between them. Note that regions 800, 801, and 802 are usually multidimensional regions, but in Figure 7, they are represented as two-dimensional regions for simplicity of explanation.

[0060] Furthermore, similarity can be calculated based on the vector of keywords included in the keyword information and the vector of elements included in the knowledge information. In this specification, these two vectors are referred to as the keyword information vector and the knowledge information vector, respectively. The keyword information vector may be the average or representative value of the vectors of multiple keywords included in the keyword information, and the knowledge information vector may be the average or representative value of the vectors of multiple elements included in the knowledge information.

[0061] More specifically, the information processing device 400 converts multiple keywords contained in keyword information into vector values, for example, using a method such as Word2Vec, calculates the average value of these vectors, and obtains the keyword information vector. Keywords with similar meanings, such as synonyms, will have similar vector values. Similarly, for knowledge information, multiple elements contained in the knowledge information are converted into vector values, calculates the average value of these vectors, and obtains the knowledge information vector. Elements with similar meanings, such as synonyms, will have similar vector values. The information processing device 400 then calculates the cosine similarity based on the keyword information vector and the knowledge information vector. The cosine similarity between the keyword information vector and the knowledge information vector is also called the distance between the two vectors.

[0062] Furthermore, similarity can be calculated between a network of keyword information and a network of knowledge information based on the node agreement rate and the distance between nodes. For example, in the network with "hypertension" as the root, as illustrated in Figure 4, knowledge far from the root is not important, so subsequent levels are ignored, and the nodes from the root to a predetermined level can be used to calculate the node agreement rate and the distance between nodes up to the nth level.

[0063] Furthermore, the information comparison unit 412 can assign weights to specific keywords or groups of keywords in the keyword information when calculating similarity. This allows the contribution of specific keywords or groups of keywords to be greater than that of other keywords or groups of keywords in the similarity calculation. For example, if the input data contains the keyword "treatment," assigning a weight to the keyword "treatment" allows its contribution to the similarity calculation to be greater than that of other keywords. This enables an approximate comparison between keyword information and knowledge information. For example, if the point of comparison is "treatment" for "hypertension," and keywords other than "treatment" are not particularly important, similarity can be calculated without being constrained by minor details. Therefore, the comparison between keyword information and knowledge information can be performed more efficiently.

[0064] Thus, when comparing keyword information of input data with knowledge information of a specific user, the information comparison unit 412 determines whether the keyword information is included in the knowledge information, or whether the keyword information and the knowledge information match (or are similar). When determining whether the keyword is included in the knowledge information, it is possible to accurately determine whether the user has knowledge about the keyword in the input data. However, if the knowledge information does not include the keyword in the input data, but does include a synonym for that keyword, the fact that the user has knowledge about the synonym for the keyword is not taken into account in the comparison result between the keyword information and the knowledge information. On the other hand, when calculating the similarity between the keyword information and the knowledge information and determining whether they match or are similar, a certain degree of variation is allowed in the comparison between the keyword information and the knowledge information, and if the knowledge information includes a synonym for the keyword, the fact that the user has knowledge about the synonym for the keyword may be taken into account. As a result, even if there is no element in the user's knowledge information that perfectly matches the keyword, if there is a synonym or a similar concept in the knowledge information, the similarity between the keyword information and the knowledge information can be increased.

[0065] The annotation unit 413 adds annotations to keywords in the input data based on the comparison results from the information comparison unit 412. For example, the annotation unit 413 can determine whether the user can understand the content of the input data, and if it is determined that the user will have difficulty understanding the content of the input data, it can add annotations to the keywords.

[0066] Whether a user can understand the content of the input data can be determined, for example, by setting a predetermined threshold for the parameters of the input data to determine whether the user can understand the input data, and checking whether the parameters exceed the threshold. For example, the number of keywords may be set as a parameter, and the minimum number of keywords that a user is considered to be able to understand the input data may be set as the threshold. For example, if the number of keywords among the multiple keywords in the keyword information that are included in the user's knowledge information, or the number of keywords that match or are similar to elements of the knowledge information, is less than the threshold, the annotation unit 413 will determine that it is difficult for the user to understand the content of the input data and will add annotations to some of the keywords in the input data. In this case, for example, the annotation unit 413 may add annotations to keywords that are not included in the knowledge information, i.e., keywords that the user does not know, based on the comparison results by the information comparison unit 412.

[0067] The threshold can be adjusted according to attributes such as the field and type of input data. For example, if the input data is a document in a highly specialized field, it will likely contain many technical terms, and understanding these terms will often be impossible. Therefore, the threshold will be set higher for such highly specialized documents. In contrast, general documents used in daily life tend to use simpler language and fewer technical terms. Therefore, the threshold will be set lower for general documents. This improves the accuracy of determining whether or not it is difficult for the user to understand the content of the input data.

[0068] Furthermore, the difficulty level of the input data can also be defined. The difficulty level of the input data can be calculated, for example, based on the threshold described above. For example, if the content of the input data belongs to a field that is generally considered difficult to understand, the number of keywords required for the user to understand the content of the input data tends to be large, and a high threshold may be set. Therefore, the annotation unit 413 is configured to calculate a higher difficulty level the higher the threshold.

[0069] The annotation unit 413 can also add annotations according to the difficulty level of the input data. For example, the annotation unit 413 can adjust the number of keywords to be annotated and the quality (detail) of the annotations to be added, according to the difficulty level of the input data.

[0070] For example, for input data with high difficulty, annotations can be added not only for keywords not included in the user's knowledge, but also for keywords corresponding to lower-ranked elements (knowledge), taking into account the user's knowledge ranking. This allows for a deeper understanding of even high-difficulty input data.

[0071] Furthermore, the annotation unit 413 can perform simple annotations, which add only text to keywords, or detailed annotations, which add diagrams to keywords. For example, simple annotations may be performed when the difficulty level is low. This is because, when the difficulty level is low, it is assumed that the user can understand the content of the input data with text-based explanations without the need for diagrams or other illustrations. On the other hand, detailed annotations may be performed when the difficulty level is high. This is because, when the difficulty level is high, it is assumed that it is difficult for the user to understand with text-based explanations alone.

[0072] Furthermore, the annotation unit 413 can also add annotations to keywords based on the comparison results, regardless of whether the user can understand the content of the input data. For example, the annotation unit 413 can add annotations to keywords in the keyword information that are not included in the knowledge information.

[0073] The output generation unit 414 generates output data based on the annotations added to the keywords by the annotation addition unit 413. The output data includes the input data and the data of the annotations added to the keywords. The output data is stored in the auxiliary storage unit 440.

[0074] <Client terminals A501~C503> Figure 8 is a schematic block diagram illustrating the hardware configuration of client terminals A501 to C503 shown in Figure 1. Each of the client terminals A501 to C503 is a computer equipped with a CPU 510, RAM 520, ROM 530, auxiliary storage unit 540, communication unit 550, and operation display unit 560, etc. Client terminals A501 to C503 can be, for example, personal computers, PDAs (Personal Digital Assistants), smartphones, etc.

[0075] Furthermore, the configurations of the CPU 510, RAM 520, ROM 530, auxiliary storage unit 540, and communication unit 550 are the same as those of the CPU 210, RAM 220, ROM 230, auxiliary storage unit 240, and communication unit 250 of the data server 200, so a detailed explanation is omitted. In this embodiment, the auxiliary storage unit 540 stores user information, such as the user's name, department, area of ​​expertise, extension number, and email address.

[0076] The operation display unit 560 has an input unit and an output unit. The input unit includes, for example, a keyboard, mouse, etc., and is used by the user to input various instructions (inputs) such as character input and various settings using the keyboard, mouse, etc. The output unit includes a display 561 (see Figures 10 and 11) and is used to present documents created by application software to the user. In this embodiment, the output unit also displays output data on the display 561 to the user in accordance with instructions from the CPU 510. The output unit also has a speaker and can provide the user with audio information about the content of the input data and the content of annotations attached to keywords in the input data.

[0077] <Control method for information processing systems> Figure 9 is a sequence chart illustrating a schematic processing procedure for the control method of the information processing system 100 according to the first embodiment. The processing shown in the sequence chart is realized by the CPU 210 executing a control program for the data server 200 and the CPU 410 executing a control program for the information processing device 400. Figure 10 is a schematic diagram showing an example of displaying the output data of the information processing device shown in Figure 1, and Figure 11 is a schematic diagram showing another example of displaying the output data.

[0078] In this embodiment, for example, we assume that user A manually uploads input data to the information processing system 100, and the information processing device 400 adds annotations to the keywords in the input data and displays them on the display of the client terminal A501. In the example shown in Figure 9, it is assumed that the input data is not a document created by user A, and that user A is unaware of the content of the input data before uploading it. Before uploading the input data, the input data contains keywords that user A is unaware of. User A understands the content of the input data by checking the annotations displayed on the display.

[0079] As shown in Figure 9, the input data stored in client terminal A501 is sent (uploaded) to data server 200 along with user information. The user information includes information about user A. Data server 200 retrieves the input data and user information and temporarily stores them in RAM 220 or auxiliary storage unit 240 (step S101).

[0080] Next, the data server 200 generates keyword information and stores it in the RAM 220 or auxiliary storage unit 240 (step S102). User information, input data, and generated keyword information are sent to the information processing device 400. The information processing device 400 also requests knowledge information for user A from the knowledge server 300, and the knowledge server 300 sends user A's knowledge information to the information processing device 400 in response to the request.

[0081] Next, the information processing device 400 adds annotations to the keywords based on the keyword information and knowledge information, and generates output data (step S103). The content of the annotations is read from the auxiliary storage unit 440. The generated output data is sent to the client terminal A501. Detailed processing of the information processing device 400 will be described later with reference to Figure 12.

[0082] Next, the client terminal A501 displays the output data on the display 561 (step S104). As shown in Figure 10, the screen 600 of the display 561 displays not only the content of the input data but also annotations 603 of the keywords of the input data. In the example shown in Figure 10, since user A is not familiar with the two keywords "heart disease" and "antihypertensive drugs," annotations are added for these two keywords. Also, as shown by reference numerals 605 and 606 in Figure 11, the annotations may be configured to be displayed, for example, by callouts.

[0083] In this way, user A can easily understand the meaning of keywords by checking the annotations displayed on the display 561, thus efficiently understanding the content of the input data.

[0084] <Processing by information processing device 400 (S103)> Figure 12 is a flowchart illustrating the general processing procedure for step S103 of the sequence chart in Figure 9. The processing in the flowchart is realized by the CPU 410 executing a control program. Figure 13 is a schematic diagram illustrating the comparison between keyword information and knowledge information. Figures 14 and 15 are conceptual diagrams illustrating the method for determining whether or not to add annotations to keywords in the examples and comparative examples.

[0085] As shown in Figure 12, the subset extraction unit 411 acquires keyword information and user A's knowledge information, and extracts a subset of user A's knowledge information based on the keyword information (steps S201, S202).

[0086] The information comparison unit 412 compares the keyword information with the knowledge information (step S203). More specifically, as shown in Figure 13, the information comparison unit 412 compares the network of keyword information with the network of user A's knowledge information. Keywords not included in the subset correspond to keywords that user A does not know (for example, the gray area in Figure 13).

[0087] The annotation unit 413 adds annotations to the keywords of the input data based on the comparison results by the information comparison unit 412 (step S204). For example, as shown in Figure 14, assume that the input data includes keywords W1 to W8 (area enclosed by solid lines), and the subset of user A includes keywords W2, W3, W6, W7, W9, W10, W11 (area enclosed by dashed lines). In this embodiment, of the keywords W1 to W8 in the input data, keywords W2, W3, W6, and W7 are included in user A's knowledge information and are keywords that user A knows, so there is no need to add annotations to them. Therefore, the keywords to which annotations are added are W1, W4, W5, and W8, and the keywords to which annotations are not added are W2, W3, W6, and W7.

[0088] On the other hand, in the comparative example, User A's area of ​​expertise is considered, but the knowledge User A possesses is not. Keywords belonging to User A's area of ​​expertise are stored in the area of ​​expertise database, for example, W2, W12, and W13 (areas enclosed by dashed lines). The area of ​​expertise database comprehensively stores keywords used in each area of ​​expertise. Of the input data keywords W1 to W8, keyword W2, which belongs to the user's area of ​​expertise, is a keyword that User A is likely to know, so there is no need to add an annotation. Therefore, the keywords to which annotations are added are W1, W3, W4, W5, W6, W7, and W8, and the keyword to which no annotation is added is W2. As a result, keywords W2, W3, W6, and W7 are annotated even though the user already has knowledge of them. However, this increases the amount of information that User A has to check, which may cause inconvenience or make it difficult to find annotations for other keywords that are needed.

[0089] Furthermore, as shown in Figure 15, we assume a case where the input data includes keywords W1 to W8 and W13 (area enclosed by solid lines), and the subset of user A includes keywords W3, W5, W9, W10, and W11 (area enclosed by dashed lines). In this embodiment, of the keywords W1 to W8 and W13 in the input data, keywords W3 and W5 are included in user A's knowledge information and are keywords that user A knows, so there is no need to add annotations. Therefore, the keywords to which annotations are added are W1, W2, W4, W6, W7, W8, and W13, and the keywords to which annotations are not added are W3 and W5.

[0090] On the other hand, in the comparative example, User A's area of ​​expertise is considered, but the knowledge User A possesses is not. Keywords belonging to User A's area of ​​expertise are, for example, W2, W6, W7, W12, and W13 (areas enclosed by dashed lines). Of the input data keywords W1 to W8 and W13, keywords W2, W6, W7, and W13, which belong to the user's area of ​​expertise, are keywords that User A is likely to know, so there is no need to add annotations. Therefore, the keywords to which annotations are added are W1, W3, W4, W5, and W8, and the keywords to which annotations are not added are W2, W6, W7, and W13. As a result, keywords W3 and W5 are not annotated even though User A does not have knowledge of them. In this case, User A may have difficulty understanding the input content because they do not have knowledge of keywords W3 and W5.

[0091] The output generation unit 414 generates output data (step S205). The generated output data is sent to the client terminal A501, and the information processing device 400 terminates processing (end).

[0092] As described above, in the sequence chart in Figure 9 and the flowchart in Figure 12, input data is acquired, keyword information is compared with user A's knowledge information, and based on the comparison results, annotations are added to the keywords in the input data. The annotated input data is sent to the client terminal A501 as output data, and the output data is displayed on the display 561.

[0093] According to the information processing system 100 of the first embodiment described above, annotations are added to the keywords of the input data based on the comparison result between the keyword information of the input data and the knowledge information. Therefore, annotations are added to the keywords of the input data according to the knowledge possessed by the user.

[0094] Furthermore, by comparing keyword information with a subset, comparisons can be made within a limited scope of knowledge information, thereby improving the efficiency of the comparison process.

[0095] (Second embodiment) In the first embodiment, a case was described in which input data is uploaded from the client terminal A501 to the data server 200 and the information processing device 400 retrieves the input data from the data server 200. In the second embodiment, a case is described in which the information processing device 400 retrieves documents that meet monitoring conditions as input data.

[0096] Figure 16 is a sequence chart illustrating the acquisition of input data in the second embodiment. In the second embodiment, the information processing system 100 further includes an application server and a mail server. The application server stores user information for multiple users, including users A, B, and C, in an auxiliary storage unit. In this embodiment, the method of acquiring input data differs from that of the first embodiment, but the configuration of the data server 200, knowledge server 300, and information processing device 400 is the same as in the first embodiment. In the following description, to avoid repetition, the same configuration as in the first embodiment will be omitted from the explanation.

[0097] As shown in Figure 16, login information is sent from client terminal A501 to the application server. The application server performs login authentication for user A (step S301), and if the login authentication is successful, it notifies client terminal A501 of this. The authentication information is also sent from the application server to the mail server. The mail server performs authentication based on the authentication information (step S302), and if the authentication is successful, it notifies the application server of this.

[0098] Next, the application server sets monitoring conditions (predetermined conditions) for emails addressed to user A that the mail server has received (step S303). The monitoring conditions may be, for example, whether the sender of the received email belongs to a specific group. The monitoring conditions are sent to the mail server.

[0099] Next, monitoring of the mail server is initiated (step S304). The application server monitors emails addressed to user A on the mail server using email software such as Microsoft Outlook®. The mail server checks whether each email addressed to user A meets the monitoring conditions (step S305) and sends the confirmation result to the application server. The application server and the mail server function as a mail monitoring unit.

[0100] The application server retrieves user information for user A based on the login information. The application server also retrieves emails addressed to user A that meet the monitoring conditions as input data (step S306) based on the verification results, and sends them to the data server 200 along with the user information.

[0101] The data server 200 generates and stores keyword information. User information, input data, and keyword information are sent to the information processing device 400. The information processing device 400 requests knowledge information for a specific user (user A) from the knowledge server 300, and the knowledge server 300 sends user A's knowledge information to the information processing device 400. The information processing device 400 annotates the keywords based on the keyword information and user A's knowledge information (subset) and generates output data (step S307). Details of the processing of the information processing device 400 are as described above and will be omitted here. The generated output data is sent to the mail server.

[0102] The mail server updates the email (step S308). More specifically, the mail server updates the original email data that formed the basis of the input data using the output data generated by the information processing device 400. The mail server also sends the updated email data to the application server upon request from the application server.

[0103] The application server generates email display data using email software and sends it to client terminal A501. Client terminal A501 displays the email based on the display data (step S309). The annotated email is displayed on display 561.

[0104] Although not shown in the diagram, the system may also be configured to include a file monitoring unit that constantly monitors files moved to the user's folder on the application server or client terminal, in addition to received emails. In this case, the information processing device 400 acquires files moved to the user's folder as input data, or acquires files moved to the user's folder as input data if they have predetermined attributes (e.g., a predetermined file name or predetermined creation date and time). The system may also have a file reading unit that reads files moved to the user's folder and determines whether the file reading result satisfies the monitoring conditions. For example, the system may be configured to determine that the monitoring conditions are met if the content of a file moved to the user's folder is a document in a specific field.

[0105] According to the information processing system 100 of the second embodiment described above, the document to be annotated (for example, an received email or a file in a specific folder) is selected based on monitoring conditions, so the user does not need to upload input data to be annotated.

[0106] As described above, the information processing system 100, the information processing device 400, and the control program have been explained in the embodiments. However, the present invention can be appropriately added to, modified, and omitted by those skilled in the art within the scope of its technical concept.

[0107] For example, in the first and second embodiments described above, a case was described in which keyword information is generated from input data in the data server 200 and user knowledge information is stored in the knowledge server 300. However, the present invention is not limited to such a case, and the information processing device 400 can also be configured to generate keyword information from input data and store user knowledge information. In this case, the CPU 410 and the communication unit 460 perform the functions of the data acquisition unit, the CPU 410 performs the function of the keyword generation unit, and the auxiliary storage unit 440 performs the function of the information storage unit.

[0108] Furthermore, in the first and second embodiments described above, a case was described in which user knowledge information is read from the knowledge server 300 to the information processing device 400, and the information comparison unit 412 of the information processing device 400 performs a comparison between keyword information and knowledge information. However, the system is not limited to this case; the knowledge server 300 can also be configured to perform the comparison between keyword information and knowledge information.

[0109] Furthermore, while the first embodiment described a case where user information is stored in the auxiliary storage unit 540 and input data and user information are transmitted to the data server 200, the system is not limited to this case. For example, similar to the second embodiment, the information processing system 100 may have an application server, perform user login authentication, and be configured to acquire user information based on login information.

[0110] Furthermore, the control program may be provided on a computer-readable recording medium such as a USB memory stick, flexible disk, or CD-ROM, or it may be provided online via a network such as the Internet. In this case, the program recorded on the computer-readable recording medium is usually transferred to memory or storage and stored therein. This control program may also be provided, for example, as a standalone application software, or it may be incorporated into the software of each device as a function of the server.

[0111] Furthermore, in the embodiment, some or all of the processing performed by the control program may be replaced and executed by hardware such as circuits. [Explanation of symbols]

[0112] 100 Information Processing Systems, 200 data servers, 210 CPU, 220 RAM, 230 ROM, 240 Auxiliary storage, 250 Communications Department, 261 Data acquisition unit, 262 Keyword Information Generation Unit, 300 knowledge servers, 310 databases, 400 information processing devices, 410 CPU, 411 Subset extraction unit, 412 Information Comparison Department, 413 Annotation section, 414 Output generation unit, 420 RAM, 430 ROM, 440 Auxiliary storage, 450 Communications Department, Client terminals A to C are listed below (501-503).

Claims

1. Data acquisition unit, An information storage unit that stores knowledge information for each user, which includes multiple elements corresponding to multiple pieces of information that constitute knowledge, An information comparison unit compares keyword information related to keywords extracted from data acquired as input data by the data acquisition unit with knowledge information associated with the user. The system includes an annotation unit that adds annotations to the keywords of the input data based on the comparison results from the information comparison unit, The information comparison unit determines whether the knowledge information contains the keywords from the keyword information, or whether the keyword information and the knowledge information match or are similar. The annotation unit is an information processing system that, when the number of keywords among the multiple keywords in the keyword information that are included in the user's knowledge information, or the number of keywords that match or are similar to elements of the knowledge information, is less than a threshold, adds annotations to the keywords among the multiple keywords that are not included in the user's knowledge information.

2. The aforementioned keyword information is information that includes multiple keywords, The information processing system according to claim 1, wherein the information comparison unit determines whether the keyword information and the knowledge information match or are similar by calculating a degree of similarity based on the vector of keywords included in the keyword information and the vector of elements included in the knowledge information.

3. The information processing system according to claim 1, wherein the keyword information is information represented by a network including a plurality of nodes each representing a plurality of the keyword and links defining the relationships between the nodes.

4. The aforementioned knowledge information is information that networks the relationships between the elements corresponding to multiple pieces of information that constitute the knowledge, The information processing system according to claim 3, wherein the information comparison unit determines whether the keyword information and the knowledge information match or are similar by calculating a similarity between the network of keyword information and the network of knowledge information based on the node match rate and the distance between nodes.

5. The system further includes a subset extraction unit that extracts a subset of the user's knowledge information based on the aforementioned keywords. The subset extraction unit searches the user's knowledge information for nodes tagged with the keyword, and extracts a subset containing the nodes tagged with the keyword. The information processing system according to claim 4, wherein the information comparison unit compares the keyword information with the subset.

6. The information processing system according to any one of claims 1 to 5, further comprising an output generation unit that generates output data based on annotations added to the keyword by the annotation addition unit.

7. The information processing system according to any one of claims 1 to 6, wherein the data acquisition unit acquires data uploaded by the user as input data.

8. The system further includes a mail monitoring unit that monitors emails received by the aforementioned user, The information processing system according to any one of claims 1 to 6, wherein the data acquisition unit acquires the received email as input data when the received email satisfies predetermined conditions.

9. The system further includes a file monitoring unit that monitors files being moved to a folder where the user's files are saved, and a file reading unit that reads files saved in the folder. The information processing system according to any one of claims 1 to 6, wherein the data acquisition unit acquires the file in the folder, or the result of reading the file, as input data when predetermined conditions are met.

10. An information processing system having a data server, a knowledge server, and an information processing device, The aforementioned data server is Data acquisition unit, It includes a keyword information generation unit that generates keyword information from data acquired as input data by the data acquisition unit, The aforementioned knowledge server has an information storage unit that stores knowledge information for each user, which includes multiple elements corresponding to multiple pieces of information that constitute knowledge. The keyword information generated by the keyword information generation unit is stored, and the keyword information and the knowledge information are transmitted to the information processing device. The aforementioned information processing device is An information comparison unit that compares the acquired keyword information with the knowledge information associated with the user, The system includes an annotation unit that adds annotations to the keywords of the input data based on the comparison results from the information comparison unit, The information comparison unit determines whether the knowledge information contains the keywords from the keyword information, or whether the keyword information and the knowledge information match or are similar. The annotation unit is an information processing system that, when the number of keywords among the multiple keywords in the keyword information that are included in the user's knowledge information, or the number of keywords that match or are similar to elements of the knowledge information, is less than a threshold, adds annotations to the keywords among the multiple keywords that are not included in the user's knowledge information.

11. The information processing system according to claim 10, wherein the annotation unit adjusts the threshold according to the attributes of the input data.

12. The information storage unit is provided by a knowledge server and stores the knowledge information for each of the multiple users, according to any one of claims 1 to 9.

13. The information processing system according to claim 11, wherein the annotation unit performs simple annotation, which adds an annotation containing only text to the keyword, or detailed annotation, which adds an annotation containing at least a figure to the keyword.

14. The information processing system according to claim 13, wherein the annotation unit calculates the difficulty level of the input data based on the threshold and the comparison result, and performs the detailed annotation if the difficulty level is high, and performs the simplified annotation if the difficulty level is low.

15. The information processing system according to claim 6, wherein the output data includes the input data and annotations attached to the keywords.

16. The information processing system according to any one of claims 1 to 15, wherein the knowledge information is generated based on at least one of data relating to social networking services (SNS) used by the user, data relating to the web viewed by the user, and document data created by the user.

17. A keyword information acquisition unit that acquires keyword information from data acquired as input data, A knowledge information acquisition unit acquires knowledge information for a specific user that includes multiple elements corresponding to multiple pieces of information that constitute knowledge, An information comparison unit that compares the keyword information with the knowledge information associated with the specific user, The system includes an annotation unit that adds annotations to the keywords of the input data based on the comparison results from the information comparison unit, The information comparison unit determines whether the keyword information contains the keyword, or whether the keyword information and the knowledge information match or are similar. The annotation unit is an information processing device that, when the number of keywords among the multiple keywords in the keyword information that are included in the knowledge information of the specific user, or the number of keywords that match or are similar to elements of the knowledge information, is less than a threshold, adds annotations to the keywords among the multiple keywords that are not included in the knowledge information of the specific user.

18. Keyword information acquisition step (a) involves acquiring keyword information from data obtained as input data, Knowledge information acquisition step (b) acquires knowledge information for a specific user that includes multiple elements corresponding to multiple pieces of information that constitute knowledge, A comparison step (c) involves comparing the keyword information with the knowledge information associated with the specific user, A process comprising: an annotation step (d) which adds annotations to the keywords of the input data based on the comparison results in the comparison step (c); In the comparison step (c) above, it is determined whether the knowledge information contains the keywords of the keyword information, or whether the keyword information and the knowledge information are identical or similar. In the annotation step (d) described above, if the number of keywords among the multiple keywords in the keyword information that are included in the knowledge information of the specific user, or the number of keywords that match or are similar to elements of the knowledge information, is less than a threshold, a control program to cause a computer to perform a process of adding annotations to the keywords among the multiple keywords that are not included in the knowledge information of the specific user.

Images