Abstract:
A document information processing apparatus includes: a retention unit that retains attention probability weight corresponding to a plurality of factor information for each users; a selection unit that selects a document, the document being inferred to be paid attention to, from a document group by using the attention probability weight of the plurality of the factor information; and a presentation unit that presents information corresponding to at least one of the plurality of the factor information used by the selection unit.

Description:
BACKGROUND 
       [0001]    1. Technical Field 
         [0002]    This invention relates to a document information processing apparatus for estimating the attention degree for each user about the processed document. 
         [0003]    2. Related Art 
         [0004]    In recent years, document management using a computer has become widespread and the number of documents viewed by the user has also increased. Under the circumstances, an art of searching for the document to which the user should pay attention is demanded. 
       SUMMARY 
       [0005]    It is therefore an object of the invention to provide a document information processing apparatus that can analyze the factor for the user to pay attention to a document from various factors not only a limited keyword. 
         [0006]    According to first aspect of the invention, a document information processing apparatus comprising: a retention unit that retains attention probability weight corresponding to a plurality of factor information for each users; a selection unit that selects a document, the document being inferred to be paid attention to, from a document group by using the attention probability weight of the plurality of the factor information; and a presentation unit that presents information corresponding to at least one of the plurality of the factor information used by the selection unit. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]    Exemplary embodiment of the present invention will be described in detail based on the following figures, wherein: 
           [0008]      FIG. 1  is a block diagram to show the configuration of an example of a document information processing apparatus according to an embodiment of the invention; 
           [0009]      FIG. 2  is a functional block diagram to show an example of the document information processing apparatus according to the embodiment of the invention; 
           [0010]      FIG. 3  is a conceptual drawing to show an example of a Bayesian network generated and used by the document information processing apparatus according to the embodiment of the invention; and 
           [0011]      FIG. 4  is a schematic representation to show an example of attention probability weight for each piece of factor information retained for each user by the document information processing apparatus according to the embodiment of the invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0012]    Referring now to the accompanying drawings, there is shown an exemplary embodiment of the invention. A document information processing apparatus according to the embodiment of the invention is made up of a control section  11 , a storage section  12 , a communication section  13 , an operation section  14 , and a display section  15 . 
         [0013]    The control section  11  is a program control device of a CPU, etc., and operates in accordance with a program stored in the storage section  12 . In the embodiment, the control section  11  authenticates the user and retains a history of manipulations on a document for each authenticated user. The manipulation history includes read (view) operation, print operation, deletion operation, etc., for example, and also retains information of the operation execution dates and times. The control section  11  generates information of attention probability weight for each user (called user profile information) for factor information that can be extracted from the manipulated document (profiling processing). 
         [0014]    Further, the control section  11  uses the user profile information based on the factor information to select the document estimated to be noted from among the processed documents, and presents information for determining the factor information about at least a part of the used factor information to the user (factor presentation processing). The profiling processing and the factor presentation processing of the control section  11  are described later in detail. 
         [0015]    The storage section  12  is implemented including a storage device of RAM, ROM, etc., and a disk device of a hard disk, etc. The storage section  12  retains programs executed by the control section  11 . The storage section  12  also operates as work memory of the control section  11 . The communication section  13  is a network interface, etc., for acquiring a document through a network in accordance with a command input from the control section  11  and storing the document in the storage section  12 . 
         [0016]    The operation section  14  is a keyboard, a mouse, etc., and receives user operation and outputs the description of the command operation to the control section  11 . The display section  15  is a display, etc., and displays information in accordance with the command input from the control section  11 . 
         [0017]    The document information processing apparatus of the embodiment provides functions as shown in  FIG. 2  by software as the control section  11  executes profiling processing and attention degree computation processing. That is, the document information processing apparatus of the embodiment is functionally made up of a profiling section  21 , a profile information retention section  22 , a document manipulation processing section  23 , a document selection section  24 , a factor estimation section  25 , and an information presentation section  26 , as shown in  FIG. 2 . 
         [0018]    It is assumed that the control section  11  previously authenticates the user and obtains information for identifying the user. For authentication, various methods such as a method of using a user name and a password are available as widely known and therefore the authentication will not be discussed here in detail. 
         [0019]    The profiling section  21  forms a Bayesian network containing each piece of factor information selected from among predetermined factor information candidates as a node. The Bayesian network contains a node concerning the description of command operation of the user and a node indicting that the target document is to be noted by the user. 
         [0020]    The Bayesian network becomes conceptually a network as shown in  FIG. 3 . Information of attention probability weight is set in each node of factor information in association with each other. For example, if the target document is a patent document, keyword information extracted from the document, applicant information contained in bibliographic information, classification information of international patent classification value and others, the inventor name, etc., can be adopted as factor information candidates. 
         [0021]    The profile information retention section  22  retains for each user a profile database associating information for identifying the node of factor information (a character string describing the factor information, for example, “applicant is A” or the like) and information of attention probability weight in association with each other as shown in  FIG. 4 . 
         [0022]    Upon reception of the description of the command operation of the user for a document from the document manipulation processing section  23 , the profiling section  21  extracts factor information concerning the document to be manipulated and changes the attention probability weight of the node corresponding to the extracted factor information, stored in the profile information retention section  22  in association with the information for identifying the user. 
         [0023]    For example, if the information output by the document manipulation processing section  23  contains the user&#39;s read (view) start date and time and end date and time, the profiling section  21  calculates the read (view) time of the user from the information. It extracts the factor information corresponding to the node contained in the Bayesian network from the read (viewed) document. For example, the profiling section  21  extracts keyword, classification information, etc. On the hypothesis that the longer the read (view) time, the higher the attention probability, the profiling section  21  increases the attention probability weight of the node corresponding to the extracted factor information according to a predetermined method. To increase the attention probability weight, various methods of a method of increasing the attention probability weight at a given ratio, a method of increasing the attention probability weight by the amount responsive to the read (view) time, for example, are available. For example, a method widely known as a method of estimating the importance of electronic mail, etc., can be adopted as the method of updating the Bayesian network in response to user&#39;s operation. 
         [0024]    For example, the document manipulation processing section  23  acquires document data through the network in response to user&#39;s command operation and displays the document data on the display section  15 . Upon reception of input of user&#39;s command operation for the document (read (view) start command, read (view) end command, deletion command, etc.,), the document manipulation processing section  23  outputs information indicating the command operation to the profiling section  21  together with the date and time information indicating the date and time of the command operation. The date and time information can be acquired from a calendar IC, etc., (not shown). 
         [0025]    The document selection section  24  acquires a document group to which processing is applied from the network or a predetermined document database at a predetermined timing such as the timing specified by the user. For example, a predetermined number of documents stored in a predetermined URL (Uniform Resource Locator) in order starting at the newest storage date and time may be acquired. All documents stored in the document database (not shown) may be acquired as processing targets. 
         [0026]    The document selection section  24  extracts the factor information corresponding to the node contained in the Bayesian network formed by the profiling section  21  from each of the documents acquired as the processing targets. It calculates the probability that each document is a document to be noted (attention probability) using the information of the attention probability weight associated with the extracted factor information. The document selection section  24  selects the document with the probability exceeding a predetermined threshold value as the selected document and stores the selected document in the storage section  12 . The calculation of the probability that each document is a document to be noted is similar to the calculation of the importance using a usual Bayesian network and therefore will not be discussed here in detail. 
         [0027]    The factor estimation section  25  selects at least a part of the factor information used for the document selection in the document selection section  24  satisfying a predetermined condition and outputs the information for determining the selected factor information to the information presentation section  26 . 
         [0028]    Using Bayes&#39; theorem, about the value of the attention probability calculated based on the attention probability weight of each piece of factor information when the selected document is determined a document to be noted, the probability of the factor information used when the selected document is determined a document to be noted is calculated inversely from the value of the attention probability. That is, the Bayes&#39; theorem associates the probability of B when A and the probability of A when B with each other and therefore the cause and effect relationship is inversed and the probability that each piece of factor information may be used for document selection can be calculated from the document selection probability. 
         [0029]    For each selected document, the factor estimation section  25  calculates the probability that each piece of factor information may be used for selection of the document. The factor estimation section  25  selects as many pieces of factor information as the predetermined number of presentations in order starting at that with the highest probability and outputs the information for determining the selected factor information (a character string describing the factor information or the like) to the information presentation section  26 . 
         [0030]    The information presentation section  26  lists the information for determining the factor information input from the factor estimation section  25  on the display section  15 . At this time, the documents selected by the document selection section  24  may also be listed on the display section  15 . 
         [0031]    If factor information candidates which do not become factor information are common to the document group selected by the document selection section  24  (corresponding to addition criterion) at a predetermined ratio or more, the factor estimation section  25  may send the factor information candidates to the profiling section  21  as the addition targets. 
         [0032]    In this case, the profiling section  21  adds the nodes corresponding to the factor information candidates sent as the addition targets to the Bayesian network and initializes the information of the attention probability weight (for example, to  1 ). 
         [0033]    According to the embodiment, if the user reads (views) a patent document whose applicant is A for long hours without concern, the attention probability weight relating to the node that “applicant is A” in the Bayesian network is raised and the document whose “applicant is A” is selected as the document to be noted. Inversely from the selection result, the node that “applicant is A” is selected as the node with high probability of use for document selection and the factor information that “applicant is A” representing the node is presented to the user. 
         [0034]    Accordingly, it is made possible for the user to know the attention factor of the document not in mind. In the embodiment, using the Bayesian network, as the information that can be extracted from documents, not only the keywords, but also various pieces of factor information containing the keywords can be contained as the nodes in the Bayesian network. Thus, the factors when the user pays attention to a document can be analyzed from various factors containing the keywords.