Abstract:
A non-transitory computer-readable storage medium storing therein a retrieval control program that causes a computer to execute a process includes: receiving a retrieval condition, extracting an incident corresponding to the received retrieval condition from a storage that stores an incident in association with a handling method, classifying the extracted incident in accordance with the handling method associated with the extracted incident and, outputting the classified incident as a retrieval result.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
       [0001]    This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2016-045646, filed on Mar. 9, 2016, the entire contents of which are incorporated herein by reference. 
       FIELD 
       [0002]    The present invention relates to a retrieval control program, a retrieval control apparatus, and a retrieval control method. 
       BACKGROUND 
       [0003]    A provider who provides a service to a user (hereafter also simply called provider), for example, constructs a business system in accordance with the intended use (hereafter also called information processing system), and operates the system in order to provide various services to the user. When an inquiry on a service (hereafter also called retrieval condition) is received from a user, for example, the information processing system refers to a storage unit storing events generated in the past during services provided to the user (hereafter also simply called incidents), and specifies an incident of which content is closest to the received inquiry. Then referring to the storage unit storing handling methods for incidents, the information processing system retrieves a handling method corresponding to the specified incident, for example. Then the information processing system sends the retrieved handling method to the user, for example. 
         [0004]    Thereby the provider can allow the user to access the handling method corresponding to the inquiry received from the user (e.g. see Japanese Laid-open Patent Publication No. 2000-357175, No. 2006-92473, No. H11-219368 and No. 2003-30224). 
       SUMMARY 
       [0005]    According to an aspect of the embodiments a non-transitory computer-readable storage medium storing therein a retrieval control program that causes a computer to execute a process includes: receiving a retrieval condition, extracting an incident corresponding to the received retrieval condition from a storage that stores an incident in association with a handling method, classifying the extracted incident in accordance with the handling method associated with the extracted incident and, outputting the classified incident as a retrieval result. 
         [0006]    The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. 
         [0007]    It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0008]      FIG. 1  is a diagram depicting a configuration of an information processing system  10 . 
           [0009]      FIG. 2  is a diagram depicting a retrieval of a handling method. 
           [0010]      FIG. 3  is a diagram depicting a retrieval of a handling method. 
           [0011]      FIG. 4  is a diagram depicting the hardware configuration of the information processing apparatus  1 . 
           [0012]      FIG. 5  is a functional block diagram of the information processing apparatus  1 . 
           [0013]      FIG. 6  is a flow chart depicting an outline of a retrieval control processing according to Embodiment 1. 
           [0014]      FIG. 7  is a diagram depicting an outline of the retrieval control processing according to Embodiment 1. 
           [0015]      FIG. 8  is a flow chart depicting the details of the retrieval control processing according to Embodiment 1. 
           [0016]      FIG. 9  is a flow chart depicting the details of the retrieval control processing according to Embodiment 1. 
           [0017]      FIG. 10  is a flow chart depicting the details of the retrieval control processing according to Embodiment 1. 
           [0018]      FIG. 11  is a drawing for describing details on the retrieval control processing according to Embodiment 1. 
           [0019]      FIG. 12  is a drawing for describing details on the retrieval control processing according to Embodiment 1. 
           [0020]      FIG. 13  is a drawing for describing details on the retrieval control processing according to Embodiment 1. 
           [0021]      FIG. 14  is a drawing for describing details on the retrieval control processing according to Embodiment 1. 
           [0022]      FIG. 15  is a table for describing an example of the first teacher data  131 . 
           [0023]      FIG. 16  is a table for describing an example of the keyword information extracted from the second handling method  131   c.    
           [0024]      FIG. 17  is a table for describing an example of the second teacher data  131 . 
           [0025]      FIG. 18  is a table for describing an example of the keyword information extracted from the second retrieval condition  131   a.    
           [0026]      FIG. 19  is a table for describing an example of the first retrieval condition  141   a  sent from the provider terminal  11 . 
           [0027]      FIG. 20  is a table for describing an example of the pre-conversion keyword information. 
           [0028]      FIG. 21  is a table for describing an example of the second parameters  133 . 
           [0029]      FIG. 22  is a table for describing an example of the second correlation information. 
           [0030]      FIG. 23  is a table for describing an example of the post-conversion keywords. 
           [0031]      FIG. 24  is a table for describing an example of the first incidents  141   b  retrieved in the processing in S 25 . 
           [0032]      FIG. 25  is a table for describing an example of the retrieval target data  136 . 
           [0033]      FIG. 26  is a table for describing an example of the first handling method  141   c  associated with the first incident  141   b  extracted in the processing in S 25 . 
           [0034]      FIG. 27  is a table for describing an example of keywords extracted from the first handling method  141   c.    
           [0035]      FIG. 28  is a table for describing an example of the first parameters  132 . 
           [0036]      FIG. 29  is a table for describing an example of the first correlation information. 
           [0037]      FIG. 30  is an example of the output apparatus  21  in the state of outputting the first incidents  141   b.    
       
    
    
     DESCRIPTION OF EMBODIMENTS 
       [0038]    When the incident corresponding to the inquiry received from the user is retrieved, as mentioned above, the information processing system may extract a plurality of incidents in some cases. In this case, for example, the provider specifies an incident which seems to be closest to the content of the inquiry received from the user, out of the extracted plurality of incidents. Then, for example, the provider outputs a handling method corresponding to the specified incident to an output apparatus, by which the user can access the handling method. 
         [0039]    However, if the number of retrieved incidents is enormous, the provider may have difficulty to specify an incident that is closest to the content of the inquiry received from the user. In this case, there is a possibility that the user is not able to access an appropriate handling method corresponding to the content of the inquiry sent by the user. The first embodiment will be explained hereinbelow. 
         [0040]    [Configuration of Information Processing System] 
         [0041]      FIG. 1  is a diagram depicting a configuration of an information processing system  10 . The information processing system  10  in  FIG. 1  includes, for example, an information processing apparatus  1  (hereafter also called retrieval control apparatus  1 ), a storage unit  2 , and a plurality of provider terminals  11 . 
         [0042]    When a retrieval condition is received from a provider terminal  11 , which is a terminal used by a provider, the information processing apparatus  1  retrieves a handling method corresponding to the received retrieval condition. In other words, the information processing apparatus  1  retrieves a handling method corresponding to a content inquired by a user. Then the information processing apparatus  1  sends the retrieved handling method to the provider terminal  11 . 
         [0043]    The provider terminal  11  is a terminal used by the provider, and sends a retrieval condition to the information processing apparatus  1 , for example. In concrete terms, the provider terminal  11  specifies a retrieval condition from the content of an e-mail (e.g. e-mail including an inquiry content for a service) sent from a user, and sends the retrieval condition to the information processing apparatus  1 , for example. The provider terminal  11  also specifies a retrieval condition from a content input by an individual in charge who received a phone call from a user (e.g. content of an inquiry on a service), and sends the retrieval condition to the information processing apparatus  1 , for example. 
         [0044]    [Retrieval of Handling Method] 
         [0045]    Retrieval of a handling method will be described next.  FIG. 2  and  FIG. 3  are diagrams depicting a retrieval of a handling method. 
         [0046]    As depicted in  FIG. 2 , when the provider terminal  11  receives an e-mail sent by a user, or when an individual in charge who received a phone call from a user inputs the content of the phone call to the provider terminal  11 , for example, the provider terminal  11  sends the specified retrieval condition to the information processing apparatus  1  (( 1 ) in  FIG. 2 ). 
         [0047]    Then when the information processing apparatus  1  receives the retrieval condition sent by the provider terminal  11 , the information processing apparatus  1  retrieves an incident corresponding to the received retrieval condition (( 2 ) in  FIG. 2 ). In concrete terms, when the information processing apparatus  1  receives a retrieval condition from the provider terminal  11 , the information processing apparatus  1 , for example, morphologically parses the sentence included in the received retrieval condition, and generates a keyword group constituted by a plurality of keywords. Then the information processing apparatus  1  accesses the storage unit  2  storing each incident corresponding to each retrieval condition, and, for example, extracts an incident(s) which includes the highest number of keywords included in the generated keyword group. Then the information processing apparatus  1  sends the retrieved incident to the provider terminal  11  (( 3 ) in  FIG. 2 ). 
         [0048]    Then the provider terminal  11  specifies an incident used for retrieving a handling method from the extracted incidents, for example (( 4 ) in  FIG. 3 ). In concrete terms, if a plurality of incidents were extracted, the provider specifies an incident which is closest to the content of the inquiry received from the user. Then the provider terminal  11  sends the incident specified by the provider to the information processing apparatus  1  (( 5 ) in  FIG. 3 ) 
         [0049]    When the information processing apparatus  1  receives the incident sent by the provider terminal  11  thereafter, the information processing apparatus  1  retrieves a handling method corresponding to the received incident (( 6 ) in  FIG. 3 ). In concrete terms, when the information processing apparatus  1  receives an incident from the provider terminal  11 , the information processing apparatus  1  morphologically parses the sentence included in the received incident, for example, and generates a keyword group constituted by a plurality of keywords. Then the information processing apparatus  1  accesses the storage unit  2  storing each handling method corresponding to each incident, and, for example, extracts a handling method which includes the highest number of the keywords included in the generated keyword group. Furthermore, the information processing apparatus  1  sends the retrieved handling method to the provider terminal  11  (( 7 ) in  FIG. 3 ). 
         [0050]    Thereby the provider terminal  11  can output the handling method sent from the information processing apparatus  1  to an output apparatus (not illustrated) which the user can access, for example. Therefore the user can access the handling method corresponding to the content of the inquiry sent to the provider terminal  11 . 
         [0051]    However, if the number of retrieved incidents is enormous in the example in  FIG. 3 , the provider may have difficulty to specify an incident having content closest to the inquiry received from the user. In this case, there is a possibility that the user is not able to access an appropriate handling method corresponding to the content of the inquiry that the user sent. 
         [0052]    Therefore the information processing apparatus  1  according to this embodiment receives a retrieval condition (hereafter also called first retrieval condition), and extracts an incident corresponding to the received first retrieval condition (hereafter also called first incident) from the storage unit  2  storing incidents in association with handling methods. Then the information processing apparatus  1  classifies the extracted first incident in accordance with the handling method associated with the first incident (hereafter also called first handling method), and outputs the classified first incident as the retrieval result. 
         [0053]    In other words, the information processing apparatus  1  according to this embodiment classifies the first incident, which was extracted based on the first retrieval condition, in accordance with the content of the first handling method associated with the first incident respectively. Then the information processing apparatus  1  sends the classified first incident to the provider terminal  11 . Thereby the provider can access the first incident of the state, which is categorized in accordance with the content, from the provider terminal  11 . Hence the provider can easily specify a first incident that is used for retrieving a first handling method. 
         [0054]    [Hardware Configuration of Information Processing Apparatus] 
         [0055]    The hardware configuration of the information processing apparatus  1  will be described next.  FIG. 4  is a diagram depicting the hardware configuration of the information processing apparatus  1 . 
         [0056]    The information processing apparatus  1  includes a CPU  101  which is a processor, a memory  102 , an external interface (I/O unit)  103 , and a storage medium  104 . Each unit is interconnected via a bus  105 . 
         [0057]    The storage medium  104  stores a program  110 , which executes a process to classify the first incident in accordance with the content of the first handling method (hereafter also called retrieval control processing), in a program storage area (not illustrated) within the storage medium  104 . The storage medium  104  also includes an information storage area  130  in which information used for performing the retrieval control processing is stored (hereafter also called storage unit  130 ), for example. 
         [0058]    As illustrated in  FIG. 4 , the CPU  101  loads the program  110  from the storage medium  104  to the memory  102  when the program  110  is executed, and performs the retrieval control processing in cooperation with the program  110 . The external interface  103  communicates with the provider terminal  11  via a network NW constituted by an intranet, internet or the like, for example. 
         [0059]    [Functions of Information Processing Apparatus] 
         [0060]    The functions of the information processing apparatus  1  will be described next.  FIG. 5  is a functional block diagram of the information processing apparatus  1 . 
         [0061]    The CPU  101  of the information processing apparatus  1  operates as a keyword extraction unit  111  (hereafter also simply called extraction unit  111 ), a machine learning execution unit  112 , an information receiving unit  113 , and a keyword estimation unit  114 , for example, by working in cooperation with the program  110 . The CPU  101  of the information processing apparatus  1  operates as an information retrieval unit  115 , a category specification unit  116 , and a result output unit  117  (hereafter category specification unit  116  and result output unit  117  are also simply called output unit  117  collectively), for example, by working in cooperation with the program  110 . Furthermore, in the information storage area  130 , a teacher data  131 , a first parameter  132  (hereafter also called classification parameter  132 ), a second parameter  133  (hereafter also called additional parameter  133 ), a first identification function  134 , a second identification function  135 , and a retrieval target data  136 , for example, are stored. 
         [0062]    It is assumed that the teacher data  131  includes a first teacher data  131 , which includes a retrieval condition  131   a  (hereafter also called second retrieval condition  131   a  or learning retrieval condition  131   a ), and an additional keyword  131   d . Further, it is assumed that the teacher data  131  includes a second teacher data  131 , which includes a handling method  131   c  (hereafter also called second handling method  131   c  or learning handling method  131   c ), and a category information  131   e  which indicates a category of an incident corresponding to the second handling method  131   c  (hereafter also called second incident  131   b  or learning incident  131   b ). 
         [0063]    Hereafter the area, in which the teacher data  131 , the first parameter  132 , the second parameter  133 , the first identification function  134 , and the second identification function  135  are stored, is also called an information storage area  130   a , and the area, in which the retrieval target data  136  is stored, is also called an information storage area  130   b . Further, the storage unit  2 , described with reference to  FIG. 1  or the like, corresponds to the information storage area  130   b , for example. 
         [0064]    The keyword extraction unit  111  extracts keywords from the second retrieval condition  131   a  included in the first teacher data  131 , which is stored in the information storage area  130 . The keyword extraction unit  111  also extracts keywords from the second handling method  131   c  included in the second teacher data  131 , which is stored in the information storage area  130 . 
         [0065]    As mentioned later, the keyword extraction unit  111  extracts keywords from the first retrieval condition  141   a  before the information retrieval unit  115  retrieves the first incident  141   b  using the first retrieval condition  141   a . Further, if the information retrieval unit  115  retrieves the first handling method  141   c  using the first incident  141   b , as mentioned later, the keyword extraction unit  111  extracts keywords from the first handling method  141   c.    
         [0066]    The machine learning execution unit  112  machine-learns the first parameter  132  to classify the second incident  131   b , associated with the second handling method  131   c , into a plurality of categories, based on the keywords which the keyword extraction unit  111  extracted from the second handling method  131   c.    
         [0067]    In concrete terms, the machine learning execution unit  112  inputs the keywords extracted from the second handling method  131   c  and the category information  131   e  of the second incident  131   b  to the first identification function  134  as the learning data, and calculates the first parameter  132 , for example. The first identification function  134  is a function that outputs the category information  131   e  of the second incident  131   b  when the keywords extracted from the second handling method  131   c  and the first parameter  132  are input, for example. Then the machine learning execution unit  112  machine-learns each of the first parameters in the relationship between the keywords extracted from the second handling method  131   c  and the category information  131   e  of the second incident  131   b.    
         [0068]    In other words, every time the learning data is input to the first identification function  134 , the machine learning execution unit  112  adjusts the first parameter  132 , so that the first identification function  134  is established not only for the learning data which was input in the past, but also for the newly input learning data. Thereby the machine learning execution unit  112  can increase the accuracy of the first parameter  132  every time the learning data is input to the first identification function  134 . As a result, the category specification unit  116  can estimate and output the category of the first incident  141   b , even if a keyword, which has not yet been machine-learned, is included in the keywords extracted from the first handling method  141   c  by the generalization function of the machine learning. 
         [0069]    The machine learning execution unit  112  also machine-learns the second parameter  133 , to convert the keywords extracted from the second retrieval condition  131   a . In other words, the keyword estimation unit  114  converts the keywords extracted from the first retrieval condition  141   a  in order to increase the retrieval accuracy of the first incident  141   b  when the first incident  141   b  is retrieved, as mentioned later. Therefore the machine learning execution unit  112  machine-learns the second parameter  133  to convert the keywords extracted from the first retrieval condition  141   a.    
         [0070]    In concrete terms, the machine learning execution unit  112  inputs the keywords extracted from the second retrieval condition  131   a  and the additional keywords  131   d  corresponding to the second retrieval condition  131   a  included in the second teacher data  131  to the second identification function  135  as the learning data, and calculates the second parameter  133 , for example. The additional keywords  131   d  are keywords which are added when the first incident  141   b  is searched, in order to increase the retrieval accuracy of the first incident  141   b . The second identification function  135  is a function to output the additional keywords  131   d  corresponding to the second retrieval condition  131   a  when the keywords extracted from the second retrieval condition  131   a  and the second parameter  133  are input, for example. Then the machine learning execution unit  112  machine-learns each of the second parameters in the relationship between the additional keywords extracted from the second retrieval condition  131   a  and the additional keywords  131   d  corresponding to the second retrieval condition  131   a , for example. 
         [0071]    In other words, every time the learning data is input to the second identification function  135 , the machine learning execution unit  112  adjusts the second parameter  133 , so that the second identification function  135  is established not only for the learning data which was input in the past, but also for the newly input learning data. Thereby the machine learning execution unit  112  can increase the accuracy of the second parameter  133  every time the learning data is input to the second identification function  135 . As a result, the keyword estimation unit  114  can estimate and output the keywords to be added when the first incident  141   b  is searched, even if a keyword, which has not yet been machine-learned, is included in the keywords extracted from the first retrieval condition  141   a  by the generalization function of the machine learning. 
         [0072]    The machine learning execution unit  112  may operate according to such an algorithm as adaptive regularization of weight vectors (AROW), confidence weighted (CW) or soft confidence weighted (SCW) learning. The first identification function  134  and the second identification function  135  may be determined by the algorithm which the machine learning execution unit  112  uses. 
         [0073]    The information receiving unit  113  receives the first retrieval condition  141   a  which is a new retrieval condition sent by the provider terminal  11 . 
         [0074]    The keyword estimation unit  114  converts the keywords extracted from the first retrieval condition  141   a  (hereafter also called pre-conversion keywords) by using the machine-learned second parameter  133 , and acquires new keywords (hereafter all called post-conversion keywords). In concrete terms, the keyword estimation unit  114  inputs the pre-conversion keywords and the second parameter  133  to the second identification function  135 , and acquires the output keywords as post-conversion keywords. 
         [0075]    The information retrieval unit  115  retrieves the first incident  141   b  corresponding to the first retrieval condition  141   a  by using the post-conversion keywords acquired by the keyword estimation unit  114 . In concrete terms, the information retrieval unit  115  retrieves the first incident  141   b  from the retrieval target data  136 , including a plurality of first incidents  141   b  which the provider prepared in advance. The retrieval target data  136  may include an incident which is the same as the second incident  131   b  included in the teacher data  131 , for example. 
         [0076]    The information retrieval unit  115  may retrieve the first incident  141   b  by using only a part of the post-conversion keywords acquired by the keyword estimation unit  114 . In concrete terms, the information retrieval unit  115  may extract only those keywords having a predetermined threshold or higher priority, out of the post-conversion keywords, and use those keywords for retrieving the first incident  141   b , for example. 
         [0077]    The provider may determine a number of keywords to be used for retrieving the first incident  141   b  in advance. Then, out of the post-conversion keywords, the information retrieval unit  115  may determine a keywords to be used for retrieving the first incident  141   b  in sequence from the higher priority, for example. 
         [0078]    If a plurality of first incidents  141   b  are retrieved, the category specification unit  116  classifies each first incident  141   b  into one of a plurality of categories based on the keywords extracted from the first handling method  141   c  corresponding to each first incident  141   b  respectively by using the machine-learned first parameter  132 . In concrete terms, the category specification unit  116  inputs the keywords extracted from the first handling method  141   c  and the first parameter  132  to the first identification function  134 , and specifies the category indicated by the output category information  131   e  as the category of the first incident  141   b . Thereby the provider can access the incidents categorized in accordance with the content in the provider terminal  11 . As a result, the provider can easily specify an incident to be used for retrieving the handling method. 
         [0079]    Then using the first incident  141   b  specified by the provider to receive the first handling method  141   c , the information retrieval unit  115  retrieves the first handling method  141   c  corresponding to the first incident  141   b . In concrete terms, the information retrieval unit  115  retrieves the first handling method  141   c  corresponding to the first incident  141   b  from the retrieval target data  136 , including a plurality of first handling methods  141   c  prepared by the provider in advance. 
         [0080]    The result output unit  117  transmits the first handling method  141   c , which was retrieved by the information retrieval unit  115 , to the provider terminal  11 . Then the provider terminal  11  outputs the received first handling method  141   c  to the output apparatus (an output operation in which the user can access the information), for example. 
       Embodiment 1 
       [0081]    Embodiment 1 will be described next.  FIG. 6  is a flow chart depicting an outline of a retrieval control processing according to Embodiment 1.  FIG. 7  is a diagram depicting an outline of the retrieval control processing according to Embodiment 1. The outline of the retrieval control processing in  FIG. 6  will be described with reference to  FIG. 7 . 
         [0082]    As depicted in  FIG. 7 , the information processing apparatus  1  stands by until a first retrieval condition  141   a  is received from a provider terminal  11  (NO in S 1 ). When the first retrieval condition  141   a  is received (YES in S 1 ), the information processing apparatus  1  extracts a first incident  141   b  corresponding to the first retrieval condition  141   a , which was received in the processing in S 1 , from an information storage area  130 , in which an incident and a handling method are stored in association with each other (S 2 ). 
         [0083]    In other words, in the processing in S 2 , the information processing apparatus  1  extracts one or more first incident(s)  141   b  that satisfy the content of the inquiry (first retrieval condition  141   a ), which the user sent to the provider terminal  11 , for example. 
         [0084]    Then the information processing apparatus  1  classifies the first incidents  141   b  extracted in the processing in S 2 , in accordance with the first handling method  141   c  associated with the first incident(s)  141   b , and outputs the classified first incident(s)  141   b  as the retrieval result (S 3 ). 
         [0085]    In other words, the information processing apparatus  1  classifies the first incident(s)  141   b , which were extracted based on the first retrieval condition  141   a , in accordance with the content of the first handling method  141   c  corresponding to each of the first incidents  141   b . Then the information processing apparatus  1  sends the classified first incident(s)  141   b  to the provider terminal  11 . Thereby the provider can access the first incident(s)  141   b , categorized in accordance with the content, in the provider terminal  11 . As a result, the provider can easily specify a first incident  141   b  to be used for retrieval of the first handling method  141   c.    
         [0086]    In this way, the information processing apparatus  1  according to this embodiment receives the first retrieval condition  141   a , extracts the first incident(s)  141   b  corresponding to the received first retrieval condition  141   a  from the storage unit  130  storing each incident in association with a handling method, classifies the extracted first incident(s)  141   b  in accordance with the first handling method  141   c  associated with the first incident(s)  141   b , and outputs the classified first incident(s)  141   b  as the retrieval result. 
         [0087]    Thereby the provider can access the first incident(s)  141   b , which are categorized in accordance with the content, in the provider terminal  11 . As a result, the provider can easily specify the first incident  141   b  to be used for retrieving the first handling method  141   c.    
       Details of Embodiment 1 
       [0088]    Details on Embodiment 1 will be described next.  FIG. 8  to  FIG. 10  are flow charts depicting the details of the retrieval control processing according to Embodiment 1.  FIG. 11  to  FIG. 30  are drawings for describing details on the retrieval control processing according to Embodiment 1. Details of the retrieval control processing depicted in  FIG. 8  to  FIG. 10  will be described with reference to  FIG. 11  to  FIG. 30 . 
         [0089]    As depicted in  FIG. 8 , the keyword extraction unit  111  of the information processing apparatus  1  stands by until the machine learning execution timing arrives (NO in S 11 ). The machine learning execution timing is, for example, a timing when the provider performs machine learning of the teacher data  131 . In concrete terms, the machine learning execution timing may be, for example, a timing when the provider input notification that the machine learning of the teacher data  131  is performed. 
         [0090]    When the machine learning execution timing arrives (YES in S 11 ), the keyword extraction unit  111  extracts keywords from the second handling method  131   c  included in the first teacher data  131 , as depicted in  FIG. 11  (S 12 ). In concrete terms, the keyword extraction unit  111  extracts the keywords by morphologically parsing the second handling method  131   c , for example. An example of the first teacher data  131  and an example of the extracted keywords will be described herein below. 
         [0091]    [Example of First Teacher Data] 
         [0092]      FIG. 15  is a table for describing an example of the first teacher data  131 . As items, the first teacher data  131  in  FIG. 15  has: “Item number” to identify each information included in the first teacher data  131 ; and “Second handling method”, in which a second handling method  131   c  is set. Further, the first teacher data  131  in  FIG. 15  has an item of “Category”, in which category information  131   e  of the second incident  131   b  corresponding to the second handling method  131   c  being set in “Second handling method” is set. 
         [0093]    In concrete terms, according to the example in  FIG. 15 , a sentence “Please create storage place of operation result information in distribution destination system.” is set in “Second handling method” of the information of which “Item number” is “1”, and “A-1” is set in “Category”. Further, according to the example in  FIG. 15 , a sentence “Please define monitoring host.” is set in “Second handling method” of the information of which “Item number” is “2”, and “A-2” is set in “Category”. Description on the other information included in  FIG. 15  will be omitted. 
         [0094]    [Example of Keywords Extracted from Second Handling Method] 
         [0095]    An example of keywords (hereafter also called keyword information) extracted from the second handling method  131   c  will be described next.  FIG. 16  is a table for describing an example of the keyword information extracted from the second handling method  131   c.    
         [0096]    As items, the keyword information in  FIG. 16  has: “Item number” to identify each information included in the keyword information in  FIG. 16 ; and “Keywords (Second handling method)” in which keywords extracted from the second handling method  131   c  are set. 
         [0097]    In concrete terms, according to the keyword information in  FIG. 16 , in the information of which “Item number” is “1”, “distribution”, “destination”, “system”, “operation”, “result”, “information”, “storage”, “place”, “create” and “please” are set as “Keywords (Second handling method)”. Description on the other information included in  FIG. 16  will be omitted. 
         [0098]    Referring back to  FIG. 8 , the machine learning execution unit  112  of the information processing apparatus  1  performs machine learning of the first parameter  132  by providing the keywords extracted in the processing in S 12  and the category information  131   e  of the second incident  131   b  included in the first teacher data  131  to the first identification function  134  (S 13 ). 
         [0099]    In concrete terms, the machine learning execution unit  112  specifies the keywords which are set in “Keywords (Second handling method)” of the information of which “Item number” is “1” in the keyword information described in  FIG. 16 , for example. The machine learning execution unit  112  also specifies “A-1” which is set in “Category” of the information of which “Item number” is “1” in the first teacher data  131  described in  FIG. 15 , for example. Then the machine learning execution unit  112  calculates the first parameter  132  by inputting each of the specified information to the first identification function  134  as the learning data, and performs machine learning of the calculated first parameter  132 . 
         [0100]    Then the machine learning execution unit  112  performs machine learning by calculating the first parameter  132  for the other information that is set in “Keywords (Second handling method)” of the keyword information in  FIG. 16 , and for the other information that is set in “Category” of the first teacher data  131  in  FIG. 15 . 
         [0101]    In other words, the machine learning execution unit  112  adjusts the first parameter  132  every time the learning data is input to the first identification function  134 , so that the first identification function  134  is established not only for the learning data which was input in the past, but also for the newly input learning data. Thereby the machine learning execution unit  112  can increase the accuracy of the first parameter  132  every time the learning data is input to the first identification function  134 . An example of the first parameter  132  will be described later. 
         [0102]    Then the keyword extraction unit  111  extracts keywords from the second retrieval condition  131   a  included in the second teacher data  131  (S 14 ), as depicted in  FIG. 12 . In concrete terms, the keyword extraction unit  111  extracts keywords by performing morphological parsing of the second retrieval condition  131   a , for example. An example of the second teacher data  131  and an example of the extracted keywords will be described next. 
         [0103]    [Example of Second Teacher Data] 
         [0104]      FIG. 17  is a table for describing an example of the second teacher data  131 . As items, the second teacher data  131  in  FIG. 17  has: “Item number” to identify each information included in the second teacher data  131 ; “Second retrieval condition” in which the second retrieval condition  131   a  is set; and “Additional keywords” in which additional keywords  131   d  are set. 
         [0105]    The additional keywords  131   d  may be extracted from the second handling method  131   c  which the provider determines as desirable to retrieve for the second retrieval condition  131   a . In concrete terms, as the additional keywords  131   d , the provider may specify keywords which are not included in the keywords extracted from the second retrieval condition  131   a , out of the keywords extracted from the second handling method  131   c  which the provider determined as desirable to retrieve, and include these keywords in the second teacher data  131 . 
         [0106]    In concrete terms, according to the example in  FIG. 17 , in “Second retrieval condition” in the information of which “Item number” is “1”, sentences “Operation manager is not able to be started for both operation system and standby system after policy is distributed. Please instruct cause and handling method.” are set. Further, according to the example in  FIG. 17 , “storage” and “place” are set in “Additional keywords” in the information of which “Item number” is “1”. Description on the other information included in  FIG. 17  will be omitted. 
         [0107]    [Example of Keywords Extracted from Second Retrieval Condition] 
         [0108]    Examples of keywords (hereafter also called keyword information) extracted from the second retrieval condition  131   a  will be described next.  FIG. 18  is a table for describing an example of the keyword information extracted from the second retrieval condition  131   a.    
         [0109]    As items, the keyword information in  FIG. 18  has: “Item number” to identify each information included in the keyword information in  FIG. 18 ; and “Keywords (Second retrieval condition)” in which keywords extracted from the second retrieval condition  131   a  are set. Further, the keyword information in  FIG. 18  has “Keywords (Additional keywords)”, in which additional keywords are added to the keywords extracted from the second retrieval condition  131   a . In other words, in the case of the example in  FIG. 18 , machine learning of the second parameter  133  is performed based on the keywords extracted from the second retrieval condition  131   a  and on the keywords in which the additional keywords are added to the keywords extracted from the second retrieval condition  131   a.    
         [0110]    In concrete terms, according to the keyword information in  FIG. 18 , in the information of which “Item number” is “1”, “policy”, “distribution”, “operation”, “standby”, “operation”, “manager”, “start”, “cause”, “handling”, “instruct” and “please” are set as “Keywords (Second retrieval condition)”. Further, according to the keyword information in  FIG. 18 , as the “Keywords (Additional keywords)”, “storage” and “place” are set in the information of which “Item number” is “1”, in addition to the information that is set in “Keywords (Second retrieval condition)”. Description on the other information included in  FIG. 18  will be omitted. 
         [0111]    Referring back to  FIG. 8 , the machine learning execution unit  112  performs machine learning of the second parameter  133  by providing the keywords extracted in the processing in S 14  and the additional keywords included in the first teacher data  131  to the second identification function  135  (S 15 ). 
         [0112]    In concrete terms, the machine learning execution unit  112  specifies the keywords which are set in “Keywords (Second retrieval method)” of the information of which “Item number” is “1” in the keyword information described in  FIG. 18 , for example. The machine learning execution unit  112  also specifies keywords which are set in “Keywords (Additional keywords)” in the information of which “Item number” is “1” in the keyword information described in  FIG. 18 , for example. Then the machine learning execution unit  112  calculates the second parameter  133  by inputting each of the specified keywords to the second identification function  135  as the learning data, and performs machine learning of the calculated second parameter  133 . 
         [0113]    Then the machine learning execution unit  112  performs machine learning by calculating the second parameter  133  for the other information that is set in “Keywords (Second retrieval method)” of the keyword information in  FIG. 18 , and on the other information that is set in ‘Keywords (Additional keywords)” of the keyword information in  FIG. 18 . 
         [0114]    In other words, the machine learning execution unit  112  adjusts the second parameter  133  every time the learning data is input to the second identification function  135 , so that the second identification function  135  is established not only for the learning data which was input in the past, but also for the newly input learning data. Thereby the machine learning execution unit  112  can increase the accuracy of the second parameter  133  every time the learning data is input to the second identification function  135 . An example of the second parameter  133  will be described later. 
         [0115]    Referring back to  FIG. 9 , the information receiving unit  113  of the information processing apparatus  1  stands by until the information retrieval timing arrives (NO in S 21 ). The information retrieval timing is, fore example, a timing when the first retrieval condition  141   a  is received from the provider terminal  11  (timing when the first retrieval condition  141   a  is input to the information processing apparatus  1 ). When the information retrieval timing arrives (YES in S 21 ), the keyword extraction unit  111  extracts the pre-conversion keywords from the first retrieval condition  141   a  sent from the provider terminal  11  (S 22 ), as depicted in  FIG. 13 . In concrete terms, the keyword extraction unit  111  extracts keywords by morphologically parsing the first retrieval condition  141   a , for example. An example of the first retrieval condition  141   a  and the pre-conversion keywords will be described herein below. 
         [0116]    [Example of First Retrieval Condition Sent from Provider Terminal] 
         [0117]      FIG. 19  is a table for describing an example of the first retrieval condition  141   a  sent from the provider terminal  11 . As items, the first retrieval condition  141   a  in  FIG. 19  has: “Item number” to identify each information included in the first retrieval condition  141   a ; and “First retrieval condition” in which the content of the first retrieval condition  141   a  is set. 
         [0118]    In concrete terms, according to the first retrieval condition  141   a  in  FIG. 19 , sentences “When access from cloud to environment setting screen of AAA operation manager is attempted, popup message ‘connect request timeout’ is displayed, and access to server is disabled. Please instruct cause and handling method.” are set in “First retrieval condition” in the information of which “Item number” is “1”. 
         [0119]    [Example of Pre-Conversion Keywords Extracted from First Retrieval Condition] 
         [0120]    An example of pre-conversion keywords (hereafter also called pre-conversion keyword information) extracted from the first retrieval condition  141   a  sent from the provider terminal  11  will be described next.  FIG. 20  is a table for describing an example of the pre-conversion keyword information. 
         [0121]    As items, the pre-conversion keyword information in  FIG. 20  has “Item number” to identify each information included in the pre-conversion keyword information in  FIG. 20 , and “Keywords (First retrieval condition)” in which keywords extracted from the first retrieval condition  141   a  are set. 
         [0122]    In concrete terms, according to the pre-conversion keyword information in  FIG. 20 , in the information of which “Item number” is “1”, “cloud”, “AAA”, “operation”, “manager” and the like are set as “Keywords (First retrieval condition)”. 
         [0123]    Referring back to  FIG. 9 , the keyword estimation unit  114  of the information processing apparatus  1  calculates the correlation with the pre-conversion keywords (hereafter also called second correlation information) extracted in the processing in S 22  for each of the keywords extracted from the second retrieval condition  131   a  in the processing in S 14  and the additional keywords  131   d  (S 23 ). 
         [0124]    In concrete terms, the keyword estimation unit  114  calculates the second correlation information with the pre-conversion keywords extracted in the processing in S 22 , by providing the pre-conversion keywords extracted in the processing in S 22  and the second parameter  133  machine-learned in the processing in S 15  to the second identification function  135 . In other words, for each keyword of the keywords extracted from the second retrieval condition  131   a  in the processing in S 14  and the additional keywords, the keyword estimation unit  114  calculates the second correlation information for determining whether each keyword is included in the post-conversion keywords. Examples of the second parameter  133  and the second correlation information will be described next. 
         [0125]    [Example of Second Parameters] 
         [0126]      FIG. 21  is a table for describing an example of the second parameters  133 . The second parameters  133  in  FIG. 21  include a second parameter between each keyword of the keywords extracted from the second retrieval condition  131   a  in the processing in S 14  and the additional keywords respectively. “policy”, “distribution”, “operation” and the like in the second parameters  133  in  FIG. 21  correspond to each keyword of the keywords extracted from the second retrieval condition  131   a  in the processing in S 14  and the additional keywords. 
         [0127]    In concrete terms, if “policy” is included in the pre-conversion keywords extracted from the first retrieval condition  141   a , the keyword estimation unit  114  refers to the information in the row where “policy” is set in the left column, out of the second parameters  133  in  FIG. 21  in the processing in S 23 . In other words, in this case, the keyword estimation unit  114  refers to “0.5”, which is the information where “policy” is set, in the top row, “0.1” which is the information where “distribution” is set in the top row, “0.3” which is the information where “operation” is set in the top row and the like. Description on the other information included in  FIG. 21  will be omitted. 
         [0128]    [Example of Second Correlation Information] 
         [0129]    An example of the second correlation information will be described next.  FIG. 22  is a table for describing an example of the second correlation information. As items, the second correlation information in  FIG. 22  has: “Item number” to identify each information included in the second correlation information; “Keyword” to identify a keyword; and “Score” to indicate the second correlation information of each keyword. Each information included in the second correlation information in  FIG. 22  will be described based on the assumption that the values are set in “Score” in descending order. 
         [0130]    In concrete terms, if “policy” and “operation”, for example, are included in the pre-conversion keywords extracted from the first retrieval condition  141   a , the keyword estimation unit  114  refers to the information in the rows where “policy” and “operation” are set in the left column, out of the information included in the second parameters  133  in  FIG. 21 . Therefore in the case of calculating the second correlation information to determine whether “distribution” is included in the post-conversion keywords, for example, the keyword estimation unit  114  refers to “0.1”, which is information where “policy” is set in the left column, and “distribution” is set in the top row. Further, in this case, the keyword estimation unit  114  refers to “0.2”, which is information where “operation” is set in the left column, and “distribution” is set in the top row. Then the keyword estimation unit  114  adds “0.1” and “0.2” which are referred information, for example, and multiplies this result by a predetermined coefficient, so as to calculate the second correlation information corresponding to “distribution”. 
         [0131]    Then the keyword estimation unit  114  sets each second correlation information calculated for each word as listed in  FIG. 22 . In concrete terms, if the second correlation information calculated for “distribution” is “75.3”, for example, the keyword estimation unit  114  sets “75.3” in “Score” of the information of which “Keyword” is “distribution” (information of which “Item number” is “1”). Description on the other information included in  FIG. 22  will be omitted. 
         [0132]    Referring back to  FIG. 9 , the keyword estimation unit  114  outputs keywords, of which second correlation information calculated in the processing in S 23  is a predetermined threshold or more, as the post-conversion keywords (S 24 ). An example of the post-conversion keywords (hereafter also called post-conversion keyword information) will be described next. 
         [0133]    [Example of Post-Conversion Keywords] 
         [0134]      FIG. 23  is a table for describing an example of the post-conversion keywords. The post-conversion keyword information in  FIG. 23  has the same items as the information in  FIG. 20 . 
         [0135]    In concrete terms, if the predetermined threshold in the processing in S 24  is “20.0”, the keyword estimation unit  114  specifies the information that is set in “Keywords” of the information of which “Item number” is “1” to “24”, for example, in the second correlation information in  FIG. 22 , as the post-conversion keywords. Therefore in this case, the keyword estimation unit  114  sets “cloud”, “AAA”, “operation”, “manager”, “normal”, “connection” and the like in the column of “Keywords (Retrieval condition)” as listed in  FIG. 23 . 
         [0136]    In other words, in the second correlation information in  FIG. 22 , the information, which is set in “Keywords” of information of which “Item number” is “1” to “24”, includes “normal” and “connection” which are not included in “Keywords (First retrieval condition)” of the pre-conversion information described in  FIG. 20 . Therefore the keyword estimation unit  114  specifies “normal” and “connection” as well as the post-conversion keywords as listed in  FIG. 23 . 
         [0137]    Thereby the information processing apparatus  1  can retrieve a more appropriate first incident  141   b  for the first retrieval condition  141   a  sent from the provider terminal  11 . 
         [0138]    Referring back to  FIG. 9 , the information retrieval unit  115  of the information processing apparatus  1  executes retrieval of the first incident  141   b  by using the post-conversion keywords output in the processing in S 24  (S 25 ). An example of the first incident  141   b  retrieved in the processing in S 25  will be described next. 
         [0139]    [Example of First Incidents Retrieved in Processing in S 25 ] 
         [0140]      FIG. 24  is a table for describing an example of the first incidents  141   b  retrieved in the processing in S 25 . As items, the first incidents  141   b  in  FIG. 24  have: “Item number” to identify each information included in the first incident  141   b ; and “First incident” in which the first incident  141   b  retrieved in the processing in S 25  is set. 
         [0141]    In concrete terms, according to the first incidents  141   b  in  FIG. 24 , “popup message ‘connect request timeout’ is displayed” is set in the “First incident” in the information of which “Item number” is “1”. Description on the other information included in  FIG. 24  will be omitted. 
         [0142]    Referring back to  FIG. 10 , the keyword extraction unit  111  extracts keywords from the first handling method  141   c  associated with the first incident  141   b  extracted in the processing in S 25  (S 31 ), as depicted in  FIG. 14 . In concrete terms, the keyword extraction unit  111  refers to the retrieval target data  136  stored in the information storage area  130 , and extracts the keywords from the first handling method  141   c  associated with the first incident  141   b  extracted in the processing in S 25 . Examples of the retrieval target data  136 , the first handling method  141   c  associated with the first incident  141   b  extracted in the processing in S 25 , and the keywords extracted from the first handling method  141   c  will be described next. 
         [0143]    [Example of Retrieval Target Data] 
         [0144]      FIG. 25  is a table for describing an example of the retrieval target data  136 . As items, the retrieval target data  136  in  FIG. 25  has: “Item number” to identify each information included in the retrieval target data  136 ; “Incident” in which the incident is set; and “Handling method” in which a handling method is set. The retrieval target data  136  may include the second incident  131   b  and the second handling method  131   c.    
         [0145]    In concrete terms, according to the retrieval target data  136  in  FIG. 25 , “Operation manager is not able to be started for both operation system and standby system after policy is distributed. Please instruct cause and handling method.” is set as “Incident” in the information of which “Item number” is “1”. Further, according to the retrieval target data  136  in  FIG. 25 , “Please create storage place of operation result information in distribution destination system.” is set as “Handling method” in the information of which “Item number” is “1”. Description on the other information included in  FIG. 25  will be omitted. 
         [0146]    [Example of First Handling Method] 
         [0147]    An example of the first handling method  141   c  associated with the first incident  141   b  extracted in the processing in S 25  will be described next.  FIG. 26  is a table for describing an example of the first handling method  141   c  associated with the first incident  141   b  extracted in the processing in S 25 . 
         [0148]    As items, the first handling method  141   c  in  FIG. 26  has “Item number” to identify each information included in the first handling method  141   c ; and “First handling method” in which the first handling method  141   c  corresponding to the first incident  141   b  retrieved in the processing in S 25  is set. 
         [0149]    In concrete terms, according to the first handling method  141   c  in  FIG. 26 , “Please define monitoring host.” is set as the “First handling method” in the information of which “Item number” is “1”. In other words, the information that is set in the “First incident” of the information of which “Item number” is “1” in the first incidents  141   b  described in  FIG. 24  is the same as the information that is set in the “Incident” of the information of which “Item number” is “3” in the retrieval target data  136  described in  FIG. 25 . Therefore in the processing in S 31 , the keyword extraction unit  111  specifies the information that is set in the “Handling method” of the information of which “Item number” is “3” in the retrieval target data  136  in  FIG. 25 , for the information of which “Item number” is “1” in the first incidents  141   b  in  FIG. 24 . Then the keyword extraction unit  111  sets the specified information in the information of which “Item number” is “1” in the first handling methods  141   c  in  FIG. 26 . Description on the other information included in  FIG. 26  will be omitted. 
         [0150]    [Example of Keywords Extracted from First Handling Methods] 
         [0151]    An example of the keywords extracted from the first handling methods  141   c  described in  FIG. 26  will be described next.  FIG. 27  is a table for describing an example of keywords extracted from the first handling method  141   c . As items, the first handling methods  141   c  in  FIG. 27  has: “Item number” to identify each information included in the first handling methods  141   c ; and “Keywords (First handling method)” in which keywords extracted from the first handling method  141   c  are set. 
         [0152]    For example, in the keyword information in  FIG. 27 , “monitor”, “host”, “define”, “register” and “please” are set as “Keywords (First handling method)” in the information of which “Item number” is “1”. Description on the other information included in  FIG. 27  will be omitted. 
         [0153]    Referring back to  FIG. 10 , the category specification unit  116  of the information processing apparatus  1  calculates correlation with the keywords extracted in the processing in S 31  (hereafter also called first correlation information) for each category of the first incident  141   b  (S 32 ). 
         [0154]    In concrete terms, the category specification unit  116  calculates the first correlation information with the keywords extracted in the processing in S 31  by providing the keywords extracted in the processing in S 31  and the first parameter  132  machine-learned in the processing in S 13  to the first identification function  134 . In other words, the category specification unit  116  calculates the first correlation information to determine the category of the first incident  141   b  extracted in the processing in S 25 . Examples of the first parameters  132  and the first correlation information will be described. 
         [0155]    [Example of First Parameters] 
         [0156]      FIG. 28  is a table for describing an example of the first parameters  132 . The first parameters  132  in  FIG. 28  include each first parameter in the relationship between each keyword extracted from the second handling method  131   c , and each category of the second incident  131   b . The information that is set in the left column (e.g. “define”, “monitor”, “memory”) in the first parameters  132  in  FIG. 28  corresponds to each keyword extracted from the second handling method  131   c  in the processing in S 12 . The information that is set in the top row (e.g. “A-1”, “A-2”, “A-3”) in the first parameters  132  in  FIG. 28  corresponds to the information indicating each category of the first incident  132   b.    
         [0157]    In concrete terms, if “define” is included in the keywords extracted from the first handling method  141   c  in the processing in S 31 , the category specification unit  116  refers to the information in the row where “define” is set in the left column, out of the first parameters  132  in  FIG. 28  in the processing in S 32 . In other words, in this case, the category specification unit  116  refers to, for example, “0.2” which is information where “A-1” is set in the top row, “0.5” which is information where “A-2” is set in the top row in the column, and “0.4” which is information where “A-3” is set in the top row in the column. Description on the other information included in  FIG. 28  will be omitted. 
         [0158]    [Example of First Correlation Information] 
         [0159]    An example of the first correlation information of one first incident  141   b , out of the first incidents  141   b  extracted in the processing in S 25 , will be described next.  FIG. 29  is a table for describing an example of the first correlation information. As items, the first correlation information in  FIG. 29  has: “Item number” to identify each information included in the first correlation information; “Category” to identify each category; and “Score” to indicate the first correlation information of each keyword. Each information included in the first correlation information in  FIG. 29  will be described based on the assumption that the values are set in “Score” in descending order. 
         [0160]    In concrete terms, if “define” and “memory” are included in the keywords extracted from the first handling method  141   c , the category specification unit  116  refers to the information in the rows where “define” and “memory” are set in the left column, out of the information included in the first parameters  132  in  FIG. 28 . Therefore in the case of, for example, determining whether the category of the first incident  141   b  corresponding to the first handling method  141   c  including “define” and “memory” is “A-1”, the category specification unit  116  refers to “0.2” which is information where “define” is set in the left column and “A-1” is set in the top row. Further, in this case, the category specification unit  116  refers to “0.3” which is information where “memory” is set in the left column and “A-1” is set in the top row. Then the category specification unit  116  adds “0.2” and “0.3” which are referred information, for example, and multiplies this result by a predetermined coefficient, so as to calculate the first correlation information corresponding to “A-1”. 
         [0161]    Then the category specification unit  116  sets each first correlation information calculated for each keyword as listed in  FIG. 29 . In concrete terms, if the first correlation information calculated for “A-1” is “3.2”, for example, the category specification unit  116  sets “3.2” in “Score” of the information of which “Keyword” is “A-1” (information of which “Item number” is “4”). Description on the other information included in  FIG. 29  will be omitted. 
         [0162]    Referring back to  FIG. 10 , the category specification unit  116  specifies a category, of which second correlation information calculated in the processing in S 32  is highest, as the category of the first incident  141   b  (S 33 ). In other words, the category specification unit  116  specifies “A-2” as the category of the first incident  141   b  corresponding to the first correlation information described in  FIG. 29 , for example. 
         [0163]    Then the result output unit  117  of the information processing apparatus  1  outputs the first incident in accordance with the category specified in the processing in S 33  (S 34 ). In concrete terms, the result output unit  117  sends the first incident  141   b  extracted in the processing in S 25  to the provider terminal  11 , along with the information on the category specified in the processing in S 33 , for example. Then the provider terminal  11  outputs the first incident  141   b , extracted in the processing in S 25 , to the output apparatus  21  in accordance with the category specified in the processing in S 33 , for example. An example of the output apparatus  21 , in the state of outputting the first incidents  141   b , will be described next. 
         [0164]    [Example of State of Outputting First Incidents  141   b]   
         [0165]      FIG. 30  is an example of the output apparatus  21  in the state of outputting the first incidents  141   b . In the output apparatus  21  in  FIG. 30 , the first incidents  141   b  are separately displayed in a first display unit  21   a , a second display unit  21   b , a third display unit  21   c , and a fourth display unit  21   d.    
         [0166]    In the example in  FIG. 30 , the first incident  141   b  of which second correlation information is “A-2” is displayed in the first display unit  21   a , and the first incident  141   b  of which second correlation information is “A-3” is displayed in the second display unit  21   b . Further, in the example in  FIG. 30 , the first incident  141   b  of which second correlation information is “B-1” is displayed in the third display unit  21   c , and the first incident  141   b  of which second correlation information is “B-2” is displayed in the fourth display unit  21   d.    
         [0167]    In concrete terms, in the case of the first incident  141   b  described in  FIG. 29 , the category of which second correlation information is highest is “A-2”. Therefore the first incident  141   b  described in  FIG. 29  is output to the first display unit  21   a  as “First retrial result” as depicted in  FIG. 30 . Description on the other information included in  FIG. 30  will be omitted. 
         [0168]    Thereby the provider is enabled to access the first incident  141   b , which is in the state of being categorized in accordance with the content, in the provider terminal  11 . As a result, the provider can easily specify the first incident  141   b  which is used for retrieving the first handling method  141   c.    
         [0169]    After the processing in S 34 , the provider specifies the first incident  141   b  used for retrieving the first handling method  141   c , from the first incidents  141   b  output in the processing in S 34 , for example. In other words, the provider specifies the first incident  141   b  of which content is closest to the first retrieval condition  141   a  received by the information receiving unit  113 . 
         [0170]    Then the information retrieval unit  115  refers to the retrieval target data  136  stored in the information storage area  130 , for example, and extracts the first handling method  141   c  corresponding to the first incident  141   b  specified by the provider. Then the result output unit  117  sends the extracted first handling method  141   c  to the provider terminal  11 . 
         [0171]    Thereby the provider terminal  11  is enabled to output the first handling method  141   c , received from the information processing apparatus  1 , to the output apparatus in which the user can access information, for example. Therefore the user can access the first handling method  141   c  corresponding to the first retrieval condition  141   a.    
         [0172]    If the category of the first incident  141   b  specified in the processing in S 33  is corrected by the provider, the machine learning execution unit  112  may perform the machine learning of the first parameters  132  again. In this case, the machine learning execution unit  112  performs the machine learning of the first parameters  132  again by providing the keywords extracted from the first handling method  141   c  in the processing in S 31  and the category of the first incident  141   b  corrected by the provider to the first identification function  134 , for example. Thereby the provider can further increase the accuracy of the first parameters  132 . 
         [0173]    All examples and conditional language provided herein are intended for the pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventor to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although one or more embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.