Patent Publication Number: US-2019179841-A1

Title: Generation program, information processing apparatus and generation method

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2017-236851, filed on Dec. 11, 2017, the entire contents of which are incorporated herein by reference. 
     FIELD 
     The embodiment discussed herein relates to a generation program, an information processing apparatus and a generation method. 
     BACKGROUND 
     In recent years, in order to make it possible to smoothly distribute financial information of financial statements and so forth, an extensible business reporting language (XBRL) document (hereinafter referred to also as document) for which XBRL that is a language based on the standard of extensible markup language (XML) is used is generated. 
     This XBRL document is constituted from a concept of an instance and a taxonomy, and all financial information to be disclosed is set as instances and taxonomies. In an XBRL document, for example, financial information itself (hereinafter referred to also as item) such as amounts of sales or operating profits is set as an instance, and a definition element such as a display structure or a display method is set as taxonomy (for example, refer to Japanese Laid-open Patent Publication No. 2007-164591, Japanese Laid-open Patent Publication No. 2010-170287 and Japanese Laid-open Patent Publication No. 2017-084340). 
     A person in charge who is to perform information analysis of such financial information as described above (such person is hereafter referred to also merely as analyzer) performs various information analyses using a neural network (for example, a forward propagation type neural network) that has learned, for example, training data including instances and taxonomies. For example, the analyzer performs prediction of future operating conditions and stock prices of a company using a neural network that has learned training data including input data constituted from information included in instances and taxonomies and output data constituted from information indicative of operating conditions, stock prices and so forth of the company in the past. 
     Here, for example, in the case where the number of input nodes of the neural network is fixed and the number of kinds of information included in the instances or taxonomies is greater than the number of the input nodes of the neural network, the analyzer causes the neural network to perform learning of training data, for example, by performing inputting to the neural network after part of the information included in the instances or the taxonomies is deleted. 
     However, a plurality of pieces of information having no relationship to each other are sometimes included in an adjacent relationship to each other in an instance or a taxonomy. Therefore, in this case, it is difficult for the analyzer to perform extraction of feature values by application of various filters with high accuracy and to input an appropriate input value to the neural network. Accordingly, it is sometimes difficult for the analyzer to construct a neural network that can output information having high significance. 
     Therefore, it is desirable to provide a generation program, an information processing apparatus and a generation method that make it possible to construct a neural network that can output information having high significance. 
     SUMMARY 
     According to an aspect of the embodiments, a generation method performed in a computer includes: classifying, when a document is accepted, each of a plurality of items included in the accepted document into one of a plurality of groups by referring to a storage in which information indicative of a relationship between the plurality of items included in the document is stored; and generating, for each of the plurality of groups, input values to a neural network based on a value or values individually associated with one or more items classified in the group. 
     The object and advantages of the invention will be realized and attained by mean of the elements and combinations particularly pointed out in the claims. 
     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 
         FIG. 1  depicts a configuration of an information processing system; 
         FIG. 2  depicts an example of document information; 
         FIG. 3  depicts an example of document information; 
         FIG. 4  depicts an example of document information; 
         FIG. 5  depicts an example of document information; 
         FIGS. 6A, 6B and 6C  depict examples of document information; 
         FIGS. 7A, 7B and 7C  depict examples of document information; 
         FIG. 8  depicts a hardware configuration of an information processing apparatus; 
         FIG. 9  depicts functional blocks of an information processing apparatus; 
         FIG. 10  depicts a flow chart illustrating an outline of an information generation process according to a first embodiment; 
         FIGS. 11 to 13  depict an outline of the information generation process according to the first embodiment; 
         FIGS. 14 to 17  depict flow charts illustrating details of the information generation process according to the first embodiment; 
         FIG. 18  depicts a particular example of first item information; 
         FIG. 19  depicts a particular example of first taxonomy information; 
         FIG. 20  depicts a particular example of neural network (NN) information; 
         FIG. 21  depicts a particular example of setting information; 
         FIG. 22  depicts a particular example of filter information; 
         FIGS. 23 and 24  depict details of the information generation process according to the first embodiment; 
         FIG. 25  depicts another particular example of filter information; 
         FIG. 26  depicts a particular example of second item information; 
         FIG. 27  depicts a particular example of second taxonomy information; 
         FIGS. 28 and 29  depict details of another information generation process according to the first embodiment; and 
         FIG. 30  depicts a further example of filter information. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Configuration of Information Processing System 
       FIG. 1  depicts a configuration of an information processing system. The information processing system  10  depicted in  FIG. 1  includes, for example, an information processing apparatus  1 , a storage unit  130  and an operation terminal  3 . The operation terminal  3  depicted in  FIG. 1  is coupled to the information processing apparatus  1  through a network NW such as the Internet. 
     The operation terminal  3  is a terminal that is used, for example, by an analyzer. For example, the analyzer inputs an XBRL document  131  (hereinafter referred to also as document information  131 ) constituted from instances and taxonomies through the operation terminal  3 . 
     The information processing apparatus  1  stores the document information  131  inputted from the operation terminal  3  into the storage unit  130 . For example, the information processing apparatus  1  stores the document information  131  of each reporting year in a sorted state into the storage unit  130 . 
     Further, the information processing apparatus  1  causes, for example, a neural network (not depicted) constructed in advance to learn the document information  131  stored in the storage unit  130 . The analyzer performs various information analyses, for example, using the neural network that has learned the document information  131 . For example, the analyzer performs prediction of future operating conditions and stock prices of companies using a neural network that has learned training data including input data constituted from information included in instances and taxonomies included in the document information  131  and output data constituted from information indicative of operating conditions, stock prices and so forth of the companies in the past. In the following, a particular example of the document information  131  is described. 
     Particular Example of Document Information 
       FIGS. 2 to 7  are views illustrating particular examples of document information. The document information here may be the document information  131  depicted in  FIG. 1 . It is to be noted that the following description is given assuming that first document information  131   a  and second document information  131   b  (that is document information  131  corresponding to a reporting year newer than that of the first document information  131   a ) are stored in the storage unit  130 . Further, the following description is given assuming that a taxonomy constituting the first document information  131   a  and the second document information  131   b  includes a schema, a presentation linkbase, a definition linkbase and a label linkbase. 
     Particular Example of Document Structure in Document Information 
     First, particular examples of a document structure in the document information  131  are described.  FIG. 2  is a view illustrating a particular example of a document structure in the first document information  131   a , and  FIG. 3  is a view illustrating a particular example of a document structure in the second document information  131   b.    
     The document structure of the first document information  131   a  depicted in  FIG. 2  indicates that a file corresponding to an instance (file whose file name is “instance2016.xbr1”) is a file that is referred to from a file corresponding to a schema (a file whose file name is “schema2016.xsd”). 
     Further, the document structure of the first document information  131   a  depicted in  FIG. 2  indicates that a file corresponding to the schema is a file that is referred to from a file corresponding to the presentation linkbase (file whose file name is “presentation2016.xml”), a file corresponding to the definition linkbase (file whose file name is “definition2016.xml”) and a file corresponding to the label linkbase (file whose file name is “labe12016.xml”). 
     Meanwhile, the document structure of the second document information  131   b  depicted in  FIG. 3  indicates that a file corresponding to the instance (file whose file name is “instance2017.xbr1”) is a file that is referred to from a file corresponding to the schema (file whose file name is “schema2017.xsd”). 
     Further, the document structure of the second document information  131   b  depicted in  FIG. 3  indicates that a file corresponding to the schema is a file that is referred to from a file corresponding to the presentation linkbase (file whose file name is “presentation2017.xml”), a file corresponding to the definition linkbase (file whose file name is “definition2017.xml”) and a file corresponding to the label linkbase (file whose file name is “label2017.xml”). 
     Particular Example of Instance Constituting Document Information 
     Now, a particular example of an instance constituting the document information  131  is described.  FIG. 4  is a view depicting a particular example of a file corresponding to an instance that configures the first document information  131   a  and  FIG. 5  is a view depicting a particular example of a file corresponding to an instance that constituting the second document information  131   b.    
     The instance depicted in  FIG. 4  includes information indicating that the item values of “Sales,” “SalesDom” and “SalesUS” that are items for referring to a context (information indicative of company information, a reporting year and so forth) whose context identification (ID) is “C1” are “200,” “150” and “50,” respectively. 
     Meanwhile, the instance depicted in  FIG. 5  includes information indicating that the item values of “Sales,” “SalesDom,” “SalesDomE,” “SalesDomW,” “SalesUS” and “SalesEU” that are items that refer to a context shoes context ID is “C1” are “35,” “20,” “14,” “6,” “10” and “5,” respectively. 
     Particular Example of Taxonomy Constituting Document Information 
     Now, a particular example of a taxonomy constituting the document information  131  is described.  FIGS. 6A to 6C  are views illustrating particular examples of a file corresponding to a taxonomy constituting the first document information  131   a .  FIG. 6A  depicts a particular example of a file corresponding to the schema;  FIG. 6B  depicts a particular example of a file corresponding to the presentation linkbase; and  FIG. 6C  depicts a particular example of a file corresponding to the label linkbase. Meanwhile,  FIGS. 7A to 7C  are views illustrating particular examples of a taxonomy constituting the second document information  131   b .  FIG. 7A  depicts a particular example of a file corresponding to the schema;  FIG. 7B  depicts a particular example of a file corresponding to the presentation linkbase; and  FIG. 7C  depicts a particular example of a file corresponding to the label linkbase. It is to be noted that, in the following description, description of a particular example of a file corresponding to the definition linkbase is omitted. 
     The schema depicted in  FIG. 6A  includes information that defines, for example, “Sales,” “SalesDom,” “SalesUS” and “Cost” as items of the monetary type. Meanwhile, the presentation linkbase depicted in  FIG. 6B  includes information indicating that, for example, “Sales,” “SalesDom” and “SalesUS” constitute a hierarchical structure and “Sales” is a parent item (item included in an upper hierarchy) of “SalesDom” and “SalesUS.” Further, the presentation linkbase depicted in  FIG. 6B  includes information indicating that, for example, a hierarchical structure including “Cost” is not formed. Furthermore, the label linkbase depicted in  FIG. 6C  includes information indicating that the labels corresponding to, for example, “Sales,” “SalesDom,” “SalesUS” and “Cost” are “amount of sales,” “domestic,” “United States” and “cost of sales,” respectively. 
     Further, the schema depicted in  FIG. 7A  includes information that defines, for example, “Sales,” “SalesDom,” “SalesDomE,” “SalesDomW,” “SalesUS,” “SalesEU” and “Cost” as items of the monetary type. Further, the presentation linkbase depicted in  FIG. 7B  includes information indicating that, for example, “Sales,” “SalesDom,” “SalesUS,” “SalesEU,” “SalesDomE” and “SalesDomW” constitute a hierarchical structure and “Sales” is a parent item of “SalesDom,” “SalesUS” and “SalesEU” and “SalesDom” is a parent item of “SalesDomE” and “SalesDomW.” Further, the presentation linkbase depicted in  FIG. 7B  includes information that, for example, a hierarchical structure including “Cost” is not formed. Furthermore, the label linkbase depicted in  FIG. 7C  includes information indicating that, for example, the labels corresponding to “Sales,” “SalesDom,” “SalesDomE,” “SalesDomW,” “SalesUS,” “SalesEU” and “Cost” are “amount of sales,” “domestic,” “East Japan,” “West Japan,” “United States,” “Europe” and “cost of sales,” respectively. 
     Here, in the case where the number of input nodes of a neural network stored in the storage unit  130  is fixed and the number of pieces of information included in an instance or a taxonomy included in the document information  131  is greater than the number of the input nodes of the neural network, the analyzer performs generation of an input value to the neural network, for example, by deleting part of information included in an instance or a taxonomy included in the document information  131 . 
     However, a plurality of pieces of information having no relationship to each other is sometimes included in an adjacent relationship to each other in an instance or a taxonomy. Therefore, in this case, it is difficult for the analyzer to perform extraction of feature values by application of various filters with high accuracy and to input an appropriate input value to the neural network. Accordingly, it is sometimes difficult for the analyzer to perform construction of a neural network that can output information of high significance. 
     Therefore, if the information processing apparatus  1  in the present embodiment accepts document information  131  (hereinafter referred to merely also as document  131 ), it refers to the storage unit  130  in which information indicative of a relationship between a plurality of items included in the document information  131  is stored. Then, the information processing apparatus  1  classifies each of a plurality of items included in the accepted document information  131  into one of a plurality of groups. 
     Thereafter, the information processing apparatus  1  executes, for each of the plurality of groups, a process for generating an input value to the neural network from values individually associated with one or more items classified in the group. 
     For example, the information processing apparatus  1  classifies items included in an instance or a taxonomy into groups each from items having a given relationship to each other. Then, the information processing apparatus  1  generates, for each of the values associated with the items included in the classified groups, an input value to the neural network by performing product sum operation and so forth by application of desirable filters. 
     Consequently, when the information processing apparatus  1  generates an input value to the neural network, it is possible to suppress product sum operation or the like to be performed for values that are associated with items that have no relationship to each other. Therefore, the information processing apparatus  1  may input an appropriate input value to the neural network and may construct a neural network that is effective to perform information analysis. 
     Hardware Configuration of Information Processing Apparatus 
     Now, a hardware configuration of an information processing apparatus is described.  FIG. 8  is a view depicting a hardware configuration of an information processing apparatus. The information processing apparatus here may be the information processing apparatus  1  depicted in  FIG. 1 . 
     As depicted in  FIG. 8 , the information processing apparatus  1  includes a central processing unit (CPU)  101  that is a processor, a memory  102 , an external interface (hereinafter referred to also as input/output (I/O) unit)  103 , a storage medium  104 , and a display apparatus  105  that displays various kinds of information. The components mentioned of the information processing apparatus  1  are coupled to each other by a bus  106 . 
     The storage medium  104  stores a program  110  for performing a process for generating an input value to the neural network (such process is hereinafter referred to also as information generation process), for example, in a program storage region (not depicted) in the storage medium  104 . The storage medium  104  may be, for example, a hard disk drive (HDD). 
     Further, the storage medium  104  includes, for example, a storage unit  130  (also referred to as information storage region  130 ) that stores information to be used when an information generation process is performed. The CPU  101  executes the program  110  loaded into the memory  102  from the storage medium  104  to perform an information generation process. The I/O unit  103  performs communication, for example, with the operation terminal  3 . 
     Function of Information Processing Apparatus 
     Now, functions of an information processing apparatus are described.  FIG. 9  is a block diagram of functions of an information processing apparatus. The information processing apparatus here may be the information processing apparatus  1  depicted in  FIG. 1 . 
     The information processing apparatus  1  implements, by organic cooperation of hardware such as the CPU  101  and memory  102  with the program  110 , various functions including an information acceptance unit  111 , an NN construction unit  112 , a grouping unit  113 , an input value generation unit  114  and a machine learning execution unit  115  as depicted in  FIG. 9   
     Further, in the information storage region  130  described with reference to  FIG. 8  and so forth, for example, document information  131 , NN information  132 , setting information  133 , item information  134 , taxonomy information  135  and filter information  136  are stored. 
     The information acceptance unit  111  accepts, for example, document information  131  inputted from the operation terminal  3 . Then, the information acceptance unit  111  stores the accepted document information  131  into the information storage region  130 . 
     Further, the information acceptance unit  111  generates, from items included in an instance constituting the document information  131 , item information  134  that is information that associates information that specifies the items and contexts relating to values of the items with each other. Further, the information acceptance unit  111  generates, from taxonomies constituting the document information  131 , taxonomy information  135  indicative of information included in each taxonomy (information including a relationship between the items). Then, the information acceptance unit  111  stores the generated item information  134  and taxonomy information  135  into the information storage region  130 . A particular example of the item information  134  and the taxonomy information  135  is hereinafter described. 
     The NN construction unit  112  constructs a neural network of an arbitrary structure. For example, the NN construction unit  112  performs generation of a program that functions as a neural network. 
     If the information acceptance unit  111  accepts document information  131 , the grouping unit  113  refers to the taxonomy information  135 , for example, stored in the information storage region  130  and classifies each of a plurality of items included in the document information  131  accepted by the information acceptance unit  111  into one of a plurality of groups. 
     The input value generation unit  114  executes, for each of the plurality of groups classified by the grouping unit  113 , a process for referring to the item information  134 , for example, stored in the information storage region  130  to generate an input value to the neural network constructed by the NN construction unit  112  from values individually associated with one or more items classified in the group. 
     For example, the input value generation unit  114  refers to the setting information  133  stored in the information storage region  130  and generates filters to be applied individually to the one or more items classified in each group. The setting information  133  is information including the number of filters to be applied to each item, an expression to be performed by each filter and so forth. Then, the input value generation unit  114  generates values, which are outputted in the case where the values individually associated with the one or more items classified in each group are inputted to the filters, as input values to the neural network. 
     The machine learning execution unit  115  causes the neural network constructed by the NN construction unit  112  to learn training data (not depicted) including the instances and taxonomies constituting the document information  131  stored in the information storage region  130 . It is to be noted that the NN information  132  and the filter information  136  are hereinafter described. 
     Outline of First Embodiment 
     Now, an outline of a first embodiment is described.  FIG. 10  is a flow chart illustrating an outline of an information generation process according to the first embodiment. Further,  FIGS. 11 to 13  are views illustrating an outline of the information generation process according to the first embodiment. An outline of the information generation process depicted in  FIG. 10  is described with reference to  FIGS. 11 to 13 . 
     As depicted in  FIG. 10 , the information processing apparatus  1  waits until it accepts document information  131  (NO at S 1 ). For example, the information processing apparatus  1  waits until document information  131  inputted through the operation terminal  3  is accepted. 
     In the case where document information  131  is accepted (YES at S 1 ), the information processing apparatus  1  refers to the information storage region  130 , in which information (taxonomy information  135 ) indicative of a relationship between a plurality of items included in the accepted document information  131  is stored, and classifies each of a plurality of items included in the accepted document information  131  into one of a plurality of groups as depicted in  FIG. 11  (S 2 ). 
     Thereafter, the information processing apparatus  1  executes a process for generating, for each of the plurality of groups, an input value to the neural network from the values individually associated with the one or more items classified in the group by the process at S 2  as depicted in  FIG. 12  (S 3 ). In the following, a particular example of S 2  and S 3  is described. 
     Particular Example of S 2  and S 3   
       FIG. 13  is a view illustrating a particular example of S 2  and S 3 . The items included in a label group LB in  FIG. 13  are labels corresponding to part of items included, for example, in an instance constituting the document information  131  (labels defined in the label link included in the taxonomies). 
     For example, the label group LB depicted in  FIG. 13  indicates that the parent item of “cash and deposits,” “notes receivable-trade,” “accounts receivable-trade,” “short-term loans receivable” and “merchandises” is “current assets,” and the parent item of “property, plant and equipment” and “intangible assets” is “noncurrent assets.” Further, the label group LB depicted in  FIG. 13  indicates that the parent item of “buildings and structures,” “machinery and equipment,” “vehicles,” “land” and “construction in progress” is “property, plant and equipment” and the parent item of “software,” “goodwill,” “patent right” and “right of trademark” is “intangible assets.” 
     Then, the information processing apparatus  1  classifies each item included, for example, in the label group LB depicted in  FIG. 13  into a group for each child item corresponding to the same parent item (S 2 ). 
     For example, the information processing apparatus  1  generates a group including “cash and deposits,” “notes receivable-trade,” “accounts receivable-trade,” “short-term loans receivable” and “merchandises” that are child items of “current assets” (the group is hereinafter referred to as group A) and another group including “buildings and structures,” “machinery and equipment,” “vehicles,” “land” and “construction in progress” that are child items of “property, plant and equipment” (the group is hereinafter referred to as group B). Further, the information processing apparatus  1  generates a further group that includes “software,” “goodwill,” “patent right” and “right of trademark” that are child items of “intangible assets” (the group is hereinafter referred to as group C). 
     Then, the information processing apparatus  1  generates a filter FLT 1   a  and a filter FLT 1   b  as filters to be applied to the items included in the group A, for example, as depicted in  FIG. 13 . Further, the information processing apparatus  1  generates, for example, a filter FLT 2  as a filter to be applied to the items included in the group B. Furthermore, the information processing apparatus  1  generates, for example, a filter FLT 3  as a filter to be applied to the items included in the group C. For example, the information processing apparatus  1  refers to the setting information  133  stored in advance in the information storage region  130  to perform generation of such filters. 
     Then, the information processing apparatus  1  generates values, which are outputted when values of the items are individually inputted to the filters FLT 1   a , FLT 1   b , FLT 2  and FLT 3  as input values to the input nodes ND 1 , ND 2 , ND 3  and ND 4  of a neural network NN 1  as depicted in  FIG. 13  (S 3 ). 
     Thereafter, the information processing apparatus  1  inputs the generated input values to the nodes ND 1 , ND 2 , ND 3  and ND 4 . Then, the information processing apparatus  1  performs machine learning by comparing the values outputted from the neural network in response to inputting of the input values and the training data (not depicted) stored in the information storage region  130 . 
     For example, the information processing apparatus  1  classifies items included in instances and taxonomies into groups each constituted from items having a given relationship to each other (for example, items whose parent items are same). Then, the information processing apparatus  1  performs, for each value associated with an item included in a classified group, product sum operation or the like by application of a desirable filter to generate an input value to the neural network. 
     Consequently, when the information processing apparatus  1  generates an input value to the neural network, it is possible to suppress performance of product sum operation and so forth for values associated with items having no relationship to each other (for example, values individually associated with “merchandises” and “noncurrent assets” in  FIG. 13 ). Therefore, it becomes possible for the information processing apparatus  1  to input an appropriate input value to the neural network and becomes possible to perform construction of a neural network that is effective to perform information analysis. 
     Details of First Embodiment 
     Now, details of the first embodiment are described.  FIGS. 14 to 17  are flow charts illustrating details of the information generation process in the first embodiment. Further,  FIGS. 15 to 30  are views illustrating details of the information generation process in the first embodiment. Details of the information generation process of  FIGS. 14 to 17  are described with reference to  FIGS. 15 to 30 . It is to be noted that, in the following description, “item name” and “label” are collectively referred to as “item name (label).” For example, “Sales” and “amount sold” that is the label of “Sales” are collectively referred to also as “Sales (amount sold).” 
     Process Where Input Value Is Generated from First Document Information 
     First, the information generation process in the case where an input value to the neural network is generated from information included in the first document information  131   a  described with reference to  FIG. 2  and so forth. 
     The information acceptance unit  111  of the information processing apparatus  1  waits till an information generation timing as depicted in  FIG. 14  (NO at S 11 ). The information generation timing may be, for example, a timing at which inputting that an information generation process is to be performed is performed through the operation terminal  3 . 
     When the information generation timing comes (YES at S 11 ), the information acceptance unit  111  acquires the first document information  131   a  stored in the information storage region  130  (S 12 ). 
     Then, the information acceptance unit  111  generates first item information  134   a  and first taxonomy information  135   a  from the first document information  131   a  acquired by the process at S 12  (S 13 ). Then, the information acceptance unit  111  stores the generated first item information  134   a  and first taxonomy information  135   a  into the information storage region  130 . In the following, a particular example of the first item information  134   a  and the first taxonomy information  135   a  is described. 
     Particular Example of First Item Information 
     First, a particular example of the first item information  134   a  is described.  FIG. 18  is a view illustrating a particular example of the first item information  134   a.    
     The first item information  134   a  depicted in  FIG. 18  includes “item number” for identifying each piece of information included in the first item information  134   a , “item name” to which an item name is set, “context ID” to which a context ID corresponding to a context referred to by an item having an item name set in “item name,” and “item value” to which an item value corresponding to an item having an item name set in “item name” is set. 
     For example, the instance described with reference to  FIG. 4  includes information indicating that the item value of “Sales” that is an item that refers to a context whose context ID is “C1” is “200.” Therefore, the information acceptance unit  111  sets “Sales” as “item name” of information whose “item number” is “1,” sets “C1” as “context ID” and sets “200” as “item value” as depicted in  FIG. 18 . Description of the other information included n  FIG. 18  is omitted. 
     Particular Example of First Taxonomy Information 
     Now, particular information of the first taxonomy information  135   a  is described.  FIG. 19  is a view illustrating a particular example of the first taxonomy information  135   a.    
     The first taxonomy information  135   a  depicted in  FIG. 19  includes “item number” for identifying each piece of information included in the first taxonomy information  135   a  and “item name” to which an item name is set. The first taxonomy information  135   a  depicted in  FIG. 19  further includes “presentation link parent” to which an item name corresponding to a parent item of an item having an item name set in “item name” from within information included in the presentation linkbase is set and “label” to which a label of an item having an item name set in “item name” from within information included in the label linkbase is set. Furthermore, the first taxonomy information  135   a  depicted in  FIG. 19  includes “definition substance” to which the definition substance of an item having an item name set to “item name” from within information included in the schema is set. It is to be noted that, in the case where a taxonomy constituting the first document information  131   a  includes a different linkbase (for example, a calculation linkbase, a reference linkbase or the like), the information acceptance unit  111  may include information included in the different linkbase into the first taxonomy information  135   a.    
     For example, the schema described with reference to  FIG. 6A  includes information that defines “Sales” as an item of the monetary type, and the label linkbase described with reference to  FIG. 6C  includes information indicating that the label corresponding to “Sales” is “amount of sales.” Therefore, the information acceptance unit  111  sets “Sales” as “item name” of the information whose “item number” is “1,” sets “amount of sales” as “label” and sets “monetary type” as “definition substance” as depicted in  FIG. 19 . Then, the information acceptance unit  111  sets “-” indicating that information is not set as “presentation link parent” of the information whose “item number” is “1” as depicted in  FIG. 19 . 
     Meanwhile, the schema described with reference to  FIG. 6A  includes information that defines “SalesDom” as an item of the monetary type, and the presentation linkbase described with reference to  FIG. 6B  includes information indicating that “Sales” is a parent item of “SalesDom.” Further, the label linkbase described with reference to  FIG. 6C  includes information indicating that the label corresponding to “SalesDom” is “domestic.” Therefore, as depicted in  FIG. 19 , the information acceptance unit  111  sets “SalesDom” as “item name” of the information whose “item number” is “2,” sets “Sales” as “presentation link parent,” sets “domestic” as “label,” and sets “monetary type” as “definition substance” as depicted in  FIG. 19 . 
     Referring back to  FIG. 14 , the NN construction unit  112  of the information processing apparatus  1  decides whether or not a neural network of an arbitrary structure is constructed already (S 14 ). 
     In the case where it is decided that a neural network is not constructed as yet (NO at S 14 ), the NN construction unit  112  constructs a neural network of an arbitrary structure (S 15 ). For example, the NN construction unit  112  may refer to a specific taxonomy (hereinafter referred to also as base taxonomy) stored in advance in the information storage region  130  to perform construction of a neural network corresponding to the structure of the base taxonomy. Alternatively, the NN construction unit  112  may refer to a plurality of taxonomies stored in advance, for example, in the information storage region  130  to perform construction of a neural network based on information included in common in the plurality of taxonomies. 
     Then, the NN construction unit  112  generates, in response to the construction of a neural network by the process at S 15 , NN information  132  indicative of the structure of the neural network constructed by the process at S 15  (S 16 ). Then, the NN construction unit  112  stores the generated NN information  132  into the information storage region  130 . 
     On the other hand, in the case where it is decided that a neural network is constructed already (YES at S 14 ), the NN construction unit  112  does not perform any of the processes at S 15  and S 16 . In the following, a particular example of the NN information  132  is described. 
     Particular Example of NN Information 
       FIG. 20  is a view illustrating a particular example of the NN information  132 . The NN information  132  depicted in  FIG. 20  includes, as items, “item number” for identifying each piece of information included in the NN information  132 , “ID” for identifying each neural network, “base taxonomy” for identifying a base taxonomy to be referred to upon construction of each neural network, and “configuration information” to which information indicative of a configuration of each neural network is set. 
     For example, in the NN information  132  depicted in  FIG. 20 , “NN 10 ” is set as “ID” and “taxonomy A01” is set as “base taxonomy” to the information whose “item number” is “1.” Further, in the NN information  132  depicted in  FIG. 20 , to the information whose “item number” is “1,” the numbers of input nodes, intermediate nodes and output nodes (“3,” “4” and “3,” respectively) and values indicative of weights between the nodes of the neural network whose “ID” is “NN 10 ” are set as “configuration information.” 
     Referring back to  FIG. 15 , the grouping unit  113  of the information processing apparatus  1  refers to the first item information  134   a  and the first taxonomy information  135   a  stored in the information storage region  130  and specifies one parent item for each item in order from an item whose hierarchy in the hierarchical structure is higher (S 21 ). 
     For example, in the first taxonomy information  135   a  described with reference to  FIG. 19 , “Sales” is set in “presentation link parent” of information whose “item name” is “SalesDom” (information whose “item number” is “2”). Further, in the first taxonomy information  135   a  described with reference to  FIG. 19 , “Sales” is set in “presentation link parent” of information whose “item name” is “SalesUS” (information whose “item number” is “3”). Therefore, the grouping unit  113  specifies, in the process at step S 21 , “Sales” from within the information set in “item name” of the first item information  134   a  described with reference to  FIG. 18  as a parent item. 
     Then, the grouping unit  113  decides whether or not a parent item is specified by the process at step S 21  (S 22 ). 
     In the case where it is decided by the process at S 21  that a parent item is specified (YES at S 22 ), the grouping unit  113  decides whether or not the parent item specified by the process at S 21  is in a decoupled state from the neural network constructed by the process at S 15  (S 23 ). 
     In the case where the parent item specified by the process at S 21  is in a decoupled state from the neural network constructed by the process at S 15  (YES at S 23 ), the input value generation unit  114  of the information processing apparatus  1  refers to the setting information  133  stored in the information storage region  130  and generates a filter for child items of the item specified by the process at S 21  (S 24 ). In the following, a particular example of the setting information  133  is described. 
     Particular Example of Setting Information 
       FIG. 21  is a view illustrating a particular example of setting information. The setting information  133  depicted in  FIG. 21  includes “item number” for identifying each piece of information included in the setting information  133 , “name” to which a name of each piece of information is set, and “value” to which a value of each piece of information is set. 
     For example, in the setting information  133  depicted in  FIG. 21 , to the information whose “item number” is “1,” “generation filter” is set as “name.” Further, in the setting information  133  depicted in  FIG. 21 , to the information whose “item number” is “1,” “fixed filter ( 1 ), learning filter ( 1 )” indicating that one fixed filter and one learning filter are to be generated is set as “value.” 
     Therefore, for example, in the case where the parent item specified by the process at S 21  is “Sales” and “Sales” is in a decoupled state from the neural network, the input value generation unit  114  refers to the setting information  133  stored in the information storage region  130  and generates a fixed filter and a learning filter for “SalesDom” and “SalesUS,” respectively, which are child items of “Sales” (YES at S 22 , YES at S 23  and S 24 ). In the following, a particular example of an expression used for a filter is described. 
     Particular Example of Expression Used for Filter 
     The input value generation unit  114  generates a fixed filter that performs arithmetic operation, for example, in accordance with one of expressions 1 to 4 given below. For example, the expression 1 given below is an expression for calculating the total of values of child items of “Sales” as an output value, and the expression 2 given below is an expression for calculating the ratio of the value of “SalesDom” to the total of the values of the child items of “Sales” as an output value. Further, the expression 3 given below is an expression for calculating an average of the values of child items of “Sales” as an output value, and the expression 4 given below is an expression for calculating a variance of the values of the child items of “Sales” as an output value. 
       Output value=SalesDom+SalesUS  (expression 1)
 
       Output value=SalesDom×100/(SalesDom+SalesUS)  (expression 2)
 
       Output value=(SalesDom+SalesUS)/2  (expression 3)
 
       Output value={[SalesDom−(SalesDom+SalesUS)/2] 2 +[SalesUS−(SalesDom+SalesUS)/2] 2 }/2  (expression 4)
 
     For example, in the process at S 24 , the input value generation unit  114  uses expressions that can exhaustively reflect the substance of the child items corresponding to the parent item specified by the process at S 21  and can generate a number of input values equal to the number of the input nodes of the neural network like the expressions 1 to 4 above. 
     Consequently, the information processing apparatus  1  may generate a number of input values equal to the number of the input nodes of the neural network in such a form that the substance of instances and taxonomies included in the first document information  131   a  acquired by the process at S 12  is exhaustively reflected without relying upon the substance of the instances or the taxonomies (for example, the number of items included in the instances or the hierarchical structure of each item) included in the first document information  131   a  acquired by the process at step S 12 . 
     Further, the input value generation unit  114  generates a learning filter for performing arithmetic operation, for example, in accordance with the following expression 5. 
       Output value= W 11*SalesDom+ W 12*SalesUS  (expression 5)
 
     Here, the expression 5 above is an expression that uses, for example, values of two items from among the child items of “Sales.” Therefore, for example, in the case where the number of child items of “Sales” is 3 or more, the input value generation unit  114  may perform pooling for the child items of “Sales.” 
     For example, the input value generation unit  114  generates an output value (input value to the neural network) in the case where values of two child items that maximize the output value from among the child items of “Sales.” For example, the input value generation unit  114  may generate an output value in the case where values corresponding to a combination that maximizes the output value from among combinations of two child items included in an adjacent relationship to the presentation linkbase described with reference to  FIG. 6B . 
     Further, in this case, the input value generation unit  114  may generate, for example, an output value in the case where values of two child items from among the child items of “Sales” for each combination of two child items and further generate an average value of the generated output values. For example, the input value generation unit  114  may generate for each combination of two child items included in an adjacent relationship to the presentation linkbase described with reference to  FIG. 6B , an output value in the case where values corresponding to each combination are used, and generate an average value of the generated output values. 
     Consequently, similarly as in the case of a fixed filter, the information processing apparatus  1  may generate an input value to the neural network without depending upon the substance (configuration) of instances or taxonomies included in the first document information  131   a  acquired by the process at S 12 . 
     Referring back to  FIG. 15 , the input value generation unit  114  generates filter information  136  on which the information relating to the filters generated by the process at S 24  is reflected (S 25 ). In the following, a particular example of the filter information  136  is described. 
     Particular Example of Filter Information 
       FIGS. 22, 25 and 30  are views illustrating particular examples of the filter information  136 . The filter information  136  depicted in  FIG. 22  and so forth includes “item number” for identifying each piece of information included in the filter information  136 , “ID” for identifying a filter group that include filters to be applied to a same item, and “NN” for identifying a neural network. The filter information  136  depicted in  FIG. 22  and so forth further includes “configuration information” to which information indicative of each filter is set, and “coupling information” to which information indicative of a coupling situation of each filter is set. 
     For example, in the filter information  136  depicted in  FIG. 22 , to information whose “item number” is “1,” “SET 1 ” for identifying a filter group including a filter FLT 11   a  and another filter FLT 11   b  is set as “ID,” and “NN 10 ” for identifying the a neural network NN 10  to which the filter FLT 11   a  and the filter FLT 11   b  are coupled is set as depicted in  FIG. 23 . 
     Further, in the filter information  136  depicted in  FIG. 22 , to information whose “item number” is “1,” “FLT 11   a  (fixed)” indicating that the filter FLT 11   a  that is a fixed filter is generated and “FLT 11   b  (learning)” indicating that the filter FLT 11   b  that is a learning filter is generated are set as “configuration information” as depicted in  FIG. 23 . 
     Furthermore, in the filter information  136  depicted in  FIG. 22 , to information whose “item number” is “1,” “child item of Sales-FLT 11   a  input” indicating that a child item of “Sales” and an input of the filter FLT 11   a  are coupled to each other and “child item of Sales-FLT 11   b  input” indicating that a child item of “Sales” and an input of the filter FLT 11   b  are coupled to each other are set as “configuration information” as depicted in  FIG. 23 . 
     Consequently, when the information processing apparatus  1  generates an input value to the neural network, it may suppress inputting of values associated with items having no relationship to each other (child items corresponding to a same parent item) to a same filter. Therefore, it becomes possible for the information processing apparatus  1  to input an appropriate value to the neural network and perform construction of a neural network effective to perform information analysis. 
     Referring back to  FIG. 16 , the input value generation unit  114  decides whether or not there exists an input node that is in a decoupled state to the neural network constructed by the process at S 15  (S 31 ). 
     As a result, in the case where it is decided that a decoupled input node exists (YES at S 32 ), the input value generation unit  114  couples the parent item specified by the process at S 21  and the output of the filter generated by the process at S 24  to the decoupled input node decided to exist by the process at S 31 . Then, the input value generation unit  114  reflects the information relating to the coupling upon the filter information  136  stored in the information storage region  130  (S 33 ). Thereafter, the input value generation unit  114  performs the processes at the steps beginning with step S 21  again. 
     For example, input nodes ND 11 , ND 12  and ND 13  in the neural network NN 10  depicted in  FIG. 23  are all decoupled input nodes. Therefore, in this case, the input value generation unit  114  couples, for example, “Sales (amount of sales)” that is the parent item specified by the process at S 21  and the input node ND 11  of the neural network NN 10  as depicted in  FIG. 24  to each other. Further, the input value generation unit  114  couples, for example, the filter FLT 11   a  and the filter FLT 11   b  generated by the process at S 24  and the input node ND 12  and the input node ND 13  of the neural network NN 10  to each other, respectively. 
     Then, in this case, the input value generation unit  114  sets (adds) “Sales-ND 11 ” indicating that “Sales” and the input node ND 11  are coupled to each other to “coupling information” of the information whose “ID” is “SET 1 ” (information whose “item number” is “1”) as depicted in  FIG. 25 . Then, the input value generation unit  114  sets (adds), to “coupling information” of the information whose “ID” is “SET 1 ,” “FLT 11   a  output-ND 12 ” indicating that the output of the filter FLT 11   a  and the input node ND 12  are coupled to each other and “FLT 11   b  output-ND 13 ” indicating that the output of the filter FLT 11   b  and the input node ND 13  are coupled to each other. 
     It is to be noted that, in the case of the number of decoupled input nodes decided to exist by the process at S 31  is smaller than the number of parent items specified by the process at S 21  and outputs of the filters generated by the process at S 24 , the input value generation unit  114  may couple part of the parent items specified by the process at S 21  and the outputs of the filters generated by the process at S 24  to the input nodes. 
     On the other hand, in the case where it is decided by the process at S 22  that a parent item is not specified by the process at S 21  (NO at S 22 ), the input value generation unit  114  refers to the item information  134  and the filter information  136  stored in the information storage region  130  and inputs values of the items to the filters corresponding to the items (S 41 ) as depicted in  FIG. 17 . 
     Then, the input value generation unit  114  inputs the outputs of the filters in response to inputting of a value by the process at S 41  to the neural network constructed by the process at S 15  (S 42 ). 
     Then, the machine learning execution unit  115  of the information processing apparatus  1  performs machine learning by comparing the training data corresponding to the first document information  131   a  acquired by the process at S 12  from among training data (not depicted) stored in the information storage region  130  and the values outputted from the neural network from the information storage region  130  with each other (S 43 ). For example, the machine learning execution unit  115  may compare the training data (hereinafter referred to also as teacher data) corresponding to a value outputted from the neural network from among the training data corresponding to the first document information  131   a  acquired by the process at step S 12  and a value actually outputted from the neural network with each other to perform machine learning by an error back propagation method. 
     It is to be noted that the machine learning execution unit  115  may perform, for example, machine learning by comparison between the value inputted to the neural network by the process at S 41  and the value actually outputted from the neural network (namely, machine learning that does not use teacher data). 
     Thereafter, the machine learning execution unit  115  reflects the value corresponding to the weight of the neural network updated by the machine learning performed by the process at S 43  on the NN information  132  stored in the information storage region  130  (S 44 ). Further, the machine learning execution unit  115  reflects information corresponding to the weight of the filter updated by the machine learning performed by the process at S 43  on the filter information  136  stored in the information storage region  130  (S 45 ). Then, the information processing apparatus  1  ends the information generation process. 
     For example, the machine learning execution unit  115  performs updating of the information corresponding to the weight of the neural network from within the information set in “configuration information” of the NN information  132  described with reference to  FIG. 20 . Further, the machine learning execution unit  115  performs, for example, updating of the information corresponding to the weight of the filter from within the information set in “configuration information” of the filter information  136  described with reference to  FIG. 22 . 
     Process Where Input Value Is Generated from Second Document Information 
     Now, the information generation process in the case where an input value to the neural network is generated from information included in the second document information  131   b  described with reference to  FIG. 3  and so forth is described. It is to be noted that the following description is given assuming that an information generation process in the case where an input value to the neural network is generated from information included in the first document information  131   a  is performed already. 
     The information acceptance unit  111  waits till an information generation timing as indicated in  FIG. 14  (NO at S 11 ). 
     In the case where an information generation timing comes (YES at S 11 ), the information acceptance unit  111  acquires the second document information  131   b  stored in the information storage region  130  (S 12 ). 
     Then, the information acceptance unit  111  generates second item information  134   b  and second taxonomy information  135   b  from the second document information  131   b  acquired by the process at S 12  (S 13 ). Then, the information acceptance unit  111  stores the generated second item information  134   b  and second taxonomy information  135   b  into the information storage region  130 . In the following, a particular example of the second item information  134   b  and the second taxonomy information  135   b  is described. 
     Particular Example of Second Item Information 
     First, a particular example of the second item information  134   b  is described.  FIG. 26  is a view illustrating a particular example of the second item information  134   b.    
     The second item information  134   b  depicted in  FIG. 26  includes “item number” for identifying each piece of information included in the second item information  134   b , “item name” to which an item name is set, “context ID” to which a context ID corresponding to a context to be referred to by an item having an item name set to “item name” is set, and “item value” to which an item value corresponding to an item having an item name set to “item name” is set. 
     For example, the instance described with reference to  FIG. 5  includes information indicating that the item value of “Sales” that is an item that refers to a context whose context ID is “C1” is “35.” Therefore, the information acceptance unit  111  sets “Sales” as “item name” of the information whose “item number” is “1,” sets “C1” as “context ID” and sets “35” as “item value” as depicted in  FIG. 26 . Description of the other information included in  FIG. 26  is omitted. 
     Particular Example of Second Taxonomy Information 
     Now, a particular example of the second taxonomy information  135   b  is described.  FIG. 27  is a view illustrating a particular example of the second taxonomy information  135   b.    
     The second taxonomy information  135   b  depicted in  FIG. 27  includes “item number” for identifying each piece of information included in the second taxonomy information  135   b  and “item name” to which an item name is set. The second taxonomy information  135   b  depicted in  FIG. 27  further includes “presentation link parent” to which an item name corresponding to a parent item of an item having an item name set to “item name” from within information included in the presentation linkbase is set and “label” to which a label of an item having the item name set to “item name” is set from within information included in the label linkbase. Furthermore, the second taxonomy information  135   b  depicted in  FIG. 27  includes “definition substance” to which the definition substance of the item having the item name set to “item name” from within information included in the schema. It is to be noted that, in the case where a different linkbase (for example, a calculation linkbase, a reference linkbase or the like) is included in a taxonomy constituting the second document information  131   b , the information acceptance unit  111  may include information included in the different linkbase into the second taxonomy information  135   b.    
     For example, the schema described with reference to  FIG. 7A  includes information that defines “Sales” as an item of the monetary type, and the label linkbase described with reference to  FIG. 7C  includes information indicating that the label corresponding to “Sales” is “amount of sales.” Therefore, the information acceptance unit  111  sets “Sales” as “item name” of the information whose “item number” is “1,” sets “amount of sales” as “label” and sets “monetary type” as “definition substance” as depicted in  FIG. 27 . Then, the information acceptance unit  111  sets “-” indicating that information is not set as “presentation link parent” of the information whose “item number” is “1” as depicted in  FIG. 27 . 
     Meanwhile, the schema described with reference to  FIG. 7A  includes information that defines “SalesDomE” as an item of the monetary type; the presentation linkbase described with reference  FIG. 7B  includes information indicating that “SalesDom” is a parent item of “SalesDomE”; and the label linkbase described with reference to  FIG. 7C  includes information indicating that the label corresponding to “SalesDomE” is “East Japan.” Therefore, the information acceptance unit  111  sets “SalesDomE” as “item name” of the information whose “item number” is “3”; sets “SalesDom” as “presentation link parent”; sets “East Japan” as “label”; and sets “monetary type” as “definition substance” as depicted in  FIG. 27 . Description of the other information included in  FIG. 27  is omitted. 
     Referring back to  FIG. 13 , the NN construction unit  112  decides that a neural network of an arbitrary structure is constructed already (YES at S 14 ). 
     Then, the grouping unit  113  refers to the second item information  134   b  and the second taxonomy information  135   b  stored in the information storage region  130  and specifies one parent item in order beginning with an item having a higher hierarchy in the hierarchical structure (S 21 ). 
     For example, in the second taxonomy information  135   b  described with reference to  FIG. 27 , “Sales” is set in “presentation link parent” of information whose “item name” is “SalesDom” (information whose “item number” is “2”). Further, in the second taxonomy information  135   b  described with reference to  FIG. 27 , “Sales” is set in “presentation link parent” of information whose “item name” is “SalesUS” (information whose “item number” is “5”). Furthermore, in the second taxonomy information  135   b  described with reference to  FIG. 27 , “Sales” is set in “presentation link parent” of information whose “item name” is “SalesEP” (information whose “item number” is “6”). Therefore, the grouping unit  113  specifies, as the parent item, “Sales” from within the information set in “item name” of the second item information  134   b  described with reference to  FIG. 26  by the process at S 21 . 
     Thereafter, the grouping unit  113  decides that a parent item is specified by the process at S 21  (YES at S 22 ). 
     Then, in the example depicted in  FIG. 24 , “Sales (amount of sales)” and the neural network NN 10  are coupled to each other. Therefore, for example, in the case where the parent item specified by the process at S 21  is “Sales,” the grouping unit  113  decides that the parent item specified by the process at S 21  is coupled to the neural network constructed by the process at S 15  (NO at S 23 ). Accordingly, the input value generation unit  114  performs the processes at the steps beginning with step S 21  again. 
     For example, in the second taxonomy information  135   b  described with reference to  FIG. 27 , “SalesDom” is set in “presentation link parent” of the information whose “item name” is “SalesDomE” (information whose “item number” is “3”). Further, in the second taxonomy information  135   b  described with reference to  FIG. 27 , “SalesDom” is set in “presentation link parent” of the information whose “item name” is “SalesDomW” (information whose “item number” is “4”). Therefore, in the process at S 21 , the grouping unit  113  specifies, as the parent item, “SalesDom” from within the information set in “item name” of the second item information  134   b  described with reference to  FIG. 26 . 
     Thereafter, the grouping unit  113  decides that a parent item is specified by the process at S 21  (YES at S 22 ). 
     In the example depicted in  FIG. 24 , “SalesDomE (East Japan)” and “SalesDomW (West Japan)” are not coupled to the neural network NN 10 . For example, in the case where the parent item specified by the process at S 21  is “SalesDom,” the grouping unit  113  decides that the parent item specified by the process at S 21  is not coupled to the neural network NN 10  constructed by the process at S 15  (YES at S 23 ). Therefore, the input value generation unit  114  performs the processes at the steps beginning with step S 21  again. 
     Then, the input value generation unit  114  refers to the setting information  133  stored in the information storage region  130  and generates filters for child items of the item specified by the process at S 21  (S 24 ). 
     For example, in the case where the parent item specified by the process at S 21  is “SalesDom,” the input value generation unit  114  generates a fixed filter and a learning filter (filter FLT 12   a  and filter FLT 12   b ) for “SalesDomE (East Japan)” and “SalesDomW (West Japan),” respectively, that are child items of “SalesDom (domestic)” as depicted in  FIG. 28 . 
     Thereafter, the input value generation unit  114  generates filter information  136  on which information relating to the filters generated by the process at S 24  is reflected (S 25 ). 
     Then, the input value generation unit  114  decides whether or not a decoupled input node exists in the neural network constructed by the process at S 15  (S 31 ). 
     For example, all of the input nodes ND 11 , ND 12  and ND 13  in the neural network NN 10  depicted in  FIG. 29  are coupled input nodes (NO at S 32 ). Therefore, the input value generation unit  114  performs processes at the steps beginning with step S 34 . 
     Then, the input value generation unit  114  generates filters (hereinafter referred to also as weighted averaging filters) for weighted averaging outputs of the filters of the parent item specified by the process at S 21  and outputs of the filters generated by the process at S 24 , and inserts the weighted averaging filters between the filters for the parent item specified by the process at S 21  and the neural network (S 34 ). 
     For example, the input value generation unit  114  generates a filter FLT 13   a  for weighted averaging an output of the filter FLT 11   a  of the parent item specified by the process at S 21  and an output of the filter FLT 12   a  generated by the process at S 24  as depicted in  FIG. 29 . Further, the input value generation unit  114  generates a filter FLT 13   b  for weighted averaging, for example, an output of the filter FLT 11   b  of the parent item specified by the process at S 21  and an output of the filter FLT 12   b  generated by the process at S 24 . 
     Furthermore, the input value generation unit  114  reflects the information relating to the filters generated at S 34  on the filter information  136  stored in the information storage region  130  (S 35 ). Thereafter, the input value generation unit  114  performs the processes at the steps beginning with S 21  again. 
     For example, the input value generation unit  114  sets “FLT 12   a  (fixed)” and “FLT 12   b  (learning)” indicative of the filter FLT 12   a  that is a fixed filter and the filter FLT 12   b  that is a learning filter to “configuration information” of the information whose “ID” is “SET 1 ” (information whose “item number” is “1”) as depicted in  FIG. 30 . Further, the input value generation unit  114  sets, for example, “child item of SalesDom-FLT 12   a  input” indicating that a child item of “SalesDom” and an input of the filter FLT 12   a  are coupled to each other is set to “coupling information” of the information whose “ID” is “SET 1 .” Furthermore, for example, “weighted average of FLT 11   a  output and FLT 12   a  output-ND 12 ” indicating that the filter FLT 13   a  for weighted averaging an output of the filter FLT 11   a  and an output of the filter FLT 12   a  and the input node ND 12  are coupled to each other is set to “coupling information” of the information whose “ID” is “SET 1 .” Description of the other information included in  FIG. 30  is omitted. In the following, a particular example of an expression used for each weighted averaging filter is described. 
     Particular Example (2) of Expression Used for Filter 
     The input value generation unit  114  generates a weighted averaging filter for performing arithmetic operation, for example, in accordance with an expression 6 given below. The following description is given assuming that the parent item specified by the process at S 21  is “SalesDom.” 
       Output value=output of filter FLT11a*(number of child items of SalesDom−1)/number of child items of SalesDom+output of filter FLT12a*1/number of child items of SalesDom  (expression 6)
 
     Consequently, even in the case where the number of filters generated by the process at S 24  becomes greater than the number of input nodes to the neural network, the information processing apparatus  1  may generate a number of input values equal to the number of the input nodes to the neural network while exhaustively utilizing all filters generated by the process at S 24 . Therefore, the information processing apparatus  1  may generate a number of input values equal to the number of input nodes to the neural network without depending upon the number of filters generated by the process at S 24  (number of parent items specified by the process at S 21 ). 
     According to the disclosed embodiment, it is made possible to construct a neural network that may output information having high significance. 
     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.