Patent Publication Number: US-9886666-B2

Title: Information processing device, information processing system, information processing method and computer-readable medium

Description:
This application is a National Stage Entry of PCT/JP2012/007930 filed on Dec. 12, 2012, which claims priority from Japanese Patent Application 2011-274792 filed on Dec. 15, 2011, the contents of all of which are incorporated herein by reference, in their entirety. 
     TECHNICAL FIELD 
     The present invention relates to an information processing device, an information processing system, an information processing method and a computer program that presents inference results obtained by inference processing. 
     BACKGROUND ART 
     An information processing device is known that obtains inference results by applying inference rules to context information inputted and presents the inference results obtained. Here, the context information is information that becomes an application target of the inference rules and is, for example, text information or image information included in document data, measurement data outputted from sensor equipment, operation log data about a device or application software and so on. 
     As an inference method that such information processing device uses, there exists, for example, deductive inference. In case the deductive inference is used, the information processing device holds an inference rule, for example, shown in an IF-THEN form as indicated by the following expression (1).
 
IF Temperature&gt;100&amp;liquid==water
 
THEN liquid-&gt;(changes)-&gt;gas  (1)
 
     First line of expression (1) shows a condition (IF information) that says “in case temperature is no smaller than 100 degrees centigrade and liquid is water.” Also, second line of expression (1) shows a phenomenon (THEN information) that says, in case the IF information is satisfied, “liquid changes into gas.” The information processing device that uses such inference rule infers, in case the context information inputted satisfies the IF information, that the THEN information set to the IF information concerned occurs. 
     Such information processing device holds in advance, for example, in designing of a certain device, case information such as important cases in the past, defect cases and so on, and conditions that such cases can occur as inference rules. And such information processing device extracts context information from design document data of the device and applies the inference rules to the context information extracted. In this case, such information processing device can present defect cases that can occur by designing on the basis of such design document data, important cases in the past related to the design concerned and so on as the inference results. As a result, such information processing device can support work of a user who checks contents of the design document data. 
     By the way, in such information processing device, in case the inference rules are accumulated in large quantities, a great deal of inference results will be obtained. And when the inference results presented become large in quantity, a problem occurs that the inference results become difficult to grasp for the user. As a related technology that solves such a problem, there is a technology that presents a large quantity of information after narrowing it down to a number that is easy to grasp for the user. 
     For example, in patent document 1, an information processing device is described that accumulates defect occurrence history in a production process in the past, calculates frequency of occurrence, degree of influence and degree of detection for each class of a fault on the basis the defect occurrence history accumulated, and calculates priority of danger by integrating the calculated information. And the information processing device presents to the user fault information sorted by the priority of danger. Accordingly, even in case there is a large quantity of fault information such the user cannot grasp all, the information processing device can present with priority the fault information with high priority of danger to the user. 
     Also, in non-patent document 1, an information processing device is described that presents, by collaborative filtering using an access history of a plurality of users about a plurality of information, information that is unknown and useful for each user. Even in case there is a large quantity of information such that the user cannot grasp all, the information processing device can present information that is unknown and useful for the user among others. 
     Patent Document 1: Japanese Unexamined Patent Application Publication No. 2007-280301 
     Non-Patent Document 1: Naoki Ohsugi et al., “Software Function Recommender System Based on Collaborative Filtering”, Transaction of Information Processing Society of Japan Vol. 45, No. 1, 2004. 
     SUMMARY OF INVENTION 
     Technical Problem 
     However, while the information processing device described in the patent document 1 can present the fault information with high priority of danger to the user, there is a following problem. 
     Even for the fault information with high priority of danger, depending on the user, there is a case when detailed knowledge about the fault information is possessed by the user. On the other hand, even for the fault information with low priority of danger, depending on the user, there is a case when a mistake related to such fault information may be easy to occur since detailed knowledge about the fault information is not possessed by the user. However, the information processing device described in the patent document 1 does not necessarily present the fault information for which each user does not have detailed knowledge at higher position. 
     Also, the information processing device described in the non-patent document 1 regards information that each user has not accessed as unknown information and can present information that is determined to be useful for each user among others, though, there is a following problem. 
     For example, assume a case when the information processing device described in the non-patent document 1 makes defect case information in designing as a target. There is a case when a certain user may, even if defect case information related to a certain kind of context information is already being accessed, when the kind of the context information is different, overlook such defect case information. In such a case, it can be said that the user does not possess truly detailed knowledge for the defect case information. However, since the information processing device described in the non-patent document 1 does not present the information that the user already accessed, there is a case when the defect case information that the user does not have detailed knowledge cannot be presented. 
     Also, there is a case when information that a certain user does not access yet is similar to the information that the user has already accessed. Even in this case, the information processing device described in the non-patent document 1 may determine the information similar to the information that is already accessed to be useful and present it. There is a case when the user already has detailed knowledge for such similar information. Accordingly, there is a case when the information processing device described in the non-patent document 1 presents the information that the user already has detailed knowledge. 
     Thus, even if the technologies described in the patent document 1 and the non-patent document 1 are applied to the information processing device that presents inference results, there is a problem that there exists a case when inference results for which each user does not have detailed knowledge cannot be presented with priority. 
     The present invention has been made in order to solve the problem described above, and makes it a main object to provide the information processing device and so on that presents information for which each user does not have detailed knowledge with priority among inference results inferred from the context. 
     Technical Solution 
     An information processing device of the present invention includes: an inference unit that obtains inference results by applying inference rules to context information; an inference result index value calculation unit that, by using information that shows a reading user who reads the inference results, acquires a knowledge level of the reading user about each inference rule that was applied until the inference results are obtained, and on the basis of each knowledge level acquired, calculates index values that show depth of the knowledge of the reading user about the inference results comprehensively, an inference result presentation unit that presents the inference results on the basis of the index values calculated by the inference result index value calculation unit; and a knowledge level update unit that, for each inference rule that was applied until the inference results are obtained, updates the knowledge level of the reading user about the inference rule concerned on the basis of evaluation information in which the degree of the knowledge that the reading user possesses is evaluated about the inference results presented by the inference result presentation unit. 
     Also, an information processing system of the present invention includes: the information processing device described above and a terminal that collects context information and sends it to the information processing device, outputs the inference results presented from the information processing device to an output device, and sends the evaluation information inputted from an input device to the information processing device. 
     Also, an information processing method of the present invention: obtains inference results by applying inference rules memorized in advance to context information inputted; acquires a numerical value memorized in advance as a knowledge level that shows depth of the knowledge that a reading user who reads the inference results possesses for each inference rule that was applied until the inference results are obtained; calculates index values that show the depth of the knowledge of the reading user about the inference results comprehensively on the basis of each knowledge level acquired; presents the inference results on the basis of the index value; acquires, for the inference results that are presented, evaluation information in which degree of the knowledge that the reading user possesses is evaluated; and updates, for each inference rule that was applied until the inference results are obtained, a numerical value memorized as the knowledge level of the reading user about the inference rule concerned on the basis of the evaluation information. 
     Also, a computer program of the present invention makes a computer device execute: a context information acquisition processing that acquires context information; an inference processing that obtains inference results by applying inference rules memorized in a memory device in advance to context information; an inference result index value calculation processing that acquires a numerical value memorized in the memory device in advance as a knowledge level that shows depth of the knowledge that a reading user who reads the inference results possesses for each inference rule that was applied until the inference results are obtained, and calculates index values that show the depth of the knowledge of the reading user about the inference results comprehensively on the basis of each knowledge level acquired; an inference result presentation processing that presents the inference results on the basis of the index value; an evaluation information acquisition processing that acquires, for the inference results presented by the inference result presentation processing, evaluation information in which degree of the knowledge that the reading user possesses is evaluated; and a knowledge level update processing that updates, for each inference rule that was applied until the inference results are obtained, a numerical value memorized in a memory device as the knowledge level of the reading user about the inference rule concerned on the basis of the evaluation information. 
     Advantageous Effects 
     The present invention can, among inference results inferred from a context, present information for which each user does not have detailed knowledge with priority. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a functional block diagram of an information processing device according to a first exemplary embodiment of the present invention. 
         FIG. 2  is a flow chart explaining inference result presentation operation of an information processing device according to the first exemplary embodiment of the present invention. 
         FIG. 3  is a flow chart explaining knowledge level update operation of an information processing device according to the first exemplary embodiment of the present invention. 
         FIG. 4  is a functional block diagram of an information processing device according to a second exemplary embodiment of the present invention. 
         FIG. 5  is a figure indicating an example of information stored in a case information memory unit in the second exemplary embodiment of the present invention. 
         FIG. 6  is a figure indicating an example of information stored in an inference rule memory unit in the second exemplary embodiment of the present invention. 
         FIG. 7  is a figure indicating an example of information stored in a knowledge level memory unit in the second exemplary embodiment of the present invention. 
         FIG. 8  is a figure indicating an example of information stored in a knowledge level alteration rule memory unit in the second exemplary embodiment of the present invention. 
         FIG. 9  is a flow chart explaining inference result presentation operation of an information processing system according to the second exemplary embodiment of the present invention. 
         FIG. 10  is a flow chart explaining knowledge level update operation of an information processing system according to the second exemplary embodiment of the present invention. 
         FIG. 11  is a schematic diagram explaining a specific example of inference result presentation operation of an information processing system according to the second exemplary embodiment of the present invention. 
         FIG. 12  is a figure indicating an example of an inference result presentation screen in the second exemplary embodiment of the present invention. 
         FIG. 13  is a figure indicating an example of an evaluation information input screen in the second exemplary embodiment of the present invention. 
         FIG. 14  is a schematic diagram explaining a specific example of knowledge level update operation of an information processing system according to the second exemplary embodiment of the present invention. 
         FIG. 15  is a functional block diagram of an information processing device according to a third exemplary embodiment of the present invention. 
         FIG. 16  is a figure indicating an example of information stored in a knowledge level memory unit in the third exemplary embodiment of the present invention. 
         FIG. 17  is a figure indicating an example of information stored in an inference rule memory unit in the third exemplary embodiment of the present invention. 
         FIG. 18  is a figure indicating an example of information stored in a case information memory unit in the third exemplary embodiment of the present invention. 
         FIG. 19  is a flow chart explaining inference result presentation operation of an information processing system according to the third exemplary embodiment of the present invention. 
         FIG. 20  is a flow chart explaining knowledge level update operation of an information processing system according to the third exemplary embodiment of the present invention. 
         FIG. 21  is a functional block diagram of an information processing device according to a fourth exemplary embodiment of the present invention. 
         FIG. 22  is a figure indicating an example of information stored in an action history conversion rule memory unit in the fourth exemplary embodiment of the present invention. 
         FIG. 23  is a figure indicating an example of information stored in a knowledge level alteration rule memory unit in the fourth exemplary embodiment of the present invention. 
         FIG. 24  is a flow chart explaining knowledge level update operation of an information processing system according to the fourth exemplary embodiment of the present invention. 
         FIG. 25  is a hardware block diagram of an information processing device according to the first exemplary embodiment of the present invention. 
         FIG. 26  is a hardware block diagram of a server according to the second to the fourth exemplary embodiment of the present invention. 
         FIG. 27  is a hardware block diagram of a terminal according to the second to the fourth exemplary embodiment of the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, each exemplary embodiment of the present invention will be explained in detail with reference to drawings. 
     (The First Exemplary Embodiment) 
     Functional block configuration of an information processing device  1  according to the first exemplary embodiment of the present invention is indicated in  FIG. 1 . In  FIG. 1 , the information processing device  1  includes: an inference rule memory unit  101 , a knowledge level memory unit  102 , a context information acquisition unit  103 , an inference unit  104 , an inference result index value calculation unit  105 , an inference result presentation unit  106 , an evaluation information acquisition unit  107  and a knowledge level update unit  109 . Next, hardware configuration of the information processing device  1  is indicated in  FIG. 25 . In  FIG. 25 , the information processing device  1  is configured from a computer  2500  equipped with CPU (Central Processing Unit)  2501 , RAM (Random Access Memory)  2502 , ROM (Read Only Memory)  2503 , a memory device  2504  such as a hard disk, an input device  2505  and a display device  2506 . Also, the inference rule memory unit  101  and the knowledge level memory unit  102  are configured from the memory device  2504 . Also, the context information acquisition unit  103  and the evaluation information acquisition unit  107  are configured from the input device  2505  and the CPU  2501  that reads a computer program memorized in the ROM  2503  or the memory device  2504  into RAM  2502  and executes it. Also, the inference unit  104 , the inference result index value calculation unit  105  and the knowledge level update unit  109  are configured from the CPU  2501  that reads a computer program memorized in the ROM  2503  or the memory device  2504  into the RAM  2502  and execute it. Also, the inference result presentation unit  106  is configured from the display device  2506  and the CPU  2501  that reads a computer program memorized in the ROM  2503  or the memory device  2504  into the RAM  2502  and executes it. Further, hardware configuration that configures each function block is not limited to the configuration described above. 
     The inference rule memory unit  101  memorizes no smaller than one inference rule. Such inference rule shows a phenomenon that occurs corresponding to the contents of context information described below. The inference rule may be, for example, a rule of an IF-THEN form including a condition (IF information) and a phenomenon (THEN information) that occurs when the condition concerned is satisfied. The inference rule memory unit  101  memorizes such inference rule with an inference rule ID that identifies the inference rule concerned. 
     The knowledge level memory unit  102  memorizes a knowledge level that shows depth of the knowledge of each user about each inference rule. Specifically, the knowledge level memory unit  102  associates the inference rule ID, user&#39;s identification information (user ID) and the knowledge level that shows the depth of the knowledge of the user concerned about the inference rule concerned, and memorizes a plurality of records that are associated. Hereinafter, in each exemplary embodiment of the present invention, explanation will continue supposing that the smaller the numerical value of the knowledge level is, it shows the less the user is knowledgeable about the inference rule. Also, each knowledge level memorized in the knowledge level memory unit  102  can be updated by the knowledge level update unit  109  described below. Also, each knowledge level memorized in the knowledge level memory unit  102  may be registered in advance or a predetermined value such as 0 may be set as an initial value. 
     The context information acquisition unit  103  acquires context information. The context information is information that becomes a target of inference and it may be, for example, text information or image information included in document data, measurement data outputted from sensor equipment, operation log data about a device or application software and so on. 
     By applying the inference rules memorized in the inference rule memory unit  101  to the context information acquired, the inference unit  104  obtains inference results. For example, the inference unit  104  searches the inference rule that possesses IF information that agrees with the context information. And the inference unit  104  presumes that THEN information that the inference rule searched possesses to occur. Also, in case the THEN information of the inference rule applied satisfies the IF information of other inference rules, the inference unit  104  may repeat a chain reaction of the inference processing that applies other inference rules further. Also, in case there is a plurality of inference rules applicable to the context information acquired or to the THEN information of the inference rule applied, the inference unit  104  may execute a plurality of inference processing on the basis of the respective inference rules. 
     The inference result index value calculation unit  105  calculates an index value of the inference result using the knowledge level of a reading user about each inference rule that was applied until the inference result is obtained. Here, the reading user is a user who reads the inference result. And the index value of the inference result is a numerical value showing depth of the knowledge of the reading user about the inference result comprehensively. 
     Specifically, the inference result index value calculation unit  105  may acquire the user ID of the reading user from the inference result presentation unit  106  described below. And the inference result index value calculation unit  105  may acquire the knowledge level that is associated with each inference rule ID and the reading user ID from the knowledge level memory unit  102 . And the inference result index value calculation unit  105  may calculate the index value of the inference result for the reading user on the basis of each knowledge level acquired. For example, the inference result index value calculation unit  105  may calculate a multiplied value of each knowledge level acquired as the index value. As described above, when the smaller the numerical value of the knowledge level is, it shows the less the reading user is knowledgeable about the inference rules, concerning the inference result that was led using the inference rules about which the reading user does not have detailed knowledge, the numerical value of the index value as the multiplied value becomes smaller. Here, it can be considered that the inference result that was led by the inference process about which the reading user does not have the detailed knowledge is more useful for the reading user. Accordingly, in this case, concerning the index value about the inference result, the smaller the value is, it indicates the higher a degree of usefulness for the user is. 
     Also, when a plurality of inference results is obtained, the inference result index value calculation unit  105  calculates the index value for the reading user about each inference result. 
     The inference result presentation unit  106  presents the inference results on the basis of the index values calculated by the inference result index value calculation unit  105 . For example, the inference result presentation unit  106  may decide an order to present the inference results on the basis of the index values. When the smaller the value of the index value is, it shows the higher the degree of usefulness for the reading user is, the inference result presentation unit  106  may present the inference results in ascending order of the index values. 
     Also, the inference result presentation unit  106  acquires the user ID of the reading user who reads the inference results and notifies it to the inference result index value calculation unit  105 . For example, the inference result presentation unit  106  may display an input screen of a reading user ID, and in case the reading user ID already registered is inputted, present the inference results. 
     The evaluation information acquisition unit  107  acquires evaluation information that evaluated the degree of the knowledge that the reading user possesses about the inference results presented by the inference result presentation unit  106 . For example, the evaluation information acquisition unit  107  may acquire the evaluation information showing whether or not the inference result is known to the reading user via the input device. 
     The knowledge level update unit  109  updates a numerical value memorized in the knowledge level memory unit  102  as the knowledge level of the reading user about each inference rule that was applied until the inference results are obtained on the basis of the evaluation information acquired by the evaluation information acquisition unit  107 . For example, in case the evaluation information showing whether or not it is known is acquired by the evaluation information acquisition unit  107 , when the evaluation information shows “known”, the knowledge level update unit  109  may add a predetermined value to the knowledge level. Also, when the evaluation information shows “not known”, the knowledge level update unit  109  may subtract a predetermined value from the knowledge level. 
     Operation of the information processing device  1  configured as above will be explained with reference to drawings. 
     First, inference result presentation operation of the information processing device  1  is indicated in  FIG. 2 .  FIG. 2  is a flow chart explaining the inference result presentation operation of the information processing device according to the first exemplary embodiment of the present invention. In  FIG. 2  and flow charts that will be referred below, a pair of two hexagons shows that a series of processing inserted in between is repeated (looped). Here, a hexagon that is of a rectangle shape of which upper corners are cut off shows start of the repeated processing. And a hexagon that is of a rectangle shape of which lower corners are cut off shows end of the repeated processing. Further, a target of the repeated processing may be described in the hexagon showing start of the repeated processing. 
     In  FIG. 2 , first, the context information acquisition unit  103  acquires context information (Step S 1 ). For example, the context information acquisition unit  103  may extract the context information from document data given. 
     Next, by applying the inference rules memorized in the inference rule memory unit  101  to the context information acquired in Step S 1 , the inference unit  104  obtains inference results (Step S 2 ). At that time, as described above, the inference unit  104  may acquire the inference results by executing inference processing in a chain reaction. 
     Next, the inference result index value calculation unit  105  acquires a reading user ID who reads the inference results (Step S 3 ). As described above, the inference result index value calculation unit  105  may acquire the reading user ID from the inference result presentation unit  106 . 
     Next, the inference result index value calculation unit  105  executes processing of Steps S 4 -S 5  for each inference result obtained in Step S 2 . 
     Here, first, the inference result index value calculation unit  105  searches the knowledge level of the reading user about each inference rule that was used by the inference unit  104  until the inference result is obtained from the knowledge level memory unit  102  (Step S 4 ). 
     Next, the inference result index value calculation unit  105  calculates an index value showing the depth of the knowledge of the reading user about the inference result comprehensively on the basis of the knowledge level searched in Step S 4  (Step S 5 ). For example, as described above, the inference result index value calculation unit  105  may calculate a multiplied value of the knowledge level of the reading user about each inference rule that was used until the inference result is obtained as the index value. 
     Next, the inference result presentation unit  106  presents the inference results on the basis of the index values calculated by the inference result index value calculation unit  105  (Step S 6 ). For example, the inference result presentation unit  106  may present the inference results in ascending order of the index values. 
     As above, the information processing device  1  ends the inference result presentation operation. 
     Next, knowledge level update operation of the information processing device  1  is indicated in  FIG. 3 .  FIG. 3  is a flow chart explaining the knowledge level update operation of the information processing device according to the first exemplary embodiment of the present invention. 
     In  FIG. 3 , first, the evaluation information acquisition unit  107  acquires evaluation information of the reading user about one among the inference results presented in Step S 6  of  FIG. 2  (Step S 11 ). For example, as described above, by displaying on the display device so that whether or not the inference result is known to the reading user is selectable, the evaluation information acquisition unit  107  may acquire the evaluation information showing whether or not it is known via the input device. 
     Next, the knowledge level update unit  109  executes processing of the following Steps S 12 -S 13  about each inference rule that was used in Step S 2  of  FIG. 2  until the inference result is obtained. 
     Here, first, the knowledge level update unit  109  searches the knowledge level that is associated with the inference rule ID and the reading user ID from the knowledge level memory unit  102  (Step S 12 ). 
     And the knowledge level update unit  109  updates the knowledge level of the record searched on the basis of a kind of the evaluation information acquired in Step S 11  (Step S 13 ). For example, corresponding to the evaluation information acquired in Step S 11 , the knowledge level update unit  109  may add or subtract a predetermined value to or from the knowledge level of the record concerned. 
     As above, the information processing device  1  ends the knowledge level update operation. Further, in case a plurality of inference results is presented, the information processing device  1  may execute such knowledge level update operation for each inference result. 
     Further, the information processing device  1  may be without part or all of the inference rule memory unit  101 , the knowledge level memory unit  102 , the context information acquisition unit  103  and the evaluation information acquisition unit  107 . Also, these components may be outside of the information processing device  1 . Further, part or all of the inference rules, the knowledge levels, the context information and the evaluation information may be given to the information processing device  1  from outside. 
     Next, effects of the first exemplary embodiment of the present invention will be described. 
     The information processing device according to the first exemplary embodiment of the present invention can present, among information inferred from the context, information of which the user does not have detailed knowledge with priority. 
     The reason is because: the knowledge level memory unit stores the knowledge level of the reading user about the inference rules; the inference result index value calculation unit calculates the index value showing the depth of the knowledge of the reading user about the inference results comprehensively on the basis of the knowledge level of the reading user about each inference rule used by the inference process; and the inference result presentation unit presents the inference results on the basis of the index values calculated. Accordingly, the information processing device according to the first exemplary embodiment will judge the degree of usefulness of the inference results for the reading user and present it on the basis of the depth of the knowledge of the reading user about the inference process. As a result, because the information processing device according to the first exemplary embodiment can, even for the inference result known to the reading user, present it with priority if the inference result is one of which the knowledge level about the inference process is low. 
     Also, the further reason is because the evaluation information acquisition unit acquires the evaluation information of the reading user about the inference results presented, and the knowledge level update unit updates the knowledge level of the reading user about each inference rule used in the inference process on the basis of the evaluation information of the reading user. Accordingly, the information processing device according to the first exemplary embodiment can update the knowledge level of the reading user about each inference rule used in the inference process on the basis of the evaluation of the degree of the knowledge of the reading user about the inference results. For example, when evaluation information that the inference result presented is known is obtained, the information processing device according to the first exemplary embodiment can update the knowledge level of the reading user about each inference rule until the inference result is obtained higher than before. Also, when evaluation information that the inference result presented is unknown is obtained, the information processing device according to the first exemplary embodiment can update the knowledge level of the reading user about each inference rule lower than before. As a result, because the information processing device according to the first exemplary embodiment can calculate the index values about other inference results in which such inference rules are used in the inference process with higher accuracy. 
     (The Second Exemplary Embodiment) 
     Next, the second exemplary embodiment of the present invention will be explained in detail with reference to drawings. In the second exemplary embodiment, an information processing system including a server as an information processing device of the present invention and a terminal will be explained. Further, in each drawing referred to in the explanation of the second exemplary embodiment, same codes are assigned to the same configurations and steps that operate similarly as the first exemplary embodiment of the present invention, and detailed explanation in the second exemplary embodiment is omitted. 
     An information processing system  20  according to the second exemplary embodiment of the present invention includes a server  2  as the information processing device of the present invention and a terminal  8 . Function block of each device is indicated in  FIG. 4 . 
     In  FIG. 4 , the server  2  is equipped with an inference rule memory unit  201 , a knowledge level memory unit  202 , a context information acquisition unit  203 , an inference unit  204 , an inference result index value calculation unit  205 , an inference result presentation unit  206 , an evaluation information acquisition unit  207 , a knowledge level alteration rule memory unit  208 , a knowledge level update unit  209  and a case information memory unit  210 . Next, hardware configuration of the server  2  is indicated in  FIG. 26 . In  FIG. 26 , the server  2  is configured from a computer  2600  equipped with CPU  2601 , RAM  2602 , ROM  2603 , a memory device  2604  and a network interface  2605 . Also, the inference rule memory unit  201 , the knowledge level memory unit  202 , the knowledge level alteration rule memory unit  208  and the case information memory unit  210  are configured from the memory device  2604 . Also, the context information acquisition unit  203 , the evaluation information acquisition unit  207  and the inference result presentation unit  206  are configured from the network interface  2605  and the CPU  2601  that reads a computer program memorized in the ROM  2603  or the memory device  2604  into the RAM  2602  and executes it. Also, the inference unit  204 , the inference result index value calculation unit  205  and the knowledge level update unit  209  are configured from the CPU  2601  that reads a computer program memorized in the ROM  2603  or the memory device  2604  into the RAM  2602  and executes it. 
     Also, the terminal  8  is equipped with a context information collection unit  801  and an information input/output unit  802 . Next, hardware configuration of the terminal  8  is indicated in  FIG. 27 . In  FIG. 27 , the terminal  8  is configured from a computer  2700  equipped with CPU  2701 , RAM  2702 , ROM  2703 , a memory device  2704 , an input device  2705 , a display device  2706  and a network interface  2707 . The context information collection unit  801  is configured from the network interface  2707  and the CPU  2701  that reads and a computer program memorized in the ROM  2703  or the memory device  2704  into the RAM  2702  and executes it. The information input/output unit  802  is configured from the input device  2705 , the display device  2706 , the network interface  2707  and the CPU  2701  that reads a computer program memorized in the ROM  2703  or the memory device  2704  into the RAM  2702  and executes it. 
     Further, hardware configuration that configures each function block of each device is not limited to the configuration described above. Also, the server  2  and the terminal  8  are connected so that they are possible to communicate via a network configured from the internet, LAN (Local Area Network), a public line network, a wireless communication network, combination of them, and so on. While one terminal  8  is indicated in  FIG. 4 , it does not mean that the number of the terminals to which the information processing device of the present invention is connected is limited to it. 
     First, each function block of the server  2  will be explained. 
     The case information memory unit  210  memorizes case information showing cases related to the inference rules. An example of the information memorized in the case information memory unit  210  is indicated in  FIG. 5 . In  FIG. 5 , the case information that each line shows includes information that identifies a case (case ID), name of the case and a related URL (Uniform Resource Locator). Also, the case information may include registration date and time and user ID of a registrant. 
     The inference rule memory unit  201  memorizes each inference rule explained in the first exemplary embodiment of the present invention further including the case ID. An example of the information memorized in the inference rule memory unit  201  is indicated in  FIG. 6 . In  FIG. 6 , the inference rule that each line indicates includes an inference rule ID, IF information, THEN information and the case ID. In addition, the inference rule may include registration date and time and user ID of a registrant. Also, the inference rule may include a plurality of IF information. In  FIG. 6 , the inference rule in which a plurality of IF information is included means that THEN information is inferred when all of the IF information is satisfied. 
     Further, the inference rule in which a plurality of IF information is included may mean that THEN information is inferred when either of the plurality of IF information is satisfied. Or, the inference rule in which a plurality of IF information is included may further include information that shows either of “and condition” or “or condition” as application condition of own rules. Here, “and” condition means it is applied when all of the IF information is satisfied. Also, “or condition” means that it is applied when either of the IF information is satisfied. Further, the inference rule may include a plurality of THEN information. 
     The case ID included in the inference rule indicates the case information related to the inference rule. When no case information related to a certain inference rule exists, the inference rule memory unit  201  does not have to include the case ID in the inference rule. 
     As a knowledge level of each user about each inference rule, the knowledge level memory unit  202  memorizes a numerical value included in a predetermined range. An example of information memorized in the knowledge level memory unit  202  is indicated in  FIG. 7 . In the example of  FIG. 7 , the range that the knowledge level can take is assumed to be a range of no smaller than 0 and no more than 1. Also, the smaller the numerical value is, it shows the less the user is knowledgeable about the inference rule. For example, the knowledge level about inference rule P-0001 of user U-0001 is 0.6, and the knowledge level about inference rule P-0004 is 0.1. In this case, knowledge about the inference rule P-0004 of the user U-0001 shows that it is less detailed compared with the knowledge about the inference P-0001. Further, the range that the knowledge level can take is not limited to this and can be set arbitrary. 
     The context information acquisition unit  203  receives the context information from the terminal  8  via a network. 
     By applying the inference rules memorized in the inference rule memory unit  201  in a chain reaction to the context information acquired by the context information acquisition unit  203 , the inference unit  204  obtains the case information as the inference result. Here, to apply in a chain reaction means to apply other inference rules possessing IF information that THEN information of the inference rule applied satisfies. And the inference unit  204  ends inference when the inference rules further applicable do not exist any more. In case a plurality of inference rules is applicable in the inference process, the inference unit  204  continues the inference processing in a chain reaction about each of a plurality of inference rules. And when the inference ends, the inference unit  204  obtains the case information that the case ID associated with each inference rule that was applied in the inference process indicates as the inference result. That is, the inference unit  204  obtains the case information related to each inference rule applied in the inference process as the inference result. Also, if there exists no case ID associated with each inference rule that was applied until the inference ends, the inference unit  204  supposes that the inference result is not obtained. Here, concerning each case information obtained as the inference result, a list of the inference rules until the inference rule related to the case information is applied is called an inference step list of the case information. 
     The inference result index value calculation unit  205  calculates, for each case information derived as the inference result, an index value for the reading user on the basis of the inference step list of the case information. For example, the inference result index value calculation unit  205  may calculate the index value for the reading user of the case information obtained as the inference result using the following expression (2). 
     
       
         
           
             
               
                 
                   
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     In expression (2), a shows the user ID and L(a, k) shows the knowledge level of the user a about the inference rule at inference step k. Also, Case shows the case information as the inference result, and Loot shows the inference step list of the case information Case. k shows application order of each inference rule (inference step) included in the inference step list Loot, and S(a, Case) shows the index value of the case Case for the user a. Here, there is a case when one case information Case is obtained by a plurality of inference step lists Loot. Accordingly, expression (2) shows that, among no smaller than one inference step list Loot until the case information Case is obtained, one of which the multiplied value of the knowledge level of the reading user about each inference rule included is minimum is selected and made as the index value of the case information Case for the user a. 
     Or, the inference result index value calculation unit  205  may make an average value, a powered average and so on of the knowledge level multiplied value calculated in a plurality of inference step lists as the index value of the case information for the reading user. Additionally, the inference result index value calculation unit  205  may calculate a total, a minimum value, a power sum and so on, not limited to the multiplied value of the knowledge level of the reading user about each inference rule included in the inference step list. 
     The inference result presentation unit  206  sorts or filters the case information as the inference result obtained by the inference unit  204  on the basis of the index values, and sends it to the terminal  8 . For example, in case the index value with a lower numerical value shows higher degree of usefulness, the inference result presentation unit  206  may send the case information that is sorted in ascending order of the index values to the terminal  8 . Also, the inference result presentation unit  206  may, for example, send the case information of which a numerical value of the index value is below a threshold value to the terminal  8 . 
     The evaluation information acquisition unit  207  receives one of a plurality of classes set in advance as the evaluation information in which the degree of the knowledge of the reading user about the case information as the inference result is evaluated from the terminal  8 . For example, the evaluation information acquisition unit  207  may notify the terminal  8  to display the evaluation information of a plurality of classes set in advance about each case information as the inference result in a display form such as a drop down list that is selectable. In this case, the evaluation information acquisition unit  207  receives the class of the evaluation information selected by the user operation to the terminal  8  from the terminal  8 . 
     The knowledge level alteration rule memory unit  208  memorizes knowledge level alteration rules that associate the classes of the evaluation information and increase/decrease values of the knowledge level. An example of the information memorized in the knowledge level alteration rule memory unit  208  is indicated in  FIG. 8 . In  FIG. 8 , the knowledge level alteration rule that each line shows includes information showing the class of the evaluation information and the increase/decrease value of the knowledge level. For example, with the evaluation information of the class as “specialized area”, information as “+0.5” that means the numerical value that added 0.5 to the knowledge level so far is made a new knowledge level is associated. 
     Also, in this example, as the classes of the evaluation information, it is assumed that the followings are set in advance.
         “Reading”: shows that the reading user accessed the URL that is associated with the case information that is the inference result. In other words, it shows the evaluation that, about the case information as the inference result, the reading user deepened the knowledge by reading. Accordingly, the knowledge level alteration rule about the evaluation information shows that 0.1 is added to the respective knowledge levels supposing that the knowledge level of the reading user about each inference rule applied until the case information evaluated as “reading” is derived deepened to a certain degree.   “Verification executed”: shows that the reading user verified actually that such case information is led by such inference basis. In other words, it shows the evaluation that the reading user deepened the knowledge by verification execution about the case information as the inference result. Accordingly, the knowledge level alteration rule about the evaluation information shows that 0.2 is added to the respective knowledge levels supposing that the knowledge of the reading user about each inference rule applied until the case information evaluated as “verification executed” is derived deepened to a certain degree.   “Specialized area”: shows that the reading user already possesses expertise about the inference results and the inference basis. In other words, it shows the evaluation that the reading user already possesses detailed knowledge about the case information as the inference result. Accordingly, the knowledge level alteration rule about the evaluation information shows that 0.5 is added to the respective knowledge levels supposing that the knowledge of the reading user about each inference rule that was applied until the case information evaluated as “specialized area” is derived is fairly deep from the beginning.   “Oversight occurred”: shows that the reading user overlooked the inference result by such inference basis, and realized it for the first time when the inference result was presented. In other words, it shows the evaluation that the reading user does not possess detailed knowledge about the case concerned such as to overlook the case information as the inference result. Accordingly, the knowledge level alteration rule about the evaluation information shows that 0.5 is subtracted from the respective knowledge levels supposing that the knowledge of the reading user about each inference rule that was applied until the case information evaluated as “oversight occurred” is derived is shallow.   “Defect embedded”: shows that the reading user is a person who executed the defect case or has caused the defect same as the defect case actually. In other words, it shows the evaluation that the reading user does not possess detailed knowledge about the case concerned such as to put the defect as the inference result into effect actually. Accordingly, the knowledge level alteration rule about the evaluation information shows that 1.0 is subtracted from the respective knowledge levels supposing that the knowledge of the reading user about each inference rule that was applied until the case information evaluated as “defect embedded” is derived is very shallow.       

     The knowledge level update unit  209  acquires the increase/decrease values associated with the classes of the evaluation information that is acquired by the evaluation information acquisition unit  207  from the knowledge level alteration rule memory unit  208 . Also, the knowledge level update unit  209  searches, about the case information that is a target of the evaluation information, a record that stored the knowledge level of the reading user about each inference rule included in the inference step list from the knowledge level memory unit  202 . And the knowledge level update unit  209  updates the knowledge level of each record searched according to the knowledge level alteration rule acquired. 
     Also, the knowledge level update unit  209  updates the knowledge level of the target record memorized in the knowledge level update unit  209  within a range of no smaller than 0 and no more than 1. For example, the knowledge level update unit  209  updates the knowledge level that exceeds 1 by performing addition according to the knowledge level alteration rule to 1 that is the upper limit value. Also, the knowledge level update unit  209  updates the knowledge level that becomes negative by performing subtraction according to the knowledge level alteration rule to 0 that is the lower limit. Further, the knowledge level update unit  209  may update the knowledge level not only within the range of no smaller than 0 and no more than 1 but also within the predetermined range set in advance. 
     Next, each function block of the terminal  8  will be explained 
     The context information collection unit  801  collects context information from the target data. For example, the context information collection unit  801  may collect the information showing each part and the attribute information as the context information from the design document data memorized in the memory device. And the context information collection unit  801  sends the context information collected to the server  2 . 
     The information input/output unit  802  displays the inference results received from the server  2  on the display device. The inference results received are the case information sorted or filtered on the basis of the index value as described above. 
     Also, the information input/output unit  802  acquires the evaluation information about the inference results displayed via the input device. At that time, the information input/output unit  802  may acquire the evaluation information of either class by displaying the evaluation information of a plurality of classes that are notified from the server  2  on the display device in a form such as a drop down list so that they are selectable. And the information input/output unit  802  sends the class of the evaluation information acquired to the server  2 . 
     Operation of the information processing system  20  configured as above will be explained with reference to drawings. First, operation of the information processing system  20  after it starts inference until it presents the inference results will be explained with reference to  FIG. 9 .  FIG. 9  is a flow chart explaining inference result presentation operation of the information processing system according to the second exemplary embodiment of the present invention. Further, in  FIG. 9 , it is supposed that a figure on the left shows operation of the terminal  8 , a figure on the right shows operation of the server  2 , and arrows of dashed lines that connect left and right show a flow of data. 
     In  FIG. 9 , first, the context information collection unit  801  of the terminal  8  collects the context information and sends it to the server  2  (Step S 20 ). 
     Next, the context information acquisition unit  203  of the server  2  receives the context information (Step S 21 ). 
     Next, the inference unit  204  applies the inference rules memorized in the inference rule memory unit  201  in a chain reaction to the context information received. And when the inference rules further applicable do not exist any more, the inference unit  204  acquires the case ID that is associated with each inference rule applied as the inference results (Step S 22 ). 
     Next, the inference result index value calculation unit  205  acquires the reading user ID (Step S 23 ). 
     Next, the inference result index value calculation unit  205  executes processing of Steps S 24 -S 25  for each case ID obtained in Step S 22 . 
     Here, first, the inference result index value calculation unit  205  searches the knowledge level of the reading user about each inference rule included in the inference step list of the case information from the knowledge level memory unit  202  (Step S 24 ). 
     Next, by applying the knowledge level searched in Step S 24  to expression (2), the inference result index value calculation unit  205  calculates the index value of the case information (Step S 25 ). 
     Next, the inference result presentation unit  206  notifies the terminal  8  to present the case information that each case ID obtained in Step S 22  indicates on the basis of the index values calculated in Step S 25  (Step S 26 ). For example, the inference result presentation unit  206  may notify the terminal  8  to sort the case information in ascending order of the index values and present it. Further, the inference result presentation unit  206  may notify the terminal  8  to perform filtering using whether or not the numerical value of the index value is below the threshold value as a condition, and to present the case information on the basis of the result. 
     Next, the information input/output unit  802  of the terminal  8  presents the case information as the inference result according to the notification from the server  2  (Step S 27 ). 
     As above, the information processing system  20  ends the inference result presentation operation. 
     Next, knowledge level update operation of the information processing system  20  will be explained with reference to  FIG. 10 .  FIG. 10  is a flow chart explaining the knowledge level update operation of the information processing system according to the second exemplary embodiment of the present invention. Further, in  FIG. 10 , it is supposed that a figure on the left shows operation of the terminal  8 , a figure on the right shows operation of the server  2 , and arrows of dashed lines that connect left and right show a flow of data. 
     In  FIG. 10 , first, the information input/output unit  802  of the terminal  8  displays the evaluation information of a plurality of classes as the degree of the knowledge of the reading user about the case information as the inference result so that it is selectable (Step S 30 ). The evaluation information of a plurality of classes is notified from the server  2  in advance. For example, the evaluation information of a plurality of classes may be, as described above, information showing “reading”, “verification executed”, “specialized area”, “oversight occurred” and “defect embedded” respectively. 
     Next, the information input/output unit  802  sends, for one among the case information presented, the class of the evaluation information selected by the operation of the input device to the server  2  (Step S 31 ). 
     Next, the evaluation information acquisition unit  207  of the server  2  receives the class of the evaluation information from the terminal  8  (Step S 32 ). 
     Next, the knowledge level update unit  209  searches the knowledge level alteration rule about the evaluation information of the kind acquired in Step S 32  from the knowledge level alteration rule memory unit  208  (Step S 33 ). 
     Next, the knowledge level update unit  209  executes processing of Steps S 34 -S 35  for each inference rule included in the inference step list of the target case information. 
     Here, first, the knowledge level update unit  209  searches the record including the knowledge level of the reading user about the inference rule from the knowledge level memory unit  202  (Step S 34 ). 
     And the knowledge level update unit  209  updates the knowledge level of the record searched within the predetermined range according to the knowledge level alteration rule acquired in Step S 33  (Step S 35 ). 
     As above, the information processing system  20  ends the knowledge level update operation. 
     Next, a specific example of the operation of the information processing system  20  according to the second exemplary embodiment of the present invention will be explained with reference to  FIG. 11 .  FIG. 11  is a schematic diagram explaining the specific example of the inference result presentation operation of the information processing system according to the second exemplary embodiment of the present invention. Here, it is assumed that circuit design drawing data is stored in the terminal  8 . 
     First, the context information collection unit  801  of the terminal  8  extracts parts information and attribute information of each part from the circuit design drawing data as the context information. Here, it is supposed that the context information collection unit  801  extracted the context information such as “case Y” as the parts information, “plastics”, “slide type” and “high temperature” as the attribute information from the circuit design drawing data (Step S 20 ). 
     Next, the context information acquisition unit  203  of the server  2  receives the context information from the terminal  8  (Step S 21 ). 
     Next, the inference unit  204  judges that the attribute information “plastics” and “high temperature” of the context information satisfy IF information of the inference rule P-0001 shown in  FIG. 6 . 
     Next, the inference unit  204  judges that THEN information “parts=&gt;melt” of the inference rule P-0001 and the attribute information “plastics” of the case Y satisfy IF information of the inference rule P-0004. 
     And since there are no inference rules in which THEN information “parts=&gt;molten deformation” of the inference rule P-0004 satisfies IF information, the inference unit  204  ends the inference processing. 
     Next, the inference unit  204  acquires the case ID associated for each of the inference rule P-0001 and P-0004 used in Step S 22 . Here, no case ID is associated with P-0001, and as the case ID, C-0001 is associated with P-0004. Accordingly, as the inference result, the inference unit  204  acquires the case C-0001 (Step S 22 ). 
     Similarly, it is supposed that the inference unit  204  obtained the cases C-0007-C-0009 as indicated in  FIG. 11  as the inference results by applying other inference rules (not shown) in a chain reaction to the attribute information “slide type” of the context information obtained in Step S 21 . 
     Further, in the example of  FIG. 11 , it is supposed that the case ID&#39;s are not associated with the inference rules that were applied during the inference processing. If there exists a case ID that is associated with the inference rules applied in between, the inference unit  204  also makes the case ID the inference result. 
     Next, the inference result index value calculation unit  205  calculates the index value of each case ID for the reading user. Here, it is supposed that the reading user ID is U-0001. 
     For example, the inference result index value calculation unit  205  obtains {inference rule P-0001, inference rule P-0004} as the inference step list for the case C-0001 as the inference result. 
     And the inference result index value calculation unit  205  obtains 0.6 as the knowledge level of the user U-0001 about the inference rule P-0001 by referring to the knowledge level memory unit  202  indicated in  FIG. 7  (Step S 24 ). 
     Similarly, the inference result index value calculation unit  205  obtains 0.1 as the knowledge level of the user U-0001 about the inference rule P-0004 by referring to the knowledge level memory unit  202  indicated in  FIG. 7  (Step S 24 ). 
     And the inference result index value calculation unit  205  obtains 0.06 as the index value for the user U-0001 of the case C-0001 using expression (2) (Step S 25 ). 
     In the similar way, it is supposed that the inference result index value calculation unit  205  also calculated 0.5, 0.3 and 0.08 as the knowledge level respectively about the cases C-0007-C-0009. Further, in case the case ID is led by a plurality of inference step lists like the case C-0009 as the inference result in  FIG. 11 , the inference result index value calculation unit  205  may adopt, on the basis of expression (2), a minimum value of the knowledge level multiplied values of the reading user among each inference step list. 
     Next, the inference result presentation unit  206  searches the case information of the cases C-0001, C-0007-C-0009 that are inference results from the case information memory unit  210 . And the inference result presentation unit  206  sorts each case information in ascending order of the index values calculated in Step S 25  and sends it to the terminal  8  (Step S 26 ). 
     Accordingly, the information input/output unit  802  of the terminal  8  displays information as indicated in  FIG. 12  on the display device as the inference results.  FIG. 12  is a figure indicating an example of the inference result presentation screen in the second exemplary embodiment of the present invention. In the example, the information input/output unit  802  of the terminal  8  displays the case information that is the inference results in ascending order of the index values. Further, in  FIG. 12 , each line indicates the case information that is the inference result. Also, operation button of “not inputted” indicated in a cell on the right end of each line is one for opening the input screen of information to evaluate the degree of the knowledge of the reading user about the case information. Also, the information where “already inputted” is indicated instead of the operation button of “not inputted” shows that the evaluation information of the degree of the knowledge of the reading user about the case information is already inputted. 
     As above, explanation of the specific example of the inference result presentation operation of the information processing system  20  ends. 
     Next, taking the specific inference results indicated in  FIG. 12  as an example, a specific example of the knowledge level update operation of the information processing system  20  will be explained with reference to  FIGS. 13-14 . 
       FIG. 13  is an example of the input screen of the evaluation information displayed by the information input/output unit  802  of the terminal  8 . The example is a screen to input the evaluation information about the case C-0001. The information input/output unit  802  displays, for example, corresponding to button down of “not inputted” operation button indicated in  FIG. 12 , the input screen indicated in  FIG. 13 . In the example, the information input/output unit  802  displays a pull-down list that can select either of “reading”, “verification executed”, “specialized area”, “oversight occurred” and “defect embedded” as the evaluation information of a plurality of classes (Step S 30 ). 
     And for example, assume a case when the information input/output unit  802  acquired the evaluation information of “verification executed” (Steps S 31 -S 32 ). 
     In this case, as indicated in  FIG. 14 , the knowledge level update unit  209  acquires the increase/decrease value “+0.2” associated with the evaluation information of “verification executed” from the knowledge level alteration rule memory unit  208  (Step S 33 ). Further,  FIG. 14  is a schematic diagram explaining a specific example of the knowledge level update operation of the information processing system according to the second exemplary embodiment of the present invention. 
     Next, the knowledge level update unit  209  acquires, as the ID of each inference rule included in the inference step list until the case C-0001 is obtained, P-0001 and P-0004. And the knowledge level update unit  209  searches a record including P-0001 and the reading user U-0001 and a record including P-0004 and the reading user U-0001 from the knowledge level memory unit  202  (Step S 34 ). 
     And the knowledge level update unit  209  updates the knowledge level included in the records searched to a numerical value that added 0.2 to the numerical value thereof. Further, at that time, in case there is no such record concerned yet, the knowledge level update unit  209  may store a numerical value to which the increase/decrease value was applied supposing the knowledge level of the user ID about the inference rule ID concerned is 0 in the knowledge level memory unit  202  as a new record. Also, in the example, the predetermined range of the knowledge level is made no smaller than 0 and no more than 1 as described above. Accordingly, when the new knowledge level by updating exceeds 1, the knowledge level update unit  209  updates the knowledge level to 1. Similarly, when the new knowledge level updating is less than 0, the knowledge level update unit  209  updates the knowledge level to 0. 
     As above, explanation of the specific example of the knowledge level update operation of the server  2  ends. 
     Further, the server  2  may be without part or all of the inference rule memory unit  201 , the knowledge level memory unit  202 , the context information acquisition unit  203 , the evaluation information acquisition unit  207 , the knowledge level alteration rule memory unit  208  and the case information memory unit  210 . Also, these components may be outside the server  2 . Further, part or all of the inference rules, the knowledge levels, the context information, the evaluation information, the knowledge level alteration rules and the case information may be given to the server  2  from outside. 
     Next, effects of the second exemplary embodiment of the present invention will be described. 
     The information processing system according to the second exemplary embodiment of the present invention can present, among case information inferred from the context, the case information of which the user does not have detailed knowledge with priority. 
     The reason is because: the inference unit makes the case information associated with each inference rule applied to the context in a chain reaction the inference results; and the inference result evaluation unit calculates, on the basis of the knowledge levels of the reading user about each inference rule used in the inference step list until the case information is obtained, the index value showing the depth of the knowledge of the reading user about the case information comprehensively. Accordingly, the information processing system according to the second exemplary embodiment can, to the user who knows the case information but does not have the detailed knowledge about the process until the case is reached, present such case information with priority. That is, the information processing system according to the second exemplary embodiment can, about the case information as the inference result, present with priority from those of which the knowledge that the reading user has about the inference process is least. As a result, the information processing system according to the second exemplary embodiment can present with high accuracy the case information about which the user does not have detailed knowledge truly, and the case information that the user tends to make a mistake accordingly. 
     Also, the information processing system according to the second exemplary embodiment of the present invention can determine with higher accuracy the case information presented to each reading user with priority. 
     The reason is because: the evaluation information acquisition unit acquires the information for which the degree of the knowledge about the case information as the inference result was evaluated by the reading user; and the knowledge level update unit updates the knowledge level of the reading user about each inference rule used until the case information as the inference result is obtained using the knowledge level alteration rule associated with the evaluation information acquired. Accordingly, the information processing system according to the second exemplary embodiment will improve accuracy of the index values of other inference results obtained using the inference rules of which the knowledge levels were updated. As a result, even for the case information that has not been read by the user, the information processing system according to the second exemplary embodiment will not display such case information to the user with priority when the user already possesses detailed knowledge about the inference process. Also, the information processing system according to the second exemplary embodiment can, even for the case information read many times by the user, if it is the case information that tends to cause a mistake by not having the detailed knowledge truly about the inference process, display the case information to the user with priority. 
     (The Third Exemplary Embodiment) 
     Next, the third exemplary embodiment of the present invention will be explained in detail with reference to drawings. Further, in each drawing referred to in the explanation of the third exemplary embodiment, same codes are assigned to the same configurations and steps that operate similarly as the second exemplary embodiment of the present invention, and detailed explanation in the third exemplary embodiment is omitted. 
     An information processing system  30  according to the third exemplary embodiment of the present invention includes a server  3  as the information processing device of the present invention and the terminal  8 . Function block of each device is indicated in  FIG. 15 . In  FIG. 15 , compared to the server  2  according to the second exemplary embodiment of the present invention, the server  3  is different in a point that it is equipped with an inference rule memory unit  301  in place of the inference rule memory unit  201 , a knowledge level memory unit  302  in place of the knowledge level memory unit  202 , an inference result index value calculation unit  305  in place of the inference result index value calculation unit  205 , a knowledge level update unit  309  in place of the knowledge level update unit  209  and a case information memory unit  310  in place of the case information memory unit  210 . Further, the server  3  is configured from the computer  2600  described in  FIG. 26  similar to the server  2 . Also, the inference rule memory unit  301 , the knowledge level memory unit  302 , the inference result index value calculation unit  305 , the knowledge level update unit  309  and the case information memory unit  310  are configured from the components of the computer  2600  same as the inference rule memory unit  201 , the knowledge level memory unit  202 , the inference result index value calculation unit  205 , the knowledge level update unit  209  and the case information memory unit  210 . 
     The inference rule memory units  301  memorizes each inference rule as illustrated in the second exemplary embodiment of the present invention in  FIG. 6  further including its probability of occurrence. An example of the information memorized in the inference rule memory unit  301  is indicated in  FIG. 16 . In  FIG. 16 , for example, the inference rule P-0002 includes “0.5” as the probability of occurrence. This shows that, when the condition that output from a regulator part is high voltage is satisfied, the probability that the part generates heat is 0.5. 
     The knowledge level memory unit  302  memorizes, in addition to each record as illustrated in the second exemplary embodiment of the present invention in  FIG. 7 , records including the case ID instead of the inference rule ID further. An example of the information memorized in the knowledge level memory unit  302  is indicated in  FIG. 17 . In  FIG. 17 , each record memorized in the knowledge level memory unit  302  includes either the inference rule ID or the case ID, the user ID and the knowledge level. A record including the inference rule ID is the same as the record in the first and the second exemplary embodiment of the present invention and shows the knowledge level of the user about the inference rule. A record including the case ID shows the knowledge level of the user about the case information. Such knowledge level of the user about the case information may be registered in advance or predetermined value of 0 may be set as an initial value. 
     The case information memory unit  310  memorizes, as information showing the case related to the inference result, in addition to the configuration of the case information in the second exemplary embodiment of the present invention, further, information showing degree of importance of the case. An example of the information memorized in the case information memory unit  310  is indicated in  FIG. 18 . 
     The inference result index value calculation unit  305  further calculates, for each case information derived as the inference result, in addition to the knowledge level of the reading user about each inference rule included in the inference step list, on the basis of the knowledge level of the reading user about the case information, the index values of the case information. For example, the inference result index value calculation unit  305  may calculate the index value of each case information as the inference result using the following expression (3). 
     
       
         
           
             
               
                 
                   
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     In expression (3), Lc(a, Case) is the knowledge level of the user a about the case Case. 
     Also, the inference result index value calculation unit  305  may, for the index value of each case information calculated as described above, calculate the index value by further considering the degree of importance of the case information. For example, the inference result index value calculation unit  305  may make a numerical value that multiplied a reciprocal of the degree of importance of the case information to the index value calculated using expression (3) a new index value. Accordingly, among the case information for which the knowledge of the reading user about the inference process of the case information is lower, the index value of the case information of which the degree of importance of the case information itself is higher becomes smaller. 
     Also, the inference result index value calculation unit  305  may calculate the index value of the case information with further considering the probability of occurrence of the phenomenon for each inference rule as indicated in  FIG. 16 . For example, the inference result index value calculation unit  305  may make a numerical value that multiplied further power of each probability of occurrence to the multiplied value of the knowledge level of the reading user about each inference rule included in the inference step list of the case information the index value of the case information. If in case the probability of occurrence is multiplied using power of a positive exponent, the index value that is calculated for the case information obtained via the inference rules of which the probability of occurrence is lower becomes smaller. In other words, in this case, the inference result index value calculation unit  305  calculates, about the case information as the inference result that is inferred by the phenomenon with rarer frequency of occurrence, an index value that supposed the degree of usefulness to be higher (the knowledge to be less) for the reading user. Such index value is effective for example, when the reading user is an expert. Or, in case the probability of occurrence is multiplied using power of a negative exponent, the index value that is calculated for the case information obtained via the inference rules of which the probability of occurrence is higher becomes smaller. In other words, in this case, the inference result index value calculation unit  305  calculates, about the case information as the inference result that is inferred by the phenomenon with higher frequency of occurrence, an index value that supposed the degree of usefulness to be higher (the knowledge to be less) for the reading user. Such index value is effective, for example, when the reading user is a beginner. Thus, by acquiring information showing a degree of skillfulness of the reading user, the inference result index value calculation unit  305  may alter the numerical value of the exponent in the power of the probability of occurrence used at the time of calculation of the index value. 
     The knowledge level update unit  309  is configured similar to the knowledge level update unit  209  in the second exemplary embodiment of the present invention. Further, the knowledge level update unit  309  searches a record including the knowledge level of the reading user about the case information that is a target of the evaluation information from the knowledge level memory unit  302 . And the knowledge level update unit  309  updates the knowledge level of the record searched according to the knowledge level alteration rule according to the class of the evaluation information. That is, the knowledge level update unit  309  updates the knowledge level of the reading user about the case information that is the target of the evaluation information on the basis of the evaluation information. 
     Operation of the information processing system  30  configured as above will be explained with reference to drawings. 
     Here, operation from the information processing system  30  starts inference until presents the inference results will be explained with reference to  FIG. 19 .  FIG. 19  is a flow chart explaining the inference result presentation operation of the information processing system according to the third exemplary embodiment of the present invention. 
     In  FIG. 19 , first, by operating Steps S 20  to S 23  similar to the information processing system  20  according to the second exemplary embodiment of the present invention, the information processing system  30  applies the inference rules in a chain reaction to the context information and acquires the case IDs as the inference results. 
     Next, the inference result index value calculation unit  305  executes the following Steps S 41 -S 45  for each case ID obtained in Step S 22 . 
     Here, first, the inference result index value calculation unit  305  acquires the knowledge level of the reading user about each inference rule included in the inference step list until the case information is obtained (Step S 41 ). 
     Next, the inference result index value calculation unit  305  acquires the knowledge level of the reading user about the case information (Step S 42 ). 
     Next, the inference result index value calculation unit  305  acquires the probability of occurrence of each inference rule included in the inference step list until the case information is obtained (Step S 43 ). 
     Next, the inference result index value calculation unit  305  acquires the degree of importance of the case information (Step S 44 ). 
     Next, the inference result index value calculation unit  305  calculates an index value for the reading user of the case information on the basis of each knowledge level acquired in Steps S 41 -S 42 , each probability of occurrence acquired in Step S 43  and the degree of importance acquired in Step S 44  (Step S 45 ). 
     Further, similar to the second exemplary embodiment of the present invention, if there exists a plurality of inference step lists from which the case ID is obtained, the inference result index value calculation unit  305  executes Steps S 41 -S 45  for each inference step list. And the inference result index value calculation unit  305  decides the index value of the case information on the basis of a plurality of candidates of the index values obtained. For example, the inference result index value calculation unit  305  may make a minimum value of the plurality of index values the index value of the case ID. 
     When the index value calculation processing ends for each case ID, the information processing system  30  operates Steps S 26  to S 27  similar to the second exemplary embodiment of the present invention, and presents the case information on the basis of the index values. 
     As above, the information processing system  30  ends the inference result presentation operation. 
     Next, knowledge level update operation of the information processing system  30  will be explained with reference to  FIG. 20 .  FIG. 20  is a flow chart explaining the knowledge level update operation of the information processing system according to the third exemplary embodiment of the present invention. 
     First, by operating Steps S 30  to S 35  similar to the information processing system  30  according to the second exemplary embodiment of the present invention, the information processing system  30  updates the knowledge level of the reading user about each inference rule until the case information as the inference result is obtained. 
     Next, the knowledge level update unit  309  searches a record including the knowledge level of the reading user about the case information of the target of the evaluation information acquired in Step S 32  from the knowledge level memory unit  302  (Step S 51 ). 
     Next, the knowledge level update unit  309  updates the knowledge level of the record searched in Step S 51  according to the knowledge level alteration rule obtained in Step S 33  (Step S 52 ). 
     As above, the information processing system  30  ends the knowledge level update operation. 
     Further, the server  3  may be without part or all of the inference rule memory unit  301 , the knowledge level memory unit  302 , the context information acquisition unit  203 , the evaluation information acquisition unit  207 , the knowledge level alteration rule memory unit  208  and the case information memory unit  310 . Also, these components may be outside the server  3 . Further, part or all of the inference rules, the knowledge levels the context information, the evaluation information, the knowledge level alteration rules and the case information may be given to the server  3  from outside. 
     Next, effects of the third exemplary embodiment of the present invention will be described. 
     The information processing device according to the third exemplary embodiment of the present invention can, among the case information inferred from the context, determine with higher accuracy the case information for which the user does not have detailed knowledge as the case information presented with priority. 
     The reason is because the inference result index value calculation unit considers, in addition to the knowledge level of the reading user about each inference rule included in the inference step list from which the case information as the inference result is derived, the knowledge level of the reading user about the case information further and calculates the index value of the case information. Further, because the inference result index value calculation unit calculates the index value considering the probability of occurrence of each inference rule included in the inference step list from which the case information as the inference result is derived, the degree of importance of the case information itself, and so on. Accordingly, by considering not only the knowledge level of the user about the inference process but also the probability of occurrence of each inference process, the knowledge level of the user about the case itself, the degree of importance of the case itself or combination of them, the information processing system according to the third exemplary embodiment can determine the case information presented with priority on the basis of the index values with higher accuracy. 
     (The Fourth Exemplary Embodiment) 
     Next, the fourth exemplary embodiment of the present invention will be explained in detail with reference to drawings. Further, in each drawing referred to in the explanation of the fourth exemplary embodiment, same codes are assigned to the same configurations and steps that operate similarly as the third exemplary embodiment of the present invention, and detailed explanation in the fourth exemplary embodiment is omitted. 
     An information processing system  40  according to the fourth exemplary embodiment of the present invention includes a server  4  as the information processing device of the present invention and a terminal  9 . Function block of each device is indicated in  FIG. 21 . 
     In  FIG. 21 , compared to the server  2  in the second exemplary embodiment of the present invention, the server  4  is different in a point that it is equipped with an evaluation information acquisition unit  407  in place of the evaluation information acquisition unit  207  and further, an action history information acquisition unit  411 , an action history conversion rule memory unit  412  and an action history information conversion unit  413 . Further, the server  4  is configured from the computer  2600  described in  FIG. 26  similar to the server  2 . Also, the action history information acquisition unit  411  is configured from the network interface  2605  and the CPU  2601  that reads a computer program memorized in the ROM  2603  or the memory device  2604  into the RAM  2602  and executes it. Also, the action history information conversion unit  413  is configured from the CPU  2601  that reads a computer program memorized in the ROM  2603  or the memory device  2604  into the RAM  2602  and executes it. Also, the action history conversion rule memory unit  412  is configured from the memory device  2604 . 
     The terminal  9  is equipped with an action history information collection unit  903  in addition to the configuration same as the terminal  8  in the second exemplary embodiment of the present invention. Further, the terminal  9  is configured from the computer  2700  described in  FIG. 27  similar to the terminal  8 . Also, the action history information collection unit  903  is configured from the network interface  2707  and the CPU  2701  that reads a computer program memorized in the ROM  2703  or the memory device  2704  into the RAM  2702  and executes it. 
     Further, hardware configuration of each function block from which each device is configured is not limited to the configuration described above. 
     First, each function block of the server  4  will be explained. 
     The action history information acquisition unit  411  receives from the terminal  9 , for each case information as the inference result presented on the terminal  9 , action history information showing a history of action that the reading user took. For example, the action history information acquisition unit  411  may acquire as the action history information reading time of the presentation screen of the inference results on the terminal  9 , access record to URL for the case information indicated in the inference results, reading time of the screen that the URL indicates, input operation history to an input column that is set up on the screen that the URL indicates and so on. Such information is collected in the terminal  9  described below. 
     The action history conversion rule memory unit  412  memorizes action history conversion rules that are used when the action history information about each case information is converted into the evaluation information that evaluated the degree of the knowledge of the user about the case information. An example of the information memorized in the action history conversion rule memory unit  412  is indicated in  FIG. 22 . 
     In  FIG. 22 , the action history conversion rule that each line indicates includes conditions about the action history information and the evaluation information. For example, the action history conversion rule in the first line shows that, when reading time about a detailed page of the case information as the inference result is no smaller than 10 seconds, the action history information is converted into the evaluation information “reading”. Also, the action history conversion rule in the second line shows that, when reading time about a detailed page of the case information as the inference result is no smaller than 100 seconds and input operation to a text area for verification result input that is set in the page exists, the action history information is converted into the evaluation information “verification executed”. Also, the action history conversion rule in the third line shows that, when input operation to a text area for comment input that is set in a detailed page of the case information as the inference result exists, the action history information is converted into the evaluation information “indication inputted”. Also, the action history conversion rule in the fourth line shows that, when reading time of a detailed page of the case information as the inference result is no smaller than 10 seconds and access operation to an attached file to which a link is established in the page exists, the action history information is converted into the evaluation information “detailed check”. 
     Further, an example of the information memorized in the knowledge level alteration rule memory unit  208  in case the action history conversion rule memory unit  412  memorizes the information indicated in  FIG. 22  is indicated in  FIG. 23 . The knowledge level alteration rules indicated in  FIG. 23  show the knowledge level alteration rules that are associated with the evaluation information “reading”, “verification executed”, “indication inputted” and “detailed check” respectively. 
     By applying the action history conversion rules memorized in the action history conversion rule memory unit  412  to the action history information acquired by the action history information acquisition unit  411 , the action history information conversion unit  413  converts the action history information into the evaluation information. 
     The evaluation information acquisition unit  407  acquires the evaluation information from the action history information conversion unit  413  instead of acquiring the evaluation information selected by the input operation of the reading user. 
     Next, each function block of the terminal  9  will be explained. 
     The action history information collection unit  903  acquires the information showing the operation history about the information input/output unit  802  as the action history information. In case the information input/output unit  802  is configured from web browser application, the action history information collection unit  903  may acquire page transition history or operation history of the web browser as the action history information. And the action history information collection unit  903  sends the action history information collected to the server  4 . 
     Knowledge level update operation of the information processing system  40  configured as above will be explained with reference to  FIG. 24 .  FIG. 24  is a flow chart explaining the knowledge level update operation of the information processing system according to the fourth exemplary embodiment of the present invention. Further, with regard to the inference result presentation operation of the information processing system  40 , because it is similar to the information processing system  20  according to the second exemplary embodiment of the present invention, its detailed explanation in the fourth exemplary embodiment will be omitted. 
     First, the action history information collection unit  903  of the terminal  9  collects the action history information of the user for each case information as the inference result and sends it to the server  4  (Step S 60 ). As described above, for example, the action history information collection unit  903  may acquire the operation history information of the user about application software that presents the inference results as the action history information. 
     Next, the action history information acquisition unit  411  of the server  4  receives the action history information from the terminal  9  (Step S 61 ). 
     Next, the action history information conversion unit  413  searches the action history conversion rules applicable to the action history information acquired in Step S 61  from the action history conversion rule memory unit  412  (Step S 62 ). 
     And the action history information conversion unit  413  converts the action history information into the evaluation information according to the action history conversion rule searched (Step S 63 ). 
     Hereinafter, by operating Steps S 33  to S 35  similar to the information processing system  20  according to the second exemplary embodiment of the present invention, the information processing system  40  updates the knowledge level of the reading user about each inference rule used until the case information is obtained. 
     As above, the information processing system  40  ends the knowledge level update operation. 
     Further, the server  4  may be without part or all of the inference rule memory unit  201 , the knowledge level memory unit  202 , the context information acquisition unit  203 , the evaluation information acquisition unit  407 , the knowledge level alteration rule memory unit  208 , the case information memory unit  210 , the action history information acquisition unit  411  and the action history conversion rule memory unit  412 . Also, these components may be outside the server  4 . Further, part or all of the inference rules, the knowledge levels, the context information, the evaluation information, the knowledge level alteration rules, the case information, the action history information and the action history conversion rules may be given to the server  4  from outside. 
     Next, effects of the fourth exemplary embodiment of the present invention will be described. 
     The information processing device according to the fourth exemplary embodiment of the present invention can update the knowledge level of each user about each inference rule without workload of the user. 
     The reason is because the action history information acquisition unit acquires the information showing the action history of the user about the inference results presented, and the action history information conversion unit converts the action history information into the evaluation information using the action history conversion rules memorized in advance. Accordingly, because it becomes possible for the information processing device according to the fourth exemplary embodiment to acquire the evaluation information of the user to update the knowledge level for each user about the inference rules with high accuracy without resort to input work of the user. 
     Further, in the fourth exemplary embodiment, the action history information acquisition unit has been explained as one that acquires, as the action history information, page transition information or operation history information of the application software that presents the inference results In addition, the action history information acquisition unit in the present invention may acquire any information as far as it is the information showing the action of the user about the inference results presented and information that can be acquired by the computer device. 
     Also, in the fourth exemplary embodiment, the evaluation information acquisition unit may further receive the evaluation information inputted in the information input/output unit of the terminal similar to the evaluation information acquisition unit in the second or the third exemplary embodiment of the present invention. And the evaluation information acquisition unit in the fourth exemplary embodiment may adopt either one of the evaluation information inputted in the information input/output unit of the terminal and the evaluation information converted by the action history information conversion unit. For example, the evaluation information acquisition unit in the fourth exemplary embodiment may acquire the evaluation information converted by the action history information conversion unit in case the evaluation information is not inputted in the information input/output unit of the terminal. 
     Also, as the second to the fourth exemplary embodiment of the present invention, while the information processing system including the server and the terminal has been explained, it is possible to configure the information processing device of the present invention by realizing each function of the server and the terminal in each exemplary embodiment in one computer. 
     Also, in the second to the fourth exemplary embodiment of the present invention, explanation has been made focusing on the examples in which the context information is character information extracted from the circuit design document data. In addition, the context information in each exemplary embodiment may be any information as far as it is information that can be a target of inference, such as character information, image information, measurement data outputted from sensor equipment, operation log data about a device or application software and so on. 
     Also, in each exemplary embodiment of the present invention, explanation has been made focusing on examples in which the inference rules are in IF-THEN form. In addition, the inference rules in each exemplary embodiment may be in other forms that are used when the inference results are derived from the context information. 
     Also, in each exemplary embodiment of the present invention, by storing the operation of the information processing device (server and terminal) that has been explained with reference to each flow chart indicated in  FIGS. 2, 3, 9, 10, 19, 20 and 24  in the memory device (storage medium) of the computer as a computer program of the present invention, and the CPU may read and execute the computer program. And in such a case, the present invention is configured from codes showing the computer program or the storage medium described above. 
     Also, each exemplary embodiment described above can be combined appropriately and executed. 
     Also, the present invention is not limited to each exemplary embodiment described above, and can be put into effect in various modes. 
     This application claims priority based on Japanese Patent Application No. 2011-274792 filed on Dec. 15, 2011 and the disclosure thereof is incorporated herein in its entirety. 
     Part or all of the exemplary embodiments mentioned above can also be described as the following supplementary notes, and they are not limited to the followings. 
     (Supplementary Note 1) 
     An information processing device comprising: 
     an inference unit for obtaining inference results by applying inference rules to context information; 
     an inference result index value calculation unit for, by using information that shows a reading user who reads the inference results, acquiring a knowledge level of the reading user about each inference rule that was applied until the inference results are obtained, and on the basis of each knowledge level acquired, calculating index values that show depth of the knowledge of the reading user about the inference results comprehensively; 
     an inference result presentation unit for presenting the inference results on the basis of the index values calculated by the inference result index value calculation unit; and 
     a knowledge level update unit for, for each inference rule that was applied until the inference results are obtained, updating the knowledge level of the reading user about the inference rule concerned on the basis of evaluation information in which the degree of the knowledge that the reading user possesses is evaluated about the inference results presented by the inference result presentation unit. 
     (Supplementary Note 2) 
     The information processing device according to supplementary note 1, wherein the inference unit makes case information related to each inference rule that was applied until the inference results are obtained the inference results; and 
     the inference result index value calculation unit calculates the index values for each case information as the inference results. 
     (Supplementary Note 3) 
     The information processing device according to supplementary note 2, wherein the inference result index value calculation unit calculates the index values, in addition to the knowledge level of the reading user about each inference rule that was applied until the inference results are obtained, further on the basis of degree of importance of the case information as the inference results. 
     (Supplementary Note 4) 
     The information processing device according to supplementary note 2 or supplementary note 3, wherein the inference result index value calculation unit calculates the index values, in addition to the knowledge level of the reading user about each inference rule that was applied until the inference results are obtained, further on the basis of the knowledge level of the reading user about the case information as the inference results, and 
     the knowledge level update unit further updates the knowledge level of the reading user about the case information on the basis of the evaluation information. 
     (Supplementary Note 5) 
     The information processing device according to any one of supplementary note 1 to supplementary note 4, wherein the inference result index value calculation unit calculates the index values, in addition to the knowledge level of the reading user about each inference rule that was applied until the inference results are obtained, further on the basis of probability of occurrence of a phenomenon inferred by each inference rule. 
     (Supplementary Note 6) 
     The information processing device according to any one of supplementary note 1 to supplementary note 5, wherein the inference result index value calculation unit calculates the index values on the basis of a multiplied value of the knowledge level of the reading user about each inference rule that was applied until the inference results are obtained. 
     (Supplementary Note 7) 
     The information processing device according to any one of supplementary note 1 to supplementary note 6, wherein the knowledge level update unit executes the update using an increase/decrease value associated with classes of the evaluation information. 
     (Supplementary Note 8) 
     The information processing device according to any one of supplementary note 1 to supplementary note 7 further comprising: an action history information conversion unit for converting, by applying action history conversion rules to action history information that shows action history of the reading user who read the inference results presented by the inference result presentation unit, the action history information concerned into the evaluation information 
     (Supplementary Note 9) 
     The information processing device according to supplementary note 8, wherein the action history information is operation history about application software that presents the inference results. 
     (Supplementary Note 10) 
     The information processing device according to any one of supplementary note 1 to supplementary note 9, wherein the knowledge level is a numerical value included in a predetermined range; and 
     the knowledge level update unit updates the knowledge level within the predetermined range. 
     (Supplementary Note 11) 
     An information processing system comprising: 
     the information processing device according to any one of supplementary note 1 to supplementary note 10; and 
     a terminal that collects context information and sends it to the information processing device, outputs the inference results presented from the information processing device to an output device, and sends the evaluation information inputted from an input device to the information processing device. 
     (Supplementary Note 12) 
     The information processing system according to supplementary note 11, wherein the information processing device is one according to supplementary note 8 or supplementary note 9, and 
     the terminal further collects the action history information about output of the inference results, and sends it to the information processing device. 
     (Supplementary Note 13) 
     An information processing method comprising: 
     obtaining inference results by applying inference rules memorized in advance to context information inputted; 
     acquiring a numerical value memorized in advance as a knowledge level that shows depth of knowledge that a reading user who reads the inference results possesses for each inference rule that was applied until the inference results are obtained, and calculating index values that show the depth of the knowledge of the reading user about the inference results comprehensively on the basis of each knowledge level acquired; 
     presenting the inference results on the basis of the index values; 
     acquiring, for the inference results presented, evaluation information in which degree of the knowledge that the reading user possesses is evaluated; and 
     updating, for each inference rule that was applied until the inference results are obtained, a numerical value memorized as the knowledge level of the reading user about the inference rule concerned on the basis of the evaluation information. 
     (Supplementary Note 14) 
     A computer program that makes a computer execute: 
     a context information acquisition processing that acquires context information; 
     an inference processing that obtains inference results by applying inference rules memorized in a memory device in advance to the context information; 
     an inference result index value calculation processing that acquires a numerical value memorized in the memory device in advance as a knowledge level that shows depth of knowledge that a reading user who reads the inference results possesses for each inference rule that was applied until the inference results are obtained, and calculates index values that show the depth of the knowledge of the reading user about the inference results comprehensively on the basis of each knowledge level acquired; 
     an inference result presentation processing that presents the inference results on the basis of the index values; 
     an evaluation information acquisition processing that acquires, for the inference results presented by the inference result presentation processing, evaluation information in which degree of the knowledge that the reading user possesses is evaluated; and 
     a knowledge level update processing that updates, for each inference rule that was applied until the inference results are obtained, a numerical value memorized in the memory device as the knowledge level of the reading user about the inference rule concerned on the basis of the evaluation information. 
     REFERENCE SIGNS LIST 
       1  Information processing device 
       2 ,  3 ,  4  Server 
       20 ,  30 ,  40  Information processing system 
       8 ,  9  Terminal 
       101 ,  201 ,  301  Inference rule memory unit 
       102 ,  202 ,  302  Knowledge level memory unit 
       103 ,  203  Context information acquisition unit 
       104 ,  204  Inference unit 
       105 ,  20 ,  305  Inference result index value calculation unit 
       106 ,  206  Inference result presentation unit 
       107 ,  207 ,  407  Evaluation information acquisition unit 
       208  Knowledge level alteration rule memory unit 
       109 ,  209 ,  309  Knowledge level update unit 
       210 ,  310  Case information memory unit 
       411  Action history information acquisition unit 
       412  Action history conversion rule memory unit 
       413  Action history information conversion unit 
       801  Context information collection unit 
       802  Information input/output unit 
       903  Action history information collection unit 
       2500 ,  2600 ,  2700  Computer device 
       2501 ,  2601 ,  2701  CPU 
       2502 ,  2602 ,  2702  RAM 
       2503 ,  2603 ,  2703  ROM 
       2504 ,  2604 ,  2704  Memory device 
       2505 ,  2705  Input device 
       2506 ,  2706  Display device 
       2605 ,  2707  Network interface