Patent Publication Number: US-2021183528-A1

Title: Information processing apparatus, information processing method, and non-transitory computer readable medium

Description:
CROSS-REFERENCE TO RELATED APPLICATION (S) 
     This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2019-227675, filed Dec. 17, 2019; the entire contents of which are incorporated herein by reference. 
     FIELD 
     An embodiment relates to an information processing apparatus, an information processing method, and a non-transitory computer readable medium. 
     BACKGROUND 
     Accompanying progress of Internet of things (IoT), various data about various events have been acquired. One of utilization method of such data is enhancement of efficiency in maintenance work. For example, when equipment used as so-called “social infrastructures” such as an automatic ticket gate, a mail sorter, and a bill validator has trouble, a maintenance person has to rush to a site. Thus, if a measure may in advance be taken by estimating that such equipment is in a state of being likely to have trouble, unexpected troubleshooting does not have to be performed, and a load on the maintenance person, dissatisfaction of a user, and so forth may be reduced. Thus, a procedure for precisely estimating a state of equipment has been demanded. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating one example of a state estimation system according to a first embodiment; 
         FIG. 2  is a diagram illustrating one example of measurement by a sensor; 
         FIG. 3  is a diagram illustrating one example of monitoring data; 
         FIG. 4  is a diagram illustrating one example of distribution of magnitude of a monitoring value; 
         FIG. 5  is an outline flowchart of whole processing by an information processing apparatus according to the first embodiment; 
         FIG. 6  is a block diagram illustrating one example of a state estimation system according to a second embodiment; 
         FIG. 7  is a flowchart of processing for determining an interested range based on the distribution before and after occurrence of a variation factor; 
         FIG. 8  is a diagram illustrating one example of an output result; and 
         FIG. 9  is a block diagram illustrating one example of a hardware configuration in one embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     One embodiment of the present invention provides an information processing apparatus for precisely estimating a state of equipment. 
     An information processing apparatus as one embodiment of the present invention includes: an acquirer; a calculator; and a determiner. The acquirer is configured to acquire first data about a predetermined target. The calculator is configured to calculate distribution of magnitude of a value included in the first data. 
     The determiner is configured to determine a portion of a width of the distribution as a specific range used for estimating a state of the target based on the distribution. 
     An embodiment will be explained in detail below with reference to the accompanying drawings. The present invention is not limited to the embodiment. 
     One Embodiment of the Present Invention 
       FIG. 1  is a block diagram illustrating one example of a state estimation system according to a first embodiment. The state estimation system related to this embodiment includes a monitoring target  1 , a sensor  2 , an information processing apparatus (state estimation apparatus)  3 , and an input-output apparatus  4 . The information processing apparatus  3  includes a monitoring data storage  301 , an acquirer  302 , a shaper  303 , a distribution calculator  304 , a range determiner  305 , an estimator  306 , and an output device  307 . 
     The state estimation system is a system estimating a state of the monitoring target  1 . The monitoring target is not particularly limited but may be equipment from which high reliability is demanded such as a power plant, a railroad, an automatic ticket gate, a mail sorter, and a bill validator. Equipment from which high reliability is demanded hardly becomes an abnormal state. Thus, it is difficult for an apparatus that estimates abnormality based on data in an abnormal state to precisely perform state estimation about such equipment. However, in this embodiment, estimation is performed by using data of the monitoring target  1  in a normal state such as data about processing time of the monitoring target  1 , for example. Thus, the state estimation may precisely be performed for such equipment. 
     However, in a case where the state estimation is performed by using the data in the normal state, the precision of the state estimation is lowered due to deviation of the value indicated by the data. In particular, in the equipment from which high reliability is demanded, a delay of processing or reprocessing due to failure of processing is not likely to occur, and the distribution of values of data is likely to be deviated. That is, almost the same value is indicated in most data, and when the state estimation is attempted by using an average value or the like, a peculiar value remains unknown. Thus, when the state estimation is performed based on the average value or the like of all data, precision is not improved. Further, because the value of data varies while being influenced by various factors, when the range of the value of data is in advance defined, the precision of estimation may be lowered. Accordingly, in this embodiment, a range of data preferentially used in the data used for estimation is determined, and estimation is performed mainly based on the data included in the range. Details will be described together with an internal configuration of the information processing apparatus  3 . 
     The sensor  2  measures the value of a predetermined monitoring item of the monitoring target  1 . The monitoring item is not particularly limited as long as the monitoring item is expected to be influenced by the state of the monitoring target  1  and is measurable by a known sensor. Note that plural sensors  2  may be provided, and the monitoring item may be obtained from measurement values by the plural sensors  2 . That is, a computed value based on the measurement values by the plural sensors  2  may be the value of the monitoring item. In the following, the value of the monitoring item will be denoted as “monitoring value”, and data about the monitoring value will be denoted as “monitoring data (first data)”. That is, this state estimation system collects the monitoring data about a predetermined target by using the sensor  2 . Note that one or plural monitoring items may be provided. Further, the sensor  2  may be built in the monitoring target  1  or may be installed on the outside of the monitoring target  1 . 
       FIG. 2  is a diagram illustrating one example of measurement by a sensor. In the example of  FIG. 2 , the monitoring target  1  is an automatic ticket gate, and plural sensors  2 A to  2 D are built in the automatic ticket gate. Further, it is assumed that conveyance time of a ticket conveyed in the automatic ticket gate is the monitoring item. 
     In the example of  FIG. 2 , two superimposed tickets  103 A and  103 B are simultaneously fed into the automatic ticket gate. Two rectangles illustrated by dotted lines on the right side of  FIG. 2  indicate the tickets  103 A and  103 B immediately after feeding. The tickets  103 A and  103 B are conveyed to a left side by a feed roller  101  as indicated by arrows in  FIG. 2 . However, the upper ticket  103 A is stopped by a separation roller  102 . Thus, the ticket  103 A is indicated below the separation roller  102 . The ticket  103 A is again conveyed to the left side by the feed roller  101  after the stop. Meanwhile, the lower ticket  103 B is conveyed to the left side without being stopped by the separation roller  102 . Thus, the ticket  103 B is indicated on the left side of the separation roller  102 . 
     The sensors  2 A and  2 C detect that the ticket  103 A arrives at the respective installation positions and measure the time points of the arrival. The conveyance time of the ticket  103 A becomes known from the difference between the measured time points. In the same manner, the sensors  2 B and  2 D detect that the ticket  103 B arrives at the respective installation positions and measure the time points of the arrival. Accordingly, the conveyance time of the ticket  103 B becomes known. As described above, the values of plural monitoring items may be obtained based on measurement results by the plural sensors  2 . 
     The conveyance time of the ticket, in other words, the processing time of the automatic ticket gate tends to gradually extend due to adhesion of trash and degradation of the feed roller  101  and so forth over time. In other words, the processing time of the automatic ticket gate becomes long in accordance with deterioration of the state of the automatic ticket gate. Besides, the conveyance time depends on the state of a fed ticket. For example, in a case where the ticket is deformed and has many wrinkles, the conveyance time is likely to become longer than a case where the ticket is not in such a state. Further, the conveyance time is also influenced by a surrounding environment and so forth. For example, because two tickets are easily separated in the summer with high humidity, the processing time becomes shorter than the winter with low humidity. That is, the value of the processing time fluctuates due to influences by various events. Thus, in a case where standard time related to processing is in advance defined and it is hastily assessed that the state of the monitoring target  1  is deteriorated because the processing time is longer than the standard time, estimation precision becomes low. 
     The information processing apparatus  3  performs processing for the monitoring data and then estimates the state of the monitoring target  1  based on the processing result. Thus, the information processing apparatus  3  may also be considered to be a state estimation apparatus. Specifically, the information processing apparatus  3  determines a portion preferentially used for state estimation in the monitoring data, in other words, a range. Accordingly, even if the above-described equipment from which high reliability is demanded is the monitoring target  1 , estimation may highly precisely be performed based on the data in the normal state. Further, the range is not fixed and is determined based on the monitoring data at each state estimation. Accordingly, the precision of estimation is prevented from being lowered due to a change in an environment or the like. Details will be described together with an internal configuration of the information processing apparatus  3 . 
     The input-output apparatus  4  inputs data needed by the information processing apparatus  3  other than the monitoring data obtained by the sensor  2  to the information processing apparatus  3  and outputs an estimation result or the like from the information processing apparatus  3 . For example, a condition in a case where each configuration element of the information processing apparatus  3  performs processing, for example, a threshold value or the like related to estimation may be designated through the input-output apparatus  4 . 
     Note that each apparatus and each configuration element of the information processing apparatus  3  that are illustrated in  FIG. 1  may be subdivided or integrated. Further, the state estimation system and the information processing apparatus  3  may have a configuration element not illustrated in  FIG. 1 . For example, the information processing apparatus  3  may be split into an apparatus including the distribution calculator  304 , the range determiner  305 , and so forth and determining the range of the monitoring data used for the state estimation and an apparatus including the estimator  306  and so forth and estimating the state. Further, the shaper  303  may be split into a first shaper excluding an outlier and a second shaper performing smoothing. Such division into apparatuses performing respective kinds of processing in a specialized manner is often made in an information processing system in order to disperse a processing load, maintain availability, and so forth. Further, for example, the state estimation system may have a storage such as a network area storage, and the storage may store the monitoring data. That is, the monitoring data storage  301  may be present on the outside of the information processing apparatus  3 . Further, for example, the input-output apparatus  4  may be split into an input apparatus and an output apparatus. 
     The internal configuration of the information processing apparatus  3  will be described. The monitoring data storage  301  stores the monitoring data. Note that an acquisition source of the monitoring data is not particularly limited. The monitoring data may directly be acquired from the sensor  2  or may be acquired via the monitoring target  1  collecting the monitoring data from the sensor  2 , another apparatus, or the like. 
       FIG. 3  is a diagram illustrating one example of the monitoring data. In the example of  FIG. 3 , the conveyance time as the monitoring value and date and time related to the conveyance time are indicated. Note that the monitoring item is not limited to the example of  FIG. 3 , plural monitoring items may be present as described above. That is, the monitoring data may be univariate data or may be multivariate data. For example, as the above-described example of  FIG. 2 , the respective conveyance times of two tickets may be included as the monitoring values in the monitoring data. 
     The acquirer  302  acquires the monitoring data in a time period related to the present estimation from the monitoring data stored in the monitoring data storage  301 . For example, the time period is designated via the input-output apparatus  4 , and the monitoring data in the designated time period may thereby be acquired. Alternatively, the time period related to estimation such as one hour or one day may be defined in advance in accordance with the monitoring item, and the monitoring data in a concerned time period may be acquired with the present date and time as a reference. The acquired monitoring data is used for determination of a range of the monitoring value preferentially used for at least present estimation. This range will be denoted as “interested range”. The interested range will be described later. 
     Further, in a case where the state estimation is performed, the acquirer  302  may acquire the monitoring data having the monitoring value in the determined interested range in the monitoring data in the time period related to the present estimation. A case is possible where the present estimation is performed by using the acquired monitoring data. 
     The shaper  303  shapes the monitoring data acquired by the acquirer  302 . For example, an outlier included in the acquired monitoring data may be removed. Further, because noise is considered to be included in the monitoring data, noise may be removed by a smoothing procedure such as a moving average. 
     The distribution calculator  304  calculates the distribution of the magnitude of the monitoring value based on the monitoring data in the time period related to the present estimation. For example, a histogram may be created by counting the number of monitoring values included in each of plural unit intervals (bins). Settings of the histogram, for example, the number of bins and width may be defined in advance in accordance with the monitoring item. Alternatively, the distribution calculator  304  may determine the settings of the histogram based on a distribution range or the like of data by using square-root choice, Sturges&#39; formula, or the like. 
     Note that the distribution calculator  304  may create distribution of the monitoring value throughout the whole time period related to the present estimation or may divide the time period related to the present estimation into each designated unit time period and create distribution for each unit time period. For example, in a case where the time period related to the present estimation spans several days, distribution may be created for each day. The unit time period may be defined in advance or may be designated through the input-output apparatus  4 . 
       FIG. 4  is a diagram illustrating one example of the distribution of the magnitude of the monitoring value. The horizontal axis represents the magnitude of the monitoring value, and the vertical axis represents the ratio of data included in the range of each bin of the histogram. In the example of  FIG. 4 , the monitoring values mostly fall within a range from 3.6 to 4.5, and many of those are around 3.8. 
     The range determiner  305  determines the interested range based on the distribution of the monitoring value. For example, the range determiner  305  establishes plural division intervals dividing the width of distribution as illustrated in  FIG. 4 . The division interval may or may not be set as each bin of the histogram. That is, a boundary between the division intervals may be different from a boundary between each bin of the histogram. Further, the length of each division interval does not have to be the same. The range determiner  305  may determine, as the interested range, the division interval among the plural division intervals other than the interval including the maximum value of the distribution. Accordingly, in a case where a large number of almost same monitoring values are included in the monitoring data, a specific value may be prevented from remaining unknown due to such monitoring values. 
     Further, as described above, the monitoring value fluctuates due to various factors. In a case where the monitoring item is the conveyance time as illustrated in  FIG. 2 , as the monitoring value is greater, that is, as the processing time is longer, the possibility is considered to be stronger that the state of the monitoring target  1  will change to an abnormal state in the future even if the state of the monitoring target  1  is normal. In this description, a situation in which the possibility is strong that the state of the monitoring target  1  will change to an abnormal state in the future even if the state of the monitoring target  1  is normal will be described as “normality is low”, and a situation in which the possibility is weak that the state of the monitoring target  1  will change to an abnormal state in the future when the state of the monitoring target  1  is normal will be described as “normality is high”. In other words, as the number of sets of monitoring data with large monitoring values is greater, normality of the monitoring target  1  is considered to be lower. On the other hand, as the number of sets of monitoring data with small monitoring values is greater, the monitoring target  1  is considered to be more normal, that is, normality of the monitoring target  1  is considered to be higher. Thus, in a case where it is desired to perform the state estimation based on the monitoring values with which normality of the monitoring target  1  is considered to be low, the interested range may be shifted from the maximum value of the distribution toward a position in which the monitoring values are large. On the other hand, in a case where it is desired to perform the state estimation based on the monitoring values with which normality of the monitoring target  1  is considered to be high, the interested range may be shifted from the maximum value of the distribution toward a position in which the monitoring values are small. Note that in a case where normality of the monitoring target  1  is considered to be higher as the number of sets of monitoring data with large monitoring values is greater, a shifting direction becomes opposite to the above-described case. 
     The example of  FIG. 4  illustrates an interested range A including the monitoring data with high normality and an interested range B including the monitoring data with low normality. As described above, the range determiner  305  may define the interested range between the division interval in which deviation of the distribution becomes the maximum value (the division interval including a protrusion of the distribution) and the division interval in which the deviation of the distribution drops lower than a predetermined threshold value (the division interval corresponding to a tail of the distribution). Note that as for the interested range, only one of an upper limit value and a lower limit value may be defined. 
     Further, the range determiner  305  may determine the interested range based on a slope of the distribution. As the slope of the distribution is steeper, a difference in normality of the monitoring data is considered to become larger across a line of the distribution. Thus, the range determiner  305  may determine the division interval including a portion in which the slope of the distribution is steepest as the interested range, for example. Alternatively, the division interval may be determined as the interested range, the division interval neighboring the division interval including the portion in which the slope of the distribution is steepest and being far from the division interval in which the deviation of the distribution becomes the maximum value. 
     The estimator  306  estimates the state of the monitoring target  1  based on data, the value of which is included in the interested range, in the monitoring data in a designated time period. A method of the state estimation may appropriately be defined. For example, the state may be estimated simply based on the number or ratio of sets of monitoring data having the monitoring value in the interested range. For example, when the number or the ratio of the number to all sets of monitoring data is a first threshold value or smaller, the state may be estimated as “a completely normal state”. When the number or the ratio is greater than the first threshold value and equivalent to or smaller than a second threshold value, the state may be estimated as “a normal state with a possibility of trouble”. When the number or the ratio is greater than the second threshold value, the state may be estimated as “a normal state with a strong possibility of trouble”. 
     Further, the acquirer  302  may receive the interested range determined by the range determiner  305 , acquire the monitoring data related to the interested range from the monitoring data storage  301 , cause the shaper  303  to perform smoothing or the like for the data, and then pass the data to the estimator  306 . The estimator  306  may input the data to a state estimation model based on a neural network that has already performed learning, for example, and thereby obtain an output of the state estimation model as an estimation result. 
     The output device  307  outputs processing results by the configuration elements of the information processing apparatus  3  to the input-output apparatus  4 . An output format of the output device  307  may be changed in accordance with the input-output apparatus  4 . For example, an image summarizing the processing results may be output by the output device  307 , or a file describing the processing results may be output. 
     Next, a description will be made about the flow of processes by the configuration elements.  FIG. 5  is an outline flowchart of whole processing by the information processing apparatus according to the first embodiment. 
     The acquirer  302  acquires the monitoring data in a designated time period from the monitoring data storage  301  (S 101 ). The shaper  303  performs smoothing, noise removal, and so forth and thereby shapes the acquired monitoring data (S 102 ). The distribution calculator  304  tallies the monitoring data and calculates distribution such as a histogram (S 103 ). 
     The range determiner  305  determines the interested range about the value of the acquired monitoring data based on the calculated distribution (S 104 ). The estimator  306  estimates the state of the monitoring target  1  based on the monitoring data the value of which is in the interested range (S 105 ). Note that the acquirer  302  may reacquire, from the monitoring data storage  301 , the monitoring data which is the monitoring data in the designated time period and the value of which is in the interested range. In this case also, the shaper  303  may shape newly acquired monitoring data. The output device  307  outputs the processing results by the configuration elements such as the determined interested range and an estimated state (S 106 ), and the flow finishes. 
     As described above, in a case where the state estimation is performed by using data in the normal state, the information processing apparatus  3  of this embodiment determines the interested range of the data used for the state estimation. The state estimation is performed by using the data included in the interested range, and a peculiar value is thereby prevented from remaining unknown due to deviation of the value indicated by the data in the normal state. Accordingly, in a case where the state estimation is performed by using the data in the normal state, the precision of the state estimation may be enhanced. 
     Second Embodiment 
       FIG. 6  is a block diagram illustrating one example of a state estimation system according to a second embodiment. In the state estimation system related to the second embodiment, the information processing apparatus  3  further includes a factor data storage  308 . 
     In this embodiment, in a case where a specific event causing the state of the monitoring target  1  to vary occurs in a designated time period when the monitoring data is acquired, the monitoring data before and after the occurrence of the event is utilized. For example, it is considered that when maintenance is performed for equipment as the monitoring target, the state of the monitoring target  1  is improved, the processing speed of the monitoring target  1  is recovered, and the frequency of reprocessing due to failure of processing is decreased. That is, the monitoring data after maintenance is considered to have the monitoring value in a case where normality of the monitoring target  1  is high. Thus, in a case where the interested range is defined so as to include the monitoring value in a case where the normality of the monitoring target  1  is high, the values in a case where normality is high may be collected more when the monitoring data after maintenance is used than when other monitoring data is used. Further, the difference between the monitoring data after maintenance and the monitoring data before maintenance represents the degree of improvement by the maintenance. 
     As described above, in this embodiment, the state estimation is performed by using the monitoring data before and after occurrence of a predetermined factor causing the state of the monitoring target  1  to vary such as maintenance. Accordingly, the precision of the state estimation is further enhanced. Note that an importance degree is provided, the importance degree of the monitoring data before and after the occurrence of the factor is made higher than the other monitoring data, and the weight of the monitoring data before and after the occurrence of the factor may be made higher with respect to the estimation result. For example, in a case where the state of the monitoring target  1  is estimated from the monitoring data in three unit time periods, the state estimation is performed for each of the unit time periods. The numerical values indicating the estimation results of the unit time periods closer to the time after the maintenance are multiplied by the importance degrees set higher, and a final estimation result may thereby be calculated. 
     In the following, an event in advance defined and estimated to cause the state of the monitoring target  1  to vary will be denoted as “variation factor”. The variation factor may appropriately be defined and may be a factor positively influencing or negatively influencing a target. In other words, the variation factor may raise the monitoring value or may lower the monitoring value. Further, the monitoring item of this embodiment is preferably an item the value of which is likely to vary due to maintenance or the like. 
     Processing of the second embodiment will be described. Note that description of the same matters as those of the first embodiment will be omitted. 
     The factor data storage  308  stores data indicating occurrence date and time of the variation factor. The data will be denoted as “factor data (second data)”. For example, for recording management of the monitoring target  1 , an operation is often performed in which date and time and contents of work conducted for the monitoring target are recorded. Such records may be saved in the factor data storage  308 . Alternatively, only the record related to the variation factor among the records may be selected and stored in the factor data storage  308 . 
     The acquirer  302  detects the occurrence date and time of the variation factor in a designated time period from the factor data in the designated time period before acquiring the monitoring data in the designated time period. Then, the acquirer  302  acquires the monitoring data in a designated unit time period at least either before or after occurrence of the variation factor. For example, the unit time period is a unit of one day, it is possible to acquire the monitoring data on the day before or the day after the occurrence of the variation factor. Further, if necessary, the monitoring data in the designated time period may be acquired. 
     For example, in a case where the variation factor is maintenance, normality of the monitoring target  1  is considered to be low on the day before the maintenance. Thus, in a case where the state is estimated by using the monitoring data in which normality of the monitoring target  1  is considered to be low, it is considered that precision becomes higher than the first embodiment when the monitoring data on the day before the maintenance is further used. Further, the monitoring target  1  is considered to be in a state where normality is high on the day after the maintenance. In a case where the state is estimated by using the monitoring data in which normality of the monitoring target  1  is considered to be high, it is considered that precision becomes higher than the first embodiment when the monitoring data on the day after the maintenance is further used. Thus, the acquirer  302  acquires the monitoring data at least either before or after the occurrence of the variation factor in the designated time period. 
     The shaper  303  and the distribution calculator  304  process the acquired monitoring data in the same manner as the first embodiment. The range determiner  305  may also determine a range based on the acquired monitoring data in the same manner as the first embodiment. 
     Further, in a case where the monitoring data with high normality on the day after maintenance or the like is processed, necessity for paying attention to the monitoring data with higher normality in such monitoring data is low. In other words, for the monitoring data with high normality, necessity for shifting the interested range from the average value, the median, or the like is low. Accordingly, in a case where the monitoring data with high normality is processed, the range determiner  305  may calculate the average value or median and the standard deviation from the distribution, set the sum of the average or median and the standard deviation (average or median+standard deviation) as the upper limit value of the interested range, and set the difference between the average or median and the standard deviation (average or median−standard deviation) as the lower limit value of the interested range. Further, a value designated by a user may be taken into consideration for determination of the upper limit value and the lower limit value. Note that a representative value such as an average or a median may be calculated not by the range determiner  305  but by the distribution calculator  304 . 
     Further, the range determiner  305  may determine the interested range based on the distribution before the occurrence of the variation factor or the distribution after the occurrence.  FIG. 7  is a flowchart of processing for determining the interested range based on the distribution before and after the occurrence of the variation factor. First, the range determiner  305  initializes the upper limit value and the lower limit value of the interested range (S 201 ). Next, the range determiner  305  sets candidate values of the upper limit value and the lower limit value of the interested range (S 202 ). It is assumed that an interested range candidate is a temporary interested range used for searching for the optimal interested range and is a different thing from the interested range. The candidate values may be defined in advance or determined at random. 
     Then, the range determiner  305  calculates a difference in the monitoring value included in the interested range candidate between times before and after the occurrence of the variation factor, in other words, a variation amount (S 203 ). That is, the monitoring data included in the interested range candidate in the monitoring data before the occurrence of the variation factor is compared with the monitoring data included in the interested range candidate in the monitoring data after the occurrence of the variation factor. For example, comparison may be performed between the numbers, ratios, or the like of monitoring values included in the interested range candidates before and after the occurrence. 
     For example, each bin of a histogram as illustrated in  FIG. 4  is used as the interested range candidate, and the variation amount between the height before the occurrence of the variation factor and the height after the occurrence of the variation factor may be used for comparison. Alternatively, the variation amount may be calculated by using a predetermined calculation formula. For example, the result of subtraction of the average value of values included in the interested range candidate before the occurrence of the variation factor from the average value of values included in the interested range candidate after the occurrence of the variation factor may be set as the variation amount. Alternatively, the standard deviation may be taken into consideration while fluctuations in data are taken into consideration. For example, the variation amount may be calculated by a calculation formula such as (the average value of values after the occurrence of the variation factor−the average value of values before the occurrence of the variation factor)/(the standard deviation of values after the occurrence of the variation factor+the standard deviation of values before the occurrence of the variation factor). 
     In a case where the calculated variation amount satisfies an update condition (YES in S 204 ), the upper limit value and the lower limit value of the interested range are updated to values of a present interested range candidate (S 205 ). In a case where calculated variation amount does not satisfy the update condition (NO in S 204 ), the update is not performed. Then, it is checked whether a finishing condition is satisfied, and in a case where the finishing condition is not satisfied (NO in S 206 ), the flow returns to a process of S 202 . That is, processing for the next interested range candidate is performed. In such a manner, until the finishing condition is satisfied, investigations about different interested range candidates are performed, the upper limit value and the lower limit value of the interested range are updated. Then, in a case where the finishing condition is satisfied (YES in S 206 ), the flow is finished, and the upper limit value and the lower limit value of the interested range are fixed. 
     The update condition is considered to be based on whether the variation amount is the maximum so far; however, a conditional expression or the like may be used, and the update condition may appropriately be defined. The finishing condition may be a common finishing condition in such a search procedure such as whether or not the processing is performed for all candidate values or the count of the processing. 
     Further, the variation factor may regularly occur. Thus, plural variation factors may occur in the designated time period. In such a case, one of the plural variation factors is selected, and the interested range may be determined based on the monitoring data after occurrence of the selected variation factor. Alternatively, plural variation factors are selected, the monitoring data after occurrence of the selected variation factors is taken into consideration, and the interested range may be determined based on each set of the monitoring data. Further, different kinds of variation factors may occur in the designated time period. In this case, the variation factors do not have to be distinguished, and estimation may be performed for each of the variation factors. Further, a final estimation result may be calculated based on an estimation result about each of the variation factors. 
     The estimator  306  and the output device  307  may be the same as the first embodiment.  FIG. 8  is a diagram illustrating one example of an output result. In the example of  FIG. 8 , a web page browsable by a web browser is generated and output by the output device  307 . Note that although the web page is described in the second embodiment for convenience of description, a web page as illustrated in  FIG. 8  may be capable of being output in the first embodiment. 
     An acquisition condition display space illustrated in  FIG. 8  indicates acquisition start date and time and acquisition end date and time that indicate a time period of the monitoring data acquired by the acquirer  302 . Further, the unit time period used in calculation of distribution by the distribution calculator  304  is indicated. Note that the acquisition condition display space may be generated by an input form on the web page and may be made capable of acquiring an input from the user. That is, a condition necessary for processing may be input to the information processing apparatus  3  via such a web page. 
     An acquisition time period representative value display space illustrated in  FIG. 8  displays a graph representing a representative value in each unit time period in the time period from the acquisition start date and time to the acquisition end date and time, the time period being indicated in the acquisition condition display space. The example of  FIG. 8  uses a ratio as the representative value and indicates that the monitoring data included in the interested range on January 27th, for example, is 70% of the total number of sets of monitoring data on January 27th. Further, two solid lines indicated in the acquisition time period representative value display space and being parallel with the vertical axis indicate the occurrence dates of variation factors. 
     An interested range display space illustrated in  FIG. 8  indicates the interested range. The example in  FIG. 8  displays dotted lines indicating the upper limit value and the lower limit value of the interested range determined by the range determiner  305  and an arrow indicating that a portion between the dotted lines is the interested range on an image, as a background, based on a histogram calculated by the distribution calculator  304 . For example, the interested range on the day before or on the day after the occurrence of the variation factor indicated in the acquisition time period representative value display space may be indicated. 
     Various kinds of information other than those, for example, the state estimated based on the monitoring value in the interested range displayed in the interested range display space, the name of the monitoring target  1 , and so forth may be displayed. 
     As described above, the information processing apparatus  3  of this embodiment further narrows down the range of data used preferentially in data used for the state estimation based on occurrence date and time of a factor causing a state of a target to vary. Accordingly, the precision of the estimation may be enhanced. 
     Note that at least a portion of the above embodiments may be realized by a dedicated electronic circuit (that is, hardware) such as an integrated circuit (IC) in which a processor, a memory, and so forth are implemented. Further, at least a portion of the above embodiments may be realized by executing software (program). For example, a general purpose computer apparatus is used as a basic hardware, a processor such as a CPU mounted on the computer apparatus is caused to execute a program, and the processing of the above embodiments may thereby be realized. 
     For example, a computer reads out dedicated software stored in a computer-readable storage medium, and the computer may thereby be used as an apparatus of the above embodiments. Kinds of storage media are not particularly limited. For example, the computer installs dedicated software downloaded via a communication network, and the computer may thereby be used as an apparatus of the above embodiments. Accordingly, information processing by software is specifically implemented by using a hardware resource. 
       FIG. 9  is a block diagram illustrating one example of a hardware configuration of one embodiment of the present invention. The information processing apparatus  3  includes a processor  51 , a MAIN STORAGE DEVICE  52 , an AUXILIARY STORAGE DEVICE  53 , a network interface  54 , and a device interface  55  and is realized as a computer apparatus  5  in which those are connected together via a bus  56 . The monitoring data storage  301  and the factor data storage  308  are realizable by the MAIN STORAGE DEVICE  52  or the AUXILIARY STORAGE DEVICE  53 , and the other configuration elements are realizable by the processor  51 . 
     Note that the computer apparatus  5  in  FIG. 9  includes one configuration element for each configuration element but may include the plural same configuration elements. Further, although  FIG. 9  illustrates one computer apparatus  5 , software is installed in plural computer apparatuses, and each of the plural computer apparatuses may execute a different portion of processing of the software. 
     The processor  51  is an electronic circuit including a control apparatus and a computation apparatus of the computer. The processor  51  performs computation processing based on data or a program input from each apparatus or the like of an internal configuration of the computer apparatus  5  and outputs a computation result or a control signal to each apparatus or the like. Specifically, the processor  51  executes an operating system (OS) of the computer apparatus  5 , an application, and so forth and controls each apparatus configuring the computer apparatus  5 . The processor  51  is not particularly limited as long as the processor  51  may perform the above processing. 
     The MAIN STORAGE DEVICE  52  is a storage storing commands executed by the processor  51 , various kinds of data, and so forth, and information stored in the MAIN STORAGE DEVICE  52  is directly read out by the processor  51 . The AUXILIARY STORAGE DEVICE  53  is a storage other than the MAIN STORAGE DEVICE  52 . Note that it is assumed that those storages mean arbitrary electronic components capable of storing electronic information and may be a memory or a storage. Further, a memory may be categorized into a volatile memory and a non-volatile memory, but either one may be used. 
     The network interface  54  is an interface for connection with the communication network  6  in a wireless or wired manner. Any network interface may be used as the network interface  54  as long as it conforms to an existing communication standard. The network interface  54  may exchange information with an external apparatus  7 A with which communication connection is made via the communication network  6 . 
     The device interface  55  is an interface such as a USB directly connected with an external apparatus  7 B. The external apparatus  7 B may be an external storage medium or a storage apparatus such as a database. 
     The external apparatuses  7 A and  7 B may be output apparatuses. The output apparatus may be a display apparatus for displaying an image or may be an apparatus or the like outputting sound or the like, for example. Examples may include a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display panel (PDP), a speaker, and so forth but are not limited to those. 
     The external apparatuses  7 A and  7 B may be input apparatuses. The input apparatus includes devices such as a keyboard, a mouse, and a touch panel and provides information input by those devices to the computer apparatus  5 . A signal from the input apparatus is output to the processor  51 . 
     While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the inventions.