Patent Publication Number: US-11644780-B2

Title: Image forming apparatus that provides management apparatus with data that can be utilized for data analysis, control method for the image forming apparatus, storage medium, and management system

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates to an image forming apparatus that provides a management apparatus with data that can be utilized for data analysis, a control method for the image forming apparatus, a storage medium, and a management system. 
     Description of the Related Art 
     A management system is known which monitors a status of an image forming apparatus and detects a sign of abnormality in the image forming apparatus based on information about the status of the image forming apparatus. In the management system, when a sign of abnormality in the image forming apparatus is detected, a maintenance person is requested to perform maintenance, and the maintenance person who has received the request performs maintenance of the image forming apparatus. By performing maintenance of the image forming apparatus when a sign of abnormality is detected, downtime caused by a failure of the image forming apparatus is avoided because appropriate actions can be taken before the image forming apparatus fails and becomes inoperative. 
     The management system is comprised of a management apparatus and a plurality of image forming apparatuses, and the management apparatus is connected to the plurality of image forming apparatuses via a network. For example, in the management system, the image forming apparatus transmits status information including a plurality of measured values obtained by various sensors provided in the image forming apparatuses to the management apparatus, which in turn accumulates the status information received from each of the image forming apparatuses (see, for example, Japanese Laid-Open Patent Publication (Kokai) No. 2011-166427). In this management system, the management apparatus calculates a feature value representing a status of one image forming apparatus based on status information received from the one image forming apparatus and detects a sign of abnormality in the one image forming apparatus based on a trend of the progression of the calculated feature value. In this management system, status information about the plurality of image forming apparatuses is collected in the management apparatus, and the status information includes a plurality of measured values obtained by the various sensors in the image forming apparatuses. For this reason, the status information can be utilized for data analysis other than prediction of a sign of abnormality. For example, the status information can be used to predict when maintenance of an image forming apparatus will be required (hereafter referred to merely as “the maintenance time”) before a sign of abnormality in the image forming apparatus is detected. On the other hand, since the status information includes a plurality of measured values as described above, data traffic increases when the image forming apparatus transmits the status information to the management apparatus, and significant costs are required to build and maintain a communication environment that implements such data communication. On the other hand, in another management system, the image forming apparatus calculates a feature value representing a status of the image forming apparatus based on status information and transmits information about a sign of abnormality detected based on a trend of the progression of the calculated feature value to the management apparatus (see, for example, Japanese Laid-Open Patent Publication (Kokai) No. 2020-3656). The information about the sign of abnormality does not include a plurality of measured values obtained by the various sensors described above but includes only limited information such as information that identifies a component whose sign of abnormality has been detected, and hence the information about the sign of abnormality has a smaller data amount than that of the status information. Therefore, the arrangement in which the image forming apparatus transmits information about a sign of abnormality to the management apparatus can reduce costs required to construct and maintain the communication environment as compared to the arrangement in which the status information is transmitted. 
     However, in the arrangement in which the image forming apparatus transmits information about a sign of abnormality to the management apparatus, information accumulated in the management apparatus is only limited information such as information that identifies a component whose sign of abnormality has been detected. For this reason, the information accumulated in the management apparatus cannot be utilized for data analysis other than detection of signs of abnormality. Namely, according to the prior art, it is impossible to provide the management apparatus with data that can be utilized for data analysis other tan detection of signs of abnormality while keeping down costs required to build and maintain the communication environment. It is also impossible to utilize the accumulated information in estimating the maintenance time for the image forming apparatus. Namely, according to the prior art, it is impossible to predict the maintenance time for the image forming apparatus while keeping down costs required to build and maintain the communication environment. 
     SUMMARY OF THE INVENTION 
     The present invention provides an image forming apparatus that is capable of providing a management apparatus with data that can be utilized for data analysis other than detection of signs of abnormality while keeping down costs required to build and maintain a communication environment, a control method for the image forming apparatus, a storage medium, and a management system. 
     Accordingly, the present invention provides an image forming apparatus with a sensor, comprising at least one memory that stores a set of instructions, and at least one processor that executes the instructions, the instructions, when being executed, causing the image forming apparatus to generate, based on first data comprising measured values obtained by the sensor, second data for use in detecting a sign of abnormality in the image forming apparatus, and transmit the second data directly or indirectly to a management apparatus that detects the sign of abnormality, wherein the second data is data that indicates characteristics of the image forming apparatus and has a smaller data amount than that of the first data. 
     According to the present invention, the management apparatus is provided with data that can be utilized for data analysis other than detection of signs of abnormality while keeping down costs required to build and maintain a communication environment. Moreover, according to the present invention, when maintenance of the image forming apparatus should be performed is predicted while costs required to build and maintain a communication environment are kept down. 
     Further features of the present invention will become apparent from the following description of exemplary embodiments (with reference to the attached drawings). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a view schematically showing an arrangement of an abnormality prediction system that is a management system according to an embodiment of the present invention. 
         FIG.  2    is a side view of an image forming apparatus in  FIG.  1   . 
         FIG.  3    is a block diagram schematically showing a hardware arrangement of the image forming apparatus in  FIG.  1   . 
         FIG.  4    is a block diagram schematically showing a hardware arrangement of a management apparatus in  FIG.  1   . 
         FIG.  5    is a block diagram showing a functional arrangement of a control unit in  FIG.  2   . 
         FIGS.  6 A and  6 B  are views useful in explaining feature extraction data, which is generated by the abnormality prediction system in  FIG.  1   , and details of a process using the feature extraction data. 
         FIG.  7    is a sequence diagram useful in explaining the flow of a sequential process in which in the abnormality prediction system in  FIG.  1   , feature extraction data is generated, and notification of the need for maintenance is provided. 
         FIG.  8 A to  8 D  are views showing examples of internal data and feature extraction data generated by the image forming apparatus in  FIG.  1   . 
         FIG.  9    is a flowchart showing the procedure of a feature extraction data transmission control process that is carried out by the image forming apparatus in  FIG.  1   . 
         FIG.  10    is a flowchart showing the procedure of a feature extraction data generating process in step S 901  in  FIG.  9   . 
         FIG.  11    is a flowchart showing the procedure of a data transmission deciding process in step S 903  in  FIG.  9   . 
         FIG.  12    is a flowchart showing the procedure of an abnormality prediction control process that is carried out by the management apparatus in  FIG.  1   . 
         FIGS.  13 A to  13 C  are views showing examples of execution results of a process in step S 1203  in  FIG.  12   . 
         FIG.  14    is a block diagram schematically showing an arrangement of a printer control unit included in a printer unit in  FIG.  2   . 
         FIGS.  15 A and  15 B  are views useful in explaining how a toner pattern is detected by a density sensor in  FIG.  2   . 
         FIG.  16    is a graph showing the relationship between LED driving current of the density sensor in  FIG.  2    and values detected by the density sensor in  FIG.  2   . 
         FIG.  17    is a flowchart showing the procedure of a light amount adjustment control process that is performed by the printer control unit in  FIG.  14   . 
         FIG.  18    is a flowchart showing the procedure of a feature extraction data transmission process that is carried out by the image forming apparatus in  FIG.  1   . 
         FIG.  19    is a flowchart showing the procedure of a data generating process in step S 1805  in  FIG.  18   . 
         FIG.  20    is a flowchart showing the procedure of a maintenance time notification process that is carried out by the management apparatus in  FIG.  1   . 
         FIG.  21    is a view useful in explaining how a maintenance time is calculated in step S 2003  in  FIG.  20   . 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     The present invention will now be described in detail below with reference to the accompanying showing an embodiment thereof. 
       FIG.  1    is a view schematically showing an arrangement of an abnormality prediction system  100  that is a management system according to the embodiment of the present invention. The abnormality prediction system  100  has one or more image forming apparatuses, a server  103 , and a management apparatus  104 . In the following description of the present embodiment, it is assumed that the abnormality prediction system  100  has, for example, two image forming apparatuses  101  and  102 . The image forming apparatuses  101  and  102 , the server  103 , and the management apparatus  104  are capable of communicating with one another via the Internet  105 . The abnormality prediction system  100  collects data from the image forming apparatuses  101  and  102 , and based on the collected data, detects signs of abnormality in the image forming apparatuses  101  and  102 . 
     The image forming apparatuses  101  and  102 , which are for example MFPs, have a plurality of functions such as a scanning function, a printing function, a copying function, and a fax communication function. In the present embodiment, the image forming apparatuses  101  and  102  have the same functions and arrangement, and hence the functions and arrangement of the image forming apparatus  101  will be described below as an example. 
     The image forming apparatus  101  receives a function selecting operation performed by a user and also executes a job submitted by the user. Examples of the job executed by the image forming apparatus  101  include a scan job, a print job, a copy job, and a fax transmission job. The image forming apparatus  101  transmits log data  310  and/or feature extraction data  311  in  FIG.  3   , which will be described later, required to detect a sign of abnormality in the image forming apparatus  101 , to the server  103  on a regular basis. 
     The server  103  stores (accumulates) the log data  310  and the feature extraction data  311  received from each of the image forming apparatuses  101  and  102 . The server  103  transmits the stored (accumulated) log data  310  and the stored (accumulated) feature extraction data  311  to the management apparatus  104 . 
     Upon receiving, for example, the log data  310  and the feature extraction data  311  of the image forming apparatus  101  from the server  103 , the management apparatus  104  analyzes the received feature extraction data  311  and detects a sign of abnormality in the image forming apparatus  101 . Specifically, the management apparatus  104  predicts failures, lifetimes, etc., of various components which the image forming apparatus  101  has. As a result of the prediction, when it is necessary to replace a component of the image forming apparatus  101 , the management apparatus  104  requests a maintenance inspector  106  to perform maintenance of the image forming apparatus  101 . Thus, in the present embodiment, regarding the image forming apparatus  101  to be managed by the abnormality prediction system  100 , maintenance such as replacement can be performed for the component approaching the end of its life before a component provided in the image forming apparatus  101  fails. 
       FIG.  2    is a side view of the image forming apparatus  101  in  FIG.  1   . It should be noted that for ease of understanding, an internal arrangement of the image forming apparatus  101  is shown in perspective in  FIG.  2   . Referring to  FIG.  2   , the image forming apparatus  101  has a printer unit  200  and a reader unit  240 . 
     The reader unit  240  is a scanner that reads an image formed on an original  245 . The original  245  is placed on an original platen glass  246  such that its surface with an image formed thereon is in contact with the original platen glass  246 . The reader unit  240  transmits image data, which represents the read image, to the printer unit  200 . The reader unit  240  has a reading unit  249  and a reader image processing unit  247 . 
     The reading unit  249  is configured as one unit comprised of a light emitting unit  242 , an optical system  243 , and a light receiving unit  244 . The reading unit  249 , which is, for example, a line sensor extending toward the rear in the figure, reads an image on the original  245  while moving in a direction indicated by an arrow R 248 . The light emitting unit  242  illuminates the original  245 . The light receiving unit  244  receives light, which is reflected from the original  245 , via the optical system  243 . The light receiving result is transmitted to the reader image processing unit  247 . Based on the received light receiving result, the reader image processing unit  247  generates image data representing the image formed on the original  245 . The reader image processing unit  247  also functions as a sensor that measures an image density of the image formed on the original  245  based on the received light receiving result. The reader image processing unit  247  transmits the image data and the measured image density to the printer unit  200 . 
     The image forming apparatus  101  forms a color image through an electrophotographic method. The image forming apparatus  101  uses an intermediate transfer tandem method, and in the printer unit  200 , four image forming units Pa to Pd are disposed in tandem on an intermediate transfer belt  206  (transfer body). The image forming unit Pa forms a yellow toner image. The image forming unit Pb forms a magenta toner image. The image forming unit Pc forms a cyan toner image. The image forming unit Pd forms a black toner image. It should be noted that the number of colors formed is not limited to four. 
     Recording materials S such as sheets, each on which an image is formed, are stacked inside recording material cassettes  230   a  and  230   b  of the printer unit  200 . The recording material S is fed, when the image forming units Pa to Pd perform image forming, from the recording material cassette  230   a  (or the recording material cassette  230   b ) by sheet feeding rollers  231   a  (or sheet feeding rollers  231   b ) adopting the friction separating method. The sheet feeding rollers  231   a  and  231   b  convey the recording materials S to registration rollers  232  via a conveying path. The registration rollers  232  correct for skewing of the recording materials S, adjust timing, and convey the recording materials S to a secondary transfer unit T 2 . 
     In the printer unit  200 , an image is formed by the image forming units Pa to Pd. In the present embodiment, the image forming units Pa to Pd have the same arrangement, and hence their arrangement will be described below using the image forming unit Pa as an example. The image forming unit Pa has a photosensitive body  201   a , a charging device  202   a , an exposure device  203   a , a developing device  204   a , a primary transfer unit T 1   a , and a photosensitive body cleaner  205   a . The charging device  202   a  uniformly charges a surface of the photosensitive body  201   a  which is rotationally driven. The exposure device  203   a  modulates light based on image data received from the reader unit  240  and irradiates the photosensitive body  201   a  with the modulated light. As a result, an electrostatic latent image corresponding to the image data is formed on the photosensitive body  201   a.    
     The developing device  204   a  develops the electrostatic latent image, which is formed on the photosensitive body  201   a , with a developer. In the present embodiment, toner is used as the developer. It should be noted that the developing device  204   a  according to the present embodiment holds a two-component developer in which nonmagnetic toner and a magnetic carrier are mixed, but may hold a one-component developer comprised of magnetic toner or nonmagnetic toner. By toner being attached to the photosensitive body  201   a  on which the electrostatic latent image is formed, a toner image is formed on the photosensitive body  201   a . When a predetermined amount of pressure and a predetermined amount of electrostatic load bias are applied to the primary transfer unit T 1   a , the primary transfer unit T 1   a  transfers the toner image formed on the photosensitive body  201   a  to the intermediate transfer belt  206 . Likewise, toner images formed on the photosensitive bodies  201   b  to  201   d  are transferred to the intermediate transfer belt  206 . Here, the toner images formed on the respective photosensitive bodies  201   a  to  201   d  are transferred to the intermediate transfer belt  206  such that they are superposed. Thus, the yellow, magenta, cyan, and black toner images are transferred to the intermediate transfer belt  206  such that they are superposed, forming a full-color toner image. Toner remaining on the photosensitive bodies  201   a  to  201   d  after the transfer is collected by the photosensitive body cleaners  205   a  to  205   d . In the printer unit  200 , when the amount of toner held in, for example, the developing device  204   a  has become equal to or smaller than a predetermined amount, the developing device  204  is replenished with toner from a toner bottle Ta which is a developer replenishment container. 
     The intermediate transfer belt  206 , which is provided on an intermediate transfer belt frame (not shown), is an endless belt stretched by a secondary transfer internal roller  208 , a tension roller  212 , and a secondary transfer upstream roller  213 . The intermediate transfer belt  206  is rotationally driven in a direction indicated by an arrow R 207  by the secondary transfer internal roller  208 , the tension roller  212 , and the secondary transfer upstream roller  213 . By rotating, the intermediate transfer belt  206  with the toner image in full color formed thereon conveys the toner image to the secondary transfer unit T 2 . 
     The recording material S and the toner image formed on the intermediate transfer belt  206  are conveyed with such timing that they join each other in the secondary transfer unit T 2 . The secondary transfer unit T 2  is a transfer nip unit formed by the secondary transfer internal roller  208  and a secondary transfer external roller  209 , which are disposed so as to face each other. By applying a predetermined amount of pressure and a predetermined amount of electrostatic load bias, the secondary transfer unit T 2  causes the toner image to be adsorbed onto the recording material S. The secondary transfer unit T 2  thus transfers the toner image on the intermediate transfer belt  206  onto the recording material S. Toner remaining on the intermediate transfer belt  206  after the transfer is collected by a transfer cleaner  210 . 
     The recording material S onto which the toner image has been transferred is conveyed from the secondary transfer unit T 2  to a fixing device  211  by the secondary transfer external roller  209 . The fixing device  211  applies a predetermined amount of pressure and predetermined-temperature heat to the recording material S within a fixing nip formed by rollers facing each other, and fuses and fixes the toner image on the recording material S. The fixing device  211  has a heater (not shown), which is a heat source, and is controlled to be maintained at an optimum temperature. The recording material S on which the toner image has been fixed is discharged onto a sheet discharge tray  233 . To form images on both sides of the recording material S, the recording material S is inverted by an inverting conveyance mechanism and conveyed to the registration rollers  232 , and another toner image is formed on a side of the recording material S on which the above toner image has not been fixed. 
     A density sensor  220  for detecting a toner density is provided in the vicinity of the intermediate transfer belt  206 . The density sensor  220  is disposed at a location where it is able to detect toner patterns of the respective colors formed on the intermediate transfer belt  206 , and more specifically, between the photosensitive body  201   d  and the secondary transfer external roller  209 . 
       FIG.  3    is a block diagram schematically showing a hardware arrangement of the image forming apparatus  101  in  FIG.  1   . Referring to  FIG.  3   , the image forming apparatus  101  has a control unit  301 , an operating panel  304 , a storage device  307 , and a network I/F  312  as well as the printer unit  200  and the reader unit  240  described above. The printer unit  200 , the reader unit  240 , the control unit  301 , the operating panel  304 , the storage device  307 , and the network I/F  312  are connected to one another via a data bus  315 . 
     The control unit  301  has a CPU  302  and a memory  303 . The control unit  301  integratedly controls operation of the image forming apparatus  101 . The CPU  302  is a hardware processor that executes various programs stored in the storage device  307 . For example, when power to the image forming apparatus  101  is turned on, the CPU  302  reads a program  308  stored in the storage device  307  and executes the read program  308 . As a result, the control unit  301  acts as a job control unit  501  and a data management unit  503  in  FIG.  5   , which will be described later. Also, a feature extraction data transmission control process in  FIG.  9   , which will be described later, is carried out by the CPU  302  executing the program  308 . The memory  303  is used as a work area for the CPU  302  and as a temporary storage area for each piece of data. 
     The operating panel  304  has a display unit  305  and an operating unit  306 . The display unit  305  is comprised of, for example, a color liquid crystal display, and displays various operating screens, which can be operated by the user and the maintenance inspector  106 , and information required for maintenance. The operating unit  306  is comprised of, for example, touch panel keys displayed on the display unit  305  and receives operations performed by the user and the maintenance inspector  106 . 
     The storage device  307  is a nonvolatile storage device and is, for example, a hard disk drive (HDD). The storage device  307  stores the program  308 , internal data  309 , log data  310 , and feature extraction data  311 . The internal data  309  is time-series data of sensor measured values obtained by various sensors which the reader unit  240  and the printer unit  200  have. The log data  310  is a data of, for example, job execution histories in the image forming apparatus  101  and includes detailed information about executed jobs, information about dates and times at which jobs were executed, and so forth. The feature extraction data  311  is generated based on the internal data  309 . The feature extraction data  311  is data indicating characteristics of the image forming apparatus  101  and has a smaller data amount than that of the internal data  309 . The network I/F  312  implements data communications via the Internet  105 . The image forming apparatus  101  carries out communications with the server  103  via the network I/F  312 . 
     The reader unit  240  has a sensor group  313 . The sensor group  313  includes a plurality of sensors which monitors operating states of movable components operating when the reader unit  240  reads an original. In accordance with requests received from the control unit  301 , the sensors included in the sensor group  313  output sensor measured values, which are obtained by measuring the operating states of the movable components, as one of pieces of the internal data  309  to the control unit  301 . The printer unit  200  has a sensor group  314 . The sensor group  314  includes a plurality of sensors, such as the density sensor  220 , which monitors operating states of movable components operating when the printer unit  200  forms an image. In accordance with requests received from the control unit  301 , the sensors included in the sensor group  313  output sensor characteristic values, which are obtained by measuring the operating states of the movable components, as one of pieces of the internal data  309  to the control unit  301 . 
     A description will now be given of a hardware arrangement of the server  103  and the management apparatus  104 . It should be noted that in the present embodiment, the server  103  and the management apparatus  104  have the same arrangement, and hence their arrangement will be described below by using the management apparatus  104  as an example. 
       FIG.  4    is a block diagram schematically showing the hardware arrangement of the management apparatus  104  in  FIG.  1   . Referring to  FIG.  4   , the management apparatus  104  has a CPU  401 , a memory  402 , a storage device  403 , and a network I/F  404 . The CPU  401 , the memory  402 , the storage device  403 , and the network I/F  404  are connected to one another via a system bus  405 . 
     The CPU  401  is a central processing unit that controls the overall operation of the management apparatus  104 . The memory  402  stores an activation program for the CPU  401  and data required to execute the activation program. The storage device  403  has a larger capacity than that of the memory  402  and is, for example, an HDD. It should be noted that the storage device  403  is not limited to an HDD but may be another storage device having functions equivalent to those of the HDD, for example, a solid-state drive (SSD). The storage device  403  stores a control program which is executed by the CPU  401 . 
     To activate the management apparatus  104 , the CPU  401  executes an activation program stored in the memory  402 . This activation program is a program for expanding the control program stored in the storage device  403  into the memory  402 . Then, the CPU  401  executes the control program expanded into the memory  402  to perform various types of control. The CPU  401  uses the network I/F  404  to carry out data communications with other apparatuses such as the server  103  via the Internet  105 . For example, based on data received from the image forming apparatus  101  using the network I/F  404 , the management apparatus  104  is capable of sharing a screen displayed on the operating panel  304  of the image forming apparatus  101  and displaying this screen on a display unit of the management apparatus  104 . 
       FIG.  5    is a block diagram showing a functional arrangement of the control unit  301  in  FIG.  2   . In the image forming apparatus  101 , the execution of the control program  308  by the CPU  302  causes the control unit  301  to function as the job control unit  501  and the data management unit  503 . 
     The job control unit  501  controls execution of a job in the image forming apparatus  101 . By controlling operation of the reader unit  240  and the printer unit  200 , the job control unit  501  controls execution of a job submitted by the user. The job control unit  501  includes a log recording unit  502 . When a job submitted by the user is executed, the log recording unit  502  records a job execution log as the log data  310 . 
     The data management unit  503  manages the internal data  309  and the feature extraction data  311 . The data management unit  503  includes a timing determination unit  504 , a data obtaining unit  505 , a feature extraction unit  506 , a data transmission deciding unit  507 , and a data transmission unit  508 . 
     The timing determination unit  504  determines whether or not it is time to transmit the feature extraction data  311  to the server  103  (hereafter referred to as “the data transmission time”). For example, when a predetermined time period set in advance has elapsed since the feature extraction data  311  was transmitted to the server  103  the last time (hereafter referred to as “the previous transmission of the feature extraction data  311 ”), the timing determination unit  504  determines that it is the data transmission time. 
     The data obtaining unit  505  obtains, from the storage device  307 , the internal data  309  for use in generating the feature extraction data  311  which is to be transmitted to the server  103 . Specifically, the data acquisition unit  505  outputs data obtaining requests at predetermined times, which are defined for the respective sensors included in the sensor groups  313  and  314  described above, to the sensors and acquires sensor measured values from the respective sensors. It should be noted that the predetermined times may be every predetermined time, for example, interval of several milliseconds to several seconds or may be times before and after execution of a job submitted by the user. The data obtaining unit  505  obtains the log data  310  stored in the storage device  307 . 
     The feature extraction unit  506  carries out a feature extraction process for converting the internal data  309  obtained by the data acquisition unit  505  to generate the feature extraction data  311 . The data transmission deciding unit  507  carries out a data transmission deciding process in  FIG.  11   , which will be described later, to decide whether or not to transmit the generated feature extraction data  311  to the server  103 . When the data transmission deciding unit  507  has decided to transmit the feature extraction data  311  to the server  103 , the data transmission unit  508  transmits the feature extraction data  311  to the server  103 . Thus, in the present embodiment, the feature extraction data  311  is transmitted to the server  103  only when it is the data transmission time and the data transmission deciding unit  507  decides to transmit the feature extraction data  311  to the server  103 . As a result, when it is unnecessary to transmit the feature extraction data  311  to the server  103 , the image forming apparatus  101  can be prevented from transmitting unnecessary data to the server  103 , and hence communication load between the image forming apparatus  101  and the server  103  can be reduced. 
       FIGS.  6 A and  6 B  are views useful in explaining feature extraction data, which is generated by the abnormality prediction system  100  in  FIG.  1   , and a process relating to the feature extraction data.  FIG.  6 A  shows the relationship among the internal data  309 , feature extraction processes that are carried out by the image forming apparatus  101 , and abnormality prediction processes that are carried out by the management apparatus  104 . 
     Referring to  FIG.  6 A , data items  601  represent data items of the internal data  309 , and more specifically, names of items such as sensor measured values and count values obtained from the reader unit  240  and the printer unit  200  by the data obtaining unit  505 . In the present embodiment, IDs for identifying the respective data items are assigned to the respective data items of the internal data  309 . 
     Data sources  602  represent component elements in the image forming apparatus  101  which are sources of data in the data items  601 . Data types  603  represent attributes of the data in the data items  601 . Feature extraction processes  604  represent types of feature extraction processes in which the feature extraction data  311  is generated using the data in the data items  601 . In  FIG.  6 A , the feature extraction processes  604  for the data items  601  for which the feature extraction data  311  is not generated, like scan counter, print counter, and log data, are represented by “—” which means that no feature extraction process is carried out. 
     Determination processes  605  represent types of abnormality prediction processes which are carried out by the management apparatus  104  based on the feature extraction data  311  generated using the data in the data items  601 . In the abnormality prediction system  100 , the types of the abnormality prediction processes are managed in association with data items of data used to generate the feature extraction data  311  used to the abnormality prediction processes. Prediction request IDs  606  are unique numbers correspondingly assigned to the abnormality prediction processes which are the determination processes  605 . It should be noted that when the management apparatus  104  and the image forming apparatus  101 ,  102  are configured to share the numbers of the prediction request IDs  606 , the numbers of the prediction request IDs  606  may be set for the respective abnormality prediction processes which are the determination processes  605  in advance, or the management apparatus  104  may regularly set the numbers of the prediction request IDs  606  for the respective abnormality prediction processes. Based on the numbers of the prediction request IDs  606 , the management apparatus  104  determines types of abnormality prediction processes to be carried out. For example, when the maintenance inspector  106  has instructed the management apparatus  104  to carry out an abnormality prediction process with a prediction request ID “ 3 ” so as to check a state of a transfer roller in the image forming apparatus  101 , the management apparatus  104  decides to carry out the abnormality prediction process corresponding to the prediction request ID “ 3 ”, which is for obtaining the dispersion ratio. The management apparatus  104  obtains the feature extraction data  311  corresponding to a running distance of the transfer roller, which is used to carry out the abnormality prediction process, from the server  103 , and carries out the abnormality prediction process for obtaining the dispersion ratio based on the obtained feature extraction data  311 . 
       FIG.  6 B  is a view showing an example of transmission data  607  which the image forming apparatus  101  in  FIG.  1    transmits to the server  103 . The transmission data  607  is comprised of the multiple feature extraction data  311 . Referring to  FIG.  6 B , the transmission data  607  is comprised of data items  608  and specific values  609 . Feature extraction data are set as the data items  608 . Specific values for the feature extraction data in the data items  608  are set as the specific values  609 , for every generation time of the internal data  309  which is the basis of the feature extraction data. With this arrangement, it is possible to identify that the feature extraction data was generated based on which generation time of the internal data  309 . For example, a value “y” ( 80 ), which is a result obtained by carrying out a maximum value calculation process for calculating a maximum value of sensor measured values representing temperatures of the fixing device  122  measured from a measurement time “t” ( 01 / 01 / 2020 / 00 : 00 : 00 ) to a predetermined measurement time is set in the transmission data  607  in  FIG.  6 B . Also, as the result of carry out of a histogram creating process using a predetermined rule on a sensor measured value representing a running distance of the transfer roller until the measurement time “t” ( 01 / 01 / 2020 / 00 : 00 : 00 ), for example, that a classification group in a histogram creating process is to be ( 80 ) is set in the transmission data  607  in  FIG.  6 B . The image forming apparatus  101  converts the transmission data  607  to generate data in text format and also compresses the generated data in text format if necessary and transmits the compressed data to the server  103 . 
       FIG.  7    is a sequence diagram useful in explaining the flow of a sequential process in which the feature extraction data  311  is generated, and notification of the need for maintenance is provided in the abnormality prediction system  100  in  FIG.  1   . 
     Referring to  FIG.  7   , the image forming apparatus  101  makes an internal data obtainment determination (step S 701 ) to determine whether or not it is time to obtain sensor measured values and count values from the reader unit  240  and the printer unit  200  (hereafter referred to as “the internal data obtainment time”). Upon determining that it is the internal data obtainment time, the image forming apparatus  101  obtains data such as sensor measured values and count values from the reader unit  240  and the printer unit  200  (step S 702 ) to generate the internal data  309  including the obtained data. In the internal data  309 , time-series data comprised of a plurality sensor measured values and count values are managed with respect to each item.  FIG.  8 A  shows rotational accelerations of a fixing belt motor at times T, which are examples of the time-series data comprised of the sensor measured values included in the internal data  309 , with a horizontal axis representing time (T) and a vertical axis representing rotational accelerations. 
     Next, the image forming apparatus  101  generates the feature extraction data  311  based on the internal data  309  comprised of the obtained sensor measured values and count values (step S 703 ). For example, the image forming apparatus  101  generates the feature extraction data  311  in  FIG.  8 B  by carrying out a histogram creating process on the time-series data comprised of the rotational accelerations of the fixing belt motor in  FIG.  8 A , which are the sensor measured values included in the internal data  309 . Thus, by carrying out the histogram creating process on the rotational accelerations of the fixing belt motor, data indicating characteristics relating to appearance frequencies of the sensor measured values representing the rotational accelerations of the fixing belt motor and having a smaller data amount than that of the internal data  309  can be obtained. 
     Then, the image forming apparatus  101  carries out the data transmission deciding process in  FIG.  11    (step S 704 ), which will be described later, to decide whether or not to allow transmission of the feature extraction data  311 . When deciding to allow transmission of the feature extraction data  31 , the image forming apparatus  101  transmits the feature extraction data  311  and the log data  310  to the server  103  (step S 705 ). In the step S 705 , the image forming apparatus  101  may transmit the transmission data  607  comprised of the multiple feature extraction data  311  to the server  103 . Alternatively, the image forming apparatus  101  may transmit the feature extraction data  311  updated from previously transmitted data among the multiple feature extraction data  311  to the server  103 . After that, the image forming apparatus  101  carries out the process in the step S 701 . The image forming apparatus  101  thus repeatedly carries out the processes in the steps S 701  to S 705 . 
     Upon receiving the feature extraction data  311  and the log data  310  from the image forming apparatus  101 , the server  103  carries out a process in step S 706 . In the step S 706 , the server  103  updates the have-been-managed feature extraction data  311  and the log data  310  on the image forming apparatus  101  to the above-mentioned received feature extraction data  311  and log data  310 . Then, the server  103  stores the updated feature extraction data  311  and log data  310  (step S 707 ). After that, the server  103  carries out the process in the step S 706 . The feature extraction data  103  thus repeatedly carries out the processes in the steps S 706  to S 707 . 
     The management apparatus  104  carries out a process in step S 1201 , which will be described later, to determine whether or not it is time to carry out an abnormality prediction process (step S 708 ). When determining that it is time to carry out an abnormality prediction process, the management apparatus  104  obtains prediction data, which is required to carry out the abnormality prediction process, from the server  103  (step S 709 ). The prediction data is the feature extraction data  311  and the log data  310  on the image forming apparatus  101 . Then, the management apparatus  101  carries out the abnormality prediction process associated with the obtained prediction data (step S 710 ). For example, when obtaining, as the prediction data, the feature extraction data  311  in  FIG.  8 B  obtained by carrying out the histogram creating process on the rotational accelerations of the fixing belt motor, the management apparatus  104  carries out a determination process using the dispersion ratio in the histogram as the abnormality prediction process, based on the obtained feature extraction data  311 . For example, when the calculated dispersion ratio is equal to or greater than a predetermined dispersion ratio (e.g.,  FIG.  8 C ), the management apparatus  104  determines that the fixing belt motor is normal. On the other hand, when the calculated dispersion ratio is smaller than the predetermined dispersion ratio (e.g.,  FIG.  8 D ), the management apparatus  104  determines that there is a sign of abnormality in the fixing belt motor. It should be noted that although in the above description of the present embodiment, the method as an example was described, in which the management apparatus  104  performs the determination process on the feature extraction data  311  subjected to the histogram creating process, using the calculated dispersion ratio, the management apparatus  104  may carry out the determination process using another method using, for example, the average, the distortion ratio, and the kurtosis, not the dispersion ratio. 
     Referring again to  FIG.  7   , when determining that it is necessary to provide notification to the maintenance inspector  106  as a result of carrying out the abnormality prediction process, the management apparatus  104  provides notification to the maintenance inspector  106  (step S 711 ). After that, the management apparatus  104  carries out the process in the step S 708 . The management apparatus  104  thus repeatedly carries out the processes in the step S 708  to S 711 . 
       FIG.  9    is a flowchart showing the procedure of the feature extraction data transmission control process that is carried out by the image forming apparatus  101  in  FIG.  1   . The process in  FIG.  9    is implemented by the CPU  302  of the control unit  301  executing the program  308 . The process in  FIG.  9    is carried out at predetermined time intervals set in advance or on a regular basis at predetermined times set in advance. It should be noted that prior to the process in  FIG.  9   , the processes in the steps S 701  and S 702  described above have already been carried out, and the internal data  309  has already been generated. 
     Referring to  FIG.  9   , first, the control unit  301  carries out a feature extraction data generating process in  FIG.  10    (step S 901 ), which will be described later, to generate the feature extraction data  311  (see the step S 703 ). Next, the control unit  301  determines whether or not it is the data transmission time (step S 902 ). In the step S 902 , for example, when a predetermined time period set in advance has elapsed since the previous transmission of the feature extraction data  311 , the control unit  301  determines that it is the data transmission time. On the other hand, when the predetermined time period has not elapsed, the control unit  301  determines that it is not the data transmission time. 
     As a result of the determination in the step S 902 , when it is not the data transmission time, the feature extraction data transmission control process proceeds to step S 905 . As a result of the determination in the step S 902 , when it is the data transmission time, the control unit  301  carries out process in step S 903 . In the step S 903 , the control unit  301  carries out the data transmission deciding process in  FIG.  11   , to be described later, to decide whether or not to allow transmission of the feature extraction data  311  to the server  103  (see the step S 704 ). 
     When the transmission of the feature extraction data  311  to the server  103  is allowed in the step S 903 , the control unit  301  transmits the feature extraction data  311  generated in the step S 901  to the server  103  (step S 904 ) (see the step S 705 ). In the step S 904 , as described above, the control unit  301  may transmit the transmission data  607  comprised of the multiple feature extraction data  311  to the server  103 . Further, the control unit  301  may transmit the feature extraction data  311  updated since the previous transmission of the feature extraction data  311  among the multiple feature extraction data  311  to the server  103 . When the transmission of the feature extraction data  311  is completed, the feature extraction data transmission control process proceeds to the step S 905 . On the other hand, when transmission of the feature extraction data  311  to the server  103  is not allowed, the feature extraction data transmission control process proceeds to the step S 905  without the feature extraction data  311  being transmitted to the server  103 . In the step S 905 , the control unit  301  determines whether or not a job executing instruction given by the user has been received. 
     As a result of the determination in the step S 905 , when a job executing instruction given by the user has been received, the control unit  301  executes a job instructed to execute by the user (step S 906 ). Upon completing the execution of the job, the control unit  301  updates the log data  310  (step S 907 ). Specifically, the control unit  301  sets an execution record of the job in the log data  310 . Then, the control unit  301  transmits the updated log data to the server  103  (see the step S 705 ). After that, the feature extraction data transmission control process is ended. 
       FIG.  10    is a flowchart showing the procedure of the feature extraction data generating process in the step S 901  in  FIG.  9   . 
     Referring to  FIG.  10   , the control unit  301  reads the internal data  309  from the storage device  307  and determines whether or not the internal data  309  has been updated since the previous transmission of the feature extraction data  311  (step  1001 ). 
     As a result of the determination in the step S 1001 , when the internal data  309  has been updated since the previous transmission of the feature extraction data  311 , the control unit  301  carries out a process in step S 1002 . In the step S 1002 , the control unit  301  identifies a data item that has been updated since the previous transmission of the feature extraction data  311  in the internal data  309 . Next, the control unit  301  determines a feature extraction process to be carried out (step S 1003 ). For example, when the data item identified in the step S 1002  is “fixing unit temperature” in  FIG.  6 A , the control unit  301  determines that the feature extraction process to be carried out as a “maximum value calculation process” for generating feature extraction data of the identified item. In a case where a plurality of data items is identified in the step S 1002 , the control unit  301  determines feature extraction processes to be carried out for the respective ones of the identified data items. 
     Then, the control unit  301  determines whether or not data required to carry out the determined feature extraction process is included in the internal data  309  (step S 1004 ). Here, for example, in the maximum value calculation process and a moving-average process, not only the latest data of the identified data item but also past data for a predetermined time period before that or a predetermined number of past data are required. Thus, in the present embodiment, since the number of data required to carry out varies with feature extraction processes, the number of data required to carry out each feature extraction process is managed in a management table (not shown). In the step S 1004 , it is determined whether or not the data required to carry out the determined feature extraction process including the past data is included in the internal data  309 . 
     As a result of the determination in the step S 1004 , when the data required to carry out the determined feature extraction process is included in the internal data  309 , the control unit  301  obtains data required to carry out the determined feature extraction process from the internal data  309  (step S 1005 ). Then, the control unit  301  carries out the feature extraction process determined in the step S 1003  to generate the feature extraction data  311  (step S 1006 ) and ends the feature extraction data generating process. 
     As a result of the determination in the step S 1001 , when the internal data  309  has not been updated since the previous transmission of the feature extraction data  311 , or as a result of the determination in the step S 1004 , when the data required to carry out the determined feature extraction process is not included in the internal data  309 , the feature extraction data generating process is ended without the feature extraction data  311  being generated. 
       FIG.  11    is a flowchart showing the procedure of the data transmission deciding process in the step S 903  in  FIG.  9   . 
     Referring to  FIG.  11   , the control unit  301  reads the log data  310  from the storage device  307  (step S 1101 ) and determines whether or not the log data  310  includes an execution record of jobs that have been executed since the previous transmission of the feature extraction data  311  (step S 1102 ). 
     As a result of the determination in the step S 1102 , when the log data  310  includes an execution record of jobs that have been executed since the previous transmission of the feature extraction data  311 , the control unit  301  allows transmission of the feature extraction data  311  (step S 1103 ) and ends the data transmission deciding process. 
     As a result of the determination in the step S 1102 , when the log data  310  does not include an execution record of jobs that have been executed since the previous transmission of the feature extraction data  311 , the control unit  301  carries out a process in step S 1104 . In the step S 1104 , the control unit  301  determines whether or not the feature extraction data  311  has been updated since the previous transmission, based on update date/time information included in the feature extraction data  311 . 
     As a result of the determination in the step S 1104 , when the feature extraction data  311  has been updated since the previous transmission, the data transmission deciding process proceeds to the step S 1103 . As a result of the determination in the step S 1104 , when the feature extraction data  311  has not been updated since the previous transmission, the control unit  301  prohibits transmission of the feature extraction data  311  (step S 1105 ). Namely, in the present embodiment, when it is time to transmit the feature extraction data  311  and the feature extraction data  311  generated in the step S 901  is the same as feature extraction data transmitted the last time, the feature extraction data  311  generated in the step S 901  is not transmitted to the server  103 . After that, the data transmission deciding process is ended. 
       FIG.  12    is a flowchart showing the procedure of an abnormality prediction control process that is carried out by the management apparatus  104  in  FIG.  1   . The process in  FIG.  12    is implemented by the CPU  401  of the management apparatus  104  executing a program stored in the memory  402  or the storage device  403 . 
     Referring to  FIG.  12   , the CPU  401  determines whether or not it is time to carry out an abnormality prediction process (step S 1201 ). In the present embodiment, with respect to prediction request IDs of abnormality prediction processes that can be carried out by the management apparatus  104 , execution times such as predetermined time periods and predetermined times are set in advance. The management apparatus  104  can also receive a request to carry out an abnormality prediction process from the image forming apparatus  101  that is operated by the maintenance inspector  106  or the like. In the step S 1201 , when the time set in advance has come for an abnormality prediction process to be carried out, or when an execution request including a prediction request ID of an abnormality prediction process designated by the maintenance inspector  106  has been received from the image forming apparatus  101  or the like, the CPU  401  determines that it is time to carry out the abnormality prediction process. On the other hand, when the time set in advance has not come for an abnormality prediction process to be carried out and an execution request for an abnormality prediction process has not been received from the image forming apparatus  101  or the like, the CPU  401  determines that it is not time to carry out the abnormality prediction process. 
     As a result of the determination in the step S 1201 , when it is not time to carry out the abnormality prediction process, the abnormality prediction control process is ended. As a result of the determination in the step S 1201 , when it is time to carry out the abnormality prediction process, the CPU  401  obtains a prediction request ID for identifying the abnormality prediction process to be carried out. For example, when an execution request including a prediction request ID “ 2 ”, which has been transmitted from the image forming apparatus  101  so that the maintenance inspector  106  can grasp a state of the fixing belt was received, the CPU  401  obtains this prediction request ID “ 2 ”. 
     Next, the CPU  401  obtains the feature extraction data  311  and the log data  310  required to carry out an abnormality prediction process corresponding to the obtained prediction request ID (step S 1202 ). Then, the CPU  401  carries out, based on the obtained feature extraction data  311  and log data  310 , the abnormality prediction process corresponding to the obtained prediction request ID (step S 1203 ) (abnormality sign detection means). 
     For example, as the abnormality prediction process corresponding to the obtained prediction request ID “ 2 ”, the CPU  401  carries out a process in which it performs period analysis using the feature extraction data  311  that is generated by performing spectrum formation on time-series data on sensor measured values representing rotational accelerations of the fixing belt motor and determines whether or not an abnormality has occurred or there is a sign of abnormality. For example, when the period of a wave is equal to or smaller than a predetermined value as indicated by a dotted line  1301  in  FIG.  13 A , the CPU  401  determines that the fixing belt motor is normal. On the other hand, when the period of a wave is greater than the predetermined value as indicated by a solid line  1302  in  FIG.  13 A , the CPU  401  determines that there is a sign of abnormality in the fixing belt motor. Thus, in the present embodiment, whether or not there is a sign of abnormality in the image forming apparatuses  101  is determined based on the feature extraction data  311 , which indicates characteristics of frequency components in sensor measured values and has a smaller data amount than that of the internal data  309 . 
     Further, the CPU  401  determines whether or not there is a sign of abnormality by performing a inclination analyzing process using the feature extraction data  311  obtained by on time-series data of sensor measured values, which represents the speed of the intermediate transfer belt, having been subjected to the moving-average process. For example, referring to  FIG.  13 B , when the inclination of a waveform is equal to or smaller than a predetermined value, the CPU  401  determines that the intermediate transfer belt is normal. On the other hand, referring to  FIG.  13 C , when the inclination of a waveform is greater than the predetermined value, the CPU  401  determines that there is a sign of abnormality in the intermediate transfer belt. Thus, in the present embodiment, by using the feature extraction data  311  generated by the moving-average process being carried out on sensor measured values, the trend of the sensor measured values can be grasped with only a small amount of data, and also, measurement errors in the sensor measured values can be reduced. 
     Then, the CPU  401  determines, based on an execution result of the abnormality prediction process, whether or not to provide notification to the maintenance inspector  106  (step S 1204 ). In the step S 1204 , for example, when occurrence of an abnormality or a sign of abnormality has been detected by the abnormality prediction process, the CPU  401  determines to provide notification to the maintenance inspector  106 . On the other hand, when occurrence of an abnormality or a sign of abnormality has not been detected by the abnormality prediction process, the CPU  401  determines not to provide notification to the maintenance inspector  106 . 
     In the step S 1204 , when the CPU  401  determines not to provide notification to the maintenance inspector  106 , the abnormality prediction control process is ended. In the step S 1204 , when the CPU  401  determines to provide notification to the maintenance inspector  106 , the CPU  401  generates an abnormal state notification including, for example, information about a component whose abnormality has been detected (step S 1205 ). Then, the CPU  401  outputs the abnormal state notification for the maintenance inspector  106  (step S 1206 ) and ends the abnormality prediction control process. 
     According to the embodiment described above, the image forming apparatus  101  (or the image forming apparatus  102 ) transmits the feature extraction data  311  to the management apparatus  104  (indirectly) via the server  103 . The feature extraction data  311  has a smaller data amount than that of the internal data  309 . As a result, it is possible to keep down data traffic when the image forming apparatus  101  (or  102 ) transmits data to the management apparatus  104  via the server  103 , and therefore, it is possible to keep down costs required to build and maintain a communication environment. The feature extraction data  311  is data indicating characteristics of the image forming apparatus  101  (or  102 ). Therefore, for the image forming apparatus  101  (or  102 ), it is possible to provide data that can be utilized for data analysis other than detection of a sign of abnormality. Namely, in the present embodiment, data that can be utilized for data analysis other than detection of a sign of abnormality can be provided to the management apparatus  104  while costs required to build and maintain a communication environment are kept down. 
     Moreover, in the embodiment described above, the abnormality prediction system  100  has the plurality of image forming apparatuses  101  and  102 . Thus, when the server  103  collects the feature extraction data  311  from each of a plurality of image forming apparatuses placed in many places, the processing load for transmitting the feature extraction data  311  can be reduced. As a result, in the abnormality prediction system  100 , processing can be efficiently performed when the server  103  collects the feature extraction data  311  as big data from many places around the world. 
     Furthermore, in the embodiment described above, the management apparatus  104  has the function of carrying out the abnormality prediction process. Here, in the abnormality prediction system  100 , when not the management apparatus  104  but the image forming apparatuses  101  and  102  are configured to have the function of carrying out the abnormality prediction process, a large-capacity storage device and a computation device, for implementing the function of carrying out the abnormality prediction process, need to be incorporated into each of the image forming apparatuses  101  and  102 . Therefore, regarding construct the abnormality prediction system  100 , it costs more in a case where the image forming apparatuses  101  and  102  have the function of carrying out the abnormality prediction process, than in the case where the management apparatus  104  has the function of carrying out the abnormality prediction process. In the present embodiment, the management apparatus  104  has the function of carrying out the abnormality prediction process. Thus, costs required to construct the abnormality prediction system  100  can be reduced as compared to the case where the image forming apparatuses  101  and  102  have the function of carrying out the abnormality prediction process. 
     In the embodiment described above, when it is time to transmit the feature extraction data  311  and the feature extraction data  311  generated in the step S 901  is the same data as feature extraction data transmitted the last time, the feature extraction data  311  generated in the step S 901  is not transmitted to the server  103 . Thus, in the abnormality prediction system  100 , transmission of unnecessary data such as transmission of data which the server  103  already holds from the image forming apparatus  101  (or  102 ) to the server  103  can be prevented. 
     Moreover, in the embodiment described above, the feature extraction data  311  is data obtained by creating a histogram from the internal data  309 . Thus, data that has a smaller data amount than that of the internal data  309  and indicates characteristics relating to the appearance frequency of sensor measured values can be provided to the management apparatus  104 . 
     Furthermore, in the embodiment described above, the feature extraction data  311  is data obtained by performing spectrum formation on the internal data  309 . Thus, data that has a smaller data amount than that of the internal data  309  and represents characteristics relating to frequency components of sensor measured values can be provided to the management apparatus  104 . 
     Although the present invention has been described by way of the embodiment, the present invention should not be limited to the embodiment described above. For example, the abnormality prediction system  100  may have a structure in which the server  103  is not equipped and the image forming apparatus  101 ,  102  is configured to transmit the feature extraction data  311  directly to the management apparatus  104 . 
     Moreover, although in the embodiment described above, the transmission data  607  obtained by aggregating the generated multiple feature extraction data  311  is transmitted to the server  103 , the present invention is not limited to this. For example, the generated multiple feature extraction data  311  may be individually transmitted to the server  103 . 
     Instead of the structure in the embodiment described above, the abnormality prediction system  100  may have a structure in which the management apparatus  104  obtains the latest feature extraction data  311  and at least one piece of the feature extraction data  311  generated prior to the generation of the latest feature extraction data  311  from the server  103  or the like and predicts a time when maintenance of the image forming apparatus  101  (or the image forming apparatus  102 ) will be required (hereafter referred to as “the maintenance time”) based on the obtained multiple feature extraction data  311 . A description will now be given of an example in which a maintenance time for the image forming apparatus  101  is predicted based on the feature extraction data  311  (second data) on the density sensor  220  obtained from the server  103 . 
       FIG.  14    is a block diagram schematically showing an arrangement of a printer control unit  1400  included in the printer unit  200  in  FIG.  2   . Referring to  FIG.  14   , the printer control unit  1400  has a CPU  1401 , a density sensor drive circuit  1402 , a shutter drive circuit  1403 , a density sensor detecting circuit  1405 , a ROM  1407 , and a RAM  1408 . The CPU  1401  is connected to the density sensor drive circuit  1402 , the shutter drive circuit  1403 , the density sensor detecting circuit  1405 , the ROM  1407 , and the RAM  1408 . 
     The CPU  1401  has a function of generating a command signal for performing density correction control using the density sensor  220  and a function of carrying out a computation process relating to the density correction control. The density sensor  220 , which is an optical sensor, detects densities of toner patterns formed on the intermediate transfer belt  206 . The density sensor drive circuit  1402  has a function of controlling turning on and off a light-emitting diode (hereafter referred to as the “LED”)  1501  and a photodiode (hereafter referred to as the “PD”)  1502  in  FIGS.  15 A and  15 B , which the density sensor  220  has, and controlling driving current for the LED  1501  and the PD  1502 . 
     To perform the density correction control, the CPU  1401  controls the shutter drive circuit  1403  to transmit a drive signal to a shutter drive unit  1401  of the printer unit  200 . The shutter drive unit  1401  that has received this drive signal performs control to open a shutter  1500  in  FIG.  15   , to be described later, which keeps the density sensor  220  from becoming dirty. The CPU  1401  also controls the density sensor drive circuit  1402  to transmit a drive signal to the density sensor  220 . The density sensor  220  irradiates, based on the received drive signal, an object to be measured with light and detects reflected light from the object to be measured. The light detected by the density sensor  220  is subjected to I-V conversion. The density sensor circuit  1405  transmits signals indicating detection results received from the density sensor  220  to an A/D converter  1406  of the CPU  1401 . The A/D converter  1406  captures, in time series, the signals transmitted from the density sensor circuit  1405 , and subjects the captured signals to A/D conversion. The CPU  1401  performs computations for calculating density correction information by using a calculating formula stored in the ROM  1407  in advance and the signals subjected to the A/D conversion. The CUP  1401 , based on the calculated density correction information, determines setting values in a lookup table, and based on the determined setting values, updates values stored in the RAM  1408  in advance. To form an image, the CPU  1401  reads a setting value in the lookup table from the RAM  1408  and forms the image under a condition corresponding to the read setting value. 
       FIGS.  15 A and  15 B  are views useful in explaining how a toner pattern is detected by the density sensor  101  in  FIG.  2   . The density sensor  220  is disposed so as to face the intermediate transfer belt  206  as shown in  FIG.  15 A  and detects a toner pattern  1504  formed on the intermediate transfer belt  206 . The density sensor  220  is comprised of the LED  1501  that emits infrared radiation, the PD  1502  that receives infrared radiation, and an electric substrate (not shown) on which the LED  1501  and the PD  1502  are mounted. It should be noted that a light receiving unit of the density sensor  220  is not limited to the PD, but may be a photo transistor. 
     The LED  1501  is disposed so as to irradiate the intermediate transfer belt  206  with infrared radiation at an incidence angle of 20°. The PD  1502  is disposed so as to receive diffused reflected light  1503  of the light, which has been emitted to the intermediate transfer belt  206  and the toner pattern  1504 , at a reflection angle of −50°. These optical elements are mounted on the electric substrate (not shown) comprised of a drive circuit (not shown) that supplies electric current to the LED  1501  and a light receiving circuit (not shown) that has an I-V conversion function of converting flowing current to voltage according to the amount of light received by the PD  1502 . It should be noted that in the present embodiment, the density sensor  220  is not limited to the above arrangement but has only to be an optical density sensor. For example, the density sensor  220  may, instead of being configured to detect the diffused reflected light  1503  from the toner pattern  1504 , be configured to detect light reflected from the intermediate transfer belt  206  and detect density using attenuation of light reflected from the intermediate transfer belt  206  according to the amount of toner attached to the intermediate transfer belt  206 . 
     There may be cases where paper dust derived from the conveyed recording material S and toner to be attached to the intermediate transfer belt  206  are scattered in the image forming apparatus  101 . If the scattered paper dust and toner become attached to the density sensor  220 , the amount of light emitted from and the amount of light received by the density sensor  220  will decrease, resulting in the accuracy of toner density detection by the density sensor  220  being decreased. To prevent the decrease in the accuracy of toner density detection by the density sensor  220 , the printer unit  200  has the shutter  1500  for keeping the density sensor  220  from becoming dirty. The shutter  1500  is disposed between the density sensor  220  and the intermediate transfer belt  206 . The shutter  1500  moves in a direction parallel to the density sensor  220  and the intermediate transfer belt  206 . The shutter  1500  is controlled to open and close by the shutter drive unit  1404 . For example, in a case where the density is to be detected, the shutter drive unit  1404  opens the shutter  1500  such that an opening of the shutter  1500  is formed at such a position as not to block light emitted from the density sensor  220  and reflected light to be received by the density sensor  220  (see, for example,  FIG.  15 A ). On the other hand, in a case where the density is not to be detected, the shutter drive unit  1404  closes the shutter  1500  so as to block passage between an optical unit (the LED  1501  and the PD  1502 ) of the density sensor  220  and the intermediate transfer belt  206  (see, for example,  FIG.  15 B ). 
     As described above, in the present embodiment, the amount of dirt attached to the density sensor  220  can be considerably decreased by closing the shutter  1500  in the case where the density is not to be detected. However, in the case where the density is to be detected, the shutter  1500  is opened, and hence nothing blocks the passage between the optical unit of the density sensor  220  and the intermediate transfer belt  206 , resulting in paper dust and toner becoming attached to the density sensor  220  through the opening. As the amount of toner attached to the density sensor  220  increases, the amount of light emitted from and the amount of light received by the density sensor  220  gradually decreases. When the amount of light emitted from and the amount of light received by the density sensor  220  decreases, a detected value of toner density of the toner pattern  1504  becomes smaller than actual. That is, the accuracy of toner density detection by the density sensor  220  degrades. 
     To prevent such degradation in the accuracy of toner density detection by the density sensor  220  caused by attachment of paper dust and toner, in the printer unit  200 , light amount adjustment control is performed so as to increase the LED drive current and to keep the amount of light from the density sensor  220  constant. In the light amount adjustment control, the density sensor  220  irradiates a reference plate  1505 , which maintains its constant reflectivity, with light, and detects reflected light. The reference plate  1505  is mounted on a surface of the shutter  1500  which faces the density sensor  220 , as shown in  FIG.  15 B . The printer control unit  1400  controls the shutter drive unit  1404  to perform the light amount adjustment control while the reference plate  1505  being placed so as to face the optical unit of the density sensor  220 . It should be noted that in the present embodiment, the light amount adjustment control should not be limited the mentioned-above method using reflected light from the reference plate  1505 , and for example, may be performed using reflected light from the intermediate transfer belt  206 . 
       FIG.  16    is a graph showing the relationship between LED driving current for the density sensor in  FIG.  2    and values detected by the density sensor  220  in  FIG.  2   . Referring to  FIG.  16   , the horizontal axis represents LED drive current values of the density sensor  220 , and the vertical axis represents values detected by the density sensor  220 . In the light amount adjustment control, the LED drive current values that are control values for controlling the amount of light from the LED  1501  are switched in five levels, and reflected light from the reference plate  1505  in each level of the LED drive current values is detected. In  FIG.  16   , I 1  to IS designate the LED drive current values in the five levels, and V 1  to V 5  designate values detected by the density sensor  220  when the LED drive currents I 1  to IS are supplied to the LED  1501 . Vt designates a value detected by the density sensor  220  and set as a target when the amount of light from the LED  1501  is adjusted. Namely, Vt is the value detected when the density sensor  220  has detected reflected light from the reference plate  1505  when an arbitrary LED drive current is supplied during initial installation of the image forming apparatus  101 . At the time of the initial installation, dirt derived from scattering of paper dust or toner is not attached to the density sensor  220 , and namely, Vt is the value detected when the amount of dirt is the least. 
     The printer control unit  1400  compares the measured V 1  to V 5  and Vt with each other and extracts two points sandwiching Vt, namely, the largest value among values smaller than Vt and the smallest value among values larger than Vt. Referring to  FIGS.  16   , V 3  and V 4  are extracted. The printer control unit  1400  linearly interpolates between the extracted V 3  and V 4  to calculate an LED drive current value It corresponding to Vt. The printer control unit  1400  sets the calculated LED drive current value It as an adjusted LED drive current value. Specifically, the printer control unit  1400  updates an LED drive current value for density correction stored in the RAM  1408 , to the calculated LED drive current value It. Thus, by setting the LED drive current value for density correction, to the calculated LED drive current value It, values detected by the density sensor  220  can be prevented from becoming smaller, and hence degradation in the accuracy of toner density detection by the density sensor  220  can be prevented. It should be noted that when the value of Vt is smaller than V 1 , or when the value of Vt is larger than V 5 , it is likely that the density sensor  220  could not normally detected density. For this reason, the LED drive current value for density correction, stored in the RAM  1408 , is not updated to the LED drive current value It calculated based on Vt. 
       FIG.  17    is a flowchart showing the procedure of a light amount adjustment control process that is performed by the printer control unit  1400  in  FIG.  14   . The process in  FIG.  17    is implemented by the CPU  1401  of the printer control unit  1400  executing a program stored in the ROM  1407  or the like. The process in  FIG.  17    is carried out when a predetermined condition on which the characteristics of the density sensor  220  change is satisfied, for example, when execution of a job using the printer unit  200  is completed, when the image forming apparatus  101  is started, and when the image forming apparatus  101  returns from a power saving mode. 
     Referring to  FIG.  17   , first, the CPU  1401  moves the shutter  1500  to such that the reference plate  1505  faces the optical unit of the density sensor  220  (step S 1701 ). Specifically, the CPU  1401  controls the shutter drive circuit  1403  to transmit a drive signal, which is an instruction to move the shutter  1500 , to the shutter drive unit  1404 . In accordance with the received drive signal, the shutter drive unit  1404  moves the shutter  1500  such that the reference plate  1505  faces the optical unit of the density sensor  220 . Next, the CPU  1401  controls the density sensor drive circuit  1402  to transmit a drive signal to the density sensor  220  and drive the density sensor  220  with the LED drive currents in the five levels (I 1  to I 5 ) described above (step S 1702 ). The CPU  1401  obtains the detected values V 1  to V 5 , which were obtained when the LED drive currents in the five levels (I 1  to I 5 ) were supplied, from the density sensor  220 . Then, the CPU  1401  determines whether or not Vt lies within a range between V 1  and V 5  (step S 1703 ). 
     As a result of the determination in the step S 1703 , when Vt lies within the range between V 1  and V 5 , that is, when V 1  is equal to or greater than V 1  and equal to or smaller than V 5 , the CPU  1401  extracts two detected values sandwiching Vt from V 1  to V 5  (step S 1704 ). In the step S 1704 , the CPU  1401  extracts the largest detected value (for example, V 3  in  FIG.  16   ) from detected values smaller than Vt among V 1  to V 5  and extracts the smallest detected value (for example, V 4  in  FIG.  16   ) from detected values larger than Vt among V 1  to V 5 . Next, the CPU  1401  linearly interpolates between the extracted two detected values to calculate the LED drive current value It corresponding to Vt (step S 1705 ). The calculated LED drive current value It is an LED drive current value for use in density adjustment from the next time. The LED drive current value for use in density adjustment will hereafter be referred to as “the light amount control value for density adjustment”. Then, the CPU  1401  sets the calculated LED drive current value It as the light amount control value for density adjustment from the next time (step S 1706 ) and stores the set light amount control value in the RAM  1408 . The RAM  1408  stores a plurality of light amount control values which have been set in the past as well as the light amount control value set in the step S 1706 . After that, the CPU  1401  transmits the light amount control value set in the step S 1706  to the control unit  301  (step S 1707 ) and ends the light amount adjustment control process. 
     As a result of the determination in the step S 1703 , when Vt does not lie within the range between V 1  and V 5 , that is, when V 1  is smaller than V 1  or larger than V 5 , the CPU  1401  determines that the density sensor  220  could not normally detect density. The CPU  1401  sets the light amount control value set in the previous light amount adjustment control process as the light amount control value for density adjustment from the next time (step S 1708 ) and stores the set light amount control value in the RAM  1408 . After that, the CPU  1401  ends the light amount adjustment control process. 
       FIG.  18    is a flowchart showing the procedure of a feature extraction data transmission process that is carried out by the image forming apparatus  101  in  FIG.  1   . The process in  FIG.  18    is implemented by the CPU  302  of the control unit  301  executing the program  308 . The process in  FIG.  18    is regularly carried out, for example, at predetermined time intervals set in advance or at predetermined times set in advance. 
     Referring to  FIG.  18   , the CPU  302  requests the printer control unit  1400  to transmit light amount data (step S 1801 ). The light amount data includes a plurality of data such as a light amount control value set the last time and a light amount control values set prior to the last time. Next, the CPU  302  receives the light amount data from the printer control unit  1400  (step S 1802 ) and stores the received light amount data in the memory  303  (step S 1803 ). The memory  303  stores a plurality of light amount data received from the printer control unit  1400  in the past as well as the light amount data received in the step S 1802 . Then, the CPU  302  determines whether or not the number of data included in the light amount data received in the step S 1802  is a predetermined number set in advance (for example,  30 ) (step S 1804 ). 
     As a result of the determination in the step S 1804 , when the number of data included in the received light amount data is the predetermined number (for example,  30 ), the CPU  302  carries out a data generating process in  FIG.  19    (step S 1805 ), which will be described later, to generate the feature extraction data  311 . Then, the CPU  302  transmits the generated feature extraction data  311  to the server  103  (step S 1806 ). It should be noted that in a case where the management apparatus  104  is configured to be capable of accumulating a plurality of feature extraction data  311  including past data, the CPU  302  may be configured to directly transmit the feature extraction data  311  to the management apparatus  104  as described above. 
     Then, the CPU  302  deletes the oldest light amount data among the plurality of light amount data stored in the memory  303  (step S 1807 ). After that, the CPU  302  ends the feature extraction data transmission process. 
     As a result of the determination in the step S 1804 , when the number of data included in the received light amount data is not the predetermined number (for example,  30 ), the CPU  302  ends the feature extraction data transmission process without generating or transmitting the feature extraction data  311 . 
     It should be noted that in the above-described process in  FIG.  18   , the CPU  302  may determine, in the step S 1804 , whether or not the number of data included in the light amount data received in the step S 1802  is equal to or greater than a predetermined number set in advance (for example,  30 ). When the number of data included in the light amount data received in the step S 1802  is equal to or greater than the predetermined number (for example,  30 ), the feature extraction data transmission process proceeds to the step S 1805 . When the number of data included in the light amount data received in the step S 1802  is smaller than the predetermined number (for example,  30 ), the feature extraction data transmission process is ended. 
       FIG.  19    is a flowchart showing the procedure of the data generating process in the step S 1805  in  FIG.  18   . 
     Referring to  FIG.  19   , the CPU  302  excludes a maximum value and a minimum value from data included in the light amount data received in the step S 1802  (step S 1901 ). Next, the CPU  302  calculates an average value of data that was not excluded in the step S 1901  among the data included in the light amount data received in the step S 1802  (step S 1902 ). By carrying out the processes in the steps S 1901  and S 1902 , variations in feature values of the feature extraction data  311  can be reduced. It can be considered that variations in the light amount control values are caused by, for example, changes in the amount of light emitted from the LED  1501  of the density sensor  220  arising from changes in the internal temperature of the image forming apparatus  101 . 
     Then, the CPU  302  normalizes the calculated average value (step S 1903 ). Specifically, the CPU  302  divides the calculated average value by an upper limit value of a control range for the LED drive currents. The upper limit value of the control range is a value determined based on device characteristics of the LED  1501 . Here, a value obtained by the normalization in the step S 1903  is “1” when the average value calculated in the step S 1902  is equal to the upper limit value of the control range for the LED drive currents. Namely, when the value obtained by the normalization in the step S 1903  is “ 1 ”, the light amount adjustment control is not performed, and hence degradation in the accuracy of toner density detection by the density sensor  220  cannot be prevented. To prevent this situation, the abnormality prediction system  100  uses the value obtained by the normalization in the step S 1903  for calculating the maintenance time for the image forming apparatus  101 . It should be noted that a margin which the value obtained by the normalization in the step S 1903  has relative to “ 1 ” is a margin relative to the time when maintenance is required. 
     Then, the CPU  302  stores the value obtained by the normalization in the step S 1903  as the feature extraction data  311  in the memory  303  (step S 1904 ). The feature extraction data  311  is data that indicates the feature of the light amount data on the density sensor  220  for calculating the maintenance time for the image forming apparatus  101  and is also data with a smaller data amount than that of the light amount data including a plurality of data. After that, the data generating process is ended. 
       FIG.  20    is a flowchart showing the procedure of a maintenance time notification process that is carried out by the management apparatus  104  in  FIG.  1   . The maintenance time notification process in  FIG.  20    is implemented by the CPU  401  of the management apparatus  104  executing a program stored in the memory  402  or the storage device  403 . It should be noted that in the present embodiment, the management apparatus  104  carries out the maintenance time notification process at a timing set in advance, for example, at predetermined time intervals, predetermined times, and so forth. Further, the management apparatus  104  carries out the maintenance time notification process when receiving a request to carry out the maintenance time notification process from the image forming apparatus  101  or the like which is operated by the maintenance inspector  106  or the like. 
     Referring to  FIG.  20   , the CPU  401  receives the feature extraction data  311  on the density sensor  220  from the server  103  (or directly from the image forming apparatus  101  or the like) and stores the received feature extraction data  311  on the density sensor  220  in the storage device  403  (step S 2001 ). Next, the CPU  401  determines whether or not the number of feature extraction data  311  on the density sensor  220  stored in the storage device  403  is two or more (step S 2002 ). In the step S 2002 , for example, when the feature extraction data  311  on the density sensor  220  stored in the step S 2001  and at least one piece of feature extraction data  311  on the density sensor  220  which was received prior to the feature extraction data  311  on the density sensor  220  stored at the step S 2001  are stored in the storage device  403 , the CPU  401  determines that the number of feature extraction data  311  on the density sensor  220  stored in the storage device  403  is two or more. On the other hand, when no feature extraction data  311  on the density sensor  220  other than the feature extraction data  311  on the density sensor  220  stored in the step S 2001  is stored in the storage device  403 , the CPU  401  determines that the number of feature extraction data  311  on the density sensor  220  stored in the storage device  403  is not two or more. 
     As a result of the determination in the step S 2002 , when the number of feature extraction data  311  on the density sensor  220  stored in the storage device  403  is two or more, the CPU  401  calculates the maintenance time for the image forming apparatus  101  based on the latest feature extraction data  311  on the density sensor  220  stored in the storage device  403  and at least one piece of feature extraction data  311  on the density sensor  220  received prior to the latest feature extraction data  311  (step S 2003 ). For example, the CPU  401  calculates, in a way of extrapolation, a date and time at which the feature value becomes equal to “ 1 ” using the latest feature extraction data  311  on the density sensor  220  stored in the storage device  403  (for example, a feature value Cn in  FIG.  21   ), the second latest feature extraction data  311  on the density sensor  220  stored in the storage device  403  (for example, a feature value Cn−1 in  FIG.  21   ), and dates and times at which the respective pieces of feature extraction data  311  are generated (for example, Tn, Tn−1 in  FIG.  21   ). The date and time at which the feature value becomes equal to “1” means a date and time at which it becomes impossible to perform the light amount adjustment control and to prevent degradation in the accuracy of toner density detection by the density sensor  220 . It is necessary to perform maintenance of the density sensor  220  by this date and time. The CPU  401  sets the calculated date and time as a time limit for maintenance to be performed and calculates a time period between the time limit for maintenance to be performed and one month before that as the maintenance time for the image forming apparatus  101  with consideration that the maintenance inspector  106  make a maintenance plan. 
     Then, the CPU  401  notifies the maintenance inspector  106  of the calculated maintenance time (step S 2004 ) and ends the maintenance time notification process. 
     As a result of the determination in the step S 2002 , when the number of feature extraction data  311  the density sensor  220  stored in the storage device  403  is not two or more, the CPU  401  ends the maintenance time notification process without providing notification of the maintenance time. 
     According to the embodiment described above, the management apparatus  104  obtains the latest feature extraction data  311  on the density sensor  220  generated by the image forming apparatus  101  and at least one piece of feature extraction data  311  on the density sensor  220  generated prior to the latest feature extraction data  311 , and based on the obtained multiple feature extraction data  311  on the density sensor  220 , predicts the maintenance time for the image forming apparatus  101 . The feature extraction data  311  on the density sensor  220  is data that indicates features of the light amount data on the density sensor  220 . Thus, the maintenance time for the image forming apparatus  101  can be predicted based on variations in the light amount data on the density sensor  220 . Further, the feature extraction data  311  on the density sensor  220  has a smaller data amount than that of the light amount data including a plurality of data. For this reason, it is possible to keep down data traffic when the management apparatus  104  receives data from the server  103  or the like, and therefore, it is possible to keep down costs required to build and maintain the communication environment. Namely, in the present embodiment, it is possible to predict the maintenance time for the image forming apparatus  101  while keeping down costs required to build and maintain the communication environment. 
     In the embodiment described above, the feature extraction data  311  on the density sensor  220  is data obtained by dividing the average value, which is obtained by averaging at least a part of the plurality of light amount setting values included in the light amount data, by the upper limit value of the control range for the LED drive currents. The time when the density sensor  220  is needed to be performed maintenance on can be calculated using such data indicating characteristics of the density sensor  220  based on which it is possible to determine whether or not to perform the light amount adjustment control in the image forming apparatus  101 . As a result, the maintenance time for the image forming apparatus  101  equipped with the density sensor  220  can be predicted. 
     Moreover, in the embodiment described above, extrapolation is used to predict the maintenance time for the density sensor  220  based on a plurality of accumulated feature extraction data  311  on the density sensor  220 . As a result, the maintenance time for the image forming apparatus  101  can be predicted easily using the plurality of feature extraction data  311  on the density sensor  220  accumulated in the server  103  or the like. 
     Furthermore, in the embodiment described above, the density sensor  220  is a sensor that detects the densities of toner patterns formed on the intermediate transfer belt  206 . Therefore, maintenance of the image forming apparatus  101  can be performed before occurrence of a failure such as the density sensor  220  becomes unable to detect the densities of toner patterns formed on the intermediate transfer belt  206 . 
     In the embodiment described above, since the feature extraction data  311  is accumulated in the server  103 , the accumulated feature extraction data  311  (hereafter referred to as “the accumulated data”) can be utilized to develop new technology. A description will now be given of an example in which the accumulated data is utilized to add a function of identifying failed parts. 
     In a case where a factor that causes a change in the characteristics of the density sensor  220  is dirt attached to the density sensor  220  as described above, the characteristics of the density sensor  220  have a tendency of slowly changing over a certain period of time. On the other hand, in a case where a factor that causes a change in the characteristics of the density sensor  220  is a failure of the density sensor  220 , the characteristics of the density sensor  220  have a tendency of sharply changing at the timing when the density sensor  220  has failed. For example, when the shutter drive unit  1404  of the density sensor  220  has failed, it becomes impossible to move the shutter  1500  to an appropriate position where the reference plate  1505  faces the optical unit of the density sensor  220  at the timing when the shutter drive unit  1404  is failed. Namely, in the light amount adjustment control, the density sensor  220  becomes unable to detect reflected light from the reference plate  1505  as intended. As a result, a light amount control value that greatly differs from a light amount control value set the last time is set in the light amount adjustment control. Namely, the characteristics of the density sensor  220  sharply change. In the present embodiment, details of required maintenance are predicted based on such tendencies of changes in the characteristics of the density sensor  220 . For example, when the characteristics of the density sensor  220  change relatively slowly, it is predicted that maintenance involving removal of dirt from the density sensor  220  will be required. On the other hand, when the characteristics of the density sensor  220  sharply change, it is predicted that maintenance involving repair of the shutter drive unit  1404  will be required. 
     Moreover, by comparing the accumulated data in the server  103  and the failure logs for the image forming apparatus  101  with each other, it is possible to find out tendency of change in data when a failure occurs. The function of identifying failed parts based on the accumulated data can be developed and implemented, by finding out the tendency of data at the timing when a failure occurs. With this function, the maintenance inspector  106  can be notified of a failed part in advance and head for a maintenance with preparing a replacement part, to smoothly perform maintenance. 
     Although in the embodiment described above, the maintenance time for the image forming apparatus  101  is predicted based on the plurality of feature extraction data  311  on the density sensor  220  generated at different times, the type of the feature extraction data  311  is not limited to the feature extraction data  311  on the density sensor  220 . The feature extraction data  311  may be any type as long as changes in the characteristics can be determined by comparing a plurality of feature extraction data  311  of the same type generated at different times. 
     Other Embodiments 
     Embodiment(s) of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s). The computer may comprise one or more processors (e.g., central processing unit (CPU), micro processing unit (MPU)) and may include a network of separate computers or separate processors to read out and execute the computer executable instructions. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, one or more of a hard disk, a random-access memory (RAM), a read only memory (ROM), a storage of distributed computing systems, an optical disk (such as a compact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™), a flash memory device, a memory card, and the like. 
     While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions. 
     This application claims the benefit of Japanese Patent Application No. 2020-130873, filed on Jul. 31, 2020 and Japanese Patent Application No. 2020-132484, filed on Aug. 4, 2020, which are hereby incorporated by reference herein in their entirety.