JP2015167302A - System and control method for the same, image formation apparatus and control method for image formation apparatus, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To solve a problem of outputting an unnecessary chart in outputting a plurality of sheets of charts having various patterns or configurations, considering various factors causing an abnormal image.SOLUTION: An image formation apparatus connected to a server for performing diagnosis by analyzing an image output by the image formation apparatus includes: analysis means for analyzing an image feature of each page included in a job in performing image formation; storage means for storing the image feature associated with each page; means for accepting, from a user, selection of a page included in a job whose output image includes abnormality as a result of image processing; selection means for selecting one or a plurality of charts from a plurality of diagnosis charts by using an image feature associated with the selected page; output means for outputting the selected chart; reading means for accepting, from the user, whether or not abnormality occurs in an output image of the output chart and reading an output image of a chart determined to include the abnormality; and transmission means for transmitting the read image of the chart to the server.

Description

  The present invention relates to a system, a control method therefor, an image forming apparatus, a control method therefor, and a program, and more particularly to a technique for estimating a failure location when a printer fails.

  Conventionally, when an image forming apparatus is used for a long time, parts and the like are deteriorated, and there is a possibility that an abnormality in the image quality of an output image occurs. An “abnormal image” generated due to such deterioration is difficult to automatically detect using a sensor or the like, and there are very many cases where a service person who repairs a printer responds after receiving an indication from a user. At this time, it is difficult to express an abnormal image in words. For example, even if “uneven” is present, the cause cannot be specified unless detailed information such as the direction, frequency, and period of occurrence of unevenness is known. For this reason, when an abnormal image is pointed out by the user, it is necessary for a service person to go to the site to check the image quality abnormality. The service person predicted the failure location and specified the related service parts, returned to the service base once, acquired the service parts, and then responded to the site again.

  In such a response, in addition to the cost of the service person, downtime occurs that the machine cannot be used until the response is completed, and the productivity of the user is greatly reduced. For this problem, for example, when identifying the cause of an abnormal image, instead of checking directly on the site, the service engineer outputs and transmits a test chart for diagnosing the failure, and the cause of the failure An image diagnosis technique for identifying the image is known. In the case of this diagnostic technique, an abnormal image that occurs as a failure differs depending on the failure location, so it has been necessary to output many test charts and identify the cause.

  For example, Patent Document 1 proposes a technique for reducing the number of patterns to be output. In this technique, a user visually confirms an abnormal image and selects a type that is close to the type of abnormal image displayed on the user interface. And the number of sheets is reduced by outputting a test pattern corresponding to the type of the selected abnormal image. However, when the user analyzes the type of abnormal image, there is a possibility that the determination accuracy is lowered because the user's analysis skill may make a determination error.

  On the other hand, as a method of performing image analysis that does not depend on the user's analysis skill, Patent Document 2 analyzes an abnormal image from an output job in which the user confirmed the abnormal image. In this method, a basic image obtained by scanning an image in which no failure has occurred is prepared in advance, and a problem image obtained by scanning an image in which an abnormal image has occurred is acquired. Then, by analyzing the abnormal image generated from the difference between the feature amount of the basic image and the problem image and outputting only the test pattern corresponding to the analysis result, the number of test chart output and user skill issues are improved. Yes.

Japanese Patent No. 4618185 Japanese Patent No. 4710495

  However, in Patent Document 2, since the user has to prepare in advance a scan image of an image in which no abnormal image has occurred, there is a problem that a burden is placed on the user to perform image diagnosis.

  The present invention provides a technique capable of reducing test charts for image diagnosis without depending on analysis skills of a user.

  In order to solve the above problems, the present invention has the following configuration. That is, a system that includes a server and an image forming apparatus, and that diagnoses the image forming apparatus by analyzing an output image from the image forming apparatus, and the image forming apparatus Analyzing means for analyzing the image features of each page included in the image, storage means for storing the image features analyzed by the analyzing means in association with each page, and output of the result of image processing when diagnosing the image forming apparatus A plurality of diagnostic charts using first reception means for receiving selection of a page included in a job in which an abnormality has occurred in an image from a user, and image features associated with the page selected by the first reception means Selecting means for selecting one or more charts, output means for outputting the chart selected by the selecting means, and the output means A second receiving means for accepting whether or not an abnormality has occurred in the output image of the chart output from the user, and an output image of the chart in which an abnormality has occurred in the second receiving means A reading unit that reads the image, and a transmission unit that transmits the read chart image to the server; the server analyzes the chart image transmitted from the transmission unit of the image forming apparatus; Diagnostic means for estimating the state of

  According to the present invention, it is possible to reduce the test chart output for image diagnosis without depending on the analysis skill of the user. As a result, it is possible to diagnose the image forming apparatus with a reduced burden on the user.

The figure which shows the structural example of an image diagnostic system. The figure which shows the flow of an image formation process. The figure which shows the flow of a feature-analysis process. The figure which shows the flow of the process at the time of the image diagnosis execution which concerns on 1st embodiment. The figure which shows the flow of the image selection process which concerns on 1st embodiment. The figure which shows the flow of the chart selection process according to color which concerns on 1st embodiment. The figure which shows the flow of a process at the time of the image diagnosis execution in the server side. The figure which shows the structural example of UI screen at the time of the image diagnosis execution which concerns on 1st embodiment. The figure which shows the structural example of the chart for image diagnosis. The figure which shows the example of the setting of attribute information. The figure which shows the flow of a process at the time of the image diagnosis execution which concerns on 2nd embodiment. The figure which shows the flow of the tendency estimation process which concerns on 2nd embodiment. The figure which shows the structural example of the UI screen at the time of the image diagnosis execution which concerns on 2nd embodiment. The figure for demonstrating the outline | summary of a feature-analysis process. The figure which shows the structural example of UI screen at the time of the abnormal image confirmation which concerns on 1st embodiment.

<First embodiment>
Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings.

[System configuration]
FIG. 1 shows a configuration example of an image diagnostic system according to the present embodiment. In FIG. 1, an MFP (Multi Function Printer) 101 functions as an image forming apparatus on the user side. A controller 102 included in the MFP 101 is a control unit of the MFP 101, an apparatus control unit 105 that controls the MFP 101, a diagnosis processing unit 106 that performs processing at the time of image diagnosis, and an image processing unit that optimizes image data to be processed. 103. The image processing unit 103 further includes a feature analysis unit 104 for analyzing features of the output image.

  The controller 102 uses the CPU 107 and the RAM 108 to acquire image data from the scanner 111 serving as a reading unit, processes the image data, and stores it in the storage device 110. The controller 102 controls output of image data to a recording medium such as paper via the printer 113. Various settings by a user operation using the input device 109 such as a mouse or a keyboard are notified to the controller 102, and the image processing unit 103 processes image data based on the notified settings. Alternatively, the value set by the input device 109 is stored in the storage device 110 via the controller 102, and the image processing unit 103 reads the stored setting value and processes the image data.

  The storage device 110 stores parameters for controlling the device, a program for realizing the present embodiment, an application, an OS (Operating System), and the like. The display device 112 is a UI (user interface) that displays instructions to the user and the state of the MFP 101. In the present embodiment, a case where a touch panel interface in which the display device 112 and the input device 109 are integrated will be described. The network I / F 114 also receives print data from a PC (not shown) connected via the network 115, and transmits and receives data necessary for the server 116 that implements the image diagnosis system according to the present embodiment. Do. Note that the configuration of the MFP 101 as the image forming apparatus is not limited to the above, and a network interface such as a router or a firewall or a PC (not shown) connected to the image forming apparatus is further added as necessary. Also good.

  The server 116 functions as an image diagnostic apparatus on the service center side. The server 116 is an information processing apparatus and is connected to the MFP 101 via the network 115 and exchanges image data, management data, control data, and the like. A controller 117 included in the server 116 is a control unit of the server 116, and includes a device control unit 118 that controls the server 116 and an image analysis unit 119 that analyzes image data.

  The controller 117 analyzes the image data sent from the MFP 101 using the CPU 121 and the RAM 122. Various instructions by user operation using the input device 123 such as a mouse or a keyboard are notified to the device control unit 118, and the image analysis unit 119 processes image data based on the notified instructions, and features of the image data Is extracted and specified. Alternatively, an instruction from the input device 123 is stored in the storage device 124 or the RAM 122 via the device control unit 118, and the image analysis unit 119 reads the stored instruction and analyzes the image data.

  The failure location estimation unit 120 uses the feature amount (feature information) of the image analyzed by the image analysis unit 119 to process the estimation of the failed part causing the image problem based on the notified instruction. The display device 125 is a UI that displays instructions to the user and the status of the server 116. A network I / F 126 transmits and receives data to and from the MFP 101 connected via the network 115.

  The configuration of the server 116 on the service center side is not limited to the above, and other interfaces and parts may be added as necessary. Further, the server 116 can be realized by a service center server or a PC, but is not limited to this. Some of the functions of the storage device 124 and the image analysis unit 119 are remote devices connected via the network 115 such as a cloud. The configuration realized by In FIG. 1, one MFP 101 and one server 116 are shown. However, the present invention is not limited to this. The MFP 101 and the server 116 are physically composed of a plurality of devices as necessary, and the functions are divided. Also good.

[Processing flow (MFP)]
A processing flow in the image processing unit 103 of the MFP 101 will be described with reference to FIG. FIG. 2 shows a flow of image processing performed on a raster image or an image read by the scanner 111. In the present embodiment, the process illustrated in FIG. 2 is realized by executing an application specific integrated circuit (ASIC) included in the image processing unit 103. In the following flowcharts, broken-line arrows indicate reference and recording of various data. In addition, in the processing of FIG. 2, detailed description of processing that is not directly related to the present invention is omitted because the conventional technology is used.

  In S201, the image processing unit 103 receives image data. At this time, there are two types of data to be received: scan data received from the scanner 111 and printer data sent from a PC (not shown) or the like. The printer data in the present embodiment is assumed to be image data in the CMYK color space, and is converted into the CMYK color space by a CMS (Color Management System: not shown) provided in the controller 102. The printer data here is assumed to be PDL (Page Description Language) data, but is not limited to this.

  In S202, the image processing unit 103 determines whether the data received in S201 is scan data or printer data. If it is scan data (YES in S202), the process proceeds to S203, and if it is printer data (NO in S202), the process proceeds to S209.

  In step S <b> 203, the image processing unit 103 performs image area separation processing on the image data (that is, scan data) to generate pixel attribute data (multivalue) 204. Here, the image area separation processing is processing for detecting an edge in an image and determining whether or not the image has attribute information for each pixel.

  Here, attribute information of the scan data and printer data will be described with reference to FIG. As shown in FIG. 10A, the scan data attribute information includes information including solid, characters, and halftone dots. The solid is set to “1” for a pixel determined that the difference in value between the target pixel and the surrounding pixel is within a certain signal value range in which no edge is extracted, and “0” for a non-applicable pixel. . For the character, an edge is detected at the target pixel, a pixel for which the number of detected edges of peripheral pixels is determined to be equal to or less than a threshold value is set to “1”, and a pixel that does not correspond is set to “0”. A halftone dot is defined as “1” for a pixel in which the number of detected edges in peripheral pixels is greater than or equal to a threshold, and “0” for a non-applicable pixel. Since each of the solid, character, and halftone dots is expressed as 1-bit data of “0” or “1”, in the present embodiment, the attribute information for each pixel is 3-bit multi-value data. Note that the threshold value for determination can be switched for each model or according to the image area separation level that can be set in the MFP main body.

  As shown in FIG. 10B, the printer data attribute information includes information including objects, vectors, and characters. If the received image data is printer data, the pixel attribute data (multivalue) 204 and the image data itself are received together in S201. In the present embodiment, both the scan data and the printer data are three types of attribute information, but other information may be set for switching processing for each pixel.

  The document type shown in FIG. 10 will be described. There are two types of document types according to the present embodiment, “character document” and “image document”, which are document types determined from combinations of setting values of attribute information. A character document is a portion that becomes a character area in image data. An image original is a portion that becomes an image area in image data. An example of setting attribute information for determining “character document” in the scan data is as shown in FIG. That is, it is assumed that the solid value is “0”, the character is “1”, and the halftone dot is not determined by the set value. In the present embodiment, two types of document types, a character document and an image document, are described, but other document types may be prepared. Further, the types that can be set by the attribute information may be further added or other information may be used.

  Returning to the description of FIG. In S205, since the received image data is an RGB image when the received image data is scan data, the image processing unit 103 performs color conversion processing on the image data, and converts the scan data into a color space that does not depend on the device (MFP). Generate image data.

  In S206, the image processing unit 103 determines the document type using the pixel attribute data (multi-value) 204 for the image data color-converted in S205, and performs filter processing. Here, the image processing unit 103 performs processing using an edge emphasis filter in the case of a character document, and performs processing using a smoothing filter when the document does not correspond to a character document (here, an image document).

  In step S207, the image processing unit 103 performs background removal processing, and removes the background of the image data subjected to the filter processing in step S206. In S208, the image processing unit 103 performs color conversion processing on the image data from which the background is removed in S207, and converts the image data into an image in the CMYK color space.

  In step S <b> 209, the image processing unit 103 corrects the single-color gradation characteristics of C, M, Y, and K using the 1D-LUT, and generates a CMYK image (multivalue) 217. The 1D-LUT is a one-dimensional LUT (Look Up Table) for correcting each color of C, M, Y, and K, and is assumed to be held in advance in a storage unit or the like.

  In S210, the image processing unit 103 performs feature analysis processing using the pixel attribute data (multi-value) 204 and the CMYK image (multi-value) 217, and what characteristics the input image data has. Analyze. Details of the feature analysis processing in this step will be described later with reference to FIG.

  In S211, the image processing unit 103 stores the image analysis result performed in S210 in association with each page in the job information 212. The job information here is information for storing printed settings including the color space, the number of pages, the number of copies, and the like, and is stored in the storage device 110.

  In step S213, the image processing unit 103 performs image forming processing such as screen processing and error diffusion processing to create a CMYK image (binary) 214. In step S215, the image processing unit 103 transmits the CMYK image (binary) 214 generated in step S213 to the printer 113 as image data to be printed. Then, this processing flow ends.

(Feature analysis processing)
Next, an outline for analyzing image features from image data according to the present embodiment will be described with reference to FIG. The description here is an overview of the processing in the image feature analysis of the image original in the scan data. The explanation already described is omitted. 14 is implemented by an ASIC (not shown) in the feature analysis unit 104.

  The image data 1401 is scan data received via the scanner 111 in S201, and is an RGB image. When image area separation processing is performed on the image data 1401 in S203, pixel attribute data 1402 is generated. At this time, the face mark (image) portion in the pixel attribute data 1402 is uniformly determined by only the halftone dot by the attribute information, and the character in the pixel attribute data 1402 is determined by only the character by the attribute information.

  Next, binarization processing is performed for each document type using the pixel attribute data 1402, and binarized image data 1403 is generated. At this time, it is determined whether the set value of the attribute information corresponding to each document type in FIG. 10A matches the set value of the pixel attribute data 1402. Here, a pixel that matches is “1”, and a pixel that does not match is “0”. When binarization processing is performed on an image original, only a face mark portion is extracted as in binarized image data 1403. The binarization process is a process corresponding to S307 in FIG.

  Next, the image feature of the part extracted by the binarized image data 1403 is extracted. First, labeling processing is performed on the binarized image data 1403 in order to extract image features. The labeling process is a process of assigning the same number to consecutive pixels in the binarized pixels, and it is possible to determine whether each pixel belongs to the same region. Then, image feature data 1405 for which the area (size), the degree of circular approximation, and the perimeter are obtained is calculated for each labeled region. That is, it is extracted as a feature for each object such as an image or a character. The image feature data 1405 is stored in the recording unit as one of the image analysis results. In the example shown in FIG. 14 of the present embodiment, the features of the image subjected to the labeling process are the area (size), the barycentric coordinates, and the perimeter, but other image features that can be extracted by the labeling process may be used. . The labeling process is a process corresponding to S309 in FIG. 3, and the image feature extraction process is a process corresponding to S312 in FIG.

  The CMYK image data 1407 is obtained by converting the image data 1401 into an image in the CMYK color space by performing the color conversion processing of S208 in FIG. Masking processing of the CMYK image data 1407 is performed using the binarized image data 1403. At this time, only the pixel value of “1” in the binarized image data 1403 is set to the signal value of the CMYK image as the signal value after masking, and the pixel value of “0” in the binarized image data 1403 is set to the signal value “ 0 ”. As a result, masked CMYK image data 1404 is generated from the CMYK image data 1407. The masking process is a process corresponding to S310 in FIG.

  Next, a histogram is calculated for the CMYK image data 1404 subjected to masking processing to obtain the frequency of input values for each color plate, and the histogram data 1406 for each document type is recorded as one piece of image analysis information. Stored in the department. In the histogram data 1406 shown in FIG. 14, the horizontal axis represents the input value, and the vertical axis represents the frequency of occurrence within the range of the input value. The histogram data creation process is a process corresponding to S311 in FIG.

  The feature analysis process (S210) of FIG. 2 will be described with reference to FIG. 3 is implemented by an ASIC (not shown) included in the image processing unit 103. Another feature of the present invention is that this processing step is performed at the time of image formation shown in FIG.

  In step S301, the image processing unit 103 calculates the amount of toner consumed in the page when the CMYK image (multi-value) 217 is output. As an example of a method for calculating the toner consumption, a 1D-LUT for converting the consumption of each color toner corresponding to a signal value is provided for each color plate, and the toner consumption is calculated by the 1D-LUT. There is. The calculation method here is not particularly limited.

  In step S302, the image processing unit 103 sets a document type to be read. The document type in the present embodiment is either “character document” or “image document”, and sets a document type that has not been processed since the start of this processing flow.

  In S303, the image processing unit 103 determines whether the image data received in S201 is scan data or printer data. In the case of scan data (YES in S303), in S303, the image processing unit 103 acquires setting of attribute information for scan data (FIG. 10A) corresponding to the document type set in S302. If it is not scan data (that is, printer data) (NO in S303), in S306, the image processing unit 103 sets attribute information for printer data corresponding to the document type set in S302 (FIG. 10B). )) To get.

  In S307, the image processing unit 103 generates a pixel attribute image (binary) 308 using the attribute data for each pixel of the pixel attribute data (multivalue) 204 and the attribute information set in S305 or S306. Specifically, it is determined whether the setting value of the attribute information corresponding to each document type in FIG. 10A matches the setting value of the pixel attribute data (multivalue) 204, and the matching pixel is determined as “ “1” and non-matching pixels are “0”.

  In S309, the image processing unit 103 performs a labeling process using the pixel attribute image (binary) 308, and generates a labeling image. In S310, the image processing unit 103 performs a masking process using the CMYK image (multivalue) 217 and the pixel attribute image (binary) 308, and generates a masking image.

  In step S311, the image processing unit 103 creates a histogram for each color plate for each document type using the masking image generated in step S310. In step S312, the image processing unit 103 extracts image features using the labeling image that has been stopped in step S309. Note that the image feature extraction processing is the same as that described for the image feature data 1405 in FIG.

  In step S313, the image processing unit 103 determines whether all document types (here, image document and text document) have been read. If all the types have not been read (NO in S313), the process returns to S303, and the image processing unit 103 sets another original type that has not been read, and repeats the process until reading of all the original types is completed. If all document types have been read (YES in S313), in S314, the image processing unit 103 determines whether each pixel is a chromatic color or an achromatic color pixel, and the chromatic color or achromatic color is determined. The total number of pixels is calculated. Further, the image processing unit 103 calculates the total number of monochrome pixels. Then, the process flow ends, and the process proceeds to S211 in FIG.

  In S211 of FIG. 2, the image processing unit 103 stores the histogram obtained in S311 and the image feature extraction result obtained in S312 as image analysis results in association with job information for each document type. Further, the image processing unit 103 stores the toner consumption amount for each color obtained in S301 and the number of chromatic, achromatic, and single color pixels as an image analysis result. Then, the image processing unit 103 holds the image analysis result for each page. The image analysis result to be stored is not limited to that described in the present embodiment, and any image analysis result may be used as long as it is a result of analyzing the characteristics of image data.

(Image diagnosis processing)
Subsequently, the image diagnosis processing according to the present embodiment will be described with reference to FIGS. 4 and 8. The image diagnosis processing in FIG. 4 is realized by the controller 102 executing using the CPU 107, the RAM 108, and the storage device 110.

  First, an image diagnosis execution method will be described with reference to FIG. A UI 801 in FIG. 8A is a configuration example of a UI screen for setting a main body that can execute calibration and the like. A UI 801 includes a button 802 for executing automatic gradation correction for correcting the gradation of a single color, a button 803 for correcting registration deviation for each color plate, and a component causing an abnormal image. Is provided with an image diagnostic button 804. The UI 801 also includes a button 805 for returning to the screen one level above the UI 801. Note that the automatic gradation correction and automatic color misregistration correction are executed using existing technology, and detailed description thereof is omitted.

  In the image diagnosis according to the present embodiment, when the user presses the image diagnosis button 804, an image diagnosis process (FIG. 4) described below is executed. Note that the configuration is not limited to the configuration shown in the UI 801 of the present embodiment, and a button for executing image diagnosis may be included in main body settings other than calibration.

  In step S <b> 401, the controller 102 acquires a job history from the job information 212. In S402, the controller 102 acquires basic chart data 403. The basic chart used here includes a plurality of diagnostic charts used when estimating a failure location by image diagnosis. An example of the basic chart data 403 will be described with reference to FIG. Here, description will be made assuming that three charts of a chart 901, a chart 903, and a chart 905 are used as the basic chart data 403. The chart data configuration (for example, the arrangement of patches) shown here is merely an example, and any chart data may be used as long as it is configured to detect an abnormal image.

  A patch 902 of the chart 901 is composed of uniform halftone signal values on the entire surface, and is used to detect abnormal images such as unevenness and streaks. A patch 904 of the chart 903 is configured and arranged with a plurality of rectangular data having different signal values such as color, gray, and monochrome, and is used to detect an abnormal image such as a poor color reproducibility.

  A patch 906 in the chart 905 is composed of thin line signal values expressed in a grid pattern, and is used to detect an abnormal image such as a poor line width reproduction. The chart data may be any chart data that can detect an abnormal image. Since abnormal images that can be detected differ depending on the signal values (features) of the chart 901, the chart 903, and the chart 905, it is necessary to prepare several patterns having different signal values. Also, in the case where parts are configured for each color, such as a developing device (not shown), the cause can be easily identified by outputting for each color, so the basic chart has the same configuration but different signal values. A plurality of patterns may be prepared. The image data of the basic chart is stored in the storage device 110 and is managed for each pattern having a different signal value. However, the location for storing the basic chart and the management method are not limited to the method of the present embodiment, and may take any form.

  In step S <b> 404, the controller 102 accepts selection of a job and a page in which an image quality problem has occurred using the input device 109. An example of a UI for the user to select a job and a page will be described with reference to FIG. A UI 806 in FIG. 8B is a configuration example of a UI screen for the user to select a job in which a problem has occurred. In the UI 806, the latest jobs are displayed in order from the table 807. Items constituting the table 807 indicate the date and time when the job was output, the job name, the job type, and the number of pages. In the present embodiment, there are four items, but there may be other items, and any UI may be used as long as it is a UI for selecting a job.

  A button 808 is a button for canceling the image diagnosis process on the UI 801. When the button 808 is pressed by the user, the controller 102 interrupts the image diagnosis process and transitions to display of the UI 801. A button 809 is a button for selecting when an image quality problem has occurred in the UI 806 regardless of the job. When the user presses the button 809, a chart (not shown) for detecting dirt is output, and image diagnosis is continued. The chart for detecting dirt is a chart for detecting dirt because abnormal images may be generated as a result of dirt being attached to any of various parts used when the MFP 101 performs image formation. It is. A button 810 is a button for shifting to a UI 811 for setting a page after the user selects a job in which an image quality problem has occurred from the table 807. When the user selects any job from the table 807 and presses the button 810, a UI 811 in FIG. 8C, which is a screen for selecting a page, is displayed.

  A UI 811 in FIG. 8C is a UI configuration example for selecting a page in a job in which a problem has occurred. A text box 812 is a text box for setting the number of pages of a job in which an image quality problem has occurred in the UI 811, and can be input up to the number of pages of the job selected in the UI 806. Note that an upper limit value of the number of pages may be additionally displayed. A return button 813 is a button for returning to the UI 806 for selecting a job in the UI 811. A button 814 is a button for executing image diagnostic processing after page setting is completed in the UI 811. If the page set in the text box 812 does not exceed the number of pages of the job selected in the UI 801 when the button 814 is pressed, the flow shifts to a flow for continuously executing the image diagnosis processing.

  Note that the present invention is not limited to the configuration shown in the present embodiment, and any configuration is possible as long as the UI can set the page of a job in which an image quality problem has occurred or can specify that any job is generated. Good. 8B and 8C, a first accepting unit that accepts selection of a job and a page in the job from the user is realized.

  In S405, the controller 102 determines whether or not “occurred in all jobs” (button 809) is selected by the user in the selection received in S404. If it is selected that an abnormality has occurred in all jobs (YES in S405), in S407, the controller 102 sets a chart necessary for detecting dirt in the selection information 408. The selection information 408 here is information that is set to output only the charts necessary for the image diagnosis of the present embodiment in the basic chart data 403. For example, when a number is assigned to each pattern having different signal values, only the number for the chart to be output is retained, and image data of the basic chart corresponding to the number is acquired and output at the time of output. In step S <b> 409, the controller 102 outputs only the chart selected from the basic chart using the selection information 408 using the printer 113.

  On the other hand, in the selection accepted in S404, if “occurred in all jobs” is not selected by the user (NO in S405), the controller 102 performs image selection processing and outputs the basic chart in S406. Is stored in selection information 408. The details of the image selection process in this step will be described later with reference to FIG. In step S <b> 409, the controller 102 outputs only the chart selected from the basic chart using the selection information 408 using the printer 113.

  In S410, the controller 102 causes the user to input whether or not a phenomenon similar to the phenomenon recognized by the user as an abnormal image in the chart output in S409 can be visually recognized. Here, a UI 1501 in FIG. 15 is a configuration example of a UI screen for confirming whether an abnormal image can be visually recognized in the output chart. A button 1502 is a button that is pressed when an abnormal image is confirmed in the output chart. A button 1503 is pressed when an abnormal image cannot be confirmed on the output chart. A button 1504 is a button for closing the UI 1501 and displaying the UI screen of the next higher level. The UI of FIG. 15 implements a second accepting unit that accepts from the user the presence or absence of an abnormal image in which an abnormality is reproduced in the output chart.

  If button 1502 is pressed by the user (YES in S410), the process proceeds to S412 for scanning the output chart. In step S412, the controller 102 scans the image diagnostic chart output in step S409 using the scanner 111 by user operation, and generates diagnostic scan data 413.

  When button 1503 is pressed by the user (NO in S410), in S411, controller 102 reselects and acquires chart data that is not selected in selection information 408 from basic chart data 403. Here, it is assumed that all the chart data that have not been selected are selected. Then, the controller 102 outputs the acquired chart data using the printer 113. In step S412, the controller 102 scans the image diagnosis chart output in step S412 using the scanner 111 by user operation, and generates diagnosis scan data 413.

  In S 414, the controller 102 transmits diagnostic scan data 413 and selection information 408 to the server 116. Here, the server 116 estimates the failure location using the diagnostic scan data 413 and the selection information 408, and records the failure location estimation result as the failure location estimation information 416. The failure location estimation processing by the server 116 will be described later with reference to FIG.

  In S 415, the controller 102 receives failure location estimation information 416 from the server 116. In S417, the controller 102 displays the estimation result of the failure location estimation information 416 using the display device 112. Then, this processing flow ends.

  Note that the content displayed in S417 displays the estimation result using the display device 112 only when there is no need for replacement of parts by the service person. For example, when it is determined as a result of diagnosis that there is no faulty part, or when it is determined that improvement is possible by setting the main body such as calibration, the contents are displayed. If there is a part that has a high possibility of failure, the user contacts the base of the service person in charge of maintenance of the MFP 101 and the failure point estimation information 416, and based on the failure point estimation information 416, the service person Prepare the parts to be exchanged and perform the exchange. As a result, downtime including waiting time for parts replacement can be reduced, and user productivity can be improved.

(Image selection process)
Next, the flow of the image selection process in S406 of FIG. 4 will be described with reference to FIG. The processing in FIG. 5 is realized by the controller 102 executing using the CPU 107, the RAM 108, and the storage device 110.

  In step S501, the controller 102 uses the model information 502 to determine a threshold value for determining whether to output a basic chart. The reason why the threshold value is changed according to the model is that the likelihood of an image problem varies depending on the model. In the present embodiment, the threshold value corresponding to each model is a value set in advance. However, the threshold value may be changed in accordance with the cumulative output number so far, the environment, or the like.

  In step S503, the controller 102 acquires an analysis result corresponding to the page in the job in which the image quality problem selected in step S404 occurs. In S504, the controller 102 compares the number of pixels of the monochrome data with the threshold value in the analysis result acquired in S503. As a result, when the number of pixels is equal to or greater than the threshold (YES in S504), in S505, the controller 102 selects a monochrome gradation confirmation chart. At this time, the configuration of the monochromatic gradation confirmation chart is represented by a chart 903 in FIG. 9B, and a patch having gradation characteristics of each monochromatic color in each column. This makes it possible to diagnose whether there is an abnormality in gradation.

  In step S506, the controller 102 compares the number of chromatic pixels with a threshold value in the analysis result acquired in step S503. As a result, when the number of pixels is equal to or greater than the threshold (YES in S506), in S507, the controller 102 selects a color confirmation chart. At this time, the configuration of the color confirmation chart is also represented by the chart 903 in FIG. 9B, and is composed of patches of various chromatic colors. This makes it possible to diagnose whether there is an abnormality in the color mixing characteristics.

  In S508, the controller 102 compares the number of achromatic pixels with the threshold in the analysis result acquired in S503. As a result, if the number of pixels is equal to or greater than the threshold value (YES in S508), in S505, the controller 102 selects a gray balance confirmation chart. At this time, the gray balance confirmation chart is expressed by a chart 903 in FIG. 9B, and is configured by a gray patch composed of CMY or a K single color patch. This makes it possible to diagnose whether there is an abnormality in gray balance.

  In S510, the controller 102 performs chart selection processing for each color. The chart selection process for each color uses the histogram data created in S311 and the image features extracted in S312 to check the line width reproducibility for each density divided into high density, medium density, and low density, and check for unevenness and streaks. This is a process for selecting a chart to do. Details of this step will be described later with reference to FIG.

  In step S511, the controller 102 acquires the toner consumption corresponding to the page in the job in which the image quality problem has occurred. In step S512, the controller 102 determines whether the toner consumption acquired in step S511 is equal to or greater than a threshold value. If the consumption is greater than or equal to the threshold (YES in S512), in S513, the controller 102 selects a monochrome gradation confirmation chart.

  In S <b> 514, the controller 102 stores the basic chart information selected so far as selection information 408. Then, the image selection process ends, and the process proceeds to S409 in FIG.

(Color chart selection process)
The flow of the color chart selection process (S510 in FIG. 5) will be described with reference to FIG. The processing in FIG. 6 is realized by the controller 102 executing using the CPU 107, the RAM 108, and the storage device 110.

  In step S601, the controller 102 sets a setting value for determining which density area the input value becomes from among the high density area, the medium density area, and the low density area in the histogram data 1406 of FIG. To determine. The set value of S601 is determined in advance according to the model. Note that the present invention is not limited to the configuration shown in the present embodiment, and the threshold value may be changed according to the cumulative output number, environment, and the like so far.

  In step S602, the controller 102 acquires an analysis result corresponding to the page in the job in which the image quality problem selected in step S404 occurs. In step S603, the controller 102 sets a document type to be determined. The document type in the present embodiment is either a text document or an image document, and a document type that has not been processed since the start of this processing flow is set. In step S604, the controller 102 sets a color plate to be determined. The color plate in the present embodiment is one of C, M, Y, and K, and a color plate that has not been processed since the start of this processing flow is set.

  In S605, the controller 102 determines whether or not the frequency of occurrence is higher than or equal to the threshold value for the area in which the high density area is set in the histogram data created in S311 of FIG. If the frequency of occurrence is greater than or equal to the threshold (YES in S605), in S606, controller 102 determines whether or not the document type set in S603 is a text document. If it is a character document (YES in S606), in S607, the controller 102 determines whether or not the number of characters determined in the image feature result extracted in S312 is greater than or equal to a threshold value. The threshold value used here is defined in advance, but is not limited to this, and may be changed according to the density or the distribution of the histogram. If the number of characters is equal to or greater than the threshold (YES in S607), controller 102 selects a high density line width expression chart. Here, the line width expression chart is the chart 905 of FIG. 9C, and the color of the line is the color set in S604.

  If the number of characters is smaller than the threshold value (NO in S607), in S610, controller 102 determines the medium density region. If the document type set in S603 is not a character document (NO in S606), in S608, the controller 102 selects a high-density in-plane uniform chart. Here, the in-plane uniform chart is the chart 901 in FIG. 9A, and the color of the patch 902 is the color set in S604. The signal value corresponding to the color may be a predetermined value, or may be a signal value having a high frequency in the high density region.

  In S610, the controller 102 determines whether or not the frequency of occurrence of the area in which the medium density area is set in the histogram data created in S311 is greater than or equal to a threshold value. If the frequency of occurrence is greater than or equal to the threshold (YES in S610), in S611, controller 102 determines whether or not the document type set in S603 is a character document. If it is a character document (YES in S611), in S612, the controller 102 determines whether or not the number of characters determined in the image feature result extracted in S312 is equal to or greater than a threshold value. If the number of characters is equal to or greater than the threshold (YES in S612), controller 102 selects a medium density line width expression chart. Here, the line width expression chart is the chart 905 of FIG. 9C, and the color of the line is the color set in S604.

  If the number of characters is smaller than the threshold value (at S612), the controller 102 determines a low density area at S615. If the document type set in S603 is not a character document (NO in S611), the controller 102 selects a medium density in-plane uniform chart in S613. Here, the in-plane uniform chart is the chart 901 in FIG. 9A, and the color of the patch 902 is the color set in S604. The signal value corresponding to the color may be a predetermined value, or may be a signal value having a high frequency in the medium density region.

  In S615, the controller 102 determines whether or not the frequency of occurrence of the low density region set in the histogram data created in S311 is equal to or greater than a threshold value. If the frequency of occurrence is greater than or equal to the threshold (YES in S615), in S616, controller 102 determines whether or not the document type set in S603 is a text document. If the document is a character document (YES in S616), in S617, the controller 102 determines whether the number of characters determined in the image feature result extracted in S312 is equal to or greater than a threshold value. If the number of characters is equal to or greater than the threshold (YES in S617), controller 102 selects a low density line width expression chart. Here, the line width expression chart is the chart 905 of FIG. 9C, and the color of the line is the color set in S604.

  If the number of characters is smaller than the threshold (NO in S617), the process proceeds to S620. If the document type set in S603 is not a character document (NO in S616), controller 102 selects a low-density in-plane uniform chart in S618. Here, the in-plane uniform chart is the chart 901 in FIG. 9C, and the color of the patch 902 is the color set in S604. The signal value may be a predetermined value or a signal value having a high frequency in a low density region.

  In S620, controller 102 checks whether all colors have been determined. If all colors have not been confirmed (NO in S620), the process returns to S604, and controller 102 repeats the process until all colors are determined. If all colors have been confirmed (YES in S620), in S621, controller 102 confirms whether all document types have been determined. If not all document types have been confirmed (NO in S621), the process returns to S603, and controller 102 repeats the process until all document types are determined. If all document types have been determined (YES in S621), the color-specific chart selection process ends, and the process proceeds to S511 in FIG.

[Processing flow (server)]
Processing on the server 116 side will be described with reference to FIG. The processing in FIG. 7 is realized by the controller 117 using the CPU 121, the RAM 122, and the storage device 124. The processing in the controller 117 includes an image analysis unit 119 that calculates a feature value and a failure location estimation unit 120 that performs failure location estimation from the feature value.

  In step S <b> 701, the controller 117 receives image scan data 413 and selection information 408 output from the MFP 101. In step S702, the controller 117 performs image analysis using the diagnostic scan data 413 acquired in step S701, and calculates feature amount data 703 as a result. At this time, the controller 117 performs an image analysis method corresponding to the type classified by the basic chart data 403 on the diagnostic scan data 413. Here, the selection information 408 is used to determine what kind of scan data 413 for diagnosis is present.

  An example of the image analysis method will be described. An analysis method using a chart for confirming monochromatic gradation with a patch configuration like the chart 903 in FIG. 9B will be described. The color value of the scan data read by the scanner in the output (printed) chart 903 is converted into density, and it is analyzed whether the density corresponds to the signal value of the patch or the gradation is not reversed. The result is used as feature data.

In the analysis method using the chart for confirming the color tone such as the mixed color with the configuration like the chart 903 in FIG. 9B, the color value of the scan data is converted into the L * a * b * color space, The color difference with the signal value of the registered L * a * b * color space is calculated, and the color difference is used as feature amount data. At this time, the signal value of the L * a * b * color space registered in advance at this time is set for each product, or the previously output color confirmation chart is used as a scan data signal using the scanner 111. A value converted from an L * a * b * value may be registered. Here, the color difference is calculated using the following equation (1). L ₁ , a ₁ , and b ₁ are the acquired L * a * b * values, and L ₂ , a ₂ , and b ₂ are the reference L * a * b * values.
... Formula (1)

  In the analysis method using the chart for confirming the uniformity of the in-plane density with the configuration like the chart 901 in FIG. 9A, the color value of the scan data is converted into the density, and a preset small region is obtained. The average density is calculated for each time. Then, it is analyzed whether unevenness or streaks are generated in the surface due to the variation in the density of the small area, and the analysis result according to the intensity where the unevenness or streaks are generated is used as the feature amount data.

  In the analysis method using the chart for confirming the reproducibility of the thin line with the configuration like the chart 905 in FIG. 9C, the toner is not scattered using the extracted edge component or the line is not blurred. This is analyzed, and the variation of the edge component is used as the feature amount data.

  In this embodiment, each analysis method is selected according to the type of the selected chart. However, the present invention is not limited to this, and is a method that can appropriately analyze an image quality problem to be diagnosed, and includes feature amount data. As long as it can be calculated, other methods may be used. Further, the feature amount data of the present embodiment is described as a continuous value, but the present invention is not limited to this. For example, a threshold value may be prepared in advance for feature amount data using discrete values, for example, an analysis result, and it may be determined that there is an abnormality when the threshold value is exceeded, and that there is no abnormality when the threshold value is not exceeded.

  In S704, the controller 117 estimates a failure location using the feature amount data 703 calculated in S702, and calculates failure location estimation information 416 in which probability information that each component has failed is stored. A failure location estimation method using the feature data 703 will be described. If there is a large amount of historical data in which the feature data of the same model and the location actually replaced by the serviceman are associated with each other, the probability of the failure location is calculated for each part using Bayes' theorem or Bayesian expansion formula. To calculate. That is, it is possible to calculate the cause of the failure from the feature amount data obtained using past performance data. Further, since the problem may be solved by changing the setting of the main body such as calibration instead of the failure, the probability of changing the setting of the main body may be calculated.

  Note that the above-described failure location estimation method is merely an example, and other methods such as failure location estimation such as a Bayesian network may be used.

  In step S <b> 705, the controller 117 transmits failure location estimation information 416 to the MFP 101 as a diagnosis result on the server 116 side. In S706, the controller 117 transmits failure location estimation information 416 as a diagnosis result on the server 116 side to a server (not shown) at the serviceman base. Then, the image diagnosis process in the server 116 is terminated.

  According to the present embodiment, the test chart for image diagnosis can be reduced without depending on the analysis skill of the user, and image diagnosis with reduced burden on the user is facilitated.

<Second Embodiment>
In this embodiment, unlike the first embodiment, it is estimated whether there is a tendency of the output image from the result analysis of the image of the job in a predetermined period without specifying the job and page in which the image problem has occurred. A method of reducing the number of output sheets used for image diagnosis from the estimated result will be described. Here, only a different part from 1st embodiment is demonstrated and description is abbreviate | omitted about an overlapping part.

[Processing flow]
First, the flow of processing when performing image diagnosis will be described with reference to FIG. The processing in FIG. 11 is realized by the controller 102 executing using the CPU 107, the RAM 108, and the storage device 110. Also, it is performed when the user instructs execution of image diagnosis of the UI 801 using the input device 109.

  Steps S1101 and S1103 are the same as steps S401 and S403 in FIG. An example of a UI according to the present embodiment will be described with reference to FIG. Note that the UI 801 used for executing the image diagnosis processing is the same as that of the first embodiment, and thus description thereof is omitted.

  A UI 1301 in FIG. 13A is a UI screen displayed in step S <b> 1105, and is a UI configuration example displayed when the user presses the image diagnosis button 804 of the UI 801. Similar to the table 807 of the UI 806 shown in FIG. 8B, the table 1302 displays the output jobs in order from the latest one. Since the buttons 1303, 1305, and 1306 operate in the same manner as the UI 806 of the first embodiment, the description thereof is omitted. A button 1304 is a button for performing processing for estimating an image problem that is likely to occur from a job tendency. When the user presses a button 1304, the trend estimation process is executed. Details of the trend estimation process in this step will be described later with reference to FIG.

  A UI 1307 in FIG. 13B is a UI configuration example that is displayed when the user cannot press the button 1304 of the UI 1301 in FIG. is there. The basic operation is the same as that of the UI 1301 in FIG. 13A, but the button 1310 is grayed out. As a result, tendency estimation cannot be performed, and the user selects a job and a page in which an abnormal image has occurred as in the first embodiment. Examples of cases where the trend cannot be estimated include that a job has not been output within a predetermined period, and that the number of times that all basic charts have been selected is greater than or equal to a threshold value, and the tendency cannot be estimated.

  Similar to the table 807 of the UI 806 in FIG. 8B, the table 1302 displays the output jobs in order from the latest one. Since the buttons 1303, 1305, and 1306 operate in the same manner as the UI 806 of the first embodiment, the description thereof is omitted. The UI 1313 in FIG. 13C performs the same operation as the UI 811 in FIG.

  When trend estimation is performed based on an instruction from the user (YES in S1105), in S1106, the controller 102 performs a process of estimating whether there is a tendency in the image characteristics of the output product within a predetermined period. The estimated result is stored in the selection information 1111. Details of the trend estimation process in this step will be described later with reference to FIG.

  In S1108, the controller 102 determines whether there is an output tendency estimated in S1106. If there is an output tendency (YES in S1108), in S1113, the controller 102 outputs a chart selected based on the selection information 1111 using the printer 113. If there is no output tendency (NO in S1108), in S1107, the controller 102 accepts selection of a job and a page in which an abnormal image is generated by the user, as in the first embodiment. Since S1109 to S1121 are the same processing as S405 to S417 in FIG.

(Trend estimation process)
Details of the trend estimation processing in S1106 of FIG. 11 will be described with reference to FIG. The processing in FIG. 12 is realized by the controller 102 executing using the CPU 107, the RAM 108, and the storage device 110.

  In step S1201, the controller 102 sets a job period to be referred to when the trend estimation process is performed. In this embodiment, a certain period predetermined for each model is set as a period of a job to be referred to in the trend estimation process. However, it is not limited to the above. For example, a job to be referred to can be referred to by referring to a job within one hour, enabling the user to set, or determining a reference period from the total number of output sheets. Any time period can be set as long as the period can be set.

  In step S1202, the controller 102 reads, from the job information 1102, job information of pages in the job that have not yet been processed within the reference period set in step S1201. In step S <b> 1203, the controller 102 performs image selection processing using the job information of the page read in step S <b> 1202, and records information obtained by selecting the basic chart on the processed page in the selection information 1111. Note that the image selection processing in S1203 is the same as the processing in FIG. 5 described in the first embodiment, and thus description thereof is omitted.

  In S1204, the controller 102 adds the chart selected in S1203 as the number of times selected from the basic chart data 403. Here, a page in a job within a predetermined period is processed, and the tendency is estimated by referring to the total number of selected times.

  In step S1205, the controller 102 determines whether there is a page in the job that has not yet been processed within the reference period set in step S1201. If there is a page that has not been processed (NO in step S1205), the controller 102 returns to step S1202 and repeats the image selection processing for pages in all jobs within the reference period. If pages in all jobs within the period to be referred to have been processed (YES in S1205), in S1209, the controller 102 reads chart data that has not been selected in the basic chart data 403.

  In S1210, the controller 102 determines whether or not the total number of times selected in the processing up to S1205 for the chart data selected in S1209 is greater than or equal to a threshold value. If it is equal to or greater than the threshold (YES in S1210), in S1211, the controller 102 selects the selected chart as a candidate for a chart to be output. If smaller than the threshold (NO in S1210), the process of S1212 is executed. Here, the threshold for determining as a candidate to be output may be a predetermined value or a value that is equal to or greater than a predetermined ratio based on the total number of referenced pages.

  In S1212, the controller 102 determines whether or not there is chart data that has not been selected in the basic chart data 403 in S1209. If there is chart data that has not been read in the basic chart data 403 (NO in S1212), the process returns to S1209, and the controller 102 reads all chart data and determines candidates to be output from the selected total. Repeat the process.

  When all the chart data has been read (YES in S1212), in S1213, the controller 102 checks the total of the chart data that is a candidate in S1211, determines whether the total is equal to or greater than a threshold, Check if there is a tendency to output. In other words, when the number (type) of chart data that is an output candidate is small, it can be determined that there is a tendency because the image in which an abnormality has occurred is biased. On the other hand, when many types of chart data are selected as output candidates, it cannot be determined that an abnormality has occurred in any image, so it is determined that there is no tendency. If it is equal to or greater than the threshold (YES in S1213), in S1215, controller 102 determines that there is no tendency to output. If it is less than the threshold (NO in S1213), in S1214, the controller 102 overwrites the selection information 1111 with the chart data that is a candidate in S1211. The threshold for determining the tendency to output in the present embodiment is a predetermined value, but is not limited to this, and is a value in which the upper limit number of charts output when the tendency is estimated by the user is set. Any method may be used as long as the tendency can be determined as described above.

  As described above, without specifying the job and page in which the image problem has occurred, by estimating whether there is a tendency of the output image from the image feature analysis result of the job in a predetermined period, the chart output by the image diagnosis The number of sheets can be reduced. As a result, the number of output charts can be reduced without selecting pages as compared with the first embodiment, and the diagnosis of the image forming apparatus can be performed with a reduced burden on the user without depending on the analysis skill of the user.

<Other embodiments>
The present invention is also realized by executing the following processing. That is, software (program) that realizes the functions of the above-described embodiments is supplied to a system or apparatus via a network or various storage media, and a computer (or CPU, MPU, or the like) of the system or apparatus reads the program. It is a process to be executed.

Claims (21)

A system that includes a server and an image forming apparatus, analyzes an output image from the image forming apparatus, and diagnoses the image forming apparatus;
The image forming apparatus includes:
Analyzing means for analyzing image characteristics of each page included in the job at the time of image formation for the job;
Storage means for storing image features analyzed by the analysis means in association with each page;
A first accepting unit for accepting selection of a page included in a job in which an abnormality has occurred in an output image as a result of image processing at the time of diagnosis of the image forming apparatus;
Selecting means for selecting one or more charts from a plurality of diagnostic charts using image features associated with the page selected by the first accepting means;
Output means for outputting the chart selected by the selection means;
Second receiving means for receiving from the user whether or not an abnormality has occurred in the output image of the chart output by the output means;
Reading means for reading the output image of the chart in which an abnormality has occurred in the second receiving means;
Transmission means for transmitting the image of the read chart to the server,
The server
Analyzing the image of the chart transmitted from the transmission unit of the image forming apparatus and estimating the state of the image forming apparatus;
The system characterized by having.   The system according to claim 1, wherein the analysis unit switches an item of attribute information when analyzing the image feature according to the type of image data used in the job.   The system according to claim 2, wherein the analysis unit extracts, as the image feature, an object included in each page included in a job classified using the attribute information.   The system according to claim 1, wherein the analysis unit calculates a histogram of color values of pixels constituting each image of a page included in the job as the image feature.   The analysis unit calculates the number of chromatic, achromatic, and single color pixels as the image feature based on a color value of a pixel constituting each image of a page included in a job. The system according to any one of 1 to 4. The first accepting means accepts from the user whether or not an abnormality has occurred in the output image regardless of the job,
When the first receiving unit receives an instruction indicating that an abnormality has occurred in the output image regardless of the job, the selecting unit detects dirt on a portion of the image forming apparatus that performs image formation. The system according to claim 1, wherein the chart is selected. When the second receiving unit receives an instruction indicating that no abnormality has occurred in the output image of the chart output by the output unit,
The selecting means reselects a chart that has not been selected from the plurality of diagnostic charts,
The system according to claim 1, wherein the output unit outputs the reselected chart. The first receiving means receives an instruction from the user to select a chart to be output according to the image characteristics of a job executed in the past,
When the selection unit receives the instruction, the selection unit extracts a chart candidate from the plurality of diagnostic charts based on the image characteristics of each page included in the job executed in a predetermined period, and the extracted chart The system according to claim 1, wherein when the number of candidates is smaller than a threshold value, the chart candidate is selected as a chart.   The selection means, when the number of extracted chart candidates is equal to or greater than a threshold, uses the image feature associated with the page selected by the first reception means to display a chart from the plurality of diagnostic charts. 9. The system according to claim 8, wherein the system is selected.   The system according to claim 3, wherein the selection unit selects a chart from the plurality of diagnostic charts according to an attribute of the object extracted as the image feature.   5. The system according to claim 4, wherein the selection unit selects a chart for each color constituting an image of each page based on the histogram.   5. The system according to claim 4, wherein the selecting means classifies each color constituting an image of each page into a plurality of density areas based on the histogram, and selects a chart for each classified area. .   The selecting means selects a chart for monochromatic gradation, hue, and gray balance according to the number of chromatic, achromatic, and monochromatic pixels of each page indicated by the image feature. The system according to claim 5.   The selection means selects a chart for a monochrome gradation when a toner consumption amount when the image of the page selected by the first reception means is equal to or greater than a threshold value. The system described in.   The system according to claim 1, wherein the reading unit reads an image of a chart with a scanner. The server further includes holding means for holding a history of jobs in which an abnormality has occurred in the past,
The diagnosis unit diagnoses the state of the image forming apparatus based on the history of the job held by the holding unit and the analysis result of the chart image transmitted by the transmission unit of the image forming apparatus. The system according to claim 1, wherein: The server further includes means for transmitting a diagnosis result diagnosed by the diagnosis means to the image forming apparatus,
The system according to claim 1, wherein the image forming apparatus further includes display means for displaying a diagnosis result received from the server. An image forming apparatus connected to a server that analyzes an output image from the image forming apparatus and diagnoses the image forming apparatus,
Analyzing means for analyzing image characteristics of each page included in the job at the time of image formation for the job;
Storage means for storing image features analyzed by the analysis means in association with each page;
A first accepting unit for accepting selection of a page included in a job in which an abnormality has occurred in an output image as a result of image processing at the time of diagnosis of the image forming apparatus;
Selecting means for selecting one or more charts from a plurality of diagnostic charts using image features associated with the page selected by the first accepting means;
Output means for outputting the chart selected by the selection means;
Second receiving means for receiving from the user whether or not an abnormality has occurred in the output image of the chart output by the output means;
Reading means for reading the output image of the chart in which an abnormality has occurred in the second receiving means;
An image forming apparatus comprising: a transmission unit that transmits the read chart image to the server. A control method of a system including a server and an image forming apparatus, and analyzing an output image from the image forming apparatus and diagnosing the image forming apparatus,
In the image forming apparatus,
An analysis process for analyzing image characteristics of each page included in the job at the time of image formation for the job;
A storage step of associating the image features analyzed by the analysis step for each page and storing them in a storage unit;
A first accepting step of accepting, from the user, selection of a page included in a job in which an abnormality has occurred in an output image as a result of image processing at the time of diagnosis of the image forming apparatus;
A selection step of selecting one or more charts from a plurality of diagnostic charts using the image features associated with the page selected in the first reception step;
An output step for outputting the chart selected in the selection step;
A second receiving step for receiving from the user whether or not an abnormality has occurred in the output image of the chart output in the output step;
A reading step of reading an output image of the chart in which an abnormality has occurred in the second reception step;
A transmission step of transmitting an image of the read chart to the server,
In the server,
Analyzing the image of the chart transmitted from the transmission means of the image forming apparatus, and estimating the state of the image forming apparatus;
A method for controlling a system, comprising: A method of controlling an image forming apparatus connected to a server that analyzes an output image from the image forming apparatus and diagnoses the image forming apparatus,
An analysis process for analyzing image characteristics of each page included in the job at the time of image formation for the job;
A storage step of storing the image features analyzed in the analysis step in a storage unit in association with each page;
A first accepting step of accepting, from the user, selection of a page included in a job in which an abnormality has occurred in an output image as a result of image processing at the time of diagnosis of the image forming apparatus;
A selection step of selecting one or more charts from a plurality of diagnostic charts using the image features associated with the page selected in the first reception step;
An output step for outputting the chart selected in the selection step;
A second receiving step for receiving from the user whether or not an abnormality has occurred in the output image of the chart output in the output step;
A reading step of reading an output image of the chart in which an abnormality has occurred in the second reception step;
And a transmission step of transmitting the read chart image to the server. Computer
Analyzing means for analyzing image characteristics of each page included in the job at the time of image formation for the job;
Storage means for storing image features analyzed by the analysis means in association with each page;
First receiving means for receiving, from the user, selection of a page included in a job in which an abnormality has occurred in an output image as a result of image processing at the time of diagnosis of the computer;
Selecting means for selecting one or more charts from a plurality of diagnostic charts using image features associated with the page selected by the first accepting means;
Output means for outputting the chart selected by the selection means;
Second receiving means for receiving from the user whether an abnormality has occurred in the output image of the chart output by the output means;
A reading unit that reads an output image of the chart in which an abnormality has occurred in the second receiving unit;
A program for causing the read chart image to function as transmission means for transmitting to a server.