A printing system includes processing circuitry. The processing circuitry acquires print data of a plurality of pages and extracts comparison data from the print data for each page. The processing circuitry, from first image data read from a printed material on which the print data is printed, acquires second image data at a position corresponding to the comparison data, for each page of the printed material. The processing circuitry outputs a comparison result of the comparison data and the read image data for each page.

In a printer, a controller controls a printing portion to execute a sample print to print on a sheet a plurality of barcodes. Each of the plurality of barcodes is represented by a received barcode data and printed in a different printing condition. The controller controls a reading portion to read the plurality of barcodes printed on the sheet in the sample print process to obtain a plurality of sets of scan data corresponding to respective ones of the plurality of barcodes. The controller performs a quality determination process to determine qualities of the plurality of printed barcodes based on the respective ones of the plurality of sets of scan data, and a printing condition setting process to set a printing condition based on the qualities determined in the quality determination process. The set printing condition is to be used to print a barcode by the printing portion.

There is set forth herein a system including a camera device. In one embodiment the system is operative to perform image processing for detection of an event involving a human subject. There is set forth herein in one embodiment, a camera equipped system employed for fall detection.

A method for printing defect detection includes processing and analyzing a difference image obtained by comparing an image scanned with a verifier to a reference image. The detected defects are grouped, and the grouping is refined. Confidence level values are then assigned to the refined groups, and analysis is performed to determine if one or more servicing actions should be taken.

A system and method for minimizing customer churn in device service businesses commences with execution of a customer service contract. Ongoing customer data capture is made for each contract. Customer data includes contract events, environmental events, service events, device usage analytics and personnel events. Machine learning is applied to captured customer data, which machine learning is based on a state of customer data at the time of contract determination. Customer data is assigned weights, and aggregate data for each customer is compared to a preselected threshold level. Customers above a threshold are deemed happy and customers below the threshold are deemed to be at risk. Remedial measures relative to at risk customer data generates remedial measure suggestions.

An information processing method including: acquiring a superimposed image in which electronic information corresponding to an image of an object captured by an image capturing unit of an information processing apparatus is superimposed on the image; storing the superimposed image in a storage; and displaying the superimposed image stored in the storage on a display of the information processing apparatus, and even in a case where the object is not included in an image capturing range of the image capturing unit, the superimposed image stored in the storage is displayed on the display.

An image forming apparatus includes a first detector detecting an operation sound based on a sound wave received by a receiver; a second detector detecting basis weight of recording material based on an ultrasonic wave transmitted from a transmitter and received by the receiver; a switching unit that switches destination of a reception signal from the receiver between a first path to the first detector and a second path to the second detector; and a controller that controls the switching unit such that the reception signal is output to the first path at a first timing and to the second path at a second timing. When a predetermined condition is satisfied, the controller controls the switching unit such that the reception signal is output to the second path at the first timing or to the first path at the second timing.

An information processing system configured to perform setting for an image processing apparatus based on setting edited by an application configured to operate on an information processing apparatus, wherein the system includes a startup unit that acquires a status of the image processing apparatus, downloads setting information for the image processing apparatus, determines whether or not to start up the application, and controls the startup of the application based on results of the determination, wherein the determination is performed based on the status before the setting information download has begun.

An image forming apparatus includes a printing unit, a verification unit, an insertion unit, and a display unit. The printing unit prints an image on a recording sheet. The verification unit verifies the image printed by the printing unit. The insertion unit inserts an insertion sheet, on which an identifier is printed, to partition a plurality of recording sheets, on each of which the image is printed, for each bundle of a predetermined number of sheets. The display unit displays the number of recording sheets on each of which an image associated with the identifier and determined as a normal image by the verification unit is printed.

An example of an apparatus including a communication interface to receive an image file is provided. The image file represents a scanned image of a output generated by a printing device. The apparatus further includes an identification engine to process the image file with a convolutional neural network model to identify a feature. The feature may be indicative of a potential failure. The apparatus also includes an image analysis engine to indicate a life expectancy of a part associated with the potential failure based on the feature. The image analysis engine uses the convolutional neural network model to determine life expectancy.