DETERMINING CAUSE OF PAPER JAM BACKGROUND [0001] There exist many different types of image forming apparatuses, including dedicated printers, scanners, copiers, facsimile machines, etc., and also multi-function products (MFPs) that act as an all-in-one solution to provide a combination of, e.g., print, copy, scan, and fax functions. In these image forming apparatuses, a paper jam may occur. BRIEF DESCRIPTION OF THE DRAWINGS [0002] Various examples will be described below by referring to the following figures. [0003] FIG. 1 is a diagram conceptually illustrating an image forming apparatus according to an example. [0004] FIG.2 is a diagram illustratively showing data acquired by sensors of an image forming apparatus according to an example. [0005] FIG.3 shows a plurality of causes of paper jams in an image forming apparatus according to an example. [0006] FIG.4 is a diagram conceptually illustrating operations of training a learning model to identify a cause of a paper jam according to an example. [0007] FIG. 5 is a diagram conceptually illustrating a learning model according to an example. [0008] FIG.6 is a diagram illustratively showing operations of identifying a cause of a paper jam in an image forming apparatus according to an example. [0009] FIG.7 is a diagram illustratively showing operations of identifying a cause of a paper jam in an image forming apparatus according to an example. [0010] FIG.8 is a diagram illustratively showing operations of identifying a cause of a paper jam in an image forming apparatus and re-training a learning model by which the identification was made according to an example. [0011] FIG.9 is a diagram illustratively showing an action to be performed after a cause of a paper jam in an image forming apparatus is identified according to an example. [0012] FIG.10 is a diagram illustratively showing an action to be performed after a cause of a paper jam in an image forming apparatus is identified according to an example. [0013] FIG. 11 is a flow diagram illustratively showing a method for identifying a cause of a paper jam in an image forming apparatus according to an example. [0014] FIG. 12 is a schematic illustration of a non-transitory computer readable recording medium including instructions according to an example. DETAILED DESCRIPTION [0015] Hereinafter, various examples will be described with reference to the drawings. However, the present disclosure may be implemented in several different forms and is not limited to the examples described hereinafter. [0016] In an image forming apparatus, there may occur a paper jam during an image forming operation such as printing. Upon the occurrence of the paper jam, a sensor installed in the image forming apparatus may detect the paper jam and generate a detection signal, based on which an informational message, including an error code or other information, may be indicated to a user who uses the image forming apparatus. Further, the user may respond to the current paper jam by checking, from the message, a response action including, for example, an operation to free the jammed paper, along with an approximate location where the paper is located (e.g., stuck) in the image forming apparatus. [0017] In addition to prompting such symptom-treating action to resolve the current paper jam by, for example, delivering the information regarding the location of the occurrence of the paper jam, the operation for removing the jammed paper, or the like, there may be adopted a technique for identifying and providing a basic cause of the paper jam according to an example. For example, in addition to taking the symptom-treating action, the user or a service engineer who performs maintenance of the image forming apparatus may desire to discover and resolve the basic cause of the paper jam. In an example, while the paper jam has occurred at a paper ejection port of the image forming apparatus, the basic cause of the paper jam may not be a mechanical failure in the paper ejection port, but rather correspond to use of poor-quality paper, lopsided loading of paper, trouble in a pick-up roller, etc. In such case, the basic cause is not straightforward to find by looking into a portion of the image forming apparatus, in which portion the paper jam has occurred. Accordingly, an analysis and identification may be made as to the basic cause of the paper jam so that the basic cause may be provided to the user or the service engineer. Such technique may help in providing a countermeasure against the paper jam and preventing a recurrence thereof, thereby reducing cost and time for the maintenance service and providing an enhanced user experience. [0018] Thus, in accordance with some examples, there is provided an apparatus and method to identify and provide a basic cause of a paper jam. [0019] In an example, an image forming apparatus may include a processor and a memory to store instructions that, based on execution, cause the processor to acquire a plurality of signals related to a paper jam, receive a paper jam cause that is identified as a most likely cause of the paper jam based on the plurality of signals, and perform an action in response to receiving the cause. In an example, the paper jam cause may be one of a plurality of paper jam causes that may include paper type mismatch, overloading of paper, or faulty removal of static charges, although other examples are also contemplated. In an example, the plurality of signals may include a first signal acquired from a first sensor arranged on a paper transfer path within the image forming apparatus and a second signal acquired from a second sensor that detects a status of the image forming apparatus, although other examples are also contemplated. [0020] The most likely cause of the paper jam may be identified, based on a learning model for machine learning, from among the plurality of paper jam causes. The learning model may be trained to receive the plurality of signals to compute, for each of the plurality of paper jam causes, a prediction probability of that paper jam cause being a cause of the current paper jam. [0021] As such, in an example, an analysis and identification may be made as to what is the basic cause of the paper jam so that the paper jam cause may be provided to the user or the service engineer. This may help in providing a countermeasure against the paper jam and preventing a recurrence thereof, thereby reducing cost and time for the service and providing an enhanced user experience. [0022] Various terms used in the present disclosure are chosen from a terminology of commonly used terms in consideration of their function herein, which may be appreciated differently depending on an intention, a precedent case, or an emerging new technology. In specific instances, some terms are to be construed as set forth in the detailed description. Accordingly, the terms used herein are to be defined consistently with their meanings in the context of the present disclosure, rather than simply by their plain and ordinary meaning. [0023] The terms “comprising,” “including,” “having,” “containing,” etc. are used herein based on specifying the presence of the elements listed thereafter. Unless otherwise indicated, these terms and variations thereof are not meant to exclude the presence or addition of other elements. [0024] As used herein, ordinal terms such as “first,” “second,” and so forth are meant to identify several similar elements. Unless otherwise specified, such terms are not intended to impose limitations, e.g., a particular order of these elements or of their use, but rather are used merely for referring to multiple elements separately. For instance, an element may be referred to in an example with the term “first” while the same element may be referred to in another example with a different ordinal number such as “second” or “third.” In such examples, such ordinal terms are not to limit the scope of the present disclosure. Also, the use of the term “and/or” in a list of multiple elements is inclusive of all possible combinations of the listed items, including any one or plurality of the items. [0025] The term “image forming job” as used herein may encompass any of a variety of image-related jobs that involve an operation of forming an image and/or other processing operations, e.g., creation, generation, and/or transfer of an image file. The term “job” as used herein may encompass a chain of processes that facilitate an image forming job, as well as the image forming job per se. By way of example and not limitation, an image forming device may perform an image forming job, such as a print job, a copy job, a scan job, a facsimile or other transmission job, a storage job, a coating job, or the like. [0026] The term “image forming device” or “image forming apparatus” as used herein may encompass any of a variety of devices, such as a printer, a scanner, a facsimile machine, a multi-function product (MFP), a display device, or the like, that are capable of performing an image forming job. In some examples, an image forming device may be a two-dimensional (2D) or three- dimensional (3D) image forming device. Such image forming device may provide various additional functions, as well as basic ones, for example, print, copy, and scan functions. [0027] The term “user” as used herein may refer to a person/organization who is to manipulate an image forming device to operate an image forming job. [0028] The term “administrator” as used herein may refer to a person/organization who has access to the entire functionality of an image forming device. By way of example and not limitation, an administrator may be a person/organization who can set up configuration data of a plurality of image forming apparatuses via a remote management server. In some examples, one person may have both roles of an administrator and a user. [0029] The term “service provider” as used herein may refer to a person/organization who is to supply an image forming device to a user and has control of the functions and settings of the image forming device. By way of example and not limitation, a service provider may be a reseller who lends a plurality of image forming devices to a particular user or group, applies device setting values of the plurality of image forming devices according to an established contract, and controls and manages installation of an application and a setting value of the application. In some examples, one person may have both roles of an administrator and a service provider. [0030] The terms “electronic device”, “electronic apparatus,” or “user device” as used herein may refer to any information processing device, such as, for example, a computer, a laptop, a tablet, a desktop personal computer (PC), a mobile telephone terminal, or the like, that may be used by a user. [0031] Certain examples of the present disclosure will now be described with reference to the accompanying drawings. The present disclosure may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein. Rather, these examples are given in order to provide a better understanding of the scope of the present disclosure. [0032] Various features of the examples will become apparent from the following detailed description taken in conjunction with the accompanying drawings. [0033] FIG. 1 is a diagram conceptually illustrating an image forming apparatus according to an example. [0034] As shown in FIG.1, an example image forming apparatus 100 may include a processor 110, a memory 120, a first sensor 130, a second sensor 140, a user interface 150, and a communications unit 160. Other configurations of the image forming apparatus 100 are also contemplated. In an example, the image forming apparatus 100 may also include an additional component, e.g., a power supply unit to supply power to the above-mentioned components, or other units. In another example, the image forming apparatus 100 may include some of, but not all of, the components shown in FIG. 1. Further, it may be appreciated that the image forming apparatus 100 is not a single device, but may be configured with a plurality of interconnected devices. [0035] In some examples, the processor 110 may control a function of the image forming apparatus 100. For example, the processor 110 may be implemented with a central processing unit (CPU), a graphics processing unit (GPU), or other processing circuitry to perform the example operations described herein. For example, the processor 110 may execute an instruction stored in the memory 120. The processor 110 may also read other information stored in the memory 120. In addition, the processor 110 may store new information in the memory 120 and may update some information stored in the memory 120. For example, the processor 110 may obtain, from the memory 120, information that may be used to control the image forming apparatus 100 or may store such information in the memory 120. [0036] In some examples, the memory 120 may include any computer- readable storage medium that stores data in a non-transitory form. Thus, the memory 120 may be implemented with Random Access Memory (RAM), Read- Only Memory (ROM), or any other type of storage medium. The memory 120 may have stored therein a variety of information, for example, a set of instructions that are to be executed by the processor 110. [0037] In some examples, the first sensor 130 may be installed in the image forming apparatus. For example, the first sensor 130 may include a plurality of sensors. It is to be understood that unless specified otherwise, the term “first sensor 130” may refer to either a single sensor or multiple sensors. In an example in which there is included in the image forming apparatus a transfer path along which a sheet of paper is to travel from a feeding tray so as to have an image formed thereon and to exit via an ejection port, the first sensor 130 may be installed at a certain location on the path. The first sensor 130 may detect the passage of the sheet of paper and send to the processor 110, or store in the memory 120, information regarding the detection, such as a time at which the detection is made, so that the processor 110 or another certain module of the image forming apparatus 100 may read or otherwise access the detection information. [0038] In some examples, the second sensor 140 may be installed in the image forming apparatus. For example, the second sensor 140 may include a plurality of sensors. It is to be understood that unless specified otherwise, the term “second sensor 140” may refer to either a single sensor or multiple sensors. The second sensor 140 may be installed to monitor and detect a status of the image forming apparatus 100. Examples of the status detected by the second sensor 140 may include information regarding a motor of the image forming apparatus 100, information regarding an environment of the image forming apparatus 100, information of a usage of the image forming apparatus 100, and the like, although other examples are possible. Examples of the motor-related information may include a motor lock signal, a motor current feedback signal, and the like, although other examples are possible. Examples of the environment information may include temperature and/or humidity inside the image forming apparatus 100, temperature and/or humidity outside the image forming apparatus 100, various configuration data values for use by the image forming apparatus to form an image, and the like, although other examples are possible. Examples of the usage information may include a count of pick-up attempts, a total number of printings, a type of paper jam, and the like, although other examples are possible. Further, any other types of example information are also contemplated that may be acquired from sensors installed in the image forming apparatus. [0039] In some examples, the user interface 150 may provide a user of the image forming apparatus 100 with an input interface and an output interface. In an example, the user interface 150 may include a display that serves as an output interface to provide or output a status, a certain message, an installed application, or the like, of the image forming apparatus 100. The display may be implemented as, e.g., a touch screen or other configuration. In an example, the user interface 150 may include an input interface to receive various inputs from the user. For example, the user may select his/her desired image forming job or select among various options related to the image forming job, through such input interface. Further, the user may launch, through the input interface, an application installed in the image forming apparatus 100. Example implementations of the input interface may include a keyboard, a keypad, a physical button, a touch pad, a touch screen, and many other types of devices that are capable of receiving user inputs. [0040] It will also be apparent that the input and the output interfaces included in the user interface 150 may be implemented as separate components or may be combined in a single component. For example, the user interface 150 may be implemented as a touch screen, which is operable both as the output interface to provide an output to the user and as the input interface to receive an input from the user. [0041] In some examples, the communications unit 160 may enable the image forming apparatus 100 to communicate with another electronic device, such as a host device, a user device, a server device, or the like, that is communicatively coupled with the image forming apparatus 100. The communications unit 160 may include a variety of communications module, for example, a wired communications module and/or a wireless communications module. For example, the wired communications module may support Local Area Network (LAN), Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), and any other suitable types of wired communication technologies. For example, the wireless communications module may support Wireless Fidelity (Wi- Fi), Wi-Fi Direct, Bluetooth, Ultra-Wide Band (UWB), Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), Fifth Generation (5G), Near-Field Communication (NFC), and any other suitable types of wireless communication technologies. [0042] With the above-mentioned components, the example image forming apparatus 100 may perform operations of identifying a cause of a paper jam by the processor 110 executing instructions, computer programs, machine readable instructions modules, and the like that are stored in the memory 120. Examples thereof are described below. [0043] FIG.2 is a diagram illustratively showing data acquired by sensors of an image forming apparatus according to an example. [0044] In some examples, a cause of a paper jam in the image forming apparatus 100 may be identified by using signals acquired from the first sensor 130 and the second sensor 140. [0045] Referring first to FIG.2, an example is described of a first signal that is acquired from the first sensor 130. The first sensor 130 may include a plurality of paper detection sensors. In a process of printing in which a sheet of paper is printed by the image forming apparatus 100, the paper sheet may be fed from a tray in which it is loaded, for example, Tray 1, Tray 2, or Tray 3 shown in FIG.2, and may be ejected, via an ejection port such as Ejection Port A or Ejection Port B of FIG.2, after being transferred within the apparatus by a paper sheet feeder (not shown) such as a roller and having an image formed thereon by a print unit (not shown) such as a photosensitive drum. On a path of the passage of the paper sheet within the image forming apparatus 100, there are arranged a plurality of sensors 130-1, 130-2, …, 130-n that are included in the first sensor 130 and that may be referred to below as the plurality of first sensors 130. Each of the plurality of first sensors 130 acquires time data regarding a time at which the sheet of paper passes by that sensor along the paper transfer path. For example, based on the passing paper sheet being detected by any two of the plurality of sensors 130, for example, the sensors 130-1 and 130-4, the processor 110 may be signaled of the passage of the paper sheet by an event, an interrupt, or the like and may use a real time clock (RTC), a timer, or the like to compute how much time is spent for the paper sheet to pass by the two sensors along a corresponding section of the path. That is, it is possible to acquire, from a difference between a time at which the sheet of paper is detected by a first encountered one of the two sensors and a time at which the paper sheet is detected by the next encountered other of the sensors, time information regarding a length of time for the paper sheet to traverse the corresponding section. In the image forming apparatus 100, as any two of the plurality of first sensors define one section of the path, a plurality of separate sections of the path may be set in accordance with detections made at the plurality of first sensors 130. Thus, for each of the plurality of sections, time data regarding a time at which the paper sheet is detected may be used for acquiring time information regarding how much time is used for the sheet of paper to pass that section. Alternatively, a certain one of the plurality of first sensors 130 may detect the paper sheet both at its entrance onto a section of the path and at its passage out of the section. In that case, time information regarding a time span of the section may be produced from a time difference between the detections. In other examples, different implementations are possible. [0046] The image forming apparatus 100 may have a set number of sheets of paper fed per unit time, for example, pages per minute or paper per minute (PPM). The higher the value of PPM, the more quickly the paper sheet transfer is performed within the image forming apparatus 100. The value of PPM may be set in a different manner across image forming apparatuses, each of which may yet have an identical mechanical configuration of the image forming apparatus 100. For example, these apparatuses may be configured with their respective different values of PPM according to different plans for leasing of the image forming apparatus 100 so that the apparatuses may support their respective different printing speeds. [0047] Therefore, the image forming apparatus 100 may produce, as the first signal, information regarding distances of the respective sections of the path of the passage of the paper sheet, as detected by the plurality of first sensors 130 within the image forming apparatus 100, by using the value of PPM of the apparatus and the time information acquired from the detections that are made by the plurality of first sensors 130 as the paper sheet travels along the respective sections. The distance indicated by such produced information for each of the sections may be identical to or different from an actual mechanical distance of that section. For example, based on the image forming apparatus 100 normally operating, the distance computed from the time information and the PPM value is identical to the previously known, actual mechanical distance. However, the computed distance may be different from the actual mechanical distance due to an error that has occurred within the image forming apparatus 100. In that case, this may be used as a reference for identifying a cause of a paper jam in the image forming apparatus. In an example in which, given a certain section which is known as having a mechanical distance of 10 cm, a distance of that certain section is computed as 15 cm from the time information and the PPM value, it may indicate that the paper sheet has traversed the section more slowly than intended. [0048] Since the distances of the respective sections of the paper transfer path are computed from the information acquired by the plurality of first sensors 130 regarding the times at which the paper sheet passes by the respective sensors and from the data indicative of the speed of the paper sheet, the same distance information may be produced even based on the same apparatus being configured with a different value of PPM. Therefore, based on a change being made to the operational configuration, such as PPM, of the image forming apparatus 100 and thus the plurality of first sensors 130 acquires different information regarding the times at each of which the paper sheet passes by a respective one of the sensors, the changed operational configuration of the apparatus is available for acquiring information for use in checking whether the apparatus normally operates or not. In other words, based on several identical image forming apparatuses being configured with different values of PPM, each of the apparatuses may compute, from its PPM value, a section-specific resulting distance, which may not vary across the apparatuses configured with different values of PPM. In an example in which a first one of the apparatuses has a PPM value twice that of a second one of the apparatuses and each of the first and the second apparatuses uses its plurality of first sensors to acquire a length of time for a sheet of paper to travel along a certain identical passage section, the time length acquired by the first sensors of the first apparatus may be half of that acquired by the first sensors of the second apparatus. It may be understood that in this example, the difference between the time lengths for the identical section does not represent an abnormal operation of one of the apparatuses. [0049] With continued reference to FIG. 2, there is described a second signal that is acquired from the second sensor 140. As described above, the second sensor 140 may be installed to monitor and detect a status of the image forming apparatus 100. Examples of the status detected by the second sensor 140 may include information regarding a motor of the image forming apparatus 100, information regarding an environment of the image forming apparatus 100, information of a usage of the image forming apparatus 100 and the like, although other examples are possible. Examples of the motor-related information may include a motor lock signal, a motor current feedback signal, and the like, although other examples are possible. Examples of the environment information may include temperature and/or humidity inside the image forming apparatus 100, temperature and/or humidity outside the image forming apparatus 100, various configuration data values for use by the image forming apparatus to form an image, and the like, although other examples are possible. Examples of the usage information may include a count of paper pick-up attempts, a total number of printings, a type of paper jam, and the like, although other examples are possible. Further, any other types of example information are also contemplated that may be acquired from sensors installed in the image forming apparatus. As such, the second sensor 140 may acquire information regarding the status of the image forming apparatus at any point of time, for example, each time a sheet of paper is printed out, based on a paper jam occurring, or at certain time intervals. The status information as acquired from the second sensor 140 may be used as the second signal. [0050] It can be understood that each of the first and the second signals described herein as being acquired, respectively, from the first sensor 130 and the second sensor 140 may include an event or data per se triggered or delivered from the respective sensor, such as an interrupt signal that is sent from the first sensor upon detection of a sheet of paper, and/or may include information acquired, based on the event or the data, by the processor 110 or the like, such as distance information produced as described above. [0051] If a paper jam occurs in the image forming apparatus 100, a cause of the paper jam may be identified from the first and the second signals that are acquired, respectively, from the first sensor 130 and the second sensor 140, as described above with respect to FIG. 2. Some example approaches as to a process to identify, based on these signals, the cause of the paper jam are described below. [0052] FIG.3 shows a plurality of causes of paper jams in an image forming apparatus according to an example. [0053] As shown, examples of paper jam causes may include lopsided and/or incorrect loading of paper with, for example, a paper guide of a cassette being misaligned with an edge of the paper, overloading of paper, for example, loading a more than allowable amount of paper into a cassette, paper type mismatch, for example, loading of a paper sheet having a thickness different from a predetermined one, use of poor-quality paper such as one-side-used paper, recycled paper, crumpled paper, stapled paper, etc., faulty removal of static charges with, for example, abnormal application of a saw voltage to a roller for transcription of a toner onto paper, trouble in manual feeding of paper into a tray with, for example, the paper being misaligned, error occurrence in a sensor such as a paper empty sensor, damage to a pick-up roller, for example, pick-up roller slip, pick-up roller contamination, etc. Other examples paper jam causes are also contemplated. [0054] FIG.4 is a diagram conceptually illustrating operations of training a learning model to identify a cause of a paper jam according to an example. [0055] In some examples, a paper jam cause may be identified using a machine learning-based trained model. The learning model may be an artificial intelligence (AI) model that has learned to identify/predict/diagnose a cause of a paper jam. An example learning process of the learning model may include a process of generating a paper jam in the image forming apparatus 100 and training the AI model from data acquired with the occurrence of the paper jam. [0056] As an example, one of the example paper jam causes illustrated in FIG.3 may be reproduced in the image forming apparatus 100. Further, with the occurrence of the paper jam cause, the first and the second signals may be acquired, respectively, from the first sensor 130 and the second sensor 140 of the image forming apparatus 100. Labeled data available for training the AI model may be collected, at operation 400, from the acquired signals and the reproduced paper jam cause. In an example, the first signal may include a signal acquired ahead of the occurrence of the paper jam by one fed sheet. Other examples of the first signal are also contemplated, including a signal acquired at a time point of the occurrence of the paper jam, a signal acquired ahead of the occurrence of the paper jam by a certain number of fed sheets, etc. In an example, the second signal may include sensor data acquired at a time point of the occurrence of the paper jam. Other examples of the second signal are also contemplated, including a signal acquired ahead of the occurrence of the paper jam by a number of fed sheets, etc. [0057] At operation 410, pre-processing may be performed on the labeled data so that the data may be provided as training data to the AI model. For example, the labeled data may be processed into data suitable for training the AI model. Examples of this processing may be identified in accordance with the AI model to be trained. Different implementations of the processing are possible. [0058] Further, as an option, data augmentation may be performed, at operation 420, to increase an amount of the training data as needed for the training of the AI model. In an example, a synthetic minority over-sampling technique (SMOTE) may be used for the augmentation. Other examples of the augmentation technology are also contemplated. [0059] At operation 430, the AI model may be trained using the training data acquired as mentioned above. This example process may be repeated for each of the example paper jam causes. The example process adopts a supervised learning approach to generate the machine learning-based trained model for paper jam cause identification by reproducing each example paper jam cause and training the AI model using information/data acquired with the occurrence of the paper jam, such as the first and the second signals described with reference to FIG.2. Other examples of the training approach for the AI model are also contemplated, including an unsupervised learning approach, a reinforced learning approach, and any other type of learning approach. [0060] FIG. 5 is a diagram conceptually illustrating a learning model according to an example. [0061] The learning model may be an AI model 500 for identifying a cause of a paper jam. As illustrated in FIG.5, the AI model 500 may be a neural network model, including an input layer 510, a hidden layer 520, and an output layer 530. For example, the input layer 510 may include ninety-one (91) input nodes, the hidden layer 520 may include four (4) hidden layers, each of which may include one-hundred (100) hidden nodes, and the output layer 530 may include eight (8) output nodes. In an example, the ninety-one input nodes of the input layer 510 may be inputted with data corresponding to the first and the second signals that are acquired from the first and the second sensors as described above with respect to FIG.2. In an example, the eight output nodes of the output layer 530 may correspond to the paper jam causes in the image forming apparatus that are illustrated above with respect to FIG.3. In other examples, different numbers of nodes of the input layer 510, the hidden layer 520, and the output layer 530 are possible. Other examples of the AI model are also contemplated with a different number of layers. Further, a back-propagation procedure may be used for the learning phase of the model. For example, the AI model may be trained using the training data acquired as described with respect to FIG.4. The AI model may be evaluated, based on its output result, for its reliability. Based on the evaluation not being favorable, the input data may be supplemented and used for retraining the AI model so that the reliability of the AI model may be improved beyond a certain level. Other examples of the learning procedure are also contemplated, including any other type of learning procedure that provides an identical or similar learning effect. [0062] In an example, the learning phase of the AI model 500 may be carried out at a cloud server or the like. Different implementations are also possible in which the AI model 500 may be trained at any other suitable electronic device. [0063] FIG.6 is a diagram illustratively showing operations of identifying a cause of a paper jam in an image forming apparatus according to an example. [0064] If there occurs a paper jam, the image forming apparatus 100 acquires, at operation S600, a plurality of signals related to the paper jam. The plurality of signals may include the signals as described with respect to FIGS.1 to 5, for example, the first signal that is acquired from the first sensor 130 disposed on a paper transfer path within the image forming apparatus 100 and that includes, e.g., information regarding a distance of each section of the paper transfer path, and the second signal that is acquired from the second sensor 140 for detecting a status of the image forming apparatus 100 and that includes information regarding a sensor related to the paper jam. In an example, the first signal may include a signal acquired ahead of the occurrence of the paper jam by one fed sheet. Other examples of the first signal are also contemplated, including a signal acquired at a time point of the occurrence of the paper jam, a signal acquired ahead of the occurrence of the paper jam by a certain number of fed sheets, etc. In an example, the second signal may include sensor data acquired at a time point of the occurrence of the paper jam. Other examples of the second signal are also contemplated, including a signal acquired ahead of the occurrence of the paper jam by a number of fed sheets, etc. [0065] In order that the cause of the paper jam may be identified, the image forming apparatus 100 sends, at operation S610, the acquired signals to a device, for example, a server 200, which is running an AI model or learning model trained to identify a paper jam cause as described with respect to FIGS.4 and 5. In an example, the AI model may be run on the server 200, for example, a cloud server which is communicatively connected with the image forming apparatus 100 and to which the image forming apparatus 100 sends the acquired signals. Different implementations of the AI model are possible. For example, the AI model may be stored in and used from the memory 120 of the image forming apparatus 100, rather than being run on the server 200. Thus, it is to be understood that FIG.6 shows an example environment in which the operations of identifying a cause of a paper jam are performed and that such operations as described with respect to FIG.6 may be performed in other environments, for example, an environment in which the AI model is loaded onto the memory 120 of the image forming apparatus 100 or an environment in which the AI model is loaded onto an additional, communicatively connected electronic device. [0066] The server 200, for example, the AI model running on the server 200, takes as input the plurality of signals and predicts therefrom a cause of the current paper jam from among a plurality of paper jam causes. With regard to the expression that the AI model predicts a cause of a paper jam, it may be understood that the term “predict” refers to an operation that is identical or similar to “identifying,” “judging,” “diagnosing,” or “deciding” the cause. Thus, unless stated otherwise, these terms are to be understood as the same or similar configuration. In an example in which the AI model is the neural network model of FIG. 5, the cause of the current paper jam is predicted, i.e., identified, judged, diagnosed, or decided, to be a paper jam cause that corresponds to a node with a highest output from among the plurality of nodes of the output layer 530. In other words, for each of the plurality of paper jam causes, a likelihood of the paper jam cause may be computed based on the plurality of signals received from the image forming apparatus 100, in which the current paper jam has occurred, so that the cause of the current paper jam may be predicted to be one of the plurality of paper jam causes that is identified as being most likely. [0067] At operation S630, the server 200 sends, to the image forming apparatus 100, the paper jam cause predicted via the AI model and the image forming apparatus 100 receives the cause through, for example, the communications unit. [0068] At operation S640, the image forming apparatus 100 may perform an action in response to receiving the cause. For example, this operation may include an action of indicating the cause on the user interface 150 of the image forming apparatus 100. Accordingly, a user or a service engineer may check, via the image forming apparatus 100, the cause of the paper jam in the image forming apparatus 100 and take an action to remove the cause of the paper jam in the image forming apparatus 100. [0069] As such, an analysis and identification may be made as to what is the basic cause of the paper jam so that the paper jam cause may be provided to the user or the service engineer. This may help in providing a countermeasure against the paper jam and preventing a recurrence thereof, thereby reducing cost and time for the service and providing an enhanced user experience. [0070] FIG.7 is a diagram illustratively showing operations of identifying a cause of a paper jam in an image forming apparatus according to an example. [0071] The operations S700, S710, and S720 shown in the example of FIG. 7 are respectively identical to the operations S600, S610, and S620 described with respect to FIG 6. Thus, details thereof are not repeated here. [0072] In the example of FIG. 7, at S730, the server 200 sends, to a communicatively connected electronic device 300, the paper jam cause predicted via the AI model and the electronic device 300 receives the cause through, for example, its communications unit. Examples of the electronic device 300 may include a host device, a user device, a server device, and the like. Other examples thereof are also contemplated, including the user’s or the service engineer’s PC, tablet, mobile phone, or the like. [0073] At operation 740, the electronic device 300 may perform an action in response to receiving the cause. For example, this operation may include an action of indicating the cause on a user interface of the electronic device 300. Accordingly, the user or the service engineer may check, via the electronic device 300, the cause of the paper jam in the image forming apparatus 100 and take an action to remove the cause of the paper jam in the image forming apparatus 100. [0074] FIG.8 is a diagram illustratively showing operations of identifying a cause of a paper jam in an image forming apparatus and re-training a learning model by which the identification was made according to an example. [0075] The learning model may be trained, in its initial training phase, as an AI model for identifying a cause of a paper jam, as described above with respect to FIG. 4, for example. After being trained from information/data that is acquired from a paper jam reproduced in the image forming apparatus 100, the AI model may be run on the cloud server or the like. Thereafter, the AI model may be updated or re-trained by using data acquired from a plurality of image forming apparatuses 100 with actual paper jams having occurred therein. The operations S800, S810, S820, S830, and S840 shown in the example of FIG. 8 are respectively identical to the operations S600, S610, S620, S630, and S640 described with respect to FIG 6. Thus, details thereof are not repeated here. [0076] Such image forming apparatus 100, in which there has occurred a paper jam, may be examined by the user or the service engineer so that he/she may identify a cause of the paper jam. The user or the service engineer may forward, to the cloud server 200 or the like on which the AI model is run, information regarding the paper jam cause identified by him/her. That is, in addition to the plurality of signals acquired by the image forming apparatus 100, the information regarding the actual paper jam cause identified by the user or the service engineer may be transmitted, at operation S850, to the AI model so that the signals and the information may be used as training data to train the AI model. Thus, in such circumstances where the plurality of image forming apparatuses 100 is brought to market and used, the AI model may be provided therefrom with the new training data. [0077] At operation S860, the AI model may be retrained or updated based on the received training data. This may continuously improve the performance of the AI model by using the data related to paper jams that have occurred in the actual usage environments. The paper jam cause identified by the user or the service engineer may be identical to or different from that identified by the AI model. In an example, based on the identification results being different from each other, for the retraining of the AI model, a higher weight may be assigned to the training data generated from the paper jam cause discovered at the actual usage environment. Other examples are also contemplated. [0078] In an example, the transmission of the new training data at operation S850 may be made from the image forming apparatus 100 to the server 200. In another example, the transmission at operation S850 may be made from an additional electronic device, for example, a user device, a host device, or the like such as the user’s or the service engineer’s PC, tablet, mobile phone, and the like. In still another example, the training data may be generated in a manner in which the plurality of signals related to the paper jam may be transmitted from the image forming apparatus 100 to the server 200 while the information regarding the actual cause of the paper jam may be transmitted from the electronic device 300 to the server 200. [0079] FIG.9 is a diagram illustratively showing an action to be performed after a cause of a paper jam in an image forming apparatus is identified according to an example. [0080] In an example, as shown in FIG. 9, an interface 900 may be indicated on the image forming apparatus 100 or the additional electronic device 300. The interface 900 may include an indication of an identified cause of a paper jam. In the example of FIG 9, it is indicated that there has occurred a paper jam due to an improper position of a guide of a cassette of the image forming apparatus. In this example, the paper jam has occurred at a place other than the cassette. Accordingly, in addition to the indication of where the paper jam has occurred or a process to remove the jammed paper, information regarding the basic cause of the paper jam may be intuitively presented to the user or the service engineer. [0081] FIG.10 is a diagram illustratively showing an action to be performed after a cause of a paper jam in an image forming apparatus is identified according to an example. [0082] In an example, as shown in FIG. 10, an interface 1000 may be indicated on the image forming apparatus 100 or the additional electronic device 300. The interface 1000 may include an indication of an identified cause of a paper jam. In the example of FIG 10, it is indicated that there has occurred a paper jam due to an excessive amount paper loaded into a cassette of the image forming apparatus. In this example, the paper jam has occurred at a place other than the cassette. Accordingly, in addition to the indication of where the paper jam has occurred or a process to remove the jammed paper, information regarding the basic cause of the paper jam may be intuitively presented to the user or the service engineer. [0083] FIG. 11 is a flow diagram illustratively showing a method for identifying a cause of a paper jam in an image forming apparatus according to an example. [0084] In an example, a method for identifying a paper jam cause includes acquiring, at operation S1110, a plurality of signals related to a paper jam in an image forming apparatus. The plurality of signals may include a first signal acquired from a first sensor arranged on a paper transfer path within the image forming apparatus and a second signal acquired from a second sensor that detects a status of the image forming apparatus. Examples of the first and the second signals are set forth above with respect to FIGS.1 to 10 and accordingly are not repeated here. [0085] The method further includes receiving, at operation S1120, a paper jam cause that is identified as a most likely cause of the current paper jam based on the plurality of signals. For example, in order that the cause of the paper jam may be identified, the image forming apparatus 100 may send the acquired signals to a server side running an AI model or learning model trained to identify a paper jam cause as described with respect to FIGS.4 and 5 and may receive, in response thereto, the cause of the paper jam. The AI model may be run on, for example, the (cloud) server 200 which is communicatively connected with the image forming apparatus 100 and to which the image forming apparatus 100 sends the acquired signals. Different implementations of the AI model are possible. For example, the AI model may be stored in and used from the memory 120 of the image forming apparatus 100, rather than being run on the server 200. Examples thereof are set forth above with respect to FIGS. 1 to 10 and accordingly are not repeated here. [0086] The method further includes performing, at operation S1130, an action in response to receiving the cause of the paper jam. For example, this operation may include an action of indicating the cause on the user interface 150 of the image forming apparatus 100. Examples thereof are set forth above with respect to FIGS.1 to 10 and accordingly are not repeated here. [0087] FIG. 12 is a schematic illustration of a non-transitory computer readable recording medium including instructions according to an example. [0088] As shown in FIG.12, a non-transitory computer readable recording medium 1200 stores instructions that are executable by a processor and programmable to implement some operations of the aforementioned example methodology, such as the example operations of the image forming apparatus described above with respect to FIGS.1 to 11 and the example operations of the method described above with respect to FIGS.1 to 11. [0089] Now, with reference to FIG.12, examples of the instructions stored in the non-transitory computer readable recording medium 1200 will be described. [0090] As shown in FIG. 12, the instructions may be executed by a computer, to cause the computer, for example, a processor of the computer, to perform operations. The non-transitory computer readable recording medium 1200 may include instructions 1210 to cause the computer to acquire a plurality of signals related to a paper jam, instructions 1220 to cause the computer to receive a paper jam cause that is identified as a most likely cause of the current paper jam based on the plurality of signals, and instructions 1230 to cause the computer to perform an action in response to receiving the cause. [0091] In an example, the computer may include the image forming apparatus. In another example, the computer may include a cloud server device on which an AI model is run. In still another example, the computer may include an electronic device communicatively connected with the image forming apparatus. [0092] Although not shown, the instructions may further cause the computer to indicate the cause on the image forming apparatus in which the paper jam has occurred or an electronic device communicatively connected with the image forming apparatus. [0093] Although not shown, the instructions may further cause the computer to send, through a communications unit to a server, the plurality of signals related to the paper jam and a signal that specifies the cause of the paper jam. [0094] As can be clearly seen, upon execution of the instructions stored in the non-transitory computer readable recording medium 1200, the instructions 1210, 1220, and 1230 illustrated in FIG. 12, as well as the example operations described above with respect to FIGS.1 to 11, may be performed by the computer, for example, the above-described image forming apparatus 100. [0095] The above-described computer readable recording medium may be a non-transitory readable medium. The term “non-transitory readable medium” as used herein refers to a medium that is capable of semi-permanently storing data and is readable by an apparatus, rather than a medium, e.g., a register, a cache, a volatile memory device, etc., that temporarily stores data. For example, the foregoing program instructions may be stored and provided in a CD, a DVD, a hard disk, a Blu-ray disc, a USB, a memory card, a ROM device, or any of other types of non-transitory readable media. [0096] The example methodology disclosed herein may be incorporated into a computer program product. The computer program product may be available as a product for trading between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium, e.g., compact disc read only memory (CD-ROM), or distributed online through an application store, e.g., PlayStore™. For the online distribution, a portion of the computer program product may be temporarily stored, or temporarily created, in a storage medium such as a server of the manufacturer, a server of the application store, a storage medium such as memory of a relay server, or the like. [0097] The foregoing description has been presented to illustrate and describe some examples. It should be understood that many modifications and variations are possible in light of the above teaching. In various examples, suitable results may be achieved based on the above-described techniques being performed in a different order, and/or based on some of the components of the above-described systems, architectures, devices, circuits, and the like being coupled or combined in a different manner, or substituted for or replaced by other components or equivalents thereof. [0098] Therefore, the scope of the disclosure is not to be limited to the precise form disclosed, but rather defined by the following claims and equivalents thereof.