Patent Publication Number: US-2023153978-A1

Title: Methods and systems for grading devices

Description:
CROSS REFERENCE TO RELATED PATENT APPLICATION 
     This application claims the benefit of priority to U.S. Provisional Application No. 63/280,449 filed Nov. 17, 2021, which is herein incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Mass production of user devices (e.g., mobile devices, smartphones, IoT devices, electronic devices, etc.) often results in a subset of the user devices failing to meet quality and/or functionality standards. For example, one or more user devices may be produced with defects and/or become damaged during processing (e.g., packaging, shipping, storing, etc.). Manual inspection of user devices to determine defects and/or damages is inefficient and error-prone, causing many defects and/or damages to be unidentified. 
     SUMMARY 
     It is to be understood that both the following general description and the following detailed description are exemplary and explanatory only and are not restrictive. Methods and systems for grading user devices (e.g., mobile devices, smartphones, IoT devices, electronic devices, etc.) are described. 
     Described are methods comprising: determining image data associated with a mobile device; determining, based on the image data, a type of the mobile device and a plurality of regions; determining, for each region of the plurality of regions, a number of defects; determining, for each region of the plurality of regions, a score, wherein the score is based on the type of the mobile device and one or more of: the region or the number of defects for the region; and updating, based on the score for each region of the plurality of regions, a device profile associated with the mobile device. 
     Also described are methods comprising: determining, for each mobile device of a plurality of mobile devices, image data; determining, for each mobile device of the plurality of mobile devices, based on the image data, a type of the mobile device and a plurality of regions; determining, for each mobile device of the plurality of mobile devices, for each region of the plurality of regions, a number of defects; determining, for each mobile device of the plurality of mobile devices, for each region of the plurality of regions, a score, wherein the score is based on the type of the mobile device and one or more of: the region or the number of defects for the region; and updating, for each mobile device of the plurality of mobile devices, based on the score for each region of the plurality of regions, a device profile associated with the mobile device. 
     Also described are methods comprising: determining image data associated with a mobile device; determining, based on the image data, a type of the mobile device and an aesthetic defect; predicting, based on the type of the mobile device and the aesthetic defect, a functional defect; determining, based on the predicted functional defect, a score; and updating, based on the score, a device profile associated with the mobile device. 
     Additional advantages will be set forth in part in the description which follows or may be learned by practice. The advantages will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments and together with the description, serve to explain the principles of the methods and systems: 
         FIGS.  1 A- 1 C  show an example system for grading user devices; 
         FIG.  2    shows an example system for grading user devices; 
         FIG.  3    shows an example system; 
         FIG.  4    shows an example system; 
         FIGS.  5 - 19    show examples of grading user devices; 
         FIGS.  20 - 23    show an example system for grading user devices; 
         FIG.  24    is a flowchart of an example method for grading user devices; 
         FIG.  25    is a flowchart of an example method for grading user devices; 
         FIG.  26    is a flowchart of an example method for grading user devices; and 
         FIG.  27    is an example operating environment for implementing grading of user devices. 
     
    
    
     DETAILED DESCRIPTION 
     Before the present methods and systems are disclosed and described, it is to be understood that the methods and systems are not limited to specific methods, specific components, or to particular implementations. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. 
     As used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another embodiment. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. 
     “Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where said event or circumstance occurs and instances where it does not. 
     Throughout the description and claims of this specification, the word “comprise” and variations of the word, such as “comprising” and “comprises,” means “including but not limited to,” and is not intended to exclude, for example, other components, integers or steps. “Exemplary” means “an example of” and is not intended to convey an indication of a preferred or ideal embodiment. “Such as” is not used in a restrictive sense, but for explanatory purposes. 
     Disclosed are components that may be used to perform the disclosed methods and systems. These and other components are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc. of these components are disclosed that while specific reference of each various individual and collective combinations and permutation of these may not be explicitly disclosed, each is specifically contemplated and described herein, for all methods and systems. This applies to all aspects of this application including, but not limited to, steps in disclosed methods. Thus, if there are a variety of additional steps that may be performed it is understood that each of these additional steps may be performed with any specific embodiment or combination of embodiments of the disclosed methods. 
     The present methods and systems may be understood more readily by reference to the following detailed description of preferred embodiments and the examples included therein and to the Figures and their previous and following description. 
     As will be appreciated by one skilled in the art, the methods and systems may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the methods and systems may take the form of a computer program product on a computer-readable storage medium having computer-readable program instructions (e.g., computer software) embodied in the storage medium. More particularly, the present methods and systems may take the form of web-implemented computer software. Any suitable computer-readable storage medium may be utilized including hard disks, CD-ROMs, optical storage devices, or magnetic storage devices. 
     Embodiments of the methods and systems are described below with reference to block diagrams and flowchart illustrations of methods, systems, apparatuses, and computer program products. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, may be implemented by computer program instructions. These computer program instructions may be loaded onto a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create a means for implementing the functions specified in the flowchart block or blocks. 
     These computer program instructions may also be stored in a computer-readable memory that may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including computer-readable instructions for implementing the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions that execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. 
     Accordingly, blocks of the block diagrams and flowchart illustrations support combinations of means for performing the specified functions, combinations of steps for performing the specified functions and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, may be implemented by special purpose hardware-based computer systems that perform the specified functions or steps, or combinations of special purpose hardware and computer instructions. The present disclosure relates to improvements in computer functionality and device grading. 
     A modular system/device may include a plurality of imaging devices (e.g., line scan cameras, area scan cameras, computer/machine vision cameras, etc.) positioned at different locations relative to redundant conveyors to enable high-velocity inspection and grading/scoring (e.g., cosmetic/aesthetic grading, predicted functionality grading, etc.) for user devices (e.g., mobile devices, smartphones, IoT devices, electronic devices, etc.). The plurality of imaging devices may capture image data of each side/surface of a user device. The image data may be used to determine the user device type, as well as the type, location, quantity, and/or size of any defect (e.g., a crack, a dent, a misconfiguration, damage, etc.) to the user device. The modular system/device, based on the location, quantity, and/or size of any aesthetic defect, such as a crack and/or a dent to the exterior body of a user device, may predict and/or determine a functional defect of the user device, such as an inoperable transceiver, a non-functional display, and/or misoperating speaker/microphone. 
     A grade/score may be determined for any defect, area of the user device associated with a defect, and/or user device that includes/excludes defects. Grading/scoring specifications/parameters may be preset and/or customized, for example, via a user interface. The system/device may determine to skip one or more grading/scoring steps/operations if a certain type of defect is determined, such as a crack to a screen and/or frame/body of a mobile device. 
       FIG.  1 A  shows an example system  100  in which the present methods and systems may operate. One skilled in the art will appreciate that provided herein is a functional description and that the respective functions may be performed by software, hardware, or a combination of software and hardware. 
     The system  100  may comprise a conveyor belt  101 . Although only the conveyor belt  101  is shown for simplicity, the system may include any number of conveyors (e.g., one or more conveyors), such as redundant conveyors, complementary conveyors, multi-level conveyors, and/or the like. The conveyor belt  101  is shown traveling in direction  102 . The conveyor belt  101  may travel and/or accelerate in the direction  102  at any rate/speed, such as 163 millimeters per second and/or the like. 
     One or more objects, such as one or more user devices (e.g., mobile devices, smartphones, IoT devices, electronic devices, etc.) may be placed on the conveyor belt  101 . For example, one or more mobile devices  103  may be placed on the conveyor belt  101  manually and/or via a robotic arm  120  attached to a gantry  122 . The robotic arm  120  may be, for example, a multi-axis (e.g., 4-axis, 6-axis, etc.) robotic arm/system and/or the like. The gantry  122  enables the robotic arm  120  to be moved to any position/location along the conveyor belt  101 . The robotic arm  120  and the gantry  122  may be connected to and/or in communication with a programmable logic controller (PLC)  116  (the PLC  116  may comprise a computing device, a PLC, or any other controller/processor, etc.). The PLC  116  may be configured to control precise movements of the robotic arm  120 , for example, according to an axis of the multi-axis robotic arm  120 . The PLC  116  may be configured to control precise movements of the robotic arm  120 , for example, in any direction (e.g., up, down, left, right, an angled direction, etc.), relative to the gantry  122  and/or the conveyor belt  101 . 
     The PLC  116  may send one or more signals that cause the robotic arm  120  to move in any direction and/or manipulate the position of a mobile device  103  along the conveyor belt  101 . For example, the PLC  116  may cause the robotic arm  120  to pick up, place, rotate, turn over, reposition, and/or the like a mobile device  103  along the conveyor belt  101 . The robotic arm  120  may include one or more grippers, vacuum/suction components, controllable fingers, and/or the like that enable the robotic arm  120  to pick up, place, rotate, turn over, reposition, and/or the like a mobile device  103  along the conveyor belt  101 . 
     The conveyor belt  101  may pass over a drive roll which may be driven by a motor  115  (e.g., an AC motor, a DC motor, a servo motor, a stepper motor, a belt drive motor, a pulse operated motor, etc.). The use of the motor  115  enables precise positioning of the one or more mobile devices  103  relative to a camera  105 , a camera  106 , a camera  107 , and a camera  108 . The conveyor belt  101  may be precisely advanced (or reversed) to cause each of the one or more mobile devices  103  to be moved into a field of view  109 , a field of view  110 , a field of view  111 , and a field of view  112  associated with the camera  105 , the camera  106 , the camera  107 , and the camera  108 , respectively. The PLC  116  may be configured to cause the motor  115  to execute any number of motions/steps in either direction to cause the one or more mobile devices  103  to be moved into the field of view  109 , the field of view  110 , the field of view  111 , and the field of view  112 . 
     The cameras  105 - 108  may be oriented at different positions/locations relative to the conveyor belt  101 . For example, the camera  105  may be positioned/located above the conveyor belt  101 . The camera  105  may be positioned/located above the conveyor belt  101  so that the camera  105 , via the field of view  109 , captures and/or determines image data from above (e.g., a topside, etc.) a mobile device  103 . The camera  107  may be positioned/located below, under, and/or at an angle lower than the conveyor belt  101 . The camera  107  may be positioned/located below, under, and/or at an angle lower than the conveyor belt  101  so that the camera  107 , via the field of view  111 , captures and/or determines image data from under (e.g., a bottom-side, etc.) a mobile device  103 .  FIG.  1 B  shows example orientation and/or positioning of the cameras  105  and  107 .  FIG.  1 B  includes a side view representation of the conveyor belt  101 . 
     Returning to  FIG.  1 A , the camera  106  may be positioned/located on a side (e.g., left side, etc.) of the conveyor belt  101 . The camera  106  may be positioned/located on the side of the conveyor belt  101  so that the camera  106 , via the field of view  110 , captures and/or determines image data from a side (e.g., a side view, etc.) of a mobile device  103 . The camera  108  may be positioned/located on a side (e.g., right side, etc.) of the conveyor belt  101 . The camera  108  may be positioned/located on the side of the conveyor belt  101  so that the camera  108 , via the field of view  112 , captures and/or determines image data from a side (e.g., a side view, etc.) of a mobile device  103 . The camera  108 , may capture and/or determine image data from a side (e.g., a side view, etc.) of a mobile device  103  that is opposite from the side from which the camera  106  captures and/or determines image data.  FIG.  1 C  shows example orientation and/or positioning of the cameras  106  and  108 .  FIG.  1 C  includes a top view representation of the conveyor belt  101 . 
     Returning to  FIG.  1 A , the cameras  105 - 108  may be and/or include line scan cameras, area scan cameras, 3D imaging cameras, inspection cameras, laser cameras, object detection cameras, and/or any other camera/imaging device. The cameras  105 - 108  may be configured for scanning, decoding, reading, sensing, imaging, capturing, and/or interpreting visual codes, for example, to determine a type of mobile device  103 . The cameras  105 - 108  may include an imaging component for scanning, reading, and decoding one-dimensional or two-dimensional barcodes, quick response (QR) codes, manufacturer labels, trademarks, insignia, and/or the like that indicate a type of mobile device  103 . The cameras  105 - 108  may include one or more depth cameras for capturing, processing, sensing, observing, modeling, detecting, and interacting with three-dimensional environments. The cameras  105 - 108  may recognize and detect depths and colors of objects in the field of views  109 - 112 . The cameras  105 - 108  may also provide other camera and video recorder functionalities, such as taking pictures, recording videos, streaming images or other data, storing data in image buffers, etc. The cameras  105 - 108  may determine sizes, orientations, and/or visual properties of the one or more mobile devices  103 , such as one or more defects of and/or associated with a mobile device  103 . 
     The cameras  105 - 108  may include appropriate hardware and software components (e.g., circuitry, software instructions, etc.) for transmitting/sending signals and data/information, such as imaging data, to and from a pass/fail controller  113  to conduct processes/methods as disclosed herein. The pass/fail controller may  113  comprise a computing device, a PLC, or other controllers/processors. The cameras  105 - 108  may each send/transmit image data associated with a mobile device  103  to the pass/fail controller  113 . The pass/fail controller  113  may use image data to inspect a mobile device  103  for one or more defects and determine if the mobile device  103  passes or fails the inspection. 
       FIG.  2    shows an example block diagram of the cameras  105 - 108  sending/transmitting image data  201  associated with a mobile device  103  to the pass/fail controller  113 . The pass/fail controller  113  may include a defect detection module  202  and a defect analysis module  203 . The defect detection module  202  and the defect analysis module  203  may be configured to analyze image data received from the cameras  105 - 108 , determine one or more defects associated with a mobile device  103  and determine/assign a grade/score to the mobile device  103  and/or a portion of the mobile device  103  based on defects (and/or a lack of defects) associated with a mobile device  103  (or any other user device). The defect detection module  202  may be configured to perform machine/computer vision operation, object recognition, image analysis, and/or the like. For example, the defect detection module  202  may be a trained machine learning model. 
     The defect detection module  202  and/or the defect analysis module  203  may use the image data  201  to determine a device type, as well as the type, location, quantity, and/or size of any defect (e.g., a crack, a dent, a misconfiguration, damage, etc.) to a mobile device  103 . The defect detection module  202  and/or the defect analysis module  203  may use the image data to determine/identify aesthetic defects of a mobile device  103  and predict/determine an associated/resulting functional defect of the mobile device  103 . For example, the defect detection module  202  and/or the defect analysis module  203 , based on the location, quantity, and/or size of any aesthetic defect, such as a crack and/or a dent to the exterior body of a mobile device  103 , may predict and/or determine a functional defect of the mobile device  103 , such as an inoperable transceiver, a non-functional display, and/or misoperating speaker/microphone. 
     The pass/fail controller  113  (e.g., the defect detection module  202  and/or the defect analysis module  203 , etc.) may determine a grade/score for any defect (e.g., an aesthetic defect, a functional defect, etc.), determine an area of a mobile device  103  associated with a defect, and/or determine whether the mobile device  103  includes/excludes defects. Grading/scoring specifications/parameters may be preset and/or customized, for example, via a user interface (e.g., a display  117 , etc.). 
       FIG.  3    shows a block diagram of system  300  for training/operating the defect detection module  202 . The defect detection module  202  learns from image data of both good and bad parts, components, aspects, and/or functions of a user device, such as a mobile device to reduce and/or eliminate the time required to specify defects and respond to changing requirements. The system  300  may use machine learning techniques to train, based on an analysis of one or more training datasets  310 A- 310 N by a training module  320 , at least one machine learning-based classifier  330  that is configured to classify features extracted from reference/test image and/or image data associated with user devices (e.g., mobile devices, smartphones, IoT devices, electronic devices, a mobile device  103 , etc.). The features may indicate non-functional defects, such as scratches and/or dents to a mobile device  103 , and potential functional defects, such as malfunctioning transceivers, antennas, speakers/microphones, and/or the like. 
     For example, the features may indicate aspects of the user device, such as a defect-free surface and/or design type, one or more aesthetic defects (e.g., dents, scratches, etc.), a defective surface and/or design type, and/or the like. The machine learning-based classifier  330  may classify features extracted from image data to determine/predict defects (e.g., aesthetic defects, functional defects, etc.) associated with a user device, such as a mobile device. For example, the system  300  may be trained to determine/predict that an aesthetic defect to a mobile device  103 , such as a dent to the body/frame of the mobile device  103  in a specific area, causes a functional defect to the mobile device  103 , such as an inoperable and/or misfunctioning transceiver, antenna, display, microphone, speaker, and/or the like of the mobile device  103 . The system  300  may be used to predict and/or determine any defect associated with a user device, such as a mobile device. 
     The one or more training datasets  310 A- 310 N may comprise labeled baseline data such as labeled defects, labeled non-defects, labeled ideal conditions, and/or the like. The labeled datasets may include features extracted from image data associated with mobile devices and/or any other user device. Labeled baseline data may indicate correspondences and/or associations between types of aesthetic defects of a user/mobile device and functional defects of the user/mobile device. The labeled baseline data may include any number of feature sets (labeled data that identify extracted features for a specific type of defect, etc.). Extracting features from image data may include matching a feature template to an image and identifying features in the image that match the feature-template. 
     The labeled baseline data may be stored in one or more databases. Data determined/extracted from image data may be randomly assigned to a training dataset or a testing dataset. The assignment of data to a training dataset or a testing dataset may not be completely random. In this case, one or more criteria may be used during the assignment, such as ensuring that similar device defects, dissimilar device defects, and/or the like may be used in each of the training and testing datasets. In general, any suitable method may be used to assign the data to the training or testing datasets. 
     The training module  320  may train the machine learning-based classifier  330  by extracting a feature set from the labeled baseline data according to one or more feature selection techniques. In some instances, the training module  320  may further define the feature set obtained from the labeled baseline data by applying one or more feature selection techniques to the labeled baseline data in the one or more training datasets  310 A- 310 N. The training module  320  may extract a feature set from the training datasets  310 A- 310 N in a variety of ways. The training module  320  may perform feature extraction multiple times, each time using a different feature-extraction technique. In some instances, the feature sets generated using the different techniques may each be used to generate different machine learning-based classification models  340 . In an embodiment, the feature set with the highest quality metrics may be selected for use in training. The training module  320  may use the feature set(s) to build one or more machine learning-based classification models  340 A- 340 N that are configured to determine/identify a defect (e.g., an aesthetic defect, etc.) to a user device, such as a mobile device, and/or predict/determine a potential defect (e.g., a functional defect, etc.) to a user device, such as a mobile device. 
     The training datasets  310 A- 310 N and/or the labeled baseline data may be analyzed to determine any dependencies, associations, and/or correlations between data/information in the training datasets  310 A- 310 N and/or the labeled baseline data. The term “feature,” as used herein, may refer to any characteristic of an item of data that may be used to determine whether the item of data falls within one or more specific categories. By way of example, the features described herein may comprise features associated with both good and bad parts, components, aspects, and/or functions of a user device, such as a mobile device. 
     A feature selection technique may comprise one or more feature selection rules. The one or more feature selection rules may comprise determining which features in the labeled baseline data appear over a threshold number of times in the labeled baseline data and identifying those features that satisfy the threshold as candidate features. For example, any feature that appears greater than or equal to 2 times in the labeled baseline data may be considered as candidate features. Any features appearing less than 2 times may be excluded from consideration as a feature. A single feature selection rule may be applied to select features or multiple feature selection rules may be applied to select features. The feature selection rules may be applied in a cascading fashion, with the feature selection rules being applied in a specific order and applied to the results of the previous rule. For example, the feature selection rule may be applied to the labeled baseline data to determine, identify, and/or predict defects of a user device, such as a mobile device. A final list of candidate features may be analyzed according to additional features. 
     Defect detection and analysis may be based on a wrapper method. A wrapper method may be configured to use a subset of features and train the machine learning model using the subset of features. Based on the inferences that are drawn from a previous model, features may be added and/or deleted from the subset. Wrapper methods include, for example, forward feature selection, backward feature elimination, recursive feature elimination, combinations thereof, and the like. In some instances, forward feature selection may be used to identify one or more candidate defects of a user device, such as a mobile device, for example, a crack to a frame, a dented body, a misconfigured component, and/or the like. Forward feature selection is an iterative method that begins with no feature in the machine learning model. In each iteration, the feature which best improves the model is added until the addition of a new variable does not improve the performance of the machine learning model. In an embodiment, backward elimination may be used to identify one or more candidate defects of a user device (e.g., a mobile device, etc.), for example, a crack to a frame, a dented body, a misconfigured component, and/or the like. Backward elimination is an iterative method that begins with all features in the machine learning model. In each iteration, the least significant feature is removed until no improvement is observed on the removal of features. 
     Recursive feature elimination may be used to identify one or more candidate defects of a user device (e.g., a mobile device, etc.), for example, a crack to a frame, a dented body, a misconfigured component, and/or the like. Recursive feature elimination is a greedy optimization algorithm which aims to find the best performing feature subset. Recursive feature elimination repeatedly creates models and keeps aside the best or the worst performing feature at each iteration. Recursive feature elimination constructs the next model with the features remaining until all the features are exhausted. Recursive feature elimination then ranks the features based on the order of their elimination. 
     One or more candidate defects (e.g., aesthetic defects, functional defects, etc.) of a user device (e.g., a mobile device, etc.), for example, a crack to a frame, a dented body, a misconfigured component, an inoperable/malfunctioning component (e.g., a functional defect, etc.), and/or the like may be determined according to an embedded method. Embedded methods combine the qualities of filter and wrapper methods. Embedded methods include, for example, Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression which implement penalization functions to reduce overfitting. For example, LASSO regression performs L1 regularization which adds a penalty equivalent to the absolute value of the magnitude of coefficients and ridge regression performs L2 regularization which adds a penalty equivalent to the square of the magnitude of coefficients. 
     After the training module  320  has generated a feature set(s), the training module  320  may generate a machine learning-based predictive model  340  based on the feature set(s). A machine learning-based predictive model may refer to a complex mathematical model for data classification that is generated using machine-learning techniques. In one example, this machine learning-based classifier may include a map of support vectors that represent boundary features. By way of example, boundary features may be selected from, and/or represent the highest-ranked features in, a feature set. 
     In an embodiment, the training module  320  may use the feature sets extracted from the training datasets  310 A- 310 N and/or the labeled baseline data to build a machine learning-based classification model  310 A- 310 N to predict defects (e.g., aesthetic defects, functional defects, etc.) of a user device (e.g., a mobile device, etc.), for example, a crack to a frame, a dented body, a misconfigured component, an inoperable/malfunctioning component (e.g., a functional defect, etc.), and/or the like. In some examples, the machine learning-based classification models  340 A- 340 N may be combined into a single machine learning-based classification model  340 . Similarly, the machine learning-based classifier  330  may represent a single classifier containing a single or a plurality of machine learning-based classification models  340  and/or multiple classifiers containing a single or a plurality of machine learning-based classification models  340 . The machine learning-based classifier  330  may also include each of the training datasets  310 A- 310 N and/or each feature set extracted from the training datasets  310 A- 310 N and/or extracted from the labeled baseline data. 
     The extracted features from image data may be combined in a classification model trained using a machine learning approach such as discriminant analysis; decision tree; a nearest neighbor (NN) algorithm (e.g., k-NN models, replicator NN models, etc.); statistical algorithm (e.g., Bayesian networks, etc.); clustering algorithm (e.g., k-means, mean-shift, etc.); neural networks (e.g., reservoir networks, artificial neural networks, etc.); support vector machines (SVMs); logistic regression algorithms; linear regression algorithms; Markov models or chains; principal component analysis (PCA) (e.g., for linear models); multi-layer perceptron (MLP) ANNs (e.g., for non-linear models); replicating reservoir networks (e.g., for non-linear models, typically for time series); random forest classification; a combination thereof and/or the like. The resulting machine learning-based classifier  330  may comprise a decision rule or a mapping that uses defect information (e.g., aesthetic defect information, functional defect information, etc.) to determine/predict defects of a user device (e.g., a mobile device, etc.), for example, a crack to a frame, a dented body, a misconfigured component, an inoperable/malfunctioning component (e.g., a functional defect, etc.), and/or the like. 
     The defect information and the machine learning-based classifier  330  may be used to predict defects for the test samples in the test dataset. In one example, the result for each test sample includes a confidence level that corresponds to a likelihood or a probability that the corresponding test sample accurately predicts a defect (and/or the like). The confidence level may be a value between zero and one that represents a likelihood that the predicted defect (and/or the like) is consistent with a computed value. Multiple confidence levels may be provided for each test sample and each candidate defect (and/or the like). A top-performing candidate defect (and/or the like) may be determined by comparing the result obtained for each test sample with a computed defect (and/or the like) for each test sample. In general, the top-performing candidate defects (and/or the like) will have results that closely match the computed defect (and/or the like). The top-performing candidate defect (and/or the like) may be used for defect analysis, for example, determining/predicting defects of a user device (e.g., a mobile device, etc.), for example, a crack to a frame, a dented body, a misconfigured component, an inoperable/malfunctioning component (e.g., a functional defect, etc.), and/or the like. 
       FIG.  3    is a flowchart illustrating an example training method  300  for generating the machine learning classifier  330  using the training module  320 . The training module  320  can implement supervised, unsupervised, and/or semi-supervised (e.g., reinforcement-based) machine learning-based classification models  340 . Method  400 , shown in  FIG.  4   , is an example of a supervised learning method; variations of this example of training method are discussed below, however, other training methods can be analogously implemented to train unsupervised and/or semi-supervised machine learning (predictive) models. 
     The training method  400  may determine information for determining defects of a user device (e.g., a mobile device, etc.), for example, a crack to a frame, a dented body, a misconfigured component, and/or the like at  410 . The information for determining defects may contain one or more datasets. Each dataset may include labeled baseline data. 
     The training method  400  may generate, at  420 , a training dataset and a testing dataset. The training dataset and the testing dataset may be generated by calculating and/or computing defects of a user device (e.g., a mobile device, etc.), for example, a crack to a frame, a dented body, a misconfigured component, and/or the like based on historical predictions of defects. The training dataset and the testing dataset may be generated by randomly assigning defect data to either the training dataset or the testing dataset. In some instances, the assignment of defect data as training or test samples may not be completely random. In some instances, only the labeled baseline data for a specific feature extracted from defect data may be used to generate the training dataset and the testing dataset. In some instances, a majority of the labeled baseline data extracted from defect data may be used to generate the training dataset. For example, 75% of the labeled baseline data for predicting defects of a user device (e.g., a mobile device, etc.), a crack to a frame, a dented body, a misconfigured component, and/or the like extracted from image data and/or defect data may be used to generate the training dataset and 25% may be used to generate the testing dataset. Any method or technique may be used to create the training and testing datasets. 
     The training method  400  may determine (e.g., extract, select, etc.), at  430 , one or more features that can be used by, for example, a classifier to label features extracted from image data and/or defect data. The one or more features may include and/or indicate defects of a user device (e.g., a mobile device, etc.), for example, a crack to a frame, a dented body, a misconfigured component, and/or the like. The training method  400  may determine a set of training baseline features from the training dataset. Features of image data and/or defect data may be determined by any method. 
     The training method  400  may train one or more machine learning models using the one or more features at  440 . In some instances, the machine learning models may be trained using supervised learning. In another embodiment, other machine learning techniques may be employed, including unsupervised learning and semi-supervised. The machine learning models trained at  440  may be selected based on different criteria (e.g., how close a predicted defect and/or the like is to an actual defect of a user/mobile device, etc.) and/or data available in the training dataset. For example, machine learning classifiers can suffer from different degrees of bias. Accordingly, more than one machine learning model can be trained at  440 , optimized, improved, and cross-validated at  450 . 
     The training method  400  may select one or more machine learning models to build a predictive model at  460  (e.g., a machine learning classifier, a predictive model, the defect detection module  202 , etc.). The predictive engine may be evaluated using the testing dataset. The predictive engine may analyze the testing dataset and generate classification values and/or predicted values at  470 . Classification and/or prediction values may be evaluated at  480  to determine whether such values have achieved a desired accuracy level. Performance of the predictive engine may be evaluated in a number of ways based on a number of true positives, false positives, true negatives, and/or false negative classifications of the plurality of data points indicated by the predictive engine. For example, the false positives of the predictive engine may refer to a number of times the predictive engine incorrectly predicted a defect and/or the like. Conversely, the false negatives of the predictive engine may refer to a number of times the machine learning model predicted a defect and/or the like incorrectly, when in fact, the predicted defect and/or the like matches an actual defect and/or the like of a user device (e.g., a mobile device, etc.), for example, a crack to a frame, a dented body, a misconfigured component, and/or the like. True negatives and true positives may refer to a number of times the predictive engine correctly predicted a defect and/or the like. Related to these measurements are the concepts of recall and precision. Generally, recall refers to a ratio of true positives to a sum of true positives and false negatives, which quantifies a sensitivity of the predictive engine. Similarly, precision refers to a ratio of true positives to a sum of true and false positives. 
     When such a desired accuracy level is reached, the training phase ends and the predictive engine may be output at  490 ; when the desired accuracy level is not reached, however, then a subsequent iteration of the training method  400  may be performed starting at  410  with variations such as, for example, considering a larger collection of image data and/or defect data. 
     Returning to  FIG.  2   , an output of the defect detection module  202  may be passed to the defect analysis module  203 . The output of the defect detection module  202  may include results of defect detection, such as defect locations, sizes, types, quantities, and/or the like, may be provided to the defect analysis module  202 . The defect analysis module  203  may determine/generate images and/or results, such as a map of defects that indicates a location, size, type, quantity, and/or the like. The defect analysis module  203  may determine/generate a grade/score for any defect, area of the user/mobile device associated with a defect, and/or user/mobile device that includes/excludes defects. Grading/scoring specifications/parameters may be preset and/or customized, for example, via a user interface (e.g., a display  117 , etc.). 
     The defect analysis module  203  may determine that a mobile device (e.g., a mobile device  103 , etc.) and/or smartphone should be associated with an overall rank based on values between any range, such as 0 and 55,780. The overall ranking may be indicative of defects associated with the mobile device and/or smartphone categorized by area, count, and/or region of defect. The system  100  may determine to skip one or more grading/scoring steps/operations if a certain type of defect is determined and/or predicted, such as a crack to a screen, a malfunctioning component, and/or dented frame/body of a mobile device  103 , and the mobile device  103  may be discarded, classified (e.g., classified as defective, etc.), sent for manual inspection, and/or the like. 
     Returning to  FIG.  1 A , the pass/fail controller  113  may determine, grade, and/or score defects affecting a mobile device  103 . For example, the pass/fail controller  113  may determine an overall ranking for a mobile device  103 . A mobile device  103  may be ranked, for example, between any range, such as 0 and 55,780, based upon defects in and/or to the mobile device  103  categorized by area, count, and/or region of defect. Different areas/portions of a mobile device  103  may be ranked/scored based on various ranges of values that, together, total an overall ranking for the mobile device. For example, a ranking value for a display, a back housing, a bottom edge, a top edge, a left edge, and a right edge of a mobile device  103  may be determined. 
     Table 1 shows an example overall ranking for a mobile device  103 . Table 1 indicates different regions of a mobile device  103  (e.g., a display, a back housing, a bottom edge, a top edge, a left edge, a right edge, etc.) and the associated ranking value ranges for the specific region based on where defects are determined and/or identified. The software allows for table 1 and the following tables 2-22 to be easily customizable. These ranges and values can be modified to accommodate new features added to a mobile device  103  as well as different weighting with regards to varying defect characteristics. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Region 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 Display 
                 0-34,385 
               
               
                   
                 Back Housing 
                 0-21,040 
               
               
                   
                 Bottom Edge 
                 0-100   
               
               
                   
                 Top Edge 
                 0-75    
               
               
                   
                 Left Edge 
                 0-95    
               
               
                   
                 Right Edge 
                 0-85    
               
               
                   
                 Grand Total 
                 0-55,780 
               
               
                   
                   
               
            
           
         
       
     
     Table 2 shows a breakdown of the example ranking for a display (e.g., 0-34,385 as indicated by Table 1, etc.) of a mobile device  103  based on a front side analysis of image data (e.g., image data captured/determined by the camera  105 , etc.) of a mobile device  103 . As shown in Table 2, the letter “R” is used to denote that the associated ranking value is for a “region” of the display of a mobile device  103 . 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Region 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 R1-R9 Front Side 
                 0-28,125 
               
               
                   
                 R10 - Top Notch 
                 0-3,125  
               
               
                   
                 R11 - Front Camera 
                 0-5    
               
               
                   
                 R12 - Receiver 
                 0-5    
               
               
                   
                 R13 - Home Button 
                 0-3,125  
               
               
                   
                 Grand Total Front Side 
                 0-34,385 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  5    shows an example front-side analysis of a display of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture a front side image of the mobile device  103 , for example via the camera  105 , as the mobile device traverses the conveyor belt  101 . The camera  105  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101 . The front side image of the mobile device  103 , for example, an image of a front side display may be partitioned and/or divided into 9 regions R1-R9, where the letter “R” is used to reference the “region” of the display. Each region (R1-R9) may be analyzed separately by the pass/fail controller  113  to determine the number of defects, such as the top 10 defects, per region of the display. The system  100  may capture (via image data) and/or determine the area of the region where each of the top 10 defects is found/located. Each region (R1-R9) may be ranked and/or graded based on the area of the defects and the number of defects. The ranking and/or grading for R1-R9 may be where a value of 0 is the best ranking and/or grading and a value of 3125 represents the worst ranking and/or grading for the region/area. A perfect mobile device  103  may receive a value of 0 and the worst value a mobile device  103  may receive is 28,125 for R1-R9 combined. Table 3 provides a breakdown of how regions for the display of a mobile device  103  are graded, ranked, and/or scored. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                   
                 Total 
                 Allowed 
                 Allowed 
                 Allowed 
                 Allowed 
                 Allowed 
               
               
                 Region 
                 Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
               
               
                 Grade 
                 Defects 
                 Area &gt;= 1000 
                 Area &gt;= 3000 
                 Area &gt;= 4000 
                 Area &gt;= 5000 
                 Area &gt;= 7000 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
               
               
                 5 
                 5 
                 1 
                 0 
                 0 
                 0 
                 0 
               
               
                 25 
                 7 
                 2 
                 1 
                 0 
                 0 
                 0 
               
               
                 125 
                 9 
                 2 
                 1 
                 1 
                 0 
                 0 
               
               
                 625 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 0 
               
               
                 3125 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
               
               
                   
               
            
           
         
       
     
       FIG.  6    shows an example front-side analysis of the bezel (R10) of a mobile device  103 . The bezel area may cover a top notch portion of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture a front camera image of the mobile device  103 , for example via the camera  105 , as the mobile device traverses the conveyor belt  101 . The camera  105  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101 . Image data of the front display notch (e.g., top notch) may be analyzed to determine if the mobile device  103  has any scratches or cracks (excluding the receiver and the camera). The pass/fail controller  113  may analyze image data including the region/area R10 to determine the number of defects, such as the top 10 defects, per region of the display. R10 may have a rank, grade, and/or score, for example, from 0-3125, where a value of 0 is the best ranking and/or grading and a value of 3125 represents the worst ranking and/or grading for the region/area. Table 4 provides a breakdown of how the region/area R10 is graded, ranked, and/or scored. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                   
                 Total 
                 Allowed 
                 Allowed 
                 Allowed 
                 Allowed 
                 Allowed 
               
               
                 Region 
                 Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
               
               
                 Grade 
                 Defects 
                 Area &gt;= 1000 
                 Area &gt;= 3000 
                 Area &gt;= 4000 
                 Area &gt;= 5000 
                 Area &gt;= 7000 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
               
               
                 5 
                 5 
                 1 
                 0 
                 0 
                 0 
                 0 
               
               
                 25 
                 7 
                 2 
                 1 
                 0 
                 0 
                 0 
               
               
                 125 
                 9 
                 2 
                 1 
                 1 
                 0 
                 0 
               
               
                 625 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 0 
               
               
                 3125 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
               
               
                   
               
            
           
         
       
     
       FIG.  7    shows an example front-side analysis of a front-side camera of a mobile device  103 . The region/area comprising the front-side camera may be denoted R11. The system  100  ( FIG.  1 A ) may capture a front camera image of the mobile device  103 , for example via the camera  105 , as the mobile device traverses the conveyor belt  101 . The camera  105  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101 . Image data of the front-side camera may be analyzed to determine if the mobile device  103  has any defect, damages, and/or the like. The pass/fail controller  113  may analyze image data including the region/area R11 to determine the presence of defects. R11 may have a rank, grade, and/or score, for example, from 0-5 where a value of 0 is the best ranking and/or grading and a value of 5 represents the worst ranking and/or grading for the region/area. Table 5 provides a breakdown of how the region/area R11 is graded, ranked, and/or scored based on a comparison evaluating a percent match to Golden, which refers to a registered image without defects. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 5 
               
               
                   
                   
               
               
                   
                 Comparison 
                 % Match 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 % Match to Golden 
                  90% 
                 0 
               
               
                   
                 % Match to Golden 
                 &lt;90% 
                 5 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  8    shows an example front-side analysis of a receiver of a mobile device  103 . The region/area comprising the receiver may be denoted R12. The system  100  ( FIG.  1 A ) may capture a front camera image of the mobile device  103 , for example via the camera  105 , as the mobile device traverses the conveyor belt  101 . The camera  105  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101 . Image data of the receiver may be analyzed to determine if the mobile device  103  has any defect, damages, and/or the like. The pass/fail controller  113  may analyze image data including the region/area R12 to determine the presence of defects. R12 may have a rank, grade, and/or score, for example, from 0-5 where a value of 0 is the best ranking and/or grading and a value of 5 represents the worst ranking and/or grading for the region/area. Table 6 provides a breakdown of how the region/area R12 is graded, ranked, and/or scored based on a comparison evaluating a percent match to Golden, which refers to a registered image without defects. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 6 
               
               
                   
                   
               
               
                   
                 Comparison 
                 % Match 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 % Match to Golden 
                  90% 
                 0 
               
               
                   
                 % Match to Golden 
                 &lt;90% 
                 5 
               
               
                   
                   
               
            
           
         
       
     
     Table 7 shows a breakdown of the example ranking for a back housing (e.g., 0-21,040 as indicated by Table 1, etc.) of a mobile device  103  based on a backside analysis of image data (e.g., image data captured/determined by the camera  107 , etc.) of a mobile device  103 . As shown in Table 7, the letter “R” is used to denote that the associated ranking value is for a “region” of the housing of a mobile device  103 . 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 7 
               
               
                   
                   
               
               
                   
                 Region 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 R1-R9 Enclosure 
                    0-13,500 
               
               
                   
                 R10-R12 - Rear Camera 
                    0-4,500 
               
               
                   
                 R13 - Flash 
                    0-1,500 
               
               
                   
                 R14 - Rear Mic 
                 0-5 
               
               
                   
                 R15 - Apple Logo 
                  0-25 
               
               
                   
                 R16 - Apple artwork 
                 0-5 
               
               
                   
                 R17 - Lidar 
                 0-5 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  9    shows an example backside analysis of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture a backside image of the mobile device  103 , for example via the camera  107 , as the mobile device traverses the conveyor belt  101 . The camera  107  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101  and/or is manipulated by the robotic arm  120 . The backside image of the mobile device  103 , for example, may be partitioned and/or divided into 9 regions R1-R9, where the letter “R” is used to reference the region of the housing. Each region (R1-R9) may be analyzed separately by the pass/fail controller  113  to determine the number of defects, such as the top 10 defects, per region of the back housing. The system  100  may capture (via image data) and/or determine the area of the region where each of the top 10 defects is found/located. Each region (R1-R9) may be ranked and/or graded based on the area of the defects and the number of defects. The ranking and/or grading for R1-R9 may be where a value of 0 is the best ranking and/or grading and a value of 1500 represents the worst ranking and/or grading for the region/area. A perfect mobile device  103  may receive a value of 0 and the worst value a mobile device  103  may receive is 13,500 for R1-R9 combined. Table 8 provides a breakdown of how regions for the back housing of a mobile device  103  are graded, ranked, and/or scored. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 8 
               
               
                   
               
               
                   
                 Total 
                 Allowed 
                 Allowed 
                 Allowed 
                 Allowed 
                 Allowed 
               
               
                 Region 
                 Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
                 Defect Qty. of 
               
               
                 Grade 
                 Defects 
                 Area &gt;= 1000 
                 Area &gt;= 3000 
                 Area &gt;= 4000 
                 Area &gt;= 5000 
                 Area &gt;= 7000 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
               
               
                 5 
                 5 
                 1 
                 0 
                 0 
                 0 
                 0 
               
               
                 25 
                 7 
                 2 
                 1 
                 0 
                 0 
                 0 
               
               
                 125 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 0 
               
               
                 1500 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
                 Unlimited 
               
               
                   
               
            
           
         
       
     
       FIG.  10    shows an example backside analysis of a back housing camera region (R18), cameras (R10, R11, R12), and flash (R13) of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture a backside image of the mobile device  103 , for example via the camera  107 , as the mobile device traverses the conveyor belt  101 , for example, a second level of the conveyor belt  101 , and/or is manipulated by the robotic arm  120 . The camera  107  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101 . Image data of the back housing camera region (R18), cameras (R10, R11, R12), and flash (R13) may be analyzed to determine if the mobile device  103  has any scratches in the respective regions/areas. The pass/fail controller  113  may analyze image data including the back housing camera region (R18), cameras (R10, R11, R12), and flash (R13) to determine the presence of defects. R10 may have a rank, grade, and/or score, for example, a value of 0, 25, or 1500, where a value of 0 is the best ranking and/or grading and a value of 1500 represents the worst ranking and/or grading for the region/area. Table 9 provides a breakdown of how the region/areas R10, R11, R12, R13, and R18 are graded, ranked, and/or scored based on a comparison evaluating a percent match to Golden, which refers to a registered image without defects. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 9 
               
               
                   
                   
               
               
                   
                 Comparison 
                 % Match 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 % Match to Golden 
                 &lt;60 
                 1500 
               
               
                   
                 % Match to Golden 
                 60 &lt; Value &lt; 95 
                 25 
               
               
                   
                 % Match to Golden 
                 &gt;95 
                 0 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  11    shows an example back-side analysis of a back housing microphone, artwork, and a LiDAR sensor of a mobile device  103 . The region/area comprising the microphone, artwork area, and a LiDAR sensor may be denoted R14, R16, and R17, respectively. The system  100  ( FIG.  1 A ) may capture a back camera image of the mobile device  103 , for example via the camera  107 , as the mobile device  103  traverses the conveyor belt  101  and/or is manipulated by the robotic arm  120 . The camera  107  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101 . Image data of the microphone, artwork area, and a LiDAR sensor may be analyzed to determine if the mobile device  103  has any defect, damages, and/or the like. The pass/fail controller  113  may analyze image data including the regions/areas R14, R16, and R17 to determine the presence of defects per region of the back housing. R14, R16, and R17 may each have a rank, grade, and/or score, for example, a value from 0-5, where a value of 0 is the best ranking and/or grading and a value of 5 represents the worst ranking and/or grading for the region/area. Table 10 provides a breakdown of how the regions/areas R14, R16, and R17 are graded, ranked, and/or scored based on a comparison evaluating a percent match to Golden, which refers to a registered image without defects. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 10 
               
               
                   
                   
               
               
                   
                 Comparison 
                 % Match 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 % Match to Golden 
                  90% 
                 0 
               
               
                   
                 % Match to Golden 
                 &lt;90% 
                 5 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  12    shows an example backside analysis of a back housing logo area, trademark, insignia, and/or any other type of text and/or imagery of a mobile device  103 . The region/area comprising the back housing logo, trademark, insignia, and/or any other type of text and/or imagery may be denoted R15. The system  100  ( FIG.  1 A ) may capture a backside camera image of the mobile device  103 , for example via the camera  107 , as the mobile device traverses the conveyor belt  101  and/or is manipulated by the robotic arm  120 . The camera  107  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101  and/or is manipulated by the robotic arm  120 . Image data of the back housing logo, trademark, insignia, and/or any other type of text and/or imagery may be analyzed to determine if the mobile device  103  has any defect, damages, and/or the like. The pass/fail controller  113  may analyze image data including the region/area R15 to determine the presence of defects. R15 may have a rank, grade, and/or score, for example, from 0-25 where a value of 0 is the best ranking and/or grading and a value of 25 represents the worst ranking and/or grading for the region/area. Table 11 provides a breakdown of how the region/area R15 is graded, ranked, and/or scored based on a comparison evaluating a percent match to Golden, which refers to a registered image without defects. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 11 
               
               
                   
                   
               
               
                   
                 Comparison 
                 % Match 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 % Match to Golden 
                 &lt;60 
                 25 
               
               
                   
                 % Match to Golden 
                 60 &lt; Value &lt; 90 
                 5 
               
               
                   
                 % Match to Golden 
                 &gt;90 
                 0 
               
               
                   
                   
               
            
           
         
       
     
     Table 12 shows a breakdown of the example ranking for a bottom edge (e.g., 0-100 as indicated by Table 1, etc.) of a mobile device  103  based on a side view (bottom-side) analysis of image data (e.g., image data captured/determined by the camera  108 , etc.) of a mobile device  103 . As shown in Table 12, the letter “R” is used to denote that the associated ranking value is for a “region” of the bottom-side of a mobile device  103 , for example, a bottom edge. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 12 
               
               
                   
                   
               
               
                   
                 Region 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 R1-R3 - Bottom Enclosure 
                  0-75 
               
               
                   
                 R4 - Left Speaker 
                 0-5 
               
               
                   
                 R5 - Left Screw 
                 0-5 
               
               
                   
                 R6 - Lightning Connector 
                 0-5 
               
               
                   
                 R7 - Right Screw 
                 0-5 
               
               
                   
                 R8 - Right Speaker 
                 0-5 
               
               
                   
                 Grand Total 
                  0-100 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  13    shows an example side view analysis of a bottom-side of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture a bottom-side image of the mobile device  103 , for example via the camera  108 , as the mobile device traverses the conveyor belt  101 . The camera  108  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101  and/or is manipulated by the robotic arm  120 . The bottom-side image of the mobile device  103  may be partitioned and/or divided into 3 regions R1, R2, R3, where the letter “R” is used to reference the regions of the bottom-side. Each region (R1, R2, R3) may be analyzed separately by the pass/fail controller  113  to determine the number of defects, such as the top 10 defects, per region of the back housing. The system  100  may capture (via image data) and/or determine the area of the region where each of the top 10 defects is found/located. Each region (R1, R2, R3) may be ranked and/or graded based on the area of the defects and the number of defects. The ranking and/or grading for R1, R2, R3 may be, for example, 0 or 25, where a value of 0 is the best ranking and/or grading and a value of 25 represents the worst ranking and/or grading for the region/area. A perfect mobile device  103  may receive a value of 0 and the worst value a mobile device  103  may receive is 75 for R1, R2, R3 combined. Table 13 provides a breakdown of how regions for the back housing of a mobile device  103  are graded, ranked, and/or scored. 
     
       
         
           
               
               
               
             
               
                 TABLE 13 
               
               
                   
               
               
                 Area 
                 Count 
                 Rank 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 &gt;500 
                 Min 3 
                 25 
               
               
                 &lt;500 
                 Max 3 
                 0 
               
               
                   
               
            
           
         
       
     
       FIG.  14    shows an example side view analysis of a bottom-side left speaker, left screw, lightning connector, right screw, and right speaker of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture a bottom-side image of the mobile device  103 , for example via the camera  108 , as the mobile device traverses the conveyor belt  101 . The camera  108  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101  and/or is manipulated by the robotic arm  120 . The bottom-side left speaker, left screw, lightning connector, right screw, and right speaker image of the mobile device  103  may be partitioned and/or divided into regions R4-R8, where the letter “R” is used to reference the regions of the bottom-side. Each region (R4-R8) may be analyzed based on a percentage (percent match). The ranking and/or grading for R4-R8 may be, for example, 0 or 5, where a value of 0 is the best ranking and/or grading and a value of 5 represents the worst ranking and/or grading for the region/area. Table 14 provides a breakdown of how regions for the bottom-side left speaker, left screw, lightning connector, right screw, and right speaker of a mobile device  103  are graded, ranked, and/or scored based on a comparison evaluating a percent match to Golden, which refers to a registered image without defects. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 14 
               
               
                   
                   
               
               
                   
                 Comparison 
                 % Match 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 % Match to Golden 
                 &lt;90 
                 5 
               
               
                   
                 % Match to Golden 
                 &gt;90 
                 0 
               
               
                   
                   
               
            
           
         
       
     
     Table 15 shows a breakdown of the example ranking for a top edge (e.g., 0-75 as indicated by Table 1, etc.) of a mobile device  103  based on a side view (top-side) analysis of image data (e.g., image data captured/determined by the camera  106 , etc.) of a mobile device  103 . As shown in Table 15, the letter “R” is used to denote that the associated ranking value is for a “region” of the top-side of a mobile device  103 , for example, a top edge. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 15 
               
               
                   
                   
               
               
                   
                 Region 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 R1-R3 - Top Enclosure 
                 0-75 
               
               
                   
                 Grand Total 
                 0-75 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  15    shows an example side view analysis of a top-edge of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture a top-edge image of the mobile device  103 , for example via the camera  106 , as the mobile device traverses the conveyor belt  101 . The camera  106  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101  and/or is manipulated by the robotic arm  120 . The top-edge image of the mobile device  103  may be partitioned and/or divided into 3 regions R1, R2, R3, where the letter “R” is used to reference the regions of the top edge. Each region (R1, R2, R3) may be analyzed separately by the pass/fail controller  113  to determine the number of defects, such as the top 10 defects, per region of the back housing. The system  100  may capture (via image data) and/or determine the area of the region where each of the top 10 defects is found/located. Each region (R1, R2, R3) may be ranked and/or graded based on the area of the defects and the number of defects. The ranking and/or grading for R1, R2, R3 may be, for example, 0 or 25, where a value of 0 is the best ranking and/or grading and a value of 25 represents the worst ranking and/or grading for the region/area. A perfect mobile device  103  may receive a value of 0 and the worst value a mobile device  103  may receive is 75, for example, R1, R2, R3 combined. Table 16 provides a breakdown of how regions for the top edge of a mobile device  103  are graded, ranked, and/or scored. 
     
       
         
           
               
               
               
             
               
                 TABLE 16 
               
               
                   
               
               
                 Area 
                 Count 
                 Rank 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 &gt;500 
                 Min 3 
                 25 
               
               
                 &lt;500 
                 Max 3 
                 0 
               
               
                   
               
            
           
         
       
     
     Table 17 shows a breakdown of the example ranking for a left edge (e.g., 0-95 as indicated by Table 1, etc.) of a mobile device  103  based on a side view (left-side) analysis of image data (e.g., image data captured/determined by the camera  106 , etc.) of a mobile device  103 . As shown in Table 17, the letter “R” is used to denote that the associated ranking value is for a “region” of the left-side of a mobile device  103 , for example, a left edge. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 17 
               
               
                   
                   
               
               
                   
                 Region 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 R1-R3 - Left Enclosure 
                  0-75 
               
               
                   
                 R4 - Ringer Switch 
                 0-5 
               
               
                   
                 R5 - Volume UP Button 
                 0-5 
               
               
                   
                 R6 - Volume Down Button 
                 0-5 
               
               
                   
                 R7 - SIM Tray Left Side 
                 0-5 
               
               
                   
                 Grand Total 
                  0-95 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  16    shows an example side view analysis of the left side of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture an image of the left side of a mobile device  103 , for example via the camera  106 , as the mobile device traverses the conveyor belt  101 . The camera  106  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101  and/or is manipulated by the robotic arm  120 . The image of the left side of the mobile device  103  may be partitioned and/or divided into 3 regions R1, R2, R3, where the letter “R” is used to reference the regions of the left side (left edge). Each region (R1, R2, R3) may be analyzed separately by the pass/fail controller  113  to determine the number of defects, such as the top 10 defects, per region of the back housing. The system  100  may capture (via image data) and/or determine the area of the region where each of the top 10 defects is found/located. Each region (R1, R2, R3) may be ranked and/or graded based on the area of the defects and the number of defects. The ranking and/or grading for R1, R2, R3 may be, for example, 0 or 25, where a value of 0 is the best ranking and/or grading and a value of 25 represents the worst ranking and/or grading for the region/area. A perfect mobile device  103  may receive a value of 0 and the worst value a mobile device  103  may receive is 75, for example, R1, R2, R3 combined. Table 18 provides a breakdown of how regions for the left side/edge of a mobile device  103  are graded, ranked, and/or scored. 
     
       
         
           
               
               
               
             
               
                 TABLE 18 
               
               
                   
               
               
                 Area 
                 Count 
                 Rank 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 &gt;500 
                 Min 3 
                 25 
               
               
                 &lt;500 
                 Max 3 
                 0 
               
               
                   
               
            
           
         
       
     
       FIG.  17    shows an example side view analysis of a left-side ringer switch, a volume up button, a volume down button, and a subscriber identity module (SIM) of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture a left-side image of the mobile device  103 , for example via the camera  106 , as the mobile device traverses the conveyor belt  101 . The camera  106  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101  and/or is manipulated by the robotic arm  120 . The left-side ringer switch, a volume up button, a volume down button, and a subscriber identity module (SIM) image of the mobile device  103  may be partitioned and/or divided into regions R4-R7, where the letter “R” is used to reference the “regions” of the left-side. The system  100  may capture (via image data) and/or determine the area of the region where each of the top 10 defects is found/located. Each region (R4-R7) may be ranked and/or graded based on defects. The ranking and/or grading for R4-R7 may be, for example, 0 or 5, where a value of 0 is the best ranking and/or grading and a value of 5 represents the worst ranking and/or grading for the region/area. Table 19 provides a breakdown of how regions for the left-side ringer switch, the volume up button, the volume down button, and the SIM of a mobile device  103  are graded, ranked, and/or scored. 
     
       
         
           
               
               
               
             
               
                 TABLE 19 
               
               
                   
               
               
                 Area 
                 Count 
                 Rank 
               
               
                   
               
             
            
               
                 &gt;500 
                 Min 2 
                 5 
               
               
                 &lt;500 
                 Max 2 
                 0 
               
               
                   
               
            
           
         
       
     
     Table 20 shows a breakdown of the example ranking for a right edge (e.g.,  0 - 85  as indicated by Table 1, etc.) of a mobile device  103  based on a side view (right-side) analysis of image data (e.g., image data captured/determined by the camera  108 , etc.) of a mobile device  103 . As shown in Table 20, the letter “R” is used to denote that the associated ranking value is for a “region” of the right-side of a mobile device  103 , for example, a right edge. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 20 
               
               
                   
                   
               
               
                   
                 Region 
                 Rank 
               
               
                   
                   
               
             
            
               
                   
                 R1-R3 - Right Enclosure 
                 0-75 
               
               
                   
                 R4 - Power Button 
                 0-5  
               
               
                   
                 R5 - SIM Tray/Antenna 
                 0-5  
               
               
                   
                 Grand Total 
                 0-85 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  18    shows an example side view analysis of the right side of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture an image of the right side of a mobile device  103 , for example via the camera  108 , as the mobile device traverses the conveyor belt  101 . The camera  108  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101  and/or is manipulated by the robotic arm  120 . The image of the right side/edge of the mobile device  103  may be partitioned and/or divided into 3 regions R1, R2, R3, where the letter “R” is used to reference the regions of the right side. Each region (R1, R2, R3) may be analyzed separately by the pass/fail controller  113  to determine the number of defects, such as the top 10 defects, per region of the back housing. The system  100  may capture (via image data) and/or determine the area of the region where each of the top 10 defects is found/located. Each region (R1, R2, R3) may be ranked and/or graded based on the area of the defects and the number of defects. The ranking and/or grading for R1, R2, R3 may be, for example, 0 or 25, where a value of 0 is the best ranking and/or grading and a value of 25 represents the worst ranking and/or grading for the region/area. A perfect mobile device  103  may receive a value of 0 and the worst value a mobile device  103  may receive is 75, for example, R1, R2, R3 combined. Table 21 provides a breakdown of how regions for the right side/edge of a mobile device  103  are graded, ranked, and/or scored. 
     
       
         
           
               
               
               
             
               
                 TABLE 21 
               
               
                   
               
               
                 Area 
                 Count 
                 Rank 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 &gt;500 
                 Min 3 
                 25 
               
               
                 &lt;500 
                 Max 3 
                 0 
               
               
                   
               
            
           
         
       
     
       FIG.  19    shows an example side view analysis of a right-side power button and SIM tray/antenna location of a mobile device  103 . The system  100  ( FIG.  1 A ) may capture a right-side image of the mobile device  103 , for example via the camera  108 , as the mobile device traverses the conveyor belt  101 . The camera  108  (and associated lighting to illuminate the surface of the mobile device  103 , etc.) may be triggered and/or caused to capture image data when the mobile device  103  reaches a sensor and/or the like along the path of the conveyor belt  101  and/or is manipulated by the robotic arm  120 . The right-side power button and SIM tray/antenna location of the mobile device  103  may be partitioned and/or divided into regions R4 and R5, where the letter “R” is used to reference the right-side. The system  100  may capture (via image data) and/or determine the area of the region where each of the top 10 defects is found/located. Each region (R4 and R5) may be ranked and/or graded based on defects. The ranking and/or grading for R4 and R5 may be, for example, 0 or 5, where a value of 0 is the best ranking and/or grading and a value of 5 represents the worst ranking and/or grading for the region/area. Table 22 provides a breakdown of how regions for the right-side power button and SIM tray/antenna location of a mobile device  103  are graded, ranked, and/or scored. 
     
       
         
           
               
               
               
             
               
                 TABLE 22 
               
               
                   
               
               
                 Area 
                 Count 
                 Rank 
               
               
                   
               
             
            
               
                 &gt;500 
                 Min 2 
                 5 
               
               
                 &lt;500 
                 Max 2 
                 0 
               
               
                   
               
            
           
         
       
     
     Returning to  FIG.  1 A , the cameras  105 - 108  and/or the pass/fail controller  113  may output an image and/or image data (e.g.,  FIGS.  5 - 19   , etc.), and/or one or more notifications to a display  117  (e.g., a monitor, a computing device, etc.). The pass/fail controller  113  may output a result of a determination made by the DDA engine  202  to the display  117 . For example, the pass/fail controller  113  may output a ranking and/or grading for a mobile device, as described in Tables 1-22, to the display  117 . The ranking and/or grading may be used, for example, by the system  100 , to update a device profile associated with a mobile device  103 . The updated device profile may indicate the rankings and/or gradings for the mobile device  103 . The pass/fail controller  113 , based on a determined ranking and/or grading, may determine that a mobile device  103  passes or fails a device inspection. 
     As described, the system  100  is a modular system that can be manually loaded and unloaded with user devices (e.g., mobile devices, smartphones, IoT devices, electronic devices, etc.) manually, for example via an operator of the system  100 , or automatically, for example via an automated conveyor system (e.g., the conveyor belt  101 , the robot arm  120 , etc.). The infeed and outfeed of the system  100  may be based on hardware, software, and/or a combination of hardware and software modular components that may be readily upgraded, changed, swapped, and/or omitted, based on the type of device being inspected, user preferences, and/or the like. The system  100  is a device-agnostic system that may accommodate inspection and analysis of any type of device. The system  100  enables remote and/or Online Provisioning (OLP), so that user device model information may be received from a remote computing device and ranking/grading criteria may be updated, changed, and/or modified remotely and/or manually. The OLP is a warehouse management system. The system  100  is integrated with OLP via web Application Programming Interfaces (APIs). The OLP shares information about a scanned serial number such as model information and/or inbound channel so the system  100  may automatically adjust the robotic positions, vision system settings, and grading specifications, for example. 
       FIGS.  20 - 23    show example perspective views of an example system  100 . The  FIGS.  21 - 23    show various components of the system  100 . For example,  FIG.  21    shows the camera  105 , the conveyor belt  101 , and the robotic arm  120 .  FIG.  22    shows the camera  106 , the conveyor belt  101 , a mobile device  103 , and the robotic arm  120 .  FIG.  23    shows the camera  105 , and the gantry  122 . 
       FIG.  24    is a flowchart of an example method  2400  for grading user devices. The method  2400  may be performed, for example, via the system  100 . At  2410 , determining image data associated with a mobile device. Determining the image data may include receiving, from an imaging device, based on the mobile device traversing a conveyor, the image data. The imaging device may include one or more of a line scan camera, an area scan camera, a three-dimensional (3D) imaging camera, a laser camera, or a computer vision camera. 
     At  2420 , determining, based on the image data, a type of the mobile device and a plurality of regions. Determining the type of the mobile device may include determining the type of the mobile device from one or more of a barcode or a quick response code indicated by the image data. Determining the type of the mobile device by pulling model information from the OLP through web APIs. 
     At  2430 , determining, for each region of the plurality of regions, a number of defects. Determining the number of defects may be based on one or more of object recognition, computer vision, a trained predictive model, or machine vision (e.g., one or more machine vision methods). 
     At  2440 , determining, for each region of the plurality of regions, a score, wherein the score is based on the type of the mobile device and one or more of: the region, the number of defects for the region, the area of the defects in the region and the type of defects of the region. 
     At  2450 , updating, based on the score for each region of the plurality of regions, a device profile associated with the mobile device. The method  2400  may further include causing display of the score for each region of the plurality of regions. The final scoring grade is calculated and may be saved to the OLP with the device International Mobile Equipment Identity (IMEI) or serial number. 
       FIG.  25    is a flowchart of an example method  2500  for grading user devices. The method  2500  may be performed, for example, via the system  100 . At  2510 , determining, for each mobile device of a plurality of mobile devices, image data. Determining, for each mobile device of the plurality of mobile devices the image data, may include receiving, from one more imaging devices, based on the mobile device traversing a conveyor, the image data. The one or more imaging devices comprises one or more of line scan cameras, area scan cameras, three-dimensional (3D) imaging cameras, laser cameras, or computer vision cameras. 
     At  2520 , determining, for each mobile device of the plurality of mobile devices, based on the image data, a type of the mobile device and a plurality of regions. Determining, for each mobile device of the plurality of mobile devices, the type of the mobile device may include determining the type of the mobile device from one or more of a barcode or a quick response code indicated by the image data. Determining, for each mobile device of the plurality of mobile devices, the type of the mobile device may be based on object recognition. Determining the type of each mobile device of the plurality of mobile devices by pulling model information from the OLP through web APIs. 
     At  2530 , determining, for each mobile device of the plurality of mobile devices, for each region of the plurality of regions, a number of defects. Determining, for each mobile device of the plurality of mobile devices, the number of defects may be based on one or more of machine vision methods or object recognition. Determining, for each mobile device of the plurality of mobile devices, the number of defects may be based on an output of a trained predictive model. 
     At  2540 , determining, for each mobile device of the plurality of mobile devices, for each region of the plurality of regions, a score, wherein the score is based on the type of the mobile device and one or more of: the region or the number of defects for the region. 
     At  2550 , updating, for each mobile device of the plurality of mobile devices, based on the score for each region of the plurality of regions, a device profile associated with the mobile device. The final scoring grade is calculated and may be saved to the OLP with the device International Mobile Equipment Identity (IMEI) or serial number. 
     The method  2500  may further include causing, for each mobile device of the plurality of mobile devices the image data, display of the score for each region of the plurality of regions. The method  2500  may further include determining, based on the scores for two or more mobile devices of the plurality of mobile devices, a group of mobile devices, wherein the scores, for each mobile device of the group of mobile devices, match. The method  2500  may further include causing, for each mobile device of the plurality of mobile devices, display of the score for each region of the plurality of regions. 
       FIG.  26    is a flowchart of an example method  2600  for grading user devices. The method  2600  may be performed, for example, via the system  100 . At  2610 , determining image data associated with a mobile device. Determining the image data may include receiving, from an imaging device, based on the mobile device traversing a conveyor, the image data. 
     At  2620 , determining, based on the image data, a type of the mobile device and an aesthetic defect. Determining the type of the mobile device and the aesthetic defect may be based on one or more of machine vision methods or object recognition. 
     At  2630 , predicting, based on the type of the mobile device and the aesthetic defect, a functional defect. 
     At  2640 , determining, based on the predicted functional defect, a score. 
     At  2650 , updating, based on the score, a device profile associated with the mobile device. The method  2600  may further include causing, based on one or more of the predicted functional defect or the score satisfying a threshold, the mobile device to be sent for manual inspection. 
     In an exemplary aspect, the methods and systems may be implemented on a computer  2701  as illustrated in  FIG.  27    and described below. By way of example, the cameras  105 - 108 , the PLC  116 , the pass/fail controller  113  (or a component thereof) of  FIG.  1   , and/or any other device/component described herein may be a computer  2701  as illustrated in  FIG.  27   . Similarly, the methods and systems disclosed may utilize one or more computers to perform one or more functions in one or more locations.  FIG.  27    is a block diagram illustrating an exemplary operating environment  2700  for performing the disclosed methods. This exemplary operating environment  2700  is only an example of an operating environment and is not intended to suggest any limitation as to the scope of use or functionality of operating environment architecture. Neither should the operating environment  2700  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  2700 . 
     The present methods and systems may be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the systems and methods comprise, but are not limited to, personal computers, server computers, laptop devices, and multiprocessor systems. Additional examples comprise set top boxes, programmable consumer electronics, network PCs, programmable logic controllers (PLCs), minicomputers, mainframe computers, distributed computing environments that comprise any of the above systems or devices, and the like. 
     The processing of the disclosed methods and systems may be performed by software components. The disclosed systems and methods may be described in the general context of computer-executable instructions, such as program modules, being executed by one or more computers or other devices. Generally, program modules comprise computer code, routines, programs, objects, components, data structures, and/or the like that perform particular tasks or implement particular abstract data types. The disclosed methods may also be practiced in grid-based and distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in local and/or remote computer storage media including memory storage devices. 
     Further, one skilled in the art will appreciate that the systems and methods disclosed herein may be implemented via a general-purpose computing device in the form of a computer  2701 . The computer  2701  may comprise one or more components, such as one or more processors  2703 , a system memory  2712 , and a bus  2713  that couples various components of the computer  2701  including the one or more processors  2703  to the system memory  2712 . In the case of multiple processors  2703 , the system may utilize parallel computing. 
     The bus  2713  may comprise one or more of several possible types of bus structures, such as a memory bus, memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. The bus  2713 , and all buses specified in this description may also be implemented over a wired or wireless network connection. 
     The computer  2701  typically comprises a variety of computer-readable media. Exemplary readable media may be any available media that is accessible by the computer  2701  and comprises, for example, and not meant to be limiting, both volatile and non-volatile media, removable and non-removable media. The system memory  2712  may comprise computer-readable media in the form of volatile memory, such as random access memory (RAM), and/or non-volatile memory, such as read-only memory (ROM). The system memory  2712  typically may comprise data such as image analysis data  2707  and/or program modules such as operating system  2705  and image analysis software  2706  that are accessible to and/or are operated on by the one or more processors  2703 . 
     In another aspect, the computer  2701  may also comprise other removable/non-removable, volatile/non-volatile computer storage media. The mass storage device  2704  may provide non-volatile storage of computer code, computer-readable instructions, data structures, program modules, and other data for the computer  2701 . For example, a mass storage device  2704  may be a hard disk, a removable magnetic disk, a removable optical disk, magnetic cassettes or other magnetic storage devices, flash memory cards, CD-ROM, digital versatile disks (DVD) or other optical storage, random access memories (RAM), read only memories (ROM), electrically erasable programmable read-only memory (EEPROM), and the like. 
     Optionally, any number of program modules may be stored on the mass storage device  2704 , including by way of example, an operating system  2705  and image analysis software  2706 . One or more of the operating system  2705  and image analysis software  2706  (or some combination thereof) may comprise elements of the programming and the image analysis software  2706 . Image analysis data  2707  may also be stored on the mass storage device  2704 . Image analysis data  2707  may be stored in any of one or more databases known in the art. Examples of such databases comprise, DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL, PostgreSQL, and the like. The databases may be centralized or distributed across multiple locations within the network  2715 . 
     In another aspect, the user may enter commands and information into the computer  2701  via an input device (not shown). Examples of such input devices comprise, but are not limited to, a keyboard, pointing device (e.g., a computer mouse, remote control), a microphone, a joystick, touch-enabled devices such as a touchscreen, tactile input devices such as gloves and other body coverings, motion sensors, and the like. These and other input devices may be connected to the one or more processors  2703  via a human-machine interface  2702  that is coupled to the bus  2713 , but can be connected by other interface and bus structures, such as, but not limited to, a parallel port, game port, an IEEE 1394 Port (also known as a Firewire port), a serial port, network adapter  2708 , and/or a universal serial bus (USB). 
     In yet another aspect, a display device  2711  may also be connected to the bus  2713  via an interface, such as a display adapter  2709 . It is contemplated that the computer  2701  may have more than one display adapter  2709  and the computer  2701  may have more than one display device  2711 . For example, a display device  2711  may be a monitor, an LCD (Liquid Crystal Display), a light-emitting diode (LED) display, a television, smart lens, smart glass, and/or a projector. In addition to the display device  2711 , other output peripheral devices may comprise components such as speakers (not shown) and a printer (not shown) which may be connected to the computer  2701  via Input/Output Interface  2710 . Any step and/or result of the methods may be output in any form to an output device. Such output may be any form of visual representation, including, but not limited to, textual, graphical, animation, audio, tactile, and the like. The display  2711  and computer  2701  may be part of one device, or separate devices. 
     In an aspect, the computer  2701  may be coupled to the system  100  via the Input/Output Interface  2710 . The computer  2701  may be configured to monitor and store data. The computer  2701  may be configured to store images acquired by cameras connected to the system  100 , store data related to pass/fail data (scores, grades, rankings, etc.) generated during inspections, etc. The computer  2701  may also be used as a programming interface to one or more smart devices (e.g., smart cameras) and/or embedded logic controllers that require customized firmware to operate. The computer  2701  may be used to generate, troubleshoot, upload, and store iterations of this software or firmware. 
     The computer  2701  may operate in a networked environment using logical connections to one or more remote computing devices  2714   a,b,c . By way of example, a remote computing device  2714   a,b,c  may be a personal computer, computing station (e.g., workstation), portable computer (e.g., laptop, mobile phone, tablet device), smart device (e.g., smartphone, smartwatch, activity tracker, smart apparel, smart accessory), security and/or monitoring device, a server, a router, a network computer, a peer device, edge device or other common network nodes, and so on. Logical connections between the computer  2701  and a remote computing device  2714   a,b,c  may be made via a network  2715 , such as a local area network (LAN) and/or a general wide area network (WAN). Such network connections may be through a network adapter  2708 . A network adapter  2708  may be implemented in both wired and wireless environments. Such networking environments are conventional and commonplace in dwellings, offices, enterprise-wide computer networks, intranets, and the Internet. In an aspect, the network adapter  2708  may be configured to provide power to one or more connected devices (e.g., a camera). For example, the network adapter  2708  may adhere to the Power-over-Ethernet (PoE) standard or the like. 
     For purposes of illustration, application programs and other executable program components such as the operating system  2705  are illustrated herein as discrete blocks, although it is recognized that such programs and components may reside at various times in different storage components of the computing device  2701 , and are executed by the one or more processors  2703  of the computer  2701 . An implementation of image analysis software  2706  may be stored on or transmitted across some form of computer readable media. Any of the disclosed methods may be performed by computer readable instructions embodied on computer-readable media. Computer-readable media may be any available media that may be accessed by a computer. By way of example and not meant to be limiting, computer-readable media may comprise “computer storage media” and “communications media.” “Computer storage media” may comprise volatile and non-volatile, removable and non-removable media implemented in any methods or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Exemplary computer storage media may comprise RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by a computer. 
     The methods and systems may employ artificial intelligence (AI) techniques such as machine learning and iterative learning. Examples of such techniques include, but are not limited to, expert systems, case-based reasoning, Bayesian networks, behavior-based AI, neural networks, fuzzy systems, evolutionary computation (e.g., genetic algorithms), swarm intelligence (e.g., ant algorithms), and hybrid intelligent systems (e.g., Expert inference rules generated through a neural network or production rules from statistical learning). 
     While the methods and systems have been described in connection with preferred embodiments and specific examples, it is not intended that the scope be limited to the particular embodiments set forth, as the embodiments herein are intended in all respects to be illustrative rather than restrictive. 
     Unless otherwise expressly stated, it is in no way intended that any method set forth herein be construed as requiring that its steps be performed in a specific order. Accordingly, where a method claim does not actually recite an order to be followed by its steps or it is not otherwise specifically stated in the claims or descriptions that the steps are to be limited to a specific order, it is no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including: matters of logic with respect to arrangement of steps or operational flow; plain meaning derived from grammatical organization or punctuation; the number or type of embodiments described in the specification. 
     It will be apparent to those skilled in the art that various modifications and variations may be made without departing from the scope or spirit. Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims.