Patent Publication Number: US-11398101-B2

Title: Item validation and image evaluation system

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of and claims priority to U.S. application Ser. No. 16/157,276, filed Oct. 11, 2018 and entitled “Item Validation and Image Evaluation System,” which is incorporated herein by reference in its entirety. 
     This application is related to U.S. application Ser. No. 16/157,326 filed Oct. 11, 2018, and entitled, “Image Evaluation and Dynamic Cropping System,” U.S. application Ser. No. 16/157,384 filed Oct. 11, 2018, and entitled, “Enterprise Profile Management and Control System,” and U.S. application Ser. No. 16/157,473 filed Oct. 11, 2018 and entitled, “Dynamic Profile Control System.” All of which are incorporated herein by reference in their entirety. 
    
    
     BACKGROUND 
     Aspects of the disclosure relate to electrical computers, systems, and machine learning. In particular, one or more aspects of the disclosure relate to item validation and image evaluation. 
     Thwarting unauthorized activity on one or more accounts is an important function. As unauthorized actors become more sophisticated, it can be difficult to identify unauthorized instruments. Further, even systems configured to identify unauthorized instruments can identify false positives. Accordingly, it would be advantageous to have a system for further evaluating instruments, for instance, after an initial evaluation, to determine whether the instrument is unauthorized, identify aspects of an authorized or unauthorized instrument, and the like. 
     SUMMARY 
     The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosure. The summary is not an extensive overview of the disclosure. It is neither intended to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure. The following summary merely presents some concepts of the disclosure in a simplified form as a prelude to the description below. 
     Aspects of the disclosure provide effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with accurately evaluating instruments for authenticity and validity. 
     In some examples, a system, computing platform, or the like, may receive an instrument and associated data. In some examples, the instrument may have been previously evaluated or processed to determine validity and/or authenticity. In some arrangements, the associated data may include a validity rating. 
     The instrument may be received and a user profile may be retrieved. The user profile may include a plurality of previously processed instruments that have been determined to be valid and/or authentic. The instrument may be compared to the plurality of previously processed instruments to determine whether one or more elements of the instrument being evaluated match one or more corresponding elements of the plurality of previously processed instruments. Matching or non-matching elements may be identified. In some examples, one or more user interfaces may be generated displaying the instruments and including any highlighting or enhancements identifying matching or non-matching elements. 
     These features, along with many others, are discussed in greater detail below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which: 
         FIGS. 1A and 1B  depict an illustrative computing environment for implementing item validation and image evaluation functions in accordance with one or more aspects described herein; 
         FIGS. 2A-2C  depict an illustrative event sequence for implementing item validation and image evaluation functions in accordance with one or more aspects described herein; 
         FIG. 3  depicts an illustrative method for implementing and using a system to perform item validation and image evaluation functions, according to one or more aspects described herein; 
         FIG. 4  illustrates example user interface in accordance with one or more aspects described herein; 
         FIG. 5  illustrates another example user interface in accordance with one or more aspects described herein; 
         FIG. 6  illustrates one example operating environment in which various aspects of the disclosure may be implemented in accordance with one or more aspects described herein; and 
         FIG. 7  depicts an illustrative block diagram of workstations and servers that may be used to implement the processes and functions of certain aspects of the present disclosure in accordance with one or more aspects described herein. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description of various illustrative embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which is shown, by way of illustration, various embodiments in which aspects of the disclosure may be practiced. It is to be understood that other embodiments may be utilized, and structural and functional modifications may be made, without departing from the scope of the present disclosure. 
     It is noted that various connections between elements are discussed in the following description. It is noted that these connections are general and, unless specified otherwise, may be direct or indirect, wired or wireless, and that the specification is not intended to be limiting in this respect. 
     Some aspects of the disclosure relate to item validation and image evaluation. 
     As mentioned above, protecting user data and thwarting unauthorized activity is a priority for most users. In some examples, unauthorized actors may generate unauthorized or invalid checks or other instruments. The checks or other instruments may be generated to appear similar to valid checks or other instruments of the user, but, in some examples, might not be identical. For instance, one or more of static (e.g., payee line, payor information, magnetic ink character recognition (MICR) line, date, and the like) elements, and/or one or more dynamic elements (e.g., signature, date information, amount information, payee information, or the like) may be slightly or substantially different from a valid instrument. Accordingly, systems may identify similarities or differences to determine whether the instrument is likely valid or authentic. However, these systems do not offer substantial information as to why an instrument was deemed valid or invalid and also may be inaccurate and identify false positives. 
     Accordingly, aspects described herein are directed to systems for further analyzing checks or other instruments to identify why the check or other instrument was deemed valid or invalid and also to eliminate the likelihood of false positives. In some examples, a system, computing platform, or the like, may receive a check or other instrument. In some arrangements, additional data may also be received, such as a validity score associated with the check or other instrument. 
     In some arrangements, a user profile may be retrieved, such as from a database. The user profile may include information associated with the user (e.g., name, contact information, account information, and the like), as well as a plurality of checks or other instruments associated with the user that were previously processed. The check or instrument being evaluated may be compared to the plurality of checks or other instruments in the user profile to evaluate validity, identify matching or non-matching elements, and the like. One or more user interfaces may be generated displaying the check or instrument being evaluated and one or more checks or other instruments from the user profile and similarities or differences may be identified on the user interface. 
     Accordingly, outputs from the evaluation described herein may be used to further update, validate and/or refine systems used to perform initial evaluations of checks or other instruments to determine validity. 
     These and various other arrangements will be discussed more fully below. 
       FIGS. 1A and 1B  depict an illustrative computing environment for implementing and using a system for item validation and image evaluation in accordance with one or more aspects described herein. Referring to  FIG. 1A , computing environment  100  may include one or more computing devices and/or other computing systems. For example, computing environment  100  may include a potential counterfeit check evaluation computing platform  110 , a check receiving and processing computing device  120 , a first local user computing device  150 , a second local user computing device  155 , a first remote user computing device  170 , and a second remote user computing device  175 . 
     Potential counterfeit check evaluation computing platform  110  may be configured to host and/or execute a machine learning engine to provide intelligent, dynamic image evaluation and item validation with respect to checks or other instruments or documents that have been identified as potentially fraudulent or otherwise unauthorized. For instance, check receiving and processing computing device  120  may include one or more computing devices, servers, or the like, configured to receive checks or other instruments from a variety of sources. For instance, checks may be received via online or mobile banking applications executing on a user computing device, such as remote user computing device  170 ,  175 , from an automated teller machine (ATM) or other self-service kiosk, from a banking associated within a financial institution location, or the like. 
     In some examples, the checks may be received by the check receiving and processing computing device  120  and may be processed, in real-time or near real-time, to evaluate the validity of the check or other instrument. In some examples, processing the check or other instrument may include comparing the check to one or more checks in a user profile associated with users drafting the checks (e.g., account holder, payer, or the like) and generating a score representing a likelihood that the check is fraudulent. For instance, the check receiving and processing computing device  120  may compare the received check to the plurality of checks in the user profile to evaluate various regions, fields or aspects of the check to determine whether the received check matches one or more checks in the user profile. Based on a number of matching items, a score may be determined. For instance, if several regions, fields, or the like, match, a low score may be generated indicating that there is a low likelihood that the check is fraudulent. If few or no items match, a high score may be generated indicating that it is very likely that the check is fraudulent. 
     In some examples, the check receiving and processing computing device  120  may convert the check to a digital image or may store a digital image of the check (e.g., if received via electronic systems such as online or mobile banking applications). This check image data, as well as the generated score, may be transmitted to the potential counterfeit check evaluation computing platform  110 . 
     In some examples, all checks processed by the check receiving and processing computing device  120  may be transmitted to the potential counterfeit check evaluation computing platform  110 . In other examples, only certain checks may be transmitted, such as those meeting certain criteria (e.g., having a generated score within a predetermined range, having particular data fields that match or do not match, history of fraudulent or unauthorized activity associated with an account of the check, or the like). 
     The potential counterfeit check evaluation computing platform  110  may receive the check and may perform an additional comparison of the check to the plurality of checks in a user profile. In some examples, this additional evaluation of the check or check image data may be performed to provide additional information related to why a particular score was generated for the check. In systems implemented by large entities, such as a financial institution, millions of checks are being processed and evaluated every day. Accordingly, providing a secondary system to generate additional information related to why a particular score was generated for a check would increase efficiency, accuracy of checks flagged as fraudulent, aid in updating and validating systems for generating scores, and the like. 
     In some examples, the potential counterfeit check evaluation computing platform  110  may display the check being evaluated along with one or more other checks in the user profile. A secondary comparison may be performed and regions or fields that match or do not match may be highlighted or otherwise visually indicated (e.g., by generating a border around a region or field, by inserting a callout or other icon near a region or field, or the like). For instance, if the check being evaluated matches one or more other checks (or portions or regions of the checks match) the check being evaluated and the matching check may be displayed with matching points highlighted. Alternatively, if the check being evaluated does not match one or more checks within the user profile, the check being evaluated may be displayed with the check from the profile that does not match and non-matching regions or fields may be highlighted. In some examples, the check being evaluated and the matching/non-matching check from the user profile may be displayed simultaneously. 
     In some arrangements, the potential counterfeit check evaluation computing device  110  may evaluate a plurality of fields or regions. For instance, static elements of the checks, such as the payee line, location of the magnetic ink character recognition (MICR) line, signature line, position of check number, location of name of account holder, or the like, may be evaluated. In some examples, a pixel associated with a particular point of each static element may be compared. For instance, a pixel associated with a position of a start of the MICR line on the check being evaluated may be compared to a pixel associated with a position of a start of the MICR line on checks in the user profile. If the pixels or pixel locations (e.g., based on x and y coordinates) match, that may be one factor indicating that the check is likely not fraudulent. Pixel locations for various other elements may be compared similarly. 
     In addition to static elements being compared, dynamic check elements may also be compared. For instance, a signature or position of a signature, a start of data including a date of the check, a start of data including an amount of the check, or the like may be evaluated. In some examples, machine learning may be used to evaluate these and other elements of the check. 
     Accordingly, upon identifying matching or non-matching elements, data may be provided to a user indicating the matching/non-matching elements, and/or providing options for additional actions. For instance, if the check is a match, the system may provide an option to add the check to a user profile. 
     Local user computing device  150 ,  155  and remote user computing device  170 ,  175  may be configured to communicate with and/or connect to one or more computing devices or systems shown in  FIG. 1A . For instance, local user computing device  150 ,  155  may communicate with one or more computing systems or devices via network  190 , while remote user computing device  170 ,  175  may communicate with one or more computing systems or devices via network  195 . In some examples, local user computing device  150 ,  155  may be used to access one or more entity systems, functions or processes. In some examples, local user computing device  150 ,  155  may be used to access the potential counterfeit check evaluation computing platform  110  and/or the check receiving and processing computing device  120  to control parameters of the system, update rules, modify settings, and the like. Local user computing device  150 ,  155  may also include ATMs or other self-service kiosks, as well as banking associated computing devices, that may receive checks from a user and may transmit the check or check images for further processing. 
     The remote user computing devices  170 ,  175  may be used to communicate with, for example, potential counterfeit check evaluation computing platform  110  and/or check receiving and processing computing device  120  to capture check image data, transmit check image data, and the like. For instance, remote user computing devices  170 ,  175  may include user computing devices, such as mobile devices including smartphones, tablets, laptop computers, and the like, that may be used to access and/or execute online banking applications, mobile banking applications, or the like. 
     In one or more arrangements, check receiving and processing computing device  120 , local user computing device  150 , local user computing device  155 , remote user computing device  170 , and/or remote user computing device  175  may be any type of computing device or combination of devices capable of performing the particular functions described herein. For example, check receiving and processing computing device  120 , local user computing device  150 , local user computing device  155 , remote user computing device  170 , and/or remote user computing device  175  may, in some instances, be and/or include server computers, desktop computers, laptop computers, tablet computers, smart phones, or the like that may include one or more processors, memories, communication interfaces, storage devices, and/or other components. As noted above, and as illustrated in greater detail below, any and/or all of check receiving and processing computing device  120 , local user computing device  150 , local user computing device  155 , remote user computing device  170 , and/or remote user computing device  175  may, in some instances, be special-purpose computing devices configured to perform specific functions. 
     Computing environment  100  also may include one or more computing platforms. For example, and as noted above, computing environment  100  may include potential counterfeit check evaluation computing platform  110 . As illustrated in greater detail below, potential counterfeit check evaluation computing platform  110  may include one or more computing devices configured to perform one or more of the functions described herein. For example, potential counterfeit check evaluation computing platform  110  may include one or more computers (e.g., laptop computers, desktop computers, servers, server blades, or the like). 
     As mentioned above, computing environment  100  also may include one or more networks, which may interconnect one or more of potential counterfeit check evaluation computing platform  110 , check receiving and processing computing device  120 , local user computing device  150 , local user computing device  155 , remote user computing device  170 , and/or remote user computing device  175 . For example, computing environment  100  may include private network  190  and public network  195 . Private network  190  and/or public network  195  may include one or more sub-networks (e.g., Local Area Networks (LANs), Wide Area Networks (WANs), or the like). Private network  190  may be associated with a particular organization (e.g., a corporation, financial institution, educational institution, governmental institution, or the like) and may interconnect one or more computing devices associated with the organization. For example, potential counterfeit check evaluation computing platform  110 , check receiving and processing computing device  120 , local user computing device  150 , and local user computing device  155 , may be associated with an organization (e.g., a financial institution), and private network  190  may be associated with and/or operated by the organization, and may include one or more networks (e.g., LANs, WANs, virtual private networks (VPNs), or the like) that interconnect potential counterfeit check evaluation computing platform  110 , check receiving and processing computing device  120 , local user computing device  150 , and local user computing device  155 , and one or more other computing devices and/or computer systems that are used by, operated by, and/or otherwise associated with the organization. Public network  195  may connect private network  190  and/or one or more computing devices connected thereto (e.g., potential counterfeit check evaluation computing platform  110 , check receiving and processing computing device  120 , local user computing device  150 , local user computing device  155 ) with one or more networks and/or computing devices that are not associated with the organization. For example, remote user computing device  170 , and/or remote user computing device  175  might not be associated with an organization that operates private network  190  (e.g., because remote user computing device  170  and remote user computing device  175  may be owned, operated, and/or serviced by one or more entities different from the organization that operates private network  190 , such as a second entity different from the entity, one or more customers of the organization, public or government entities, and/or vendors of the organization, rather than being owned and/or operated by the organization itself or an employee or affiliate of the organization), and public network  195  may include one or more networks (e.g., the internet) that connect remote user computing device  170  and remote user computing device  175  to private network  190  and/or one or more computing devices connected thereto (e.g., potential counterfeit check evaluation computing platform  110 , check receiving and processing computing device  120 , local user computing device  150 , local user computing device  155 ). 
     Referring to  FIG. 1B , potential counterfeit check evaluation computing platform  110  may include one or more processors  111 , memory  112 , and communication interface  113 . A data bus may interconnect processor(s)  111 , memory  112 , and communication interface  113 . Communication interface  113  may be a network interface configured to support communication between potential counterfeit check evaluation computing platform  110  and one or more networks (e.g., private network  190 , public network  195 , or the like). Memory  112  may include one or more program modules having instructions that when executed by processor(s)  111  cause potential counterfeit check evaluation computing platform  110  to perform one or more functions described herein and/or one or more databases that may store and/or otherwise maintain information which may be used by such program modules and/or processor(s)  111 . In some instances, the one or more program modules and/or databases may be stored by and/or maintained in different memory units of potential counterfeit check evaluation computing platform  110  and/or by different computing devices that may form and/or otherwise make up potential counterfeit check evaluation computing platform  110 . 
     For example, memory  112  may have, store, and/or include a check processing module  112   a . Check processing module  112   a  may store instructions and/or data that may cause or enable the potential counterfeit check evaluation computing platform  110  to receive one or more checks, check data, check image data, check validity scores, and the like, for further evaluation. In some examples, check processing module  112   a  may extract or parse data associated with the received data to identify a user or user profile or account associated with the payer of the check being evaluated, retrieve a user profile from user profile database  112   f , and the like. 
     Potential counterfeit check evaluation computing platform  110  may further have, store and/or include a match detection and evaluation module  112   b . Match detection and evaluation module  112   b  may store instructions and/or data that may cause or enable the potential counterfeit check evaluation computing platform  110  to process received check data, images, and the like, compare the received checks, check images, check data and the like to a plurality of checks in a user profile, and identify regions, fields, or other check elements that match or do not match. For instance, as discussed above, the match detection and evaluation module  112   b  may evaluate a location (e.g., based on one or more pixel locations) of static and/or dynamic elements of a check or other instrument being evaluated with corresponding locations on one or more checks in the user profile. Based on this evaluation, the match detection and evaluation module  112   b  may determine whether regions, fields, elements or the like, of the check being evaluated match one or more checks in the user profile. If so, the check is likely not fraudulent or counterfeit. If not, the check is likely fraudulent or counterfeit. 
     In some examples, matching and/or non-matching regions, fields, elements or the like may be visually displayed to a user. For instance, the match detection and evaluation module  112   b  may identify matching and/or non-matching regions on the check being evaluated and one or more checks in a user profile and may highlight or otherwise flag those regions, fields, elements, or the like. User interface generation module  112   e  may include instructions and/or data that may cause or enable the potential counterfeit check evaluation computing platform  110  to generate a user interface displaying the check being evaluated and the one or more matching or non-matching checks simultaneously. The user interface may include images of the check being evaluated and the matching/non-matching checks, as well as highlighting or other enhancements on or near the regions, elements, fields, or the like, that are matching or non-matching. In some examples, the user interface may further include options for additional processing. For instance, if the check being evaluated matches the checks on the user profile, the user interface may include a selectable option to add the check being evaluated (or image thereof) to the user profile. Various other recommendations and/or options may be provided via the user interface without departing from the invention. 
     Potential counterfeit check evaluation computing platform  110  may further have, store and/or include a machine learning engine  112   c  and machine learning datasets  112   d . Machine learning engine  112   c  and machine learning datasets  112   d  may store instructions and/or data that may cause or enable potential counterfeit check evaluation computing platform  110  to evaluate received checks as compared to checks stored within a user profile to determine a likelihood that the check being evaluated is fraudulent. The machine learning datasets  112   d  may be generated based on analyzed data (e.g., data from previously received data, previously analyzed checks, and the like), raw data, and/or received from one or more outside sources. 
     The machine learning engine  112   c  may receive check images and/or data and, using one or more machine learning algorithms, may generate one or more machine learning datasets  112   d . Various machine learning algorithms may be used without departing from the invention, such as supervised learning algorithms, unsupervised learning algorithms, regression algorithms (e.g., linear regression, logistic regression, and the like), instance based algorithms (e.g., learning vector quantization, locally weighted learning, and the like), regularization algorithms (e.g., ridge regression, least-angle regression, and the like), decision tree algorithms, Bayesian algorithms, clustering algorithms, artificial neural network algorithms, and the like. Additional or alternative machine learning algorithms may be used without departing from the invention. In some examples, the machine learning engine  112   c  may analyze data to identify patterns of activity, sequences of activity, and the like, to generate one or more machine learning datasets  112   d.    
     In some examples, the machine learning datasets  112   d  may include machine learning data linking one or more user check element characteristics to previously identified fraudulent or counterfeit checks. The machine learning datasets  112   d  may be updated and/or validated based on subsequent data received, for example, after a check has been evaluated, identified as fraudulent, added to a user profile, or the like. 
     In some examples, the machine learning datasets  112   d  may be used by, for example, match detection and evaluation module  112   b  to evaluate checks to determine whether a match exists, regions, fields or elements that match, or the like. 
     Potential counterfeit check evaluation computing platform  110  may further have, store and/or include a user profile database  112   f . User profile database  112   f  may store user profiles and associated information, such as name of a user, account information, contact information, or the like. In some examples, the user profiles may store one or more checks or check images associated with the user or user account that are identified as not fraudulent for later comparison to checks being evaluated for authenticity. The checks or check images may correspond to a plurality of previously processed checks or other instruments that, in at least some examples, were determined to be valid. 
       FIGS. 2A-2C  depict an illustrative event sequence for implementing and using item validation and image evaluation functions in accordance with one or more aspects described herein. The events shown in the illustrative event sequence are merely one example sequence and additional events may be added, or events may be omitted, without departing from the invention. 
     Referring to  FIG. 2A , at step  201 , a check may be received by a device. For instance, a check or other instrument may be received by local user computing device  150 . As discussed herein, local user computing device  150  may include an ATM or other self-service kiosk, a banking associate computing device, or the like. Although the event sequence shown and described includes receiving a check by a local user computing device  150 , in some examples, the check may be received by a remote user computing device  170  (e.g., via mobile or online banking applications) without departing from the invention. 
     At step  202 , preliminary processing of the check may be performed. For instance, if the check may be converted to a digital image. Additionally or alternatively, the check image may be stored by the local user computing device  150 , transmitted to another device for storage or the like. 
     At step  203 , a connection may be established between the local user computing device  150  and the check receiving and processing computing device  120 . For instance, a first wireless connection may be established between the local user computing device  150  and the check receiving and processing computing device  120 . Upon establishing the first wireless connection, a communication session may be initiated between the check receiving and processing computing device  120  and the local user computing device  150 . 
     At step  204 , the check or check image may be transmitted from the local user computing device  150  to the check receiving and processing computing device  120 . For instance, the check image may be transmitted during the communication session initiated upon establishing the first wireless connection. 
     At step  205 , the check image may be received by the check receiving and processing computing device  120 . At step  206 , the check image may be processed. For instance, the check image may be evaluated to determine a likelihood that the check is counterfeit or fraudulent. As discussed herein, in some examples, the check image being evaluated may be compared to check images stored in a user profile associated with the payer of the check being evaluated. For instance, a user profile including a plurality of checks written by a user may be generated and stored. Those checks may then be compared to checks being evaluated to determine whether elements of the check being evaluated match elements of one or more checks in the user profile. 
     With reference to  FIG. 2B , at step  207 , a validity score may be generated by the potential counterfeit check evaluation computing platform  110 . For instance, a validity score indicating a likelihood of validity of the check based on the matching evaluation process may be generated. 
     At step  208 , a connection may be established between the check receiving and processing computing device  120  and the potential counterfeit check evaluation computing platform  110 . For instance, a second wireless connection may be established between the check receiving and processing computing device  120  and the potential counterfeit check evaluation computing platform  110 . Upon establishing the second wireless connection, a communication session may be initiated between the check receiving and processing computing device  120  and the potential counterfeit check evaluation computing platform  110 . 
     At step  209 , the check image and/or associated data (e.g., validity score) may be transmitted from the check receiving and processing computing device  120  to the potential counterfeit check evaluation computing platform  110 . For instance, the check image and/or associated data may be transmitted during the communication session initiated upon establishing hte4 second wireless connection. 
     At step  210 , the check image and/or data may be received by the potential counterfeit check evaluation computing platform  110 . At step  211 , a user profile associated with a payer of the check being evaluated may be received. For instance, data may be extracted from the check image or associated data to identify a user, account, and the like, associated with the check. Based on this extracted information, the potential counterfeit check evaluating computing platform  110  may retrieve, e.g., from one or more databases, a user profile associated with the user and associated check images, data and the like. 
     At step  212 , the check image may be further evaluated (e.g., by a second system different from a system of the check receiving and processing computing device and subsequent to evaluation by the check receiving and processing computing device  120 ). For instance, the check image may be further compared to check images in the retrieved user profile to evaluate one or more regions, fields, elements, or the like, of the check to determine whether one or more matches exist. 
     With reference to  FIG. 2C , at step  213 , matching and/or non-matching regions, fields, elements, and the like, of the check or check image being evaluated and one or more checks or check images from the user profile may be identified. At step  214 , a user interface may be generated. In some examples, the user interface may include images of the check being evaluated and one or more checks from the user profile having one or more regions, fields, elements, or the like that match or do not match corresponding regions, fields, elements, or the like, of the check being evaluated. Further, the user interface may include highlighting of the matching or non-matching aspects or other enhancements identifying matching or non-matching regions, fields, elements, and the like. 
     Additionally or alternatively, the user interface may include one or more recommendations or selectable options. For instance, if at least a threshold number of regions, fields, elements, or the like match, the generated user interface may include an option to add the check being evaluated to the user profile. In some examples, the threshold may be retrieved from a database. In some examples, the threshold may include all regions, fields, elements, or the like, matching. In some arrangements, if a validity score is above a pre-determined threshold, the user interface may be generated to include an option to add the check image being evaluated to the user profile. Various other recommendations, selectable options, and the like, may be included in the user interface without departing from the invention. 
     At step  215 , a connection may be established between the potential counterfeit check evaluation computing platform  110  and the local user computing device  150 . For instance, a third wireless connection may be established between the potential counterfeit check evaluation computing platform  110  and the local user computing device  150 . Upon establishing the third wireless connection, a communication session may be initiated between the local user computing device  150  and the potential counterfeit check evaluation computing platform  110 . 
     At step  216 , the generated user interface may be transmitted to the local user computing device  150 . For instance, the generated user interface may be transmitted to the local user interface  150  during the communication session initiated upon establishing the third wireless connection. 
     At step  217 , the generated user interface may be received by the local user computing device  150  and may be displayed on a display of the local user computing device. 
     At step  218 , one or more machine learning datasets may be updated based on the check image evaluation, generated user interface, and the like. For instance, aspects related to number of matches, fields, regions or elements that match or do not match, validity score, and the like, determined or used in evaluating the check image may be used to update or validate one or more machine learning datasets. 
       FIG. 3  is a flow chart illustrating one example method of providing item validation and image evaluation functions according to one or more aspects described herein. The processes illustrated in  FIG. 3  are merely some example processes and functions. The steps shown may be performed in a different order, more steps may be added, or one or more steps may be omitted without departing from the invention. 
     At step  300 , a check image and associated data may be received from a system, such as check receiving and processing computing device  120 . In some examples, the check may have been previously processed by the check receiving and processing computing device and a validity score may be determined indicating a likelihood that the check or check image is valid or fraudulent. 
     At step  302 , a user profile may be retrieved. For instance, based on the received check image and associated data, a user profile may be retrieved from a database. In some examples, the user profile may include a plurality of previously processed check images associated with the user (e.g., drawn on an account of the user) that have been deemed valid and/or not fraudulent or counterfeit. 
     At step  304 , the received check image may be evaluated. For instance, the check image being evaluated may be compared to the check images in the user profile to identify points, regions, fields, elements, or the like, on the check being evaluated that match or do not match corresponding points, regions, fields, elements or the like on the check images from the user profile. In some examples, machine learning may be used to perform some or all of this evaluation. 
     At step  306 , matching and/or non-matching elements of the check being evaluated and one or more checks from the user profile may be identified. 
     At step  308 , a determination may be made as to whether there is a sufficient number of matches. For instance, a determination may be made as to whether a number of matches is above a predetermined threshold. If not, a user interface may be generated included the check images in step  310 . 
     If the number of matches is above the threshold in step  308 , an option to add the check image of the check image being evaluated to the user profile may be generated in step  312 . In step  314 , a user interface may be generated including the check images and the option to add the check image to the user profile. The user interface may be transmitted to one or more user devices and displayed on the devices. 
       FIG. 4  illustrates one example user interface that may be generated based on the item validation and image evaluation functions described herein. The interface  400  may include an image of a check  402  being evaluated and one or more images of checks  404  from a user profile associated with user 1. The user interface includes enhancements  406   a ,  406   b ,  408   a ,  408   b  indicating matching elements of the two checks. For instance, as shown in  FIG. 4 , a star is positioned adjacent to matching elements such as the position of the word “dollars” relative to the line, the position of the word “payee” relative to the payee line. Accordingly, the user interface is generated to include an enhancement to indicate matches. For example, enhancement  406   a  corresponds to an enhancement  406   b  adjacent one set of matching elements, while enhancement  408   a  corresponds to enhancement  408   b  adjacent another set of matching elements. More or fewer matching elements may be identified without departing from the invention. 
     Interface  400  further includes a selectable option  410  to add the check being evaluated (e.g., “check 1” to the user profile. Selection of this option may prompt display of one or more additional user interfaces. 
       FIG. 5  illustrates another example user interface that may be generated based on the item validation and image evaluation functions described herein. The interface  500  may include an image of a check  502  being evaluated and one or more images of checks  504  from a user profile associated with user 1. The user interface includes enhancements  506   a ,  506   b ,  508   a ,  508   b  (e.g., highlighting) indicating non-matching elements of the two checks. For instance, as shown in  FIG. 5 , the word “dollars” and corresponding line are highlighted in each check to indicate that the two checks do not match (e.g.,  506   a  illustrates the word “dollars” positioned closer to the line than  506   b ). In another example, because the signature of check 2 begins closer to the start of the signature line than the signature in check 1, the corresponding regions on each check are highlighted  508   a ,  508   b , to indicate that the elements do not match. More or fewer non-matching elements may be identified without departing from the invention. 
     In some examples, the enhancements (e.g., highlighting, embellishments, icons, or the like) used to identify matching elements may be the same or substantially similar to enhancements used to identify non-matching elements. In other examples, enhancements used to identify matching elements may be different from enhancements used to identify non-matching elements. 
     As discussed herein, aspects described are directed to systems, devices and arrangements for evaluating the validity of an item, such as a check or other instrument, and identifying aspects of the check or other instrument that formed the basis for the determination of validity. Arrangements discussed herein allow for evaluation of checks or other instruments that have been previously evaluated in order to verify the output of the evaluation, understand the basis for the output, and the like. This data may then be used to further update, validate, and/or refine not only the systems and arrangements described herein but also systems and arrangements performing an initial evaluation of a check or other instrument. 
     The above-described examples and arrangements are merely some example arrangements in which the systems described herein may be used. Various other arrangements employing aspects described herein may be used without departing from the invention. 
       FIG. 6  depicts an illustrative operating environment in which various aspects of the present disclosure may be implemented in accordance with one or more example embodiments. Referring to  FIG. 6 , computing system environment  600  may be used according to one or more illustrative embodiments. Computing system environment  600  is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality contained in the disclosure. Computing system environment  600  should not be interpreted as having any dependency or requirement relating to any one or combination of components shown in illustrative computing system environment  600 . 
     Computing system environment  600  may include potential counterfeit check evaluation computing device  601  having processor  603  for controlling overall operation of potential counterfeit check evaluation computing device  601  and its associated components, including Random Access Memory (RAM)  605 , Read-Only Memory (ROM)  607 , communications module  609 , and memory  615 . Potential counterfeit check evaluation computing device  601  may include a variety of computer readable media. Computer readable media may be any available media that may be accessed by potential counterfeit check evaluation computing device  601 , may be non-transitory, and may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, object code, data structures, program modules, or other data. Examples of computer readable media may include Random Access Memory (RAM), Read Only Memory (ROM), Electronically Erasable Programmable Read-Only Memory (EEPROM), flash memory or other memory technology, Compact Disk Read-Only Memory (CD-ROM), Digital Versatile Disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by computing device  601 . 
     Although not required, various aspects described herein may be embodied as a method, a data transfer system, or as a computer-readable medium storing computer-executable instructions. For example, a computer-readable medium storing instructions to cause a processor to perform steps of a method in accordance with aspects of the disclosed embodiments is contemplated. For example, aspects of method steps disclosed herein may be executed on a processor on potential counterfeit check evaluation computing device  601 . Such a processor may execute computer-executable instructions stored on a computer-readable medium. 
     Software may be stored within memory  615  and/or storage to provide instructions to processor  603  for enabling potential counterfeit check evaluation computing device  601  to perform various functions as discussed herein. For example, memory  615  may store software used by potential counterfeit check evaluation computing device  601 , such as operating system  617 , application programs  619 , and associated database  621 . Also, some or all of the computer executable instructions for potential counterfeit check evaluation computing device  601  may be embodied in hardware or firmware. Although not shown, RAM  605  may include one or more applications representing the application data stored in RAM  605  while potential counterfeit check evaluation computing device  601  is on and corresponding software applications (e.g., software tasks) are running on potential counterfeit check evaluation computing device  601 . 
     Communications module  609  may include a microphone, keypad, touch screen, and/or stylus through which a user of potential counterfeit check evaluation computing device  601  may provide input, and may also include one or more of a speaker for providing audio output and a video display device for providing textual, audiovisual and/or graphical output. Computing system environment  600  may also include optical scanners (not shown). 
     Potential counterfeit check evaluation computing device  601  may operate in a networked environment supporting connections to one or more remote computing devices, such as computing devices  641  and  651 . Computing devices  641  and  651  may be personal computing devices or servers that include any or all of the elements described above relative to potential counterfeit check evaluation computing device  601 . 
     The network connections depicted in  FIG. 6  may include Local Area Network (LAN)  625  and Wide Area Network (WAN)  629 , as well as other networks. When used in a LAN networking environment, potential counterfeit check evaluation computing device  601  may be connected to LAN  625  through a network interface or adapter in communications module  609 . When used in a WAN networking environment, potential counterfeit check evaluation computing device  601  may include a modem in communications module  609  or other means for establishing communications over WAN  629 , such as network  631  (e.g., public network, private network, Internet, intranet, and the like). The network connections shown are illustrative and other means of establishing a communications link between the computing devices may be used. Various well-known protocols such as Transmission Control Protocol/Internet Protocol (TCP/IP), Ethernet, File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP) and the like may be used, and the system can be operated in a client-server configuration to permit a user to retrieve web pages from a web-based server. Any of various conventional web browsers can be used to display and manipulate data on web pages. 
     The disclosure is operational with numerous other computing system environments or configurations. Examples of computing systems, environments, and/or configurations that may be suitable for use with the disclosed embodiments include, but are not limited to, personal computers (PCs), server computers, hand-held or laptop devices, smart phones, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like that are configured to perform the functions described herein. 
       FIG. 7  depicts an illustrative block diagram of workstations and servers that may be used to implement the processes and functions of certain aspects of the present disclosure in accordance with one or more example embodiments. Referring to  FIG. 7 , illustrative system  700  may be used for implementing example embodiments according to the present disclosure. As illustrated, system  700  may include one or more workstation computers  701 . Workstation  701  may be, for example, a desktop computer, a smartphone, a wireless device, a tablet computer, a laptop computer, and the like, configured to perform various processes described herein. Workstations  701  may be local or remote, and may be connected by one of communications links  702  to computer network  703  that is linked via communications link  705  to potential counterfeit check evaluation server  704 . In system  700 , potential counterfeit check evaluation server  704  may be a server, processor, computer, or data processing device, or combination of the same, configured to perform the functions and/or processes described herein. Server  704  may be used to receive check images and associated data and/or validation scores, retrieve user profile, evaluate the check image compared to the user profile, identify matching or non-matching elements, generate user interfaces, and the like. 
     Computer network  703  may be any suitable computer network including the Internet, an intranet, a Wide-Area Network (WAN), a Local-Area Network (LAN), a wireless network, a Digital Subscriber Line (DSL) network, a frame relay network, an Asynchronous Transfer Mode network, a Virtual Private Network (VPN), or any combination of any of the same. Communications links  702  and  705  may be communications links suitable for communicating between workstations  701  and potential counterfeit check evaluation server  704 , such as network links, dial-up links, wireless links, hard-wired links, as well as network types developed in the future, and the like. 
     One or more aspects of the disclosure may be embodied in computer-usable data or computer-executable instructions, such as in one or more program modules, executed by one or more computers or other devices to perform the operations described herein. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform particular tasks or implement particular abstract data types when executed by one or more processors in a computer or other data processing device. The computer-executable instructions may be stored as computer-readable instructions on a computer-readable medium such as a hard disk, optical disk, removable storage media, solid-state memory, RAM, and the like. The functionality of the program modules may be combined or distributed as desired in various embodiments. In addition, the functionality may be embodied in whole or in part in firmware or hardware equivalents, such as integrated circuits, Application-Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGA), and the like. Particular data structures may be used to more effectively implement one or more aspects of the disclosure, and such data structures are contemplated to be within the scope of computer executable instructions and computer-usable data described herein. 
     Various aspects described herein may be embodied as a method, an apparatus, or as one or more computer-readable media storing computer-executable instructions. Accordingly, those aspects may take the form of an entirely hardware embodiment, an entirely software embodiment, an entirely firmware embodiment, or an embodiment combining software, hardware, and firmware aspects in any combination. In addition, various signals representing data or events as described herein may be transferred between a source and a destination in the form of light or electromagnetic waves traveling through signal-conducting media such as metal wires, optical fibers, or wireless transmission media (e.g., air or space). In general, the one or more computer-readable media may be and/or include one or more non-transitory computer-readable media. 
     As described herein, the various methods and acts may be operative across one or more computing servers and one or more networks. The functionality may be distributed in any manner, or may be located in a single computing device (e.g., a server, a client computer, and the like). For example, in alternative embodiments, one or more of the computing platforms discussed above may be combined into a single computing platform, and the various functions of each computing platform may be performed by the single computing platform. In such arrangements, any and/or all of the above-discussed communications between computing platforms may correspond to data being accessed, moved, modified, updated, and/or otherwise used by the single computing platform. Additionally or alternatively, one or more of the computing platforms discussed above may be implemented in one or more virtual machines that are provided by one or more physical computing devices. In such arrangements, the various functions of each computing platform may be performed by the one or more virtual machines, and any and/or all of the above-discussed communications between computing platforms may correspond to data being accessed, moved, modified, updated, and/or otherwise used by the one or more virtual machines. 
     Aspects of the disclosure have been described in terms of illustrative embodiments thereof. Numerous other embodiments, modifications, and variations within the scope and spirit of the appended claims will occur to persons of ordinary skill in the art from a review of this disclosure. For example, one or more of the steps depicted in the illustrative figures may be performed in other than the recited order, one or more steps described with respect to one figure may be used in combination with one or more steps described with respect to another figure, and/or one or more depicted steps may be optional in accordance with aspects of the disclosure.