Patent Publication Number: US-2016239888-A1

Title: Systems and methods for verifying compliance in an electronic marketplace

Description:
COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material, which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves ail copyright rights whatsoever. The following notice applies to this document: Copyright© 2016 Thomson Reuters. 
     TECHNICAL FIELD 
     This disclosure relates generally to compliance verification in an electronic marketplace. More specifically, the disclosure is directed towards systems and methods for evaluating a seller in an electronic marketplace offering branded products. 
     BACKGROUND 
     With the success of the Internet, electronic marketplaces or e-marketplaces like EBAY®, AMAZON®, ALIBABA.COM®, or TAOBAO® have become increasingly popular with a plethora of products being, offered for sale and sold through those platforms. Often the products are so-called branded products as they refer to or display a trademark or belong to an established brand. The owner of a trademark or brand, also referred to as brand owner, is interested in that only authorized or genuine products are offered and sold but frequently counterfeit and grey market products are offered and sold via electronic marketplaces. Counterfeit products are fake replicas of real products and are often produced, offered and sold with the intent to take advantage of the higher value of the imitated product. Grey market products are parallel imports of products intended to be sold in one country or region and sold in another where they were not intended to be sold. The intent of selling grey market products is to take advantage of the price/margin disparities found in the manufacturer&#39;s pricing across countries. 
     Currently, marketplaces represent a seller&#39;s reputation based on generalized seller data and the seller&#39;s ability to deliver the goods that they list in their listing as well as their reputation for shipping in an expedient manner. However, there are no capabilities that exist today focused across one or more brands which allow an understanding of the seller as a seller of a particular brand or brands and the aggregation of the enforcement actions, histories, and results from those enforcements by one or more brand owners. 
     Accordingly, there exists a need for a brand centric reputational view of sellers that span across various brand owners, marketplaces, and enforcements. 
     SUMMARY 
     The present disclosure is directed towards systems and methods for evaluating a seller entity in an electronic marketplace. The seller entity offers products related to a brand owner or brand owner entity. In one aspect, the method includes obtaining, by at least one specialized computer system, market information from one or more electronic marketplaces, the market information comprising market listing information comprising one or more offered branded products, seller information comprising seller entity and activity information, and marketplace information comprising marketplace identification. Market data is derived from one or more of the market listing information, the seller information and the marketplace information and stored in a non-transient memory. The authenticity of the market data is then determined in view of the one or more authenticated branded products and the non-transient memory is updated with behavioral data in response to an authentication of the market data, wherein said behavioral data comprises one or more of brand owner claim information and marketplace determination information. At least one reputational score value is the generated, wherein said generating comprises combining the behavioral data and the market data, wherein said at least one reputational score value is indicative of the evaluation of the seller entity. The at least, one reputational score value is indicative of the evaluation of the seller entity or as to the likelihood of a seller entity being a legitimate or illegitimate seller of brand owner&#39;s goods. 
     A system, as well as articles that include a machine-readable medium storing machine-readable program code for implementing the various techniques, are disclosed. Details of various embodiments are discussed in greater detail below. 
     Additional features and advantages will be readily apparent from the following detailed description, the accompanying drawings and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic depicting an exemplary computer-based system for evaluating a seller entity in an electronic marketplace offering branded products; 
         FIG. 2  is a further schematic depicting an exemplary computer-based system for evaluating a seller entity in an electronic marketplace offering branded products; 
         FIG. 3  is a flow diagram illustrating an exemplary computer-implemented method for evaluating a seller entity in an electronic marketplace offering branded products; 
         FIG. 4  is an illustration of brand owner entities initiating search requests of marketplaces and asking for all listings selling branded products; 
         FIG. 5  illustrates the extraction of listing information from one or more marketplaces; 
         FIG. 6  is an illustration of collecting additional information by the brand owner entities; 
         FIG. 7  is an illustration of either acknowledgement or rejection by the marketplaces as to whether a listing is in violation or compliance with the rules, with resulting information used in the creation of behavioral data; and 
         FIG. 8  is an illustration with an added brand owner entity along with leveraging seller reputational data. 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     In the following description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments in which the disclosure may be practiced. It is to be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present disclosure, 
     Turning now to  FIG. 1 , an example of a suitable computing system  100  within which embodiments of the disclosure may he implemented is presented. The computing system  100  is only one example and is not intended to suggest any limitation as to the scope of use or functionality of the disclosure. Neither should the computing system  100  be interpreted, as having any dependency or requirement relating to any one or combination of illustrated components. 
     For example, the present disclosure is operational with numerous other general purpose or special purpose computing consumer electronics, network PCs, minicomputers, mainframe computers, laptop computers, as well as distributed computing environments that, include any of the above systems or devices, and the like. 
     The disclosure may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, loop code segments and constructs, and other computer instructions known to those skilled in the art that perform particular tasks or implement particular abstract data types. The disclosure can be practiced in 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 are located in both local and remote computer storage media including memory storage devices. Tasks performed by the programs and modules are described below and with the aid of figures. Those skilled in the art may implement the description and figures as processor executable instructions, which may be written on any form of a computer readable media. 
     In one embodiment, with reference to  FIG. 1 , the system  100  includes a server device  110  configured to include a processor  112 , such as a central processing unit (“CPU”), random access memory (“RAM”)  114 , one or more input-output devices  116 , such as a display device (not shown) and keyboard (not shown), non-volatile memory  120  and a data store  130 , all of which are interconnected via a common bus and controlled by the processor  112 . 
     As shown in the  FIG. 1  example, in one embodiment, the non-volatile memory  120  is configured to include a search and extraction module  122 , an update module  124 , a scoring module  126 , and a verification module  128 . 
     The search and extraction module  122  is used to initiate the retrieval of market information and to derive market data from the obtained market information. The update module  124  is used to process and update the market data and/or behavioral data. The scoring module  126  computes and calculates reputational score values and the verification module  128  supports the review of the market data and/or the subsequent correlation of market data against, the behavioral data to add, update, or delete reputational score values. 
     As shown in  FIG. 1 , in one embodiment, a network  170  is provided that can include various, devices such as routers, server, administration devices, and switching elements connected in an Intranet, Extranet or Internet configuration. The network  170  provides access to electronic marketplaces ore-marketplaces (not show in  FIG. 1 ). In one embodiment, the network  170  uses wired communications to transfer information between marketplaces, access devices  180  and  190 , brand marketplace databases or brand database servers  140 ,  150 ,  160 , and the server device  110  with the data store  130 . Access device  180  is an access device of for example, brand owner A that has access to the band A marketplace database server  140 . Access device  190  is an access device of brand owner B with access to the brand B marketplace database server  150 . The brand C marketplace database server  160  is connected to the network  170 . A brand owner C access device (not shown) has access to the band C marketplace database  160 . 
     In another embodiment, the network  170  employs wireless communication protocols to transfer information between marketplaces, the access devices  180  and  190 , the brand marketplace databases or servers  140 ,  150 ,  160 , and the server device  110  with the data store  130 . For example, the network  170  may be a cellular or mobile network employing digital cellular standards including but not limited to the 3GPP, 3GPP2 and AMPS family of standards such as Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), CDMAOne, CDMA2000, Evolution-Data Optimized (EV-DO), LTE Advanced, Enhanced Data Rates for GSM Evolution (EDGE), Universal Mobile Telecommunications System (UMTS), Digital Enhanced Cordless Telecommunications (DECT), Digital AMPS (IS-136/TDMA), and Integrated Digital Enhanced Network (iDEN). The network  170  may also be a Wide Area Network (WAN), such as the Internet, which employs one or more transmission protocols, e.g. TCP/IP. As another example, the network  170  may employ a combination of digital cellular standards and transmission protocols. In yet other embodiments, the network  170  may employ a combination of wired and wireless technologies to transfer information between the access devices  180  and  190 , the brand marketplace databases or servers  140 ,  150 ,  160 , the data store  130 , the server device  110 , and the marketplaces. 
     The data store  130  is a repository that maintains and stores information utilized by the before-mentioned modules  122  to  128 . In one embodiment, the data store  130  is a relational database. In another embodiment, the data store  130  is a directory server, such as a Lightweight Directory Access Protocol (“LDAP”). In yet another embodiment, the data store  130  is an area of non-volatile memory  120  of the server device  110 . 
     In one embodiment, as shown in the  FIG. 1  example, the data store  130  includes a market data store  132 , a behavioral data store  134  and a reputational score data store  136 . The market data store  132 , according to one embodiment, maintains market data, also referred to as seller-brand data, which is derived from the market listing information comprising one or more offered branded products, the seller information comprising the seller entity and activity information, and the marketplace information comprising marketplace identification. The behavioral data store  134 , according to one embodiment, includes brand owner claim information, such as legitimacy determination, date reviewed, brand owners decision, and marketplace determination information, such as listing takedown status, time taken for removal, repeat infringer, additional seller identification, number of times seller requested for removal. The reputational score data store  136  comprises reputational score values including brand owner reputation values, marketplace seller reputation values, and marketplace reputation values 
     Although the data store  130  shown in  FIG. 1  is connected to the network  170 , it will be appreciated by one skilled in the art that the data store  130  and/or any of the information shown therein, can be distributed across various servers and be accessible to the server  110  and/or by the access devices  180  and  190  over the network  170 ; be coupled directly to the server  110 ; be configured as part of server  110  and interconnected to processor  112 , RAM  114 , the one or more input-output devices  116  and the non-volatile memory  120  via the common bus; or be configured in an area of non-volatile memory  120  of the server  110 . 
     The access devices  180  and  190 , according to one embodiment, are general purpose or special purpose computing devices comprising; a touch-sensitive graphical user interface (“GUI”), GUI  186  and GUI  196 , respectively; a digital signal processor (“DSP”), DSP  184  and DSP  194 , respectively; each DSP having an access application module that allows a user to access application module  182  and access application module  192 , respectively; transient and persistent storage devices such as band marketplace databases  140 ,  150  and  160 ; the server  110 ; an input/output subsystem (not shown); and a bus to provide a communications path between components comprising the general purpose or special purpose computer (not shown). According to one embodiment, access application module  182  and access application module  192  are web-based and use thin client applications (not shown), such as a web browser, which allows a user to access the brand marketplace database  140 ,  150 , and the server  110 . Examples of web browsers are known in the art, and include well-known web browsers such as MICROSOFT® INTERNET EXPLORER®, GOOGLE CHROME™, MOZILLA FIREFOX® AND APPLE® SAFARI®. According to another embodiment, access devices  180  and  190 , are mobile electronic devices, each having GUI, a DSP having an access application module, internal and external storage components; a power management system; an audio component; audio input/output components: an image capture and process system; RF antenna; and a subscriber identification module (SIM) (not shown). Although system  100  is described generally herein as comprising two separate access devices, access devices  180  and  190 , it should be appreciated that the present invention does not require at least two separate access devices, nor is it limited to solely two access devices. Indeed, system  300  can include a single access device, such as access device  180  or access device  190 , or multiple access devices. 
     Further, it should be noted that the system  100  shown in  FIG. 1  is only one embodiment of the disclosure. Other system embodiments of the disclosure may include additional structures that are not shown, such as secondary storage and additional computational devices. In addition, various other embodiments of the disclosure include fewer structures than those shown in  FIG. 1 . For example, in one embodiment, the disclosure is implemented on a single computing device in a standalone configuration. Data input and requests are communicated to the computing device via various communication channels. Data output of the system is communicated from the computing device to a display device, such as a computer monitor, or other devices. According to one embodiment, data output includes at least one reputational score value or multiple reputational score value such as brand owner reputation values, marketplace seller reputation values, and marketplace reputation values. 
     Turning now to  FIG. 2 , an example of a suitable computing system  200  within which embodiments of the disclosure may be implemented is presented. Specifically,  FIG. 2  illustrates one embodiment of the present Inventive system that utilizes a reputational server system  210  that comprises the server device  110  and brand marketplace databases  140 ,  150 ,  160  as described in connection with  FIG. 1 . The reputational server system  210  and the access device  180  of brand owner A are connected to the network  170  as also discussed in connection with  FIG. 1 . Also connected to the network  170  are marketplaces  220 , which are illustrated as M-Place  1 , M-Place  2 , M-Place  3 , M-Place  4 , M-Place  5 , and M-Place  6 . According to one embodiment, marketplaces  220  includes electronic marketplaces or e-marketplaces such as EBAY®, AMAZON®, ALIBABA.COM®, or TAOBAO®. 
     The computing system  200  is another exemplary embodiment and is not intended to suggest any limitation as to the scope of use or functionality of the disclosure. Neither should the computing system  200  be interpreted as having any dependency or requirement relating to any one or combination of illustrated components. For example, the present disclosure is operational with numerous other general purpose or special purpose computing consumer electronics, network PCs, minicomputers, mainframe computers, laptop computers, as well as distributed computing environments that include any of the above systems or devices, and the like. Other system embodiments of the disclosure may include additional structures that are not shown, such as secondary storage and additional computational devices. In addition, various other embodiments of the disclosure include fewer structures than those shown in  FIG. 2 . 
     The systems  100  and  200  operate as described with respect to  FIGS. 3 through 8 . 
     Turning now to  FIG. 3 , an exemplary computer-implemented method for evaluating a seller entity in an electronic marketplace offering branded products is disclosed in the context of system  100  and  200  of  FIGS. 1 and 2 . 
     At step  310  a retrieval of market information is initiated by the server device  110  depicted in systems  100  and  200  of  FIGS. 1 and 2 , respectively. At step  320  the market information is obtained or collected from various marketplaces  220 . According to one embodiment, referring to  FIG. 2 , market information, which may include market listing information, seller information, and marketplace information, is retrieved from M-Place  1 , M-Place  2 , M-Place  3 , M-Place  4 , and M-Place  6 . For example, a listing in an electronic auction for a designer handbag will have associated with it certain market information. In one embodiment, the market listing information, or short listing, can comprise the product being sold, its price, its available quantity, its location, and the marketplace. The seller information can comprise a seller&#39;s Id, the seller&#39;s location, the seller&#39;s name and the seller&#39;s contact information. The marketplace information can comprise the marketplace name, the marketplace location, and the marketplace primary contact. Continuing from the previous example, the market listing information would include the make and model of the designer handbag, the sale price, the quantity available, where the designer handbag would be shipped from and the electronic auction website through which the designer handbag is offered for sale. The seller information would include an type of identification associated with the seller, such as the seller&#39;s auction website nickname and/or the seller&#39;s actual name and the contact information for the seller, such as e-mail address and the marketplace information would include the name and contact information for the auction website. 
     Referring back to Step  320  of  FIG. 3 , he approach used to obtain market listing information, also referred to as candidate marketplace listings, seller information, and marketplace information can be achieved through one or more of the following techniques. In one embodiment the search and extraction module  122  uses a marketplace&#39;s authorized Application Programming Interface (“API”), which allows a programmatic call to the respective databases of the marketplaces  122  requesting information which would include the market listing information, the seller information, and the marketplace information. 
     In another embodiment, respective search engines if the marketplaces  122  are leveraged by the search and extraction module  122  to submit search queries in conjunction with a crawling strategy to search for listings. Pre- and post-search screening and filtering techniques as are known in the art may also be used to either include or eliminate search results. The search results returned would include the candidate marketplace listings, which would subsequently be crawled for their detail content after which “screen scraping” techniques, as are known in the art, would be used to identify key information on the candidate listings. This information would leverage Neural Linguistic programming techniques and/or basic extraction capabilities to identify candidate listing information which may be deemed relevant for subsequent review. The information “scraped” would be similar to what is of interest in the API method above with the inclusion but not necessarily limited to the market listing information the seller information, and the marketplace information. Other techniques may be used to identify listings from one or more marketplaces beyond an API or search/scrape approach. 
     Returning to step  330  of  FIG. 3 , the market data, also referred to seller-brand data, is derived from the market listing information comprising one or more offered branded products, the seller information comprising the seller entity and activity information, and the marketplace information comprising marketplace identification, is loaded to and stored in the market data store  132 . Use collected market data is then normalized across all marketplaces such that the structured and/or unstructured data that is obtained in step  320  is stored in the market data store  132  with a list of attributes which are warehoused into the centralized repository. In addition to storing this information into market data store  132 , the market data is also collected and stored into the respective brand owner market databases  140 ,  150 , and  160 . In one embodiment, the market data comprises data elements associated with the listing that was acquired, which would include attributes such as: seller ID, seller name, seller location, product(s) sold in listing, brand(s) identified in listing, listing ID, marketplace name; brand owner requesting listing, brand owner product being requested, product cost, product quantity sold in listing and listing location. 
     At step  340 , the market data obtained is analyzed in order to determine the authenticity or legitimacy of the offer for sale of the subject product in view of one or more authenticated branded products. According to one embodiment, the market data is used to identify the legitimacy of a product or brand being sold. This review process is primarily performed automatically. Historical review processes may be used as part of the machine learning such that future listings may be electronically identified and flagged for its legitimacy. In one embodiment, certain key markers contained in the market data are identified, which is subsequently used to generate an electronic score that, identifies the confidence levels of legitimacy of products being sold and can. be used to set a threshold for allowing automated review and execution of listings. Examples of markers identified and reviewed electronically may be for example the combination of: 
       Price×Quantity
 
     where the Price for the product being offered drops below a specified threshold of Minimum Advertised Price by the brand owner such as 30% lower than the Minimum Advertised Price with a quantity of 30. This combination would create an automated trigger that identified it as highly confident of being considered counterfeit. These types of rules could be defined or derived based on either preset descriptions or automated machine learning over time. The example is not an exhaustive approach and other means may include additional attributes like 
       Price×Quantity×Number
 
     of listings by seller entity of the same product. A further example to be identified and reviewed can be 
       Marketplace Listing Location×Seller ID
 
     which may have been implicated in prior reviews or investigations. The mentioned examples should not be viewed as an exhaustive approach as to how the collected market data would be used to electronically score and create a confidence level as to an automated review process. 
     An Individual review would allow doing interrogation of the attributes collected and stored in the data store  130  such that expertise and knowledge obtained through years of experience would allow the individual to determine the likelihood of legitimacy of goods being sold. The information reviewed could be either subjective or objective based on many factors and would be a collection of information that is determined and gathered through investigative and review methods that are used based on experience and Interaction with a brand owner entity and the brand owners “tolerance” on accuracy thresholds for legitimacy. During the individual review operation information from the above process step may be leveraged used in part or in whole to assist in the individual review process. The information from the individual review may also be leveraged through machine learning such that the algorithms are adjusted or new algorithms are created which would potentially facilitate future reviews more efficient. 
     At step  350  behavioral data is updated in the behavioral data store  134  of the data repository  130  in response to an authentication of the reviewed offered branded products. The behavioral data comprises brand owner claim information, marketplace determination information or combination thereof. The update of the behavioral data would facilitate the creation of additional attributes in the behavioral data store  134  including: listing legitimacy determination, date reviewed, brand owners decision, marketplace determination, listing takedown status, time taken for removal repeat infringer, additional seller identification, number of times seller requested for removal, etc. In response to the prior step  340 , associated with the review process, the resulting information is then stored in one or more of the brand marketplace database servers  140 ,  150 ,  160  as well as the behavioral data store  134  providing key insight and relevant as well as useful information as to the legitimacy of the listings, the sellers, and the products being sold. This information is to be considered as relevant artifacts for future use. The collected information and artifacts from the reviewed results would indicate the correlation of one or more of market listing information, seller information, brand owner information, review or enforcement decision information. 
     The collected information is relevant to the method and can then be subsequently used in all phases including and not limited to step  320  “Obtaining listing”, step  330  “Storing Data”, and/or step  340  “Reviewing Listing information” as described above. 
     At step  360  at least one reputational score value is generated through combining the behavioral data and the market data. The at least one reputational score value is indicative of the evaluation of the seller entity. More precisely, the behavioral data that comprises behavioral seller information is applied against the market data with listing information to derive reputational score, values including brand owner reputation values, marketplace seller reputation values, and marketplace reputation values, which are stored in the reputational scores data store  136 . The information collected and updated in the prior step  350  can be used in an automated fashion to dynamically create multiple reputational scores or score values associated with listings, seller entities, marketplaces, brand owner entities for creating a behavioral model across the marketplace ecosystem or community. The information in the reputational scores data store  136  would contain information which may be leveraged either for a specific listing, seller and brand combination or across multiple instances and may then be used as part of a scoring or score value computation which would then be used to rate reputations of seller entities, marketplaces, and brand owner entities. Examples, but not an exhaustive list or method for calculation, for each can be as follows: 
     Setters Reputation Calculation=Seller ID×# Brand Owner Claims×Successful Uncontested Marketplace Takedowns of Listings. 
     Marketplace Reputation=# Sellers×# Repeat Enforcements against Sellers×#Times Seller allowed to continue to sell.
 
Brand Owner Reputation=# Enforcements Submitted×# False claim Reports by Seller×Brand Owner Uncontesting.
 
     It should be understood that there might he more than one reputational score value for each of the above types and various approaches is calculated. This is because each seller entity, marketplace, and brand owner entity may have multiple and various reputations for their respective activities or behaviors. These multiple reputational calculations may then be combined or used individually to make future determinations. 
     At step  370  the at least one score value or multiple score values are made available and can be provided to brand owner entities or other interested entities. These entities can then apply the reputational score values and other information for subsequent scans of their brands. The information and data obtained throughout the prior steps and stages can be leveraged so that reputational score values are applied and considered for future searches in obtaining listings, the storage of that information, review/validation, and subsequent rescoring. This creates a closed loop environment, as indicated with the process flow returning to step  340 , such that the information can be leveraged in the identification of listings that are flagged for legitimacy, seller or seller entities that are identified as legitimate for not only the originating brand owner that identified the seller in the first place but more importantly leveraging the information for future brands as to the legitimacy of listings×sellers that may also be representing a different brand owner or entity. In another embodiment, process flow continues to step  310  as depicted by the dotted line as depicted in  FIG. 3 , which illustrates that seller reputational information consisting of seller ID, marketplace ID, and one or more reputation score values could be used to initiate a query to each of the marketplaces  220  requesting snatching sellers that have listings selling one or more brands. The data with the information and score values may also be used by marketplaces for authorization and or pre-screening, by brand owners for identifying infringers, by possible resellers, and for identifying brand owners that may be overstepping their boundaries in aggressive enforcement techniques. The reputational information with the reputational score values and ongoing dynamic scoring are a relevant component in the method and ecosystem of marketplace×seller entity×listing×brand owner ecosystem. 
     In the following  FIGS. 4  to the steps of the method  300  are illustrated in more detail. 
     Turning now to FIG,  4  that, corresponds to step  310  and illustrates, in one embodiment, brand owner A and B initiating through their access devices  180  and  190  and the respective brand marketplace database servers  140  and  150 . search requests  410  to marketplaces  220  and asking for all listings selling branded products. In the example brand owner A requests information from M-Place  1 , M-Place  2 , and M-Place  4 , whereas brand owner B requests information from M-Place  1 , M-Place  2 , and M-Place  3 ,  FIG. 4  represents a first step in initiating the retrieval of, for example, seller IDs, which a brand owner makes the request by searching for listings on marketplaces  220  that, contain their brand. The marketplace request  410  is for listings or sellers. Use seller information is collected from the listing information and a seller can have many listings on the marketplace returning many results per seller. At this point, the marketplace  220 , e.g. M-Place  1 , has no knowledge that an individual seller is potentially doing a criminal activity as being a counterfeiter or infringer. Such information is not known to the marketplace and not captured. 
       FIG. 5  corresponds to step  320  and illustrates, in one embodiment, the extraction of market listing information from one or more seller entities at one or more of the marketplaces  220 , The marketplace search requests  410  return results  510  of listings via the network  170  from one or more of the marketplaces  220  to the respective brand database server  140  and  150  and the marketplace reputational server  110 . Subsequently, seller information, brand information, and marketplace information is derived by the marketplace reputational server  110  and loaded into the data repository  130 .  FIG. 5  represents the ability to extract relevant information from brand owners taking discrete listing information and capturing seller and brand attributes and subsequently (i) adding and or changing information and (ii) deriving additional information that is then deduced and stored into the market data store  132  of the marketplace reputational server  110 . Various capabilities and techniques can be used in verifying the pre-existence of a seller in the data store, the additional attributes such as the brand owner identity that is reporting the seller ID, and the ability to store additional information on the seller including any informational changes or updates. 
       FIG. 6  corresponds to step  340  and represents, in one embodiment, the collecting of additional information by request  610  once the brand owner has determined and verified which listings represent a potential infringement on their brand, as well as a potential reason code that can be used for enforcement purposes. Potential reason codes, e.g., “trademark violation”, “counterfeit product” or “product never produced”, are created by the marketplace, but usually are different from marketplace to marketplace. The offered branded products are authenticated and the information is captured and derived such that informational updates on sellers that were found on the listing itself are updated as behavioral data and stored in the behavioral data store  134  of the marketplace reputational server  110 . For example, information is captured wherein the brand owner believes its brand was infringed and that the associated seller should be identified as an infringer. The captured information is stored in the behavioral data store  134  and the subsequent, scoring to determine the reputational score values is executed, adjusting the score value of a particular seller that is having their information updated. 
     For example, the brand owner A makes a claim to the marketplace M-Place  1  that this listing and seller is infringing on its rights or distributing its products illegally or trafficking in counterfeit goods. Nevertheless, not all listings retrieved are considered by brand owner A to be infringing. As indicated above, additional information is stored in the behavioral data store  134  indicating that the brand owner A believes the seller is infringing on their brand but there is no indication as to whether the infringer has been considered by the marketplace M-Place  1  as doing criminal activity. At this point sellers or seller entities are accused as doing criminal activity and thus these are marked accordingly as potential infringers. It should be noted that the inverse might be true, A seller within a marketplace may be found to be conducting legitimate selling activities, which would be considered non-infringing. The artifacts consisting of non-infringing behavioral information and any subsequent scoring may still be captured, computed and stored in the behavioral data store  134 . 
       FIG. 7  corresponds to step  350  and represents, in one embodiment, either an 
     acknowledgement or rejection notification  710  by the marketplaces  220  as to whether the listing is in violation or in compliance with their rules. Further information from these notifications  710  is then derived and extracted such that seller information along with reason codes or acknowledgement can be stored as behavioral facts, also referred to as behavioral data, against or for a seller or seller entity. This behavioral data comprises a lookup and is constantly updated with the respective seller data as well as potential proprietary derived reputational score values as a result of the recently reported behavior. The marketplace  220  either removes a listing or keeps the listing posted after determining if a brand owner claim is valid. All the information Is captured in the data repository  130  regardless of whether the marketplace  220  deems them to be enforceable or not. From a marketplace perspective sellers at this point are agreed by the marketplace  220  to have infringed and agreed to as doing criminal activity so are marked as confirmed infringers, however some marketplaces may keep some sellers active and do not view or track them as criminals. 
       FIG. 8  represents, according to one embodiment, what occurs when a new brand owner C with the band C marketplace database server  160  is added to the systems  100  and  200 . In this case, brand owner C&#39;s search results  810  and the resulting listings with extracted seller IDs from these listings are taken to the marketplace reputational server  110  via the network  170 . A search by the brand C marketplace database server  160  through the network  170  is performed at the marketplace reputational server  110 . The results  810  that correspond to step  320  are checked against the behavioral-data store  134  to identify prior infringers regardless of whether it was an infringement specifically identified from the brand owner C, i.e. a known seller with prior infringement history on the marketplace  220 . The information of known prior brand infringers is delivered with one or more reputational score values allowing brand owners A, B and C to prioritize on the respective enforcement strategy. All the steps described in connection with  FIGS. 4 through 7  are performed with the additional step of added information pertaining to a particular seller or seller entity being returned along with additional fields from the database  130  enhancing the data the brand owner C store. These fields can comprise information to reputational score values, seller type indication (e.g., legitimate brand seller, counterfeit brand seller and rogue distributor), as well as additional Information such as the location of the seller. prior number infringements against brand owners, other marketplaces that the seller may be selling on and the industry/product type that the seller typically operates in. 
     The types of attributes and information that are stored in the reputational data repository  130  and processed by the marketplace reputational server  110  serve as example and are not a definitive list of attributes which may be acquired or derived from the various sources, in addition, the sellers in the database may show interconnection between the marketplaces and show linkages of sellers which are computationally derivable based on a variety of attributes. 
     According to one embodiment, attributes that are captured in the data repository  130  and are derived by the servers  110  and  210  include reputational score values, seller ID, seller location, infringing industry type, successful enforcements, unsuccessful enforcements, etc. 
     The data and information of the marketplace reputational server  110  can be used in enforcement by other brand owners, by marketplaces to blacklist sellers, by payment processors, by marketers for expanding channels, by government agencies for tax recovery and other purposes as is known in the art. 
       FIGS. 1 through 8  are conceptual illustrations allowing for an explanation of the present disclosure. It should be understood that various aspects of the embodiments of the present disclosure could be implemented in hardware, firmware, software, or combinations thereof. In such embodiments, the various components and/or steps would be implemented in hardware, firmware, and/or software to perform the functions of the present disclosure. That is, the same piece of hardware, firmware, or module of software could perform one or more of the illustrated, blocks (e.g., components or steps). 
     In software implementations, computer software (e.g., programs or other instructions) and/or data is stored on a machine readable medium as part of a computer program product, and is loaded into a computer system or other device or machine via a removable storage drive, hard drive, or communications interface. Computer programs (also called computer control logic or computer readable program code) are stored in a main and/or secondary memory, and executed by one or more processors (controllers, or the like) to cause the one or more processors to perform the functions of the disclosure as described herein. In this document, the terms “machine readable medium/” “computer program medium” and “computer usable medium” are used to generally refer to media such as a random access memory (RAM); a read only memory (ROM); a removable storage unit (e.g., a magnetic or optical disc, flash memory device, or the like); a hard disk; or the like. 
     Notably, the figures and examples above are not meant to limit the scope of the present disclosure to a single embodiment, as other embodiments are possible by way of Interchange of some or all of the described or illustrated elements. Moreover, where certain elements of the present disclosure can be partially or fully implemented using known components, only those portions of such known components that are necessary for an understanding of the present disclosure are described, and detailed descriptions of other portions of such known components are omitted so as not to obscure the disclosure. In the present specification, an embodiment showing a singular component should not necessarily be limited to other embodiments including a plurality of the same component, and vice-versa, unless explicitly stated otherwise herein. Moreover, the applicants do not intend for any term in the specification or claims to be ascribed an uncommon or special meaning unless explicitly set forth as such. Further, the present disclosure encompasses present, and future known equivalents to the known components referred to herein by way of illustration. 
     The foregoing description of the specific embodiments so fully reveals the general nature of the disclosure that others can, by applying knowledge within the skill of the relevant art(s), readily modify and/or adapt for various applications such specific embodiments, without undue experimentation, without departing from the general concept of the present disclosure. Such adaptations and modifications are therefore intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It is to be understood that the phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by the skilled artisan in light of the teachings and guidance presented herein, in combination with the knowledge of one skilled in the relevant art(s). 
     While various embodiments of the present disclosure have been described above, it should be understood that they have been presented by way of example, and not as limitations. It would be apparent to one skilled in the relevant art(s) that various changes in form and detail could be made therein without departing from the spirit and scope of the disclosure. Thus, the present disclosure should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.