Patent Publication Number: US-2016224658-A1

Title: Item listing categorization system

Description:
PRIORITY 
     This application is a continuation of and claims the benefit of priority to U.S. patent application Ser. No. 13/966,160, filed on Aug. 13, 2013, which is hereby incorporated by reference herein in its entirety. 
    
    
     A portion of the disclosure of this patent document contains material that 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 all copyright rights whatsoever. The following notice applies to the software and data as described below and in the drawings that form a part of this document: Copyright eBay, Inc. 2013, All Rights Reserved. 
     TECHNICAL FIELD 
     The present application relates generally to data processing systems and, in one specific example, to techniques for categorizing item listings based on parsing item listing titles. 
     BACKGROUND 
     Conventional e-commerce websites allow shoppers to browse through a wide variety of items available for sale online. Each e-commerce website generally maintains its own product category structure that defines various product categories, and various items from the various product categories may be posted for sale on the website. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings in which: 
         FIG. 1  is a network diagram depicting a client-server system, within which one example embodiment may be deployed; 
         FIG. 2  is a block diagram of an example system, according to various embodiments; 
         FIG. 3  is a flowchart illustrating an example method, according to various embodiments; 
         FIG. 4  illustrates an example of item listing information identifying item listings associated with different product categories, according to various embodiments; 
         FIG. 5  illustrates exemplary aspects of techniques for tokenizing an item listing title, according to various embodiments; 
         FIG. 6  illustrates an example of attribute information identifying various attributes associated with different product categories, according to various embodiments; 
         FIG. 7  illustrates an example of dominant n-gram model information identifying various dominant n-gram models associated with different product categories, according to various embodiments; 
         FIG. 8  is a flowchart illustrating an example method, according to various embodiments; 
         FIG. 9  illustrates an exemplary mobile device, according to various embodiments; and 
         FIG. 10  is a diagrammatic representation of a machine in the example form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. 
     
    
    
     DETAILED DESCRIPTION 
     Example methods and systems for categorizing item listings based on parsing item listing titles are described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one skilled in the art that the present invention may be practiced without these specific details. 
     According to various exemplary embodiments, a system parses item listing titles based on token symbolization and performs n-gram modeling of the parsed listing titles, in order to determine the dominant n-gram models for listing titles in various product categories. For example, the system may include a rule-based engine that may convert the title from the text domain to the semantic token domain, by converting words in the listing title to abstract semantic tokens in a token symbol space (e.g., a product token, an accessory token, a product description token, a “with” token, an unknown token, etc.). The system may then use n-gram background modeling and machine learning on the abstract semantic tokens in the token symbol space, in order to generate various n-gram models for the listing titles in various categories. 
     For example, the listing title “Leather Case for Samsong Galaxy” associated with the accessory product category includes an accessory token “A” (e.g., the words “leather case”), followed by a token “4” (e.g., the word “for”), followed by a product token “P” (e.g., “Samsong”), followed by a product token” “P” (e.g., “galaxy”), which may correspond to an A+4+P n-gram model. Accordingly, by performing n-gram modeling on a large number of listing titles associated with the accessory product category, the system may determine that, for example, the A+4+P n-gram model is a dominant n-gram model for accessory item listings. As another example, the system may determine that the P+A n-gram model (e.g., “iPhone case”) is another dominant n-gram model for accessory product listings. These techniques can be applied by the system to item listings associated with other product types or product categories, such as n-gram models for “bundles” (e.g., “Canun 600D with lens” corresponds to a P token+P token+“w” (with) token+A token, or a P+w+A n-gram model). 
     In some embodiments, after the system uses machine learning to learn the dominant n-gram models for various product categories, the system may automatically categorize item listings by parsing the listing titles and determining if the listing titles are associated with the dominant n-gram model for a particular product category. For example, a potential problem with item listings is that sellers may place items into the incorrect categories (e.g., by placing an iPhone case in the “smartphone” category rather than the “smartphone accessory category”), either due to error or due to deceptive intent to cause buyers searching for iPhones to be presented with the seller&#39;s item listing. This frustrates the search experience by making it more difficult for buyers to find the listings they are really interested in. 
     Accordingly, the system can compare listing titles with dominant n-gram models for various product categories, in order to detect the correct product category for the listing title. Thus, system can detect if, for example, the item listing has been attached to the incorrect category by the seller, and can suggest the correct product category associated with the item listing. Accordingly, the system can enhance the selling experience by assisting sellers in finding the correct product category for the items they wish to sell. Similarly, the system can enhance the search experience by ensuring that buyers are able to find the listings they are really interested in. In some embodiments, the system can assign item listings to product categories even when unknown tokens are present. For example, the system can determine that a listing title with token symbolization ?+4+P (where “?” represents an unknown token) likely corresponds to the A+4+P dominant n-gram model associated with accessory listings. 
       FIG. 1  is a network diagram depicting a client-server system  100 , within which one example embodiment may be deployed. A networked system  102  provides server-side functionality via a network  104  (e.g., the Internet or Wide Area Network (WAN)) to one or more clients.  FIG. 1  illustrates, for example, a web client  106  (e.g., a browser), and a programmatic client  108  executing on respective client machines  110  and  112 . 
     An Application Program Interface (API) server  114  and a web server  116  are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers  118 . The application servers  118  host one or more applications  120 . The application servers  118  are, in turn, shown to be coupled to one or more databases servers  124  that facilitate access to one or more databases  126 . According to various exemplary embodiments, the applications  120  may be implemented on or executed by one or more of the modules of the item listing categorization system  200  illustrated in  FIG. 2 . While the applications  120  are shown in  FIG. 1  to form part of the networked system  102 , it will be appreciated that, in alternative embodiments, the applications  120  may form part of a service that is separate and distinct from the networked system  102 . With some embodiments, the application servers  118  hosts what is referred to herein as an item listing categorization system  200 . The item listing categorization system  200  is described in more detail below in conjunction with  FIG. 2 . 
     Further, while the system  100  shown in  FIG. 1  employs a client-server architecture, the present invention is of course not limited to such an architecture, and could equally well find application in a distributed, or peer-to-peer, architecture system, for example. The various applications  120  could also be implemented as standalone software programs, which do not necessarily have networking capabilities. 
     The web client  106  accesses the various applications  120  via the web interface supported by the web server  116 . Similarly, the programmatic client  108  accesses the various services and functions provided by the applications  120  via the programmatic interface provided by the API server  114 . 
       FIG. 1  also illustrates a third party application  128 , executing on a third party server machine  130 , as having programmatic access to the networked system  102  via the programmatic interface provided by the API server  114 . For example, the third party application  128  may, utilizing information retrieved from the networked system  102 , support one or more features or functions on a website hosted by the third party. The third party website may, for example, provide one or more functions that are supported by the relevant applications of the networked system  102 . 
     Turning now to  FIG. 2 , an item listing categorization system  200  includes an identification module  202 , a tokenization module  204 , and a database  206 . The modules of the item listing categorization system  200  may be implemented on or executed by a single device such as an item listing categorization device, or on separate devices interconnected via a network. The aforementioned item listing categorization device may be, for example, one of the client machines (e.g.  110 ,  112 ) or application server(s)  118  illustrated in  FIG. 1 . 
     According to various exemplary embodiments described in more detail below, the identification module  202  is configured to access listing titles of item listings on a marketplace website, the item listings being associated with a particular product category in a product category structure of the marketplace website. The tokenization module  204  is then configured to convert words in each of the listing titles to semantic tokens in a token symbol space, based on a tokenization process. Thereafter, the tokenization module  204  may perform n-gram modeling on the tokens corresponding to each of the listing titles of the item listings in the particular product category. The tokenization module  204  may then identify one or more dominant n-gram models associated with the listing titles of the item listings in the particular product category. The operation of each of the aforementioned modules of the item listing categorization system  200  will now be described in greater detail in conjunction with  FIG. 3 . 
       FIG. 3  is a flowchart illustrating an example method  300 , according to various exemplary embodiments. The method  300  may be performed at least in part by, for example, the item listing categorization system  200  illustrated in  FIG. 2  (or an apparatus having similar modules, such as client machines  110  and  112  or application server  118  illustrated in  FIG. 1 ). In operation  301 , the identification module  202  accesses listing titles of item listings posted on a marketplace website, where the item listings are associated with a particular product category in a product category structure of the marketplace website. For example, most marketplace websites (e.g., eBay®, Amazon®, etc.) offer a wide variety of products for sale, where such marketplace websites include item listing webpages (also referred to as item listings herein) that advertise a particular item or product for sale. Moreover, such marketplace websites typically maintain a product category structure defining various product categories (e.g., computers, toys, clothes, accessories, etc.), where each of the items or products offered for sale in each of the item listings are associated with a particular product category in the product category structure. In some embodiments, the item listing title and product category of an item listing may be submitted by a seller in conjunction with a request to post an item listing or an item listing page on the marketplace website. For example, the seller may access a user interface for allowing the seller to generate an item listing page, where the seller may specify an item listing title describing the item being sold, the product category of the item, as well as other information about the item (e.g., descriptions, pictures, characteristics, properties, prices, etc.). Accordingly, each item listing is associated with a particular product category in the product category structure of the marketplace website. 
     In some embodiments, the identification module  202  may access item listing information maintained by a marketplace website to identify the various existing item listings in a particular product category. For example,  FIG. 4  illustrates an example of item listing information  400  that identifies various product categories (e.g., accessories, computers, etc.) in the product category structure of a marketplace website, as well as a list of existing item listings assigned to each of the product categories. For example, as illustrated in  FIG. 4 , the item listings  1 - 7  are associated with the “accessories” product category, while the item listings  11 - 16  are associated with the “computers” product category, and so on. The item listing information  400  may be stored locally at, for example, the database  206  illustrated in  FIG. 2 , or may be stored remotely at a database, data repository, storage server, etc., that is accessible by the item listing categorization system  200  via a network (e.g., the Internet). 
     In some embodiments, the item listing information  400  may identify all the item listings that are associated with the particular product category. However, it is possible that some item listings have been miscategorized in a particular product category, such as when a seller who has uploaded an item listing has specified the incorrect product category for the product in the item listing. Accordingly, in some embodiments, it is possible that the item listings in the item listing information  400  may only represent those item listings that are correctly associated with the corresponding product category. For example, the item listing categorization system  200  may display the item listings for review by a user (e.g., customer service personnel or administrator of a marketplace website) to confirm that the item listings do actually correspond to the appropriate product category. Accordingly, it is understood that, in some embodiments, the operation  301  in  FIG. 3  may comprise accessing the listing titles of item listings that are known to be correctly assigned to a particular product category. 
     After identifying the item listings in a particular product category, the identification module  202  may access information (e.g., in a database associated with a marketplace website) describing the item listing titles for each of the item listings. For example,  FIG. 5  illustrates an example flow  500  where an exemplary input string “leather case for Samsong galaxy” (see  501 ) corresponding to an item listing title is accessed by the determination module  202 . 
     Referring back to the method  300  and  FIG. 3 , in operation  302 , the tokenization module  204  converts words in each of the listing titles (accessed in operation  301 ) to semantic tokens in a token symbol space, based on a tokenization process. In some embodiments, the tokenizing process may involve identifying different words in the string, and may also involve normalizing each of the words and/or replacing each of the words with a substitute value or “token” representing each word. Examples of possible tokens in a token symbol space utilized by the item listing categorization system  200  may include a product token “P”, an accessory token “A”, an attribute token, a for token “4” (corresponding to the word “for”), a with token “w” (corresponding to the word “with”), an and token “&amp;” (corresponding to the word “and”), an unknown token “?” representing an unknown word that cannot be classified as another type of token, and so on. 
     For example, as illustrated in  FIG. 5 , the input string “leather case for Samsong galaxy” has been parsed into the words “leather case”, “for”, “Samsong”, and “galaxy” (see  502 ), and these words have been tokenized to produce various tokens (see  503 ). For example, as illustrated in  FIG. 5 , the words “leather case” have been replaced with an accessory token “A”, while the word “for” has been replaced with a “4” token, while the word “Samsong” has been replaced with a product token “P”, while the word “galaxy” has also been replaced the product token “P”. 
     In some embodiments, the tokenization process may involve referring to a list of predefined tokens. For example, the tokenization module  204  may compare each of the parsed words in the item listing title with lists of words associated with predefined token types. For example, the process of replacing the parsed words in the listing title with tokens may involve comparing the parsed words in the listing title with dictionaries or lists of predefined words associated with predefined tokens. 
     For example, the tokenization module  204  may access rules listing words corresponding to brand names (e.g., “Samsong”) and product names (e.g., “galaxy”) in the product inventory of a marketplace website, and if the tokenization module  204  detects any of these words in the listing title, it may replace these words with a product token “P”. Similarly, the tokenization module  204  may access rules listing words corresponding to known accessories (e.g., “accessory”, “case”, “leather case”, “charger”, etc.) that may be available for sale on a marketplace website, and if the tokenization module  204  detects any of these words in the listing title, it may replace these words with an accessory token “A”. Similarly, the tokenization module  204  may access a rule indicating that the words “for”, “with”, and “and” should be replaced with a for token “4”, a with token “W”, and an “&amp;” token, respectively, and so on. The aforementioned tokens are merely exemplary, and it is understood that the aspects of this disclosure applicable to other types of words and other types of tokens that may be applicable to item listing titles. The aforementioned rules may be stored locally at, for example, the database  206  illustrated in  FIG. 2 , or may be stored remotely at a database, data repository, storage server, etc., that is accessible by the item listing categorization system  200  via a network (e.g., the Internet). 
     In some embodiments, the tokenization module  204  may determine that one or more of the words in the item listing title are attribute values associated with predefined attributes of one or more product categories in a product category structure of the e-commerce website, and the operation  302  in  FIG. 3  may involve the tokenization module  204  tokenizing words in the input string with one or more attribute tokens. For example, the category structure of an e-commerce website (e.g., eBay.com) will typically specify various product categories in the product inventory of the e-commerce website, as well as various possible attributes and attribute values for each of the categories. For example,  FIG. 6  illustrates an example of product category information  600  associated with product category structure of an e-commerce website. The product information  600  identifies various product categories (e.g., accessories, computers, etc.) and, for each of the product categories, a number of attributes, such as the attributes “brand”, “model” and “size” associated with the product category of “accessories”. Although not shown in  FIG. 6 , the product category information  600  may also include dictionaries of qualifying attribute-value pairs corresponding to various product attributes (e.g., size, type, brand, color, etc.). For example, the product category information  600  may indicate that, for the attribute of brand, the various possible values that this attribute may take are “Gucci”, “Chanel”, etc., while for the attribute of size, the various possible values that this attribute may take include “small”, “medium”, “large”, etc., while for the attribute of color, the various possible values that this attribute may take include “red”, “blue”, “green”, etc., and so on for each of the attributes for each product category. 
     Accordingly, the tokenization module  204  may perform an attribute extraction process on the input string to determine if any of the words therein correspond to attribute values in attribute-value pairs. For example, for the input string “large red case for iPhone”, the tokenization module  204  may determine that the tokens of “large” and “red” correspond to attribute values for the attributes “size” and “color” in one or more produce categories (e.g., the product category of “accessories” as illustrated in  FIG. 6 ). In other words, the tokenization module  204  may determine that the word “large” may correspond to the known attribute-value pair of “size:large”, while the word “red” may correspond to the known attribute-value pair of “color:red”. Accordingly, the operation  302  in  FIG. 3  may involve the tokenization module  204  replacing the words “large” and “red” in the input string “large red case for iPhone” with one or more attribute tokens. Alternatively, upon determining that the words “large” and “red” correspond to attribute values, the tokenization module  204  may combine these words into an adjacent token (e.g., the tokenization module  204  may combine the words “large red case” into the accessory token “A” associated with the word “case”). 
     Various techniques for tokenizing words in a string are well understood by those skilled in the arts of linguistics, lexical analysis, and natural language processing, and will not be described in further detail herein in order to avoid occluding various aspects of this disclosure. 
     Referring back to the method  300  and  FIG. 3 , in operation  303 , the tokenization module  204  performs n-gram modeling on each of the tokenized listing titles of the item listings in a particular product category. As understood by those skilled in the art, the process of n-gram modelling involves analyzing strings or sequences of one or more words or tokens, in order to detect trends in such strings or sequences of words or tokens. For example, the tokenization module  204  may utilize the process of n-gram modeling to analyze all the tokenized listing titles in a particular category, in order to determine trends (e.g., in the occurrence and ordering of various tokens) in each of the listing titles in that particular category. 
     For example, as illustrated in  FIG. 5 , the tokenization module  204  may determine that the sequence of tokens for the input string “leather case for Samsong galaxy” (which is associated with an accessory product category) corresponds to the n-gram model of one or more accessory tokens “A” followed by a for token “4” followed by one or more product tokens “P”, otherwise referred to as an A+4+P n-gram model (see  504 ). Accordingly, in some examples, by performing n-gram modeling and machine leaning on a large number of listing titles associated with the accessory product category, the system may determine that, for example, the A+4+P n-gram model is a dominant n-gram model for accessory item listings, in that it occurs in a large portion or a statistically significant portion of the item listings in the accessory product category. As another example, the system may determine that, for example, the P+A n-gram model (e.g., “iPhone case”) is another dominant n-gram model for accessory product listings. These techniques can be applied by the item listing categorization system  200  to item listings associated with other product types or product categories, such as n-gram models for “bundles” (e.g., “Canon 600D with lens” corresponds to a P token+P token+“w” (with) token+A token). 
     Accordingly, by utilizing various techniques of n-gram modelling and machine learning, the tokenization module  204  may identify that a particular sequence of tokens occurs in a large portion or a statistically significant portion of the item listing titles in a particular category, where such a sequence of tokens may be referred to herein as a dominant n-gram model associated with the particular product category. Various techniques for performing n-gram modeling and machine learning on tokens are well understood by those skilled in the arts of linguistics, lexical analysis, and natural language processing, and will not be described in further detail herein in order to avoid occluding various aspects of this disclosure. 
     Accordingly, referring back to the method  300  in  FIG. 3 , in operation  304 , the tokenization module  204  identifies one or more dominant n-gram models associated with the listing titles of the item listings in a particular product category. As described above, in some embodiments, each dominant n-gram model identifies a sequence of one or more semantic tokens that appear in a large portion or a statistically significant portion of the listing titles of the item listings in the particular product category. 
     In some embodiments, the tokenization module  204  may repeat the method  300  with a majority or all of the available item listings in each of the product categories in the product category structure of the marketplace website, in order to determine dominant n-gram models for each of the product categories. In some embodiments, after the tokenization module  204  identifies various dominant n-gram models associated with the particular product category, the tokenization module  204  may store this information in dominant n-gram model information  700  illustrated in  FIG. 7 , which identifies various dominant n-gram models corresponding to different product categories. The dominant n-gram model information  700  may be stored locally at, for example, the database  206  illustrated in  FIG. 2 , or may be stored remotely at a database, data repository, storage server, etc., that is accessible by the item listing categorization system  200  via a network (e.g., the Internet). 
     In some embodiments, after the system  200  uses n-gram modelling and machine learning to learn the dominant n-gram models for various product categories, the system  200  may automatically categorize item listings by parsing the listing titles and determining if the listing titles are associated with the dominant n-gram model for a particular product category. For example, when a seller attempts to upload a new item listing to a marketplace website, the seller may specify an item listing title for the item listing, such as “carry bag for laptop”. The tokenization module  204  may tokenize the item listing title using the techniques described herein, and compare the resulting tokenized item listing title to the dominant n-gram models associated with various product categories (e.g., based on the dominant n-gram model information  700  illustrated in  FIG. 7 ). If the tokenization module  204  detects a match between the tokenized item listing title provided by the seller (e.g., “carry bag for laptop”) and one of the dominant n-gram models (e.g., A+4+P) associated with a given product category (e.g., accessories), the tokenization module  204  may determine that the item in the item listing title belongs to that given product category. The tokenization module  204  may transmit a notification to the seller identifying the correct product category, or the tokenization module  204  may automatically enter the correct product category (or pre-fill product category specification fields) in a user interface used by sellers to upload item listings. In some embodiments, if the seller has already specified a category for the item listing, the tokenization module  204  may utilize the techniques described above to determine if the item listings associated with the seller-specified category; if not, the tokenization module  204  may associate the item listing with the correct product category, and/or notify the seller that the item listing is not associated with the correct product category, and so on. 
     In some embodiments, the item listing categorization system  200  can assign new item listings to product categories even when unknown tokens are present. For example, if the system receives the item listing title “Gobbledygook for HTC zero” with token symbolization ?+4+P (where “?” represents an unknown token), then the tokenization module  204  may then compare this tokenized item listing title with the various dominant n-gram models for each of the product categories (see  FIG. 7 ). Even though there may not be an exact match with any particular dominant n-gram model, the tokenization module  204  may determine that, for example, the closest match is the dominant n-gram model A+4+P of the accessories product category. Accordingly, the tokenization module  204  may determine that tokenized item listing title likely corresponds to the A+4+P dominant n-gram model associated with accessory listings, and that the item listing should be associated with the accessory product category. 
       FIG. 8  is a flowchart illustrating an example method  800 , consistent with various embodiments described above. The method  800  may be performed at least in part by, for example, the item listing categorization system  200  illustrated in  FIG. 2  (or an apparatus having similar modules, such as client machines  110  and  112  or application server  118  illustrated in  FIG. 1 ). The method  800  may be performed after, for example, the method  300  and  FIG. 3 . In operation  801 , the tokenization module  204  receives a user specification of an item listing title in connection with a user request to post an item listing on a marketplace website. In operation  802 , the tokenization module  204  matches the item listing title with a dominant n-gram model associated with a particular product category in a product category structure of the marketplace website. In operation  803 , the tokenization module  204  associates the item listing title with the particular product category identified in operation  802 . The tokenization module  204  may notify a seller associated with the item listing that the item listing is going to be assigned to the particular product category in the product category structure of the marketplace website. 
     Example Mobile Device 
       FIG. 9  is a block diagram illustrating the mobile device  900 , according to an example embodiment. The mobile device may correspond to, for example, client machines  110  and  112  or application server  118  illustrated in FIG.  1 . One or more of the modules of the system  200  illustrated in  FIG. 2  may be implemented on or executed by the mobile device  900 . The mobile device  900  may include a processor  910 . The processor  910  may be any of a variety of different types of commercially available processors suitable for mobile devices (for example, an XScale architecture microprocessor, a Microprocessor without Interlocked Pipeline Stages (MIPS) architecture processor, or another type of processor). A memory  920 , such as a Random Access Memory (RAM), a Flash memory, or other type of memory, is typically accessible to the processor  910 . The memory  920  may be adapted to store an operating system (OS)  930 , as well as application programs  940 , such as a mobile location enabled application that may provide location based services to a user. The processor  910  may be coupled, either directly or via appropriate intermediary hardware, to a display  950  and to one or more input/output (I/O) devices  960 , such as a keypad, a touch panel sensor, a microphone, and the like. Similarly, in some embodiments, the processor  910  may be coupled to a transceiver  970  that interfaces with an antenna  990 . The transceiver  970  may be configured to both transmit and receive cellular network signals, wireless data signals, or other types of signals via the antenna  990 , depending on the nature of the mobile device  900 . Further, in some configurations, a GPS receiver  980  may also make use of the antenna  990  to receive GPS signals. 
     Modules, Components and Logic 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied (1) on a non-transitory machine-readable medium or (2) in a transmission signal) or hardware-implemented modules. A hardware-implemented module is tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more processors may be configured by software (e.g., an application or application portion) as a hardware-implemented module that operates to perform certain operations as described herein. 
     In various embodiments, a hardware-implemented module may be implemented mechanically or electronically. For example, a hardware-implemented module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware-implemented module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware-implemented module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the term “hardware-implemented module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired) or temporarily or transitorily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. Considering embodiments in which hardware-implemented modules are temporarily configured (e.g., programmed), each of the hardware-implemented modules need not be configured or instantiated at any one instance in time. For example, where the hardware-implemented modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware-implemented modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware-implemented module at one instance of time and to constitute a different hardware-implemented module at a different instance of time. 
     Hardware-implemented modules can provide information to, and receive information from, other hardware-implemented modules. Accordingly, the described hardware-implemented modules may be regarded as being communicatively coupled. Where multiple of such hardware-implemented modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware-implemented modules. In embodiments in which multiple hardware-implemented modules are configured or instantiated at different times, communications between such hardware-implemented modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware-implemented modules have access. For example, one hardware-implemented module may perform an operation, and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware-implemented module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware-implemented modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules. 
     Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or processors or processor-implemented modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations. 
     The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs).) 
     Electronic Apparatus and System 
     Example embodiments may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Example embodiments may be implemented using a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. 
     A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. 
     In example embodiments, operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Method operations can also be performed by, and apparatus of example embodiments may be implemented as, special purpose logic circuitry, e.g., a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). 
     The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In embodiments deploying a programmable computing system, it will be appreciated that that both hardware and software architectures require consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware may be a design choice. Below are set out hardware (e.g., machine) and software architectures that may be deployed, in various example embodiments. 
     Example Machine Architecture and Machine-Readable Medium 
       FIG. 10  is a block diagram of machine in the example form of a computer system  1000  within which instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. 
     The example computer system  1000  includes a processor  1002  (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory  1004  and a static memory  1006 , which communicate with each other via a bus  1008 . The computer system  1000  may further include a video display unit  1010  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system  1000  also includes an alphanumeric input device  1012  (e.g., a keyboard or a touch-sensitive display screen), a user interface (UI) navigation device  1014  (e.g., a mouse), a disk drive unit  1016 , a signal generation device  1018  (e.g., a speaker) and a network interface device  1020 . 
     Machine-Readable Medium 
     The disk drive unit  1016  includes a machine-readable medium  1022  on which is stored one or more sets of instructions and data structures (e.g., software)  1024  embodying or utilized by any one or more of the methodologies or functions described herein. The instructions  1024  may also reside, completely or at least partially, within the main memory  1004  and/or within the processor  1002  during execution thereof by the computer system  1000 , the main memory  1004  and the processor  1002  also constituting machine-readable media. 
     While the machine-readable medium  1022  is shown in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions or data structures. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. 
     Transmission Medium 
     The instructions  1024  may further be transmitted or received over a communications network  1026  using a transmission medium. The instructions  1024  may be transmitted using the network interface device  1020  and any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., WiFi, LTE, and WiMax WiMAX networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software. 
     Although an embodiment has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.