Patent Publication Number: US-11651417-B2

Title: Method, system, and non-transitory processor-readable medium for intelligent listing creation for a for sale object

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/912,411 titled “Computer Technology for Intelligent Listing Creation”, filed Jun. 25, 2020, which is incorporated by reference in its entirety. 
     This application is also related to U.S. patent application Ser. No. 16/253,719 titled “Temporal Disposition Of Offers Based On Decay Curves,” filed Jan. 22, 2019; U.S. patent application Ser. No. 16/288,379 titled “Probabilistic Item Matching And Searching,” filed Feb. 28, 2019; U.S. patent application titled “Inventory Ingestion And Pricing System,” Ser. No. 16/288,203, filed Feb. 28, 2019; and U.S. Provisional Application No. 62/900,764 titled “Automating The Creation Of Listings Using Augmented Reality Computer Technology,” filed Sep. 16, 2019, all of which are herein incorporated by reference in their entireties. 
    
    
     BACKGROUND 
     A number of ecommerce sites exist where users can sell their unwanted items, such as MERCARI, EBAY, AMAZON, POSHMARK, LETGO, CRAIGSLIST, etc. Conventionally, in order to sell on these sites, users must manually create listings for offering their items for sale. The quality of such listings can vary greatly, and may depend on a number of factors, such as the user&#39;s experience creating listings, the information the user has on the item (such as make, model, brand, size, color, features, etc.), the user&#39;s photo taking skills, whether the user is rushed when creating the listing, whether this is the first time the user has ever tried to sell an item of this type, etc. Since a well-constructed listing will increase the likelihood that the associated item will sell, it would be advantageous if computer technology could be employed to enhance the quality of listings. 
     Also, a key element when creating a listing is establishing an offer price (that is, a price that the associated item is being offered for sale). If the offer price is too low, the user may not receive the full value of the item. If the offer price is too high, it may take a long time for the item to sell, or the item may never sell. Again, it would be advantageous if computer technology could be employed to assist users with pricing their items for sale. 
     SUMMARY 
     Provided herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for automatic, intelligent generation of listings for a for sale object (FSO) being offered by a seller. 
     Some embodiments operate by: receiving information relating to the FSO, including specifications for selling the FSO and an election of an automatic listing option; determining a category of the FSO; generating an optimal offer price based on at least the category; and generating multiple listings for the FSO, wherein the multiple listings have varying titles, descriptions, pictures and offer prices, so as to parallelize the selling of the FSO. The information may include pictures of the FSO, and the category may be determined by analyzing the pictures. 
     The multiple listings may be generated by: identifying potential buyers based on searches and past purchases; determining tendencies and preferences of the potential buyers by analyzing the searches and past purchases; and customizing at least some of the listings based on the determined tendencies and preferences. Then, the customized listings may be provided to the respective potential buyers. 
     Also provided herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for automatic, intelligent generation of an offer price for a FSO. 
     Some embodiments may operate by: receiving information relating to the FSO, including specifications for selling the FSO, wherein the specifications include an original offer price and a time window for selling the FSO; determining a category of the FSO; generating an optimal offer price for the FSO based on one or more of: (a) past listings of previously sold FSOs that have a same or similar category of the FSO; (b) the specifications, including the time window; (c) a category decay curve applicable to the category; and (d) a seller flexibility curve of the seller. The optimal offer price may be suggested to the seller as an offer price for a listing corresponding to the FSO. 
     Generating the optimal offer price may include: identifying past listings of the previously sold FSOs that have the same or similar category of the FSO; accessing transaction information from the identified past listings; and generating the optimal price based on at least the transaction information using either a statistics based approach, or through artificial intelligence techniques such as machine learning. 
     The optimal price may be adjusted, either initially due to seller requirements, or as time goes on, based on the category decay curve applicable to the category and/or the seller flexibility curve of the seller. The optimal price may be also or alternatively adjusted based on a determination that the seller elected a charitable option. 
     Some embodiments may also include: generating zones of possible agreement (ZOPA) based on at least one of (a) the category decay curve applicable to the category; and (b) the seller flexibility curve of the seller; periodically, generating a new offer price for the listing based on a ZOPA corresponding to a current listing age; and offering the new offer price to the seller. 
     Further features and advantages of the embodiments disclosed herein, as well as the structure and operation of various embodiments, are described in details below with reference to the accompanying drawings. It is noted that this disclosure is not limited to the specific embodiments described herein. Such embodiments are presented herein for illustrative purposes only. Additional embodiments will be apparent to a person skilled in the relevant art based on the teachings contained herein. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       The accompanying drawings are incorporated herein and form a part of the specification. 
         FIG.  1    illustrates a computing environment having an interne site for selling “for sale objects” (FSO), according to some embodiments. 
         FIG.  2    illustrates an example category decay curve, according to some embodiments. 
         FIG.  3    illustrates an example seller decay curve (also called a seller flexibility curve), according to some embodiments. 
         FIG.  4    illustrates an example listing, according to some embodiments. 
         FIG.  5    illustrates various example ZOPAS (zones of possible agreement), according to some embodiments. 
         FIG.  6    illustrates a flowchart of a method for using computer technology to provide pricing guidance, and to intelligently create and process listings for selling FSOs, according to some embodiments. 
         FIGS.  7 - 9    provide more details of the method of  FIG.  6   , according to some embodiments. 
         FIG.  10    provides an example graphical user interface (GUI) for both relaying the optimal price and associated information to the user, as well as gathering information from the user, according to some embodiments. 
         FIG.  11    provides an example graphical user interface (GUI) used for gathering data points from a user to construct a seller flexibility curve, according to some embodiments. 
         FIG.  12    shows a flow diagram depicting the translation of input data from the user to data that is used in a regression model to output a predicted price and price range, according to some embodiments. 
         FIG.  13 A  shows an example of a regression model that may be used for gathering various further input data that is needed for predicting price according to some embodiments. 
         FIG.  13 B  shows an example of a neural network model that may be used for predicting price, as well as a price range confidence interval, according to some embodiments. 
         FIG.  14    shows an example of a decision tree that may be used in a random forest regression or boosted tree regression model, according to some embodiments. 
         FIG.  15    illustrates an example computer system useful for implementing various embodiments. 
     
    
    
     In the drawings, like reference numbers generally indicate identical or similar elements. Additionally, generally, the left-most digit(s) of a reference number identifies the drawing/figure in which the reference number first appears. 
     DETAILED DESCRIPTION 
     Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for using computer technology to provide pricing guidance, and to intelligently create and process listings for selling FSOs (for sale objects), according to some embodiments. 
       FIG.  1    illustrates a computing environment  102  having an internet site  110 , which may be implemented using one or more computer systems  1000  such as shown in  FIG.  10    (and further described below). In some embodiments, the site  110  enables sellers  104  and buyers  108  to sell and buy, respectively, products and/or services, which may be collectively herein referred to as for sale objects (FSOs). Examples of site  110  include MERCARI, EBAY, AMAZON, POSHMARK, LETGO, CRAIGSLIST, etc., to name just some examples. Sellers  104  and buyers  108  may access the site  110  via the Internet, for example. 
     Sellers  104  may create listings  112  on the site  110  to sell their FSOs  112 . The listings  112  may be stored in a listing database  110 . Buyers  108  may browse and search listings  112  to find FSOs  112  of interest to purchase. At any given time, a given user may be selling and/or buying FSOs  106  using the site  110  (that is, a given user may be a seller  104  and/or a buyer  108  at any given time on the site  110 ). 
     As shown in  FIG.  4   , each listing  112  may include a title  402 , description  406  and pictures  410  of the FSO  106  that is being offered for sale. Additionally, in some embodiments, the listing also includes the brand/make  412  and condition  414  of the FSO  106 . The listing  112  may also store the current otter price  404  of the FSO  106  (that is, the price that the FSO  106  is being currently offered for sale via the listing  112  on the site  110 ). According to some embodiments, a pricing module  138  may intelligently provide guidance to the seller  104 : (1) for setting the initial offer price  404  of any given FSO  106 , and also (2) for adjusting the offer price  404  over time prior to the sale of the FSO  106  on the site  110 . This is further described below. 
     The FSOs  106  may be organized into categories  152 , such as clothing, furniture, tools, electronics, fine art, painting services, accounting services, etc. The listing  112  may store the category  408  of the FSO  106 , which may also be provided by the user. Information regarding categories  408  may be cross-checked, changed appropriately as described herein if the user has indicated a wrong category  408 , and stored in a category database  150  in the site  110 . 
     The internet site  110  may include a historical database  114 . The historical database  114  may store past listings  116  of FSOs  106  that have sold in the past. That is, when a FSO  106  sells, its associated listing  112  becomes a past listing  116  that is stored in the historical database  114 . 
     Each past listing  116  may include the fields of listings  112  shown in  FIG.  4   . For example, such a past listing  116  may include transaction information  118 , including the listing information  126 . Listing information  126 , in turn, may include some or all information fields provided in sample listing  112  in  FIG.  4    (e.g., title  402 , offer price  404 , description  406 , category  408 , picture(s)  410 , brand  412 , and condition  414 ). Also or alternatively, the transaction information  118  of each past listing  116  may also include original price  120 , sale price  122 , and price history  124 . This information pertains to the pricing and sale history of the associated FSO  106  prior to its sale on the site  110 . 
     For example, the pricing history  124  may include information indicating: how long it took for the FSO  102  to sell after it was originally listed on the site  110 , pricing changes (typically price decreases, although price increases are also possible), and/or when those pricing changes took place measured from when the FSO  102  was originally listed on the site  110  (called the “age of the listing,” showed in  FIGS.  2 ,  3  and  5    as  210 ). 
     Category Decay Curves and Seller Flexibility Curves 
     The internet site  110  may also include other databases  126 . The other databases  126  may store decay curves  128 , flexibility curves  130 , and/or information relating to ZOPAs  132  (zones of possible agreement). Decay curves  128  and flexibility curves  130  shall now be described. ZOPAs  132  are described further below. 
     Decay curves  128  reflect the fact that at least some sellers  104  are more willing to sell their FSOs  106  for lower prices depending on how long their FSOs  106  have been listed (that is, the age  210  of the associated listings  112 ), and/or the respective categories  152  of their FSOs  106 . For example, the longer a given FSO  106  has been listed, the seller  104  may be more willing to accept lower prices. Also, such a decay curve  128  may be category-dependent. That is, the seller  104  may be more willing to accept lower prices for some categories  152  of FSOs  106  (such as used clothing and used furniture) as compared to other categories  152  (such as high end electronics and fine art). 
     Also, different sellers  104  may vary on the price they are willing to accept for any given FSO  106 . For example, some sellers  104  may be naturally inclined to accept lower prices than other sellers  104 . 
       FIG.  2    illustrates an example decay curve  202 . In some embodiments, each decay curve  202  corresponds to a category  152  of FSOs  106 . The decay curve  202  (also called category decay curve  202 ) takes into consideration (1) the age  210  of the listing  112 ; and (2) the category  152  (stored in  408  of the listing  112 ) of the FSO  106 . 
     The decay curves  202  may be generated and updated by the pricing module  138 . Specifically, the pricing module  138  may analyze past listings  116  in the historical database  114  for sellers  104  to generate a category decay curve  202  for each category  152 . In some embodiments, the category decay curve  202  shows, for a given category  152 , the prices at which sellers  104  were willing to sell their FSOs  106  over time (where time is based on the age  210  of the listing  112  when the FSOs  106  sold). 
     In the example of  FIG.  2   , the category decay curve  202  shows that when listings  112  in a given category  152  are created (that is, listing age  210  equals 0), sellers were willing to sell only at 100% of the original listing price  120 . However, within 5 days after listings  112  were created, at least some sellers  104  were willing to sell their FSOs  106  at 93% of the price  120  (this is indicated by  208 A). At 15 days after the listing  112  was created, some sellers  104  in the given category  152  were willing to sell their FSOs  106  at 80% of the original price  120  (this is indicated by  208 B). Similarly, at 30 days after the listing  112  was created, some sellers  104  were willing to sell their FSOs  106  at 60% of the original price  120  (this is indicated by  208 C). 
     Each of these points  208 —which may be called decay points  208  herein—may be generated by averaging the sales data collected by the pricing module  138  at particular listing ages  210 , and/or at particular percentages of the original price  120 . For example, suppose at listing age  210 =15 days, there were 5 sales (in the category  152  associated with category decay curve  202 ) at the following percentages of the original price  120 : 100%, 90%, 80%, 70% and 60%. The pricing module  138  would thereby determine the average of these percentages to be 80%, and thus create decay point  208 B of 80% in the category decay curve  202 . 
     In some embodiments, the number of sales must be greater than a threshold over a predetermined time period in order to generate a decay point  208 . The predetermined time period may be 1 month, 3 months, or any other time period. Referring again to the example of  FIG.  2   , and for the predetermined time period, if this threshold is 10, then the pricing module  138  would not create the decay point  208 B since the number of sales (5) are less than the threshold (10). 
     As just described, in some embodiments, category decay curves  202  are associated with categories  152 . In other embodiments, the pricing module  138  also tracks the past activities of sellers  104  to determine each seller&#39;s  104  willingness (that is, flexibility) to accept lower sale prices over time  210 . For example, the pricing module  138  may analyze the transaction information  118  in past listings  116  for a given seller  104  to generate a seller decay curve (also called seller flexibility curve)  302  for the seller  104  (see the example in  FIG.  3   ; shown as  130  in  FIG.  1   ). In some embodiments, the seller flexibility curve  302  may show, for a given seller  104 , the percentages by which the seller  104  reduced the offer price  404  in his listings  112 , and the times (in terms of the age  210  of the listing  112 ) such reductions occurred. The seller flexibility curve  302  may also show the percentages off the original price  120  that the seller  104  accepted offers from buyers  108 , and the times such acceptances occurred. 
     For example, the example seller flexibility curve  302  in  FIG.  3    shows that the associated seller  104  (for whom the curve  302  applies) has a history of selling at 80% of the original price  120  at 11 days after creating listings  112  (see point  304 A), and at 60% at 23 days (see  304 B). 
     Each of these points  304 —which may be called seller reduction points  304  herein—may be generated by averaging the sales data in past listings  116  (associated with the seller  104 ) at particular listing ages  210 , and/or at particular percentages of the original price  120 . For example, supposed at listing age  210 =10 days, the seller  104  reduced the price  404  to 90% of the original price  120  in a first listing, and at listing age  210 =12 days, the seller  104  accepted an offer price that was at 70% of the original price  120  in a second listing. In this case, the pricing module  138  may create a seller reduction point  304 A of 80% (that is, the average of 90% and 70%) at a listing age  210  of 11 days (that is, the average of 10 and 12). 
     In some embodiments, the pricing module  138  may analyze sales of the seller  104  as just described, by moving across the X axis using a window  301  of a predetermined size. The window  301  may have a length of 5 days (as shown in the example of  FIG.  3   ), 10 days, or any other time period. The pricing module  138  may analyze sales data of the seller  104  to determine if a seller reduction point  304  should be created in the window  301 , as the window  301  steps across the X axis in 1 day increments (or any other increment). 
     In some embodiments, the number of sales in the window  301  must be greater than a threshold over a predetermined time period in order to generate a seller reduction point  304  in the current position of the window  301 . The predetermined time period may be 1 month, 3 months, or any other time period. Referring again to the example of  FIG.  3   , assume the window  301  is currently positioned over days 9 to 13 of the X-axis, and the threshold is 10. In this case, the pricing module  138  would not create the seller reduction point  304 A since the number of sales (2) are less than the threshold (10) within the window  301  as currently positioned on the X-axis. However, if the threshold was 2, a point would be created by averaging both the X and Y values as described above, resulting in a point at 11 days and 80%, as shown in  FIG.  3   . 
     In some embodiments of  FIGS.  2  and  3   , only a limited number of data points may be present. In this case, based on the type of trend-line, regression can be used to generate a fitting-equation for the curve  302  that will be used as described later. As shown in  FIGS.  2  and  3   , because the data follows a linear pattern, a linear trend-line may be created. However, in other cases, data may follow an exponential or logarithmic pattern, for either curve  202 , curve  302 , or both. In this case, based on the type of trend-line, linear, exponential, or logarithmic regression can be used with the data points in  FIG.  3    (as generated by averaging proximate data points in the moving window described above) or with the data points in  FIG.  2    (generated from averaging percentages of original price sold (Y) for past sales at particular ages of listing (X)). With such a generated curve, the percentage of original price sold (Y), with respect to both category decay ( FIG.  2   ) and seller flexibility ( FIG.  3   ), can be predicted at any given age (X) of a listing. 
     In some embodiments if not enough seller reduction points (such as point  304  described above) can be created to depict an accurate curve, the user may also be asked themselves what they feel their flexibility may be. Alternately, the curve previously created through regression as described above may be presented to the user in a GUI such that the user may alter the curve as needed to reflect what they feel their true flexibility may be. In both embodiments, a GUI as shown in  FIG.  11    may be presented to the user. For the embodiment where there are not enough seller reduction points to depict an accurate curve, the screen may start with no points, or may display the seller reduction points that already exist. Then, at regular predetermined intervals (e.g., every 10 days, where the age of listing  210  indicates days in  FIG.  11   ), a point may be presented to the user (e.g., points  1102  and  1104  in  FIG.  11   ) wherein the user may be able to place the point on the Y axis at a percentage level they may feel comfortable lowering the original price by. For example, point  1102  may first be presented to the user, and if they feel comfortable selling for 80% of the original price at an age listing of 10 days, they may drag the point  1102  along the Y-axis to the 80% location. If  1102  is an already existing seller reduction point at, e.g. 70%, and the user instead feels comfortable with 80% (meaning 70% is too low for them), similarly, they may drag the point  1102  along the Y-axis to the 80% location. 
     In this manner, in such an embodiment, the user is free to generate their own curve at pre-determined intervals, or adjust an already-existing curve, as the need may be. This is beneficial as it may reflect current exigencies which may not be captured by a trend. For example, if a user is getting married soon, will be bankrupt by a certain death, has a death in the immediate family, or by any other unforeseen circumstance urgently needs money by a certain date, the actual curve which a user is comfortable with may tend to be more steep, with a greater negative rate of change, than a predicted curve. Conversely, if the user has had a sudden infusion of cash due to an unforeseen circumstance, such as, a gift from a family or well-wisher, they may not be in a hurry to sell, and the curve which they are comfortable with may be less steep, with a lower rate of negative change, than a predicted curve. In this manner, the user can account for their personal circumstances, taking the predicted curve as guidance, and adjust it to their needs as they see fit. The curve may then be interpolated. between the points chosen by the user (for example, if the interval at which points are presented is every 10 days as described above, then the user chooses 4 seller reduction points in  FIG.  11    defining the curve) using linear, exponential, or logarithmic regression, or a combination of the three, depending on the type of the curve. This curve may then form the seller flexibility curve  302  that will be used to generate an optimal price or initial offer price, or adjust an initial offer price over time, along with the demand curve  202 , as will be described below. 
     In some embodiments, a single seller flexibility curve  302  may be generated for a given seller  104  that covers all the categories  152 . In other embodiments, multiple seller flexibility curves  302  may be generated for a given seller  104 , with each seller flexibility curve  302  covering one of the categories  152 . A single seller flexibility curve  302  can be useful when there is not much data present for a specific category  152  for sales for a particular seller. In addition, a single seller flexibility curve  302  can also be useful when sales for a new product are expected to follow a usual trend. For products with unique or unusual sales patterns (seasonal, luxury brands, etc.), a particular category  152  seller flexibility curve  302  may be useful. 
     Example embodiments for generating and using category decay curves  202  and seller flexibility (decay) curves  302  are provided in U.S. patent application Ser. No. 16/253,719 titled “Temporal Disposition Of Offers Based On Decay Curves,” filed Jan. 22, 2019, which is herein incorporated by reference in its entirety, and any of those embodiments may be used herein. 
     Automatic Generation of Listings and Pricing Generation 
     As noted above, sellers  104  may create listings  112  on the site  110  to sell their FSOs  112 . The quality of such listings  112  can vary greatly, and may depend on a number of factors, such as the user&#39;s experience creating listings, the information the user has on the item (such as make, model, brand, condition, size, color, features, etc.), the user&#39;s photo taking skills, whether the user is rushed when creating the listing, whether this is the first time the user has ever tried to sell an item of this type, etc. Also, the creation of listings  112  may not be an intuitive or easy task for many sellers  104 . These factors may result in deterring many sellers  104  from creating listings  112 , such that their FSOs  106  are not offered for sale on the internet site  110 . 
     Thus, in some embodiments, the internet site  110  includes a listing module  134 . Upon command from a seller  104 , the listing module  134  automatically creates a listing  112  to sell the seller  104 &#39;s FSO  106 . The listing module  134  intelligently creates the listing  112  by customizing the listing  112  to the seller  104 &#39;s particular FSO  106 , as well as specifications  154  received from the seller  104 . 
     For example, one seller  104  may wish to maximize the sale price  122  that his FSO  106  ultimately sells for, and may not be as concerned with how long the FSO  106  takes to sell. Such a seller  104  may indicate such a specification  154  on a GUI such as GUI  1000  of  FIG.  10   . For example, in GUI  1000 , there is a slider  1018  presented to the user between “Maximize Profit” and “Maximize Time [Sell at an earlier time],” wherein the seller  104  can indicate that he/she would like to maximize profit at the cost of potentially waiting for some time before the FSO is sold. Such a visual form for indicating the preference as a slider  1018  represents an intuitive and easy-to-use feature for the seller  104  to use. Another seller  104  may wish to quickly sell her FSO  106 , or sell her FSO  106  within a designated time frame, provided that the sale price  122  is above a given minimum price. In this case, conversely, such a seller  104  may position slider  1018  on GUI  1000  such that it is located at the “Maximize Time” end. Another seller  104  may specify that her FSO  106  is being sold for one or more specified charities. Such a charity may, e.g., be chosen from a drop-down box  1022 , as shown in GUI  1000 , where “Charity A” and “Charity B” are listed. The drop-down box selections may further be text-editable, such that the user may write-in the charity of their choice as an additional drop-down field if it is not already present. The selections made by the user are stored and sent from the user application as inputs of specifications  154  to the listing module  134 . 
     The listing module  134  (operating in conjunction with a charity module  136  when the seller  104  has designated the sale of the FSO  106  for charitable contribution) receives these specifications  154  from sellers  104 , and automatically and intelligently creates the listings  112  for the respective FSOs  106  based on these specifications of sellers  104 . 
     As part of this automatic listing  112  creation function, the listing module  134  may operate with the pricing module  138 , for automatically and intelligently generating an offer price  404  for a particular FSO  106  (herein called the FSO  106  being offered for reference purposes) that an associated seller  104  wishes to sell. To generate the offer price  404 , the pricing module  138  may take into consideration the seller  104 &#39;s specifications  154 , the category decay curve  202  associated with the category  152  of the FSO  106  being offered, the seller  104 &#39;s flexibility curve  302 , and/or past listings  116  for FSOs  106 , either in general or having the same or similar category  152  of the FSO  106  being offered. 
     example, such past listings  116  may indicate that the same or similar FSOs  106  sold for an average price of $10. In an embodiment, the pricing module  138  may thereby suggest an optimal offer price  404  of $10. Or, instead, the pricing module  138  may also take into consideration the category decay curve  202  associated with the category  152  of the FSO  106  being offered, and/or the seller  104 &#39;s flexibility curve  302 . 
     For example, the seller  104  may inform the pricing module  138  (via the specifications  154 ) that the seller  104  wishes to sell within 20 days of the age of the listing. This may occur, for example, through the editable text field  1016  and check-box  1014  present on GUI  1000  of  FIG.  10   . For example, the seller  104  may write “20 days” in the text field  1016  and check the box  1016  to indicate that such a sell-by condition is valid and should be incorporated as part of the specifications  154 . It is assumed the example category decay curve  202  shown in  FIG.  2    applies to the category  152  of the FSO  106  being offered. In the example of  FIG.  2   , decay point  208 B of 80% is closest to listing age  210 =20 days. Thus, the pricing module  138  may pre-apply this decay point  208 B—that is 80%—to the optimal price thereby suggesting an offer price  404  of $8 (that is, 0.8×$10). As will be explained below, the seller flexibility curve  304  associated with the seller  104  can also or alternatively be applied in the same way to generate the offer price  404 . 
     In addition, in an embodiment, when the price is suggested to the seller, a forecasting via an associated demand curve can also be shown. That is, several prices and demand curves may be shown, in a similar manner as the seller flexibility curve in  FIG.  11   . In this manner, not only the demand curve for the optimal price of $10 in the example above, but also other prices can be shown with different demand curves corresponding to these different prices, in order to give the seller  104  a range of options. In such an embodiment, the demand curve  202  of  FIG.  2   , based on past listings  116 , may be shown as corresponding to the optimal price of $10, with the point  208 B indicated as corresponding to the suggested offer price of $8 on the demand curve  202 . 
     However, additional demand curves can also be shown, with steeper-sloped curves corresponding to higher prices than the optimal price, and lower-sloped curves corresponding to lower prices than the optimal price. For example, in the example above, if the FSO  106  was priced at $40 instead of $8, the rate of change in order to sell the product in 20 days, based on past listings  116 , would need to be much steeper than $10 as for the curve in  202 . As a result, a steeper curve would be shown along with the option of a $40 initial price to indicate to the user how far they would need to drop to match the trend in reduction of price of past listings  116  in order to sell the product by 20 days. Similarly, if the FSO  106  was priced at $9 the rate of change in order to sell the product in 20 days would be much less, based on past listings  116 , than it would be with the original optimal price of $10. As a result, the user can determine an option that is more suitable for them. Furthermore, in an embodiment, the user can input their own offer price (field  1004  of GUI  1000  in  FIG.  10   ), and a corresponding demand curve, relative to the curve  202  of the optimal offer price (steeper or lower sloped as described above), may be shown in a GUT similar to the GUI of  FIG.  11   . In this manner, the seller  104  can see for themselves how far off from the decay curve  202  they are if they refuse the suggested offer price, and would like to use a higher or lower initial offer price instead. 
     In some embodiments, the listing module  134  may automatically create multiple listings  112  for the FSO  106 , in order to enhance the sellability of the FSO  106  on the site  110 . This is called parallelizing the sale of the FSO  106 , and is further described below. 
     Smart Nudge Engine and ZOPA 
     By operating as described above, the pricing module  138  may automatically generate an offer price that is an optimal price based on the specifications  154  provided by the seller  104 , and the characteristics of the FSO  106  being offered. But the seller  104  may ultimately select an initial offer price  404  that is higher than the optimal price generated by the pricing module  138 . In this case, in some embodiments, the listing module  134  will post the listing  110  on the site  100  using the initial offer price  404  provided by the seller  104  (rather than the optimal price generated by the pricing module  138 ). 
     This scenario is shown in the example of  FIG.  5   , where the seller  104 &#39;s initial offer price  404  (which is shown as the original price  504  in  FIG.  5   ) is higher than the optimal price  506  that was automatically generated by the pricing module  138 . Often, because of this price difference, it is possible (and even likely) that the FSO  106  being offered will not sell quickly or even at all, or sell within the specifications  154  provided by the seller  104  (such as selling within a specified period of time). Accordingly, the site includes a smart nudge engine  140  to help the seller  104 , over time, intelligently lower the offer price  404  of the FSO  106  being offered. In other words, the smart nudge engine  140  operates to, over time, “nudge” the seller  104  to offer prices  404  that are within zones of possible agreement (ZOPA)  508 , 
     As illustrated in the example of  FIG.  5   , a ZOPA  508  is a price range where the seller  104  and buyers  108  may agree on a price for the FSO  106  being offered. The smart nudge engine  140  may generate ZOPA.  508  for a given seller  104  and category  152  of FSO  106  based on the seller  104 &#39;s flexibility curve  302  and/or the applicable category decay curve  202 . Specifically, as discussed above, the category decay curve  202  indicates the percentage discounts below the original offer price that sellers  104  and buyers  108  were able to reach agreement at various listing ages  210  for a given category  152 . The seller flexibility curve  302  indicates, for a given seller  104 , the percentage discounts below the original offer price the seller  104  was willing to make at various listing ages  210 . The smart nudge engine  140  may use these curves  202 ,  304  (either individually or in combination) to generate ZOPAs  508  for the seller  104  and the category  152  of the FSO  106  being offered at different listing ages  210 . 
     For example, the example category decay curve  202  in  FIG.  2    indicates that, for the category  152  of the FSO  106  being offered, sellers  104  and buyers  108  were able to reach agreement at 80% of the original offer price at listing age  210 =15 days. Also, the example seller flexibility curve  302  in  FIG.  3    indicates that, at listing age  210 =15 days, the seller  104  associated with the FSO  106  being offered was often willing to drop the offer price to about 70% of the original offer price. Accordingly, in this example, the smart nudge engine  140  may generate a ZOPA  508 B at listing age  210 =15 that ranges from the optimal price  506  to somewhere between 70% to 80% of the seller  104 &#39;s original price  504 . In an embodiment, the average percentage discount corresponding to the same listing age, on the category decay curve  202  and the seller flexibility curve  302 , may be used. In this case, the average would be 75%. 
     In an embodiment, the upper range of the ZOPA may be set based on a predicted range of the price estimation model, which will be described below according to three overall paradigms. Any of these three paradigms, or any combination of these paradigms, may be used to generate an upper range of a ZOPA to be used. 
     Alternatively, the upper range of the ZOPA may also be set based on the average price of listings  116  for a particular category  152  wherein such an average price of listings  116  may be adjusted over time according to an average price drop curve, such as the curve  202  described above. In a further embodiment, such average prices and price drop curves may be generated for each brand and category pair, if there are more than a predetermined number of past listings  116  providing enough data in such a respective brand and category. For example, if there were more than a predetermined number of past sales of a particular make of a product (a bag of brand A), then the average price of all listings  116  of bags of brand A may be used as a starting upper bound for the ZOPA, which may be adjusted per the average price drop curve to reach an optimal offer price  506 . 
     ZOPAs  508  may be stored in a ZOPA database  132  in the site  110 . (It is noted that the ZOPAs  508  in  FIG.  5    are provided for illustrative purposes only, and are not necessarily drawn to scale.) 
     For the first example described above, as listing age  210 = 15  approaches, the smart nudge engine  140  may encourage the seller  104  (via emails or texts, for example) to reduce the offer price  404  to be within the ZOPA  508 B that is applicable at this listing age  210 . Or, the seller  104  may instruct the smart nudge engine  140  to automatically adjust the offer price  404  so that it is within the ZOPAs  508  applicable to respective listing ages  210 . 
     As noted above, the listing module  134  may automatically create multiple listings  112  for the FSO  106  being offered. In some embodiments, the listing module  134  will have different offer prices  404  for these listings  112 . For example, the offer price  404  for one of the listings  112  may be the seller  104 &#39;s original price  504 , and another may be closer to the optimal price  506  (but at least equal to the minimum price that the seller  104  provided as part of the specifications  154 ). The smart nudge engine  140  may adjust these offer prices  404  into the applicable ZOPAs  508  (to the extent they do not already fall in the ZOPAs  508 ) as the listing age  210  of the listing  112  progresses over time. 
     Furthermore, as will be explained below, in an embodiment, monitoring of the optimal price  506  occurs periodically as the age of the listing increases. As a result, the optimal price  506  itself may change over time. For example, as opposed to the constant Optimal Price axis  506  shown in  FIG.  5   , the optimal price for ZOPA  508 B and  508 C may each be different from the optimal price at ZOPA  508 A. Factors associated with artificial intelligence evaluation of the optimal price may be evaluated over time, or alternatively, a statistical approach may be taken, and the value of the optimal price  506  may shift over time as the age of the listing  210  increases. Both approaches will be described below, with regards to the flowchart of  FIG.  6   . 
     With the embodiment of the shifted value of the optimal price  506 , in a further embodiment, an aggressive approach may be taken to help the seller reach the optimal price  506 . An example will be described with regard to the ZOPA  508 B. For example, in an embodiment, the optimal price upon reaching 15 days for the age of listing, at ZOPA  508 B, may be lower than the optimal price  506  at 5 days, in ZOPA  508 A. The optimal price may be periodically evaluated at a time period of a predetermined number of days (e.g., 5 days). In this case, from the time period of ZOPA  508 B (15 days after the listing) until the next evaluation of optimal price  506  (20 days after the listing using the above example of a time period of 5 days), the smart nudge engine  140  may break the entire ZOPA  508 B into 4 intervals (P 1 -P 2 , P 2 -P 3 , P 3 -P 4 , and P 4 -P 5 ), and 5 values, P 1 -P 5 , as shown in  FIG.  5   . 
     The first value P 1  may correspond to the already-calculated interpolation between the demand curve  202  and the seller flexibility curve  302 , which in the above example for ZOPA  508 B is around 75%. The last value P 5  would be the adjusted optimal price  506  for 15 days, which as described, may be different for the optimal price at 5 days. Accordingly, the second, third, and fourth values (P 2 -P 4 ) would be calculated between the first and last value at one-fourth, one-half, and three-fourths of the interval from the first value to the last value (intervals not drawn to scale in  FIG.  5   ). 
     Thus, in the 5 day interval from 15 days of the listing, until the next optimal price evaluation  506  at 20 days, these values of P 2 , P 3 , P 4 , and P 5  may be suggested on days 16, 17, 18, and 19, to the user, respectively. In this manner, an approach may be taken where the user is encouraged to lower the offer price, wherein the optimal price  506 , which would be most beneficial for the user in terms of both time and profit, is also suggested. 
     However, in some embodiments, the intervals suggested to the user may be altered. At each optimal price  506  evaluation period (e.g. 15 days, 20 days, 25 days, etc.), the seller&#39;s original price may be subtracted by the seller&#39;s flexibility curve at the age of listing corresponding to each respective evaluation period (e.g. seller&#39;s original price minus seller&#39;s flexibility curve value at 15 days for the evaluation period at ZOPA  508 B, seller&#39;s original price minus seller&#39;s flexibility curve value at 20 days for the next evaluation period, etc.) 
     If this figure subtracted by the newly evaluated optimal price  506 , at each evaluation period, exceeds a certain threshold, this may indicate that the seller&#39;s expectations are very far from optimal, and thus the seller may refuse if a large change in price is suggested. As a. result, in such a case, P 2  may be suggested at days 16 and 17, and P 3  may be suggested at days 18 and 19. In this manner, the smart nudge engine  140  may be geared to only suggest changes that are palatable to a seller given their individual flexibility. 
       FIG.  6    is a flowchart for a method  602  for using computer technology to provide pricing guidance, and to intelligently create and process listings  112  for selling FSOs  106 , according to some embodiments. Method  602  can be performed by processing logic that can comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executing on a processing device), or a combination thereof. It is to be appreciated that not all steps may be needed to perform the disclosure provided herein. Further, some of the steps may be performed simultaneously, or in a different order than shown in  FIG.  6   , as will be understood by a person of ordinary skill in the art. Method  602  shall be described with reference to  FIGS.  1 - 5   . However, method  602  is not limited to those example embodiments. 
     In  604 , the seller  104  provides information to the site  110  regarding a FSO  106  (referred to as the FSO  106  being offered for reference purposes). The information may include, for the FSO  106  being offered: the title, category of product, brand, make, model, condition, color, size, pictures, description, etc. At step  604 , the seller  104  may also provide specifications  154  pertaining to the sale, such as an initial offer price (corresponding to field  1004  on GUI  1000  of  FIG.  10   ), a minimum sale price, a desired sell window (that is, a date by which time the seller  104  wishes the FSO  106  to sell, corresponding to field  1016  on GUI  1000 ), etc. 
     In  606 , the seller  104  also elects an automatic listing option, to instruct the site  110  to automatically create listing(s)  112  for the FSO  106  being offered. The seller  104  may indicate that the FSO  106  is being sold for charity (e.g., by choosing a charity from a drop down box  1022  in one embodiment, as shown in GUI  1000  of  FIG.  10   ), and identify one or more charities to receive the proceeds of the sale. The website  110  in the charity  136  module may have stored in this module the collection drive dates of the respective charities shown in the drop-down box  1022 . Thus, for example, if the user has selected a charity which may have a collection drive date that is soon forthcoming, within a predetermined threshold number of days (e.g., within 50 days), then the pricing module  138  may automatically pre-select the checkbox  1014  to indicate a sell-by window to be checked. 
     In addition, the pricing module  138  may fill in the editable field  1016  with the number of days until the next collection drive date (50 days in the example above) along with an explanation of why “(before the next charity collection drive),” so that the seller  104  upon examining the GUI  1000  may intuitively determine why the checkbox  1014  is already checked. In this manner, the user can be aided in trying to sell in an efficient manner so that their FSO  106  can be of benefit to the charity as soon as possible. Such information as collective drive dates of listed charities in drop down list  1022  may be obtained from external databases, the internet, in an autonomous manner, or may be manually input by a seller  104  on GUI  1000 , etc. 
     In  608 , the listing module  134  determines a product category  152  (such as the stocking keeping unit, or SKU) of the FSO  106  being offered. The listing module  134  may do this even if the seller  104  provided the category in  604  (since the seller  104  may not have provided the correct category). The product category  152  may be analyzed on the basis of past listings  116 , including when the product was sold as part of price history  124 , disclosed above, to determine if sales of the product might be seasonal or perishable. 
     For example, a majority of heavy jackets may be sold in the winter months (in-season), and a minority of these jackets may be sold in the summer months (off-season). In the case of an item in such a category  152  being offered for sale in-season, to maximize profit it should be sold before the in-season period is over. As a result, e.g., if more than a threshold percentage of past listings  116  of a category  152  were sold within a particular season or time, then a seasonality Boolean variable associated with a listing  112  may be marked as true. 
     In such a case, an end of such a season or time, based on the date past listings  116  in the same category  152  were sold, may be tabulated. This date may then be filled in field  1016  of GUI  1000 , and the checkbox  1014  may be checked to indicate a sell-by condition. For example, in the above example of heavy jackets, the number of days until spring may be written in the editable field  1016  along with an explanation “(before the season is over)” so it is intuitive to a seller  104  why a sell-by condition has been pre-filled out. 
     A further example may be a food item, or an item with a limited shelf-life. In the case of these items, sales may not be seasonal, but instead when examining the price history  124  of past listings  116  in the same category  152 , the number of days from the initial offer price until the sold date may be analyzed. If the number of days in this calculation is less than that for other categories  152  on average by a certain threshold, then a determination of a perishable or limited-shelf-life item might be made. Additionally, such properties may also be pre-input e.g., by having a perishability Boolean variable associated with a listing  112  marked as true, where such a Boolean variable might be true for the entire category  152  (non-vegetarian food, etc.) 
     In such a case, the number of days from the initial offer price until the sold date for past listings  116  in the same category  152  may be calculated on average, and a sell-by condition using such a calculated figure may be pre-filled out in GUI  1000 . For example, the editable text field  1016  may contain this calculated figure, along with the explanation “(before the product&#39;s shelf-life is over)” with the box  1014  checked. In this manner, it is intuitive to the seller  104  that the box is checked for a sell-by condition to account for the perishable nature of the item. 
     The listing module  134  may determine the category  152  of the FSO  106  being offered by analyzing the information provided in  604 . For example, the listing module  134  may analyze the pictures using image recognition techniques, algorithms, methods, modules, components, software and/or other technology to identify the category  152  of the FSO  106  being offered. Example embodiments for automatically identifying the category  152  of FSOs  106  are provided in U.S. patent application Ser. No. 16/288,379 titled “Probabilistic Item Matching And Searching,” filed Feb. 28, 2019, which is herein incorporated by reference in its entirety, and any of those embodiments may be used herein for automatically determining the category  152  of FSOs  106 . 
     In  610 , the pricing module  138  generates an optimal offer price  506  for the FSO  106  being offered. As discussed above, to generate the optimal price  506 , the pricing module  138  may take into consideration the seller  104 &#39;s specifications  154 , the category decay curve  202  associated with the category  152  of the FSO  106  being offered, the seller  104 &#39;s flexibility curve  302 , and/or past listings  116  for FSOs  106  having the same or similar category  152  of the FSO  106  being offered. The pricing module  138  may analyze the listing information of the FSO including any of the title  402 , description  406 , category  408 , brand  412 , condition  414 , pictures  410 , etc., using artificial intelligence techniques, algorithms, methods, modules, components, software and/or other technology to identify an optimal offer price  506  of the FSO  106  being offered (The listing module  134  may suggest the optimal offer price  506  to the seller  104  to be the offer price  404  for at least one of the listings that are generated in step  612 , which is described below.) 
     An example of such processing for step  610  is shown in a flowchart  702  of  FIG.  7   , with respect to a sell-by condition specified by a seller (e.g., through GUT  1000  as described above). In  704 , the pricing module  138  identifies past listings  116  from the historical database  114  of FSOs  106  having the same or similar SKU, category  152 , brand, condition, or any of the listing information parameters in  FIG.  4   , or any of the tabulated parameters as will be described below as the FSO  106  being offered. 
     In  706 , transactional information  118  from those identified past listings  116  is accessed and retrieved. 
     In  708 , the pricing module  138  uses (1) the retrieved transactional information  118 , (2) the category decay curve  202  associated with the category  152  of the FSO  106  being offered; (3) the seller flexibility curve  302  associated with the seller  104 ; and/or (4) artificial intelligence techniques, to generate the optimal offer price  506  and associated price range. 
     These will be described herein with regard to three example overarching paradigms. Firstly, past listings with the same SKU as the FSO  106  may be identified. If there are a substantial number of such past listings  116  present, these may capture trends in a focused dataset, even within a category. For example, in the heavy jackets category, a vest may have different sale characteristics than a normal heavy jacket. Because such vests may have a common SKU, any such trends may be captured in analyzing their transaction information  118 , collectively. 
     For these past listings  116  with the same SKU, if box  1014  is checked in the GUI, these listings may optionally first be narrowed down based on the field  1016  inputted from the GUI  1000 . For example, if the field  1016  has a value of 0 days, this may indicate that the user would like to sell the product immediately. In this case, past listings  116  with the same SKU as the FSO  106  may be excluded, except for those listings which have a sold date that is proximate to the initial offer price date (within a predetermined number of days, e.g. 5 days). In this manner the dataset is limited to those samples which have a desired time window corresponding to a high degree to a seller  104 &#39;s request per field  1016 . In another example, if the field  1016  has a value of 20 days, this indicated that the user would like to sell the product within 20 days. Accordingly, past listings  116  with the same SKU as the FSO  106  may be excluded, except for those listings which have a sold date that is a predetermined number of days (e.g., 3 days) before or after 20 days (i.e., the sold date is 17 days-23 days after the initial offer price). 
     Alternatively, for the example with a sell-by condition per field  1016  of 20 days, instead of excluding listings as is done above, the previous listings  116  with the same SKU as the FSO  106  may be considered to generate an initial optimal price. Then, this initial optimal price may be altered via the previously generated demand curve  202  or seller flexibility curve  302  to generate an adjusted optimal offer price taking into account a seller  104 &#39;s sell-by condition. In an embodiment, the generated demand curve  202  may be altered to only take into account previous listings  116  in the same category  152  that have the same SKU as the FSO  106 . 
     Once the past listings  116  with the same SKU (including any exclusions described above) are accounted for, median and inter-quartile range analysis may be run on such data (e.g., the sold price of such past listings  116 ) to capture the middle 50% of sales in terms of sold price. Then, the median price value of these values may be reported as the optimal price, with the inter-quartile range as a price range. Alternatively, especially in the case of a skewed distribution, the mean sale price of the listings in the first quartile to third quartile (the middle 50% of sales) may be averaged, and this averaged value may be reported as the optimal offer price, along with the inter-quartile range as a price range. For example, as explained above, the retrieved transactional information  118  may indicate that the FSOs  106  with the same SKU sold for an average price of $10 in the first quartile to the third quartile (the middle 50% of sales). The pricing module  138  may thereby suggest an offer price  404  of $10, along with the respective inter-quartile range. In this case, the interquartile range serves as a useful measure of variability. 
     In the alternate embodiment above where the initial optimal offer price is adjusted per the generated demand curve  202  or seller flexibility curve  302  taking into account a seller  104 &#39;s sell-by condition, the further step of adjusting the median/mean price value occurs. In the above example where the seller  104 &#39;s sell-by condition is 20 days, the percentage of original price is tabulated at the 20-day-mark using the demand curve  202  and the seller flexibility curve  302 . 
     If the price percentage value of the seller flexibility curve is lower than the demand curve at 20 days, then the demand curve value may be used (since the seller will not have to go lower in price than the demand curve based). For example, if the demand curve  202  indicates that similar sales of previous listings in the category  152  (and possibly with the same SKU as per the embodiment of the demand curve  202  described above) settled at 70% in 20 days, and the seller flexibility curve indicated that the seller was okay with going as low as 60%, then the demand curve value may be used since the seller may not have to go as low as 60%. 
     In this case, the value of 70% of the initial optimal price (calculated as the median/mean above) may be output as the adjusted optimal offer price, with the same inter-quartile range given, but shifted also by 30% of the initial optimal price. That is if the inter-quartile range was initially ($8,$12), with the initial optimal price at $10, and the initial optimal price is adjusted to $10−$3=$7, then similarly the interquartile range is shifted to ($8−$3,$12−$3), which is ($5,$9). 
     However, if the seller flexibility curve is higher than the demand curve for the sell-by date (e.g. 20 days), this means that the seller is not quite as flexible as market demand. requires for settling by the sell-by date as input in  1016 , with respect to previously analyzed listings  116 . As a result, in such a case, the values of the seller flexibility curve and demand curve may be averaged. 
     This generates a “meeting in the middle” approach that bridges seller rigidity with the realities of having to sell at a lower percentage of original price in order to ensure that the FSO  106  is sold by a certain date as desired. In this manner, chances are higher that the seller may agree to such a change, rather than shifting the price all the way to what the demand curve requires (e.g., in the case above if the seller curve  302  at 20 days corresponds to 80% of the initial optimal price, and the demand curve  202  corresponds to 70%, then the average of 75% is easier for the seller to be okay with than the requirement of 70% per the demand curve). 
     As with the example above, the initial optimal price may be adjusted by such an averaged amount percentage to be output as the adjusted optimal offer price (e.g., 75%), and the inter-quartile range is similarly shifted by the same amount as described above. 
     In an embodiment, in addition to the calculating the median/mean (denoted as med_a) and interquartile range (denoted as min_a, max_a, corresponding to the first and third quartile values), a confidence threshold (denoted as ct_a) for the median or mean and such interquartile range may also be calculated. Such a confidence threshold may depend on the number of past listings  116  that are found to have the same SKU as the FS)  106 , as well as the time window to get a minimum number of items. In this manner, more importance may be placed on gathering more recent data, wherein if more recent data is gathered, this generates more confidence that such a figure may be replicable in the present listing to be created. Intuitively, this makes sense, as sales data from a long time ago may not hold true in the present day due to changes in the marketplace. 
     The second overarching paradigm is similar to the first overarching paradigm, but instead of only analyzing past listings  116  with the same SKU, similar past listings  116  of the same category  152 , brand  412 , and/or condition  414  are analyzed. A sell-by condition may be analyzed in the same manner as with the first overarching paradigm. Using the second overarching paradigm thus expands the scope of past listings  116  that may be analyzed in determining the initial optimal offer price. In cases where there may not be many past listings  116  with the same SKU but there are substantial listings that are similar in terms of condition, brand, or category  152 , such a paradigm would be useful. 
     In the same manner as with the first overarching paradigm, once the previous listings  116  have been identified, median and inter-quartile analysis is run, and depending on the previous listings  116  being excluded or not, a generated demand curve  202  and seller flexibility curve  302  may be used to adjust an initial optimal price to accommodate a sell-by condition (e.g, 20 days as described above). 
     This process, as with the first overarching paradigm generates a suggested optimal price of a median or mean (or adjusted median/mean) (denoted as med_b), and an interquartile range (denoted as min_b, max_b, corresponding to the first and third quartiles. Finally, as with the first overarching paradigm, a confidence threshold (denoted as ct_b) may be generated which may depend on the number of similar previous listings  116  found, the degree of similarity of those items identified, and the time window to get a minimum number of similar items. 
     The third overarching paradigm for the generation of an optimal offer price is herein described. The sequential operation of the third paradigm is shown in an example embodiment  1200  in  FIG.  12   . Operation of this sequence will first be described with respect to a neural network type of regression model, but it is understood that analogous steps would be applied to both the random forest regression model and the boosted decision tree regression model. 
     First, as shown in the flow diagram  1200 , text data  1202  is taken as input from a seller  104 , comprising any combination of a listing description  405 , a listing title  402 , a listing category name  408 , and a listing brand name  412 . All of these inputs are considered to be textual input. 
     In order to convert the input from textual input in  1202  to a usable form by any of the regression models present in block  1212 , the textual input in  1202  must be tokenized. In order to tokenize the textual input in this manner, algorithms such as term frequency-inverse document frequency (Tf-idf), Bag-of-words, and word2vec may be used, as shown in block  1208  where the arrow from  1202  to  1208  depicts the step where textual input is fed as input to such an algorithm to result in tokenized output. That is, by applying such algorithms to the textual input data  1202 , such data input results in numerical output, which may be useful to a regression model. In its original text form, such inputs are unusable by the regression models. 
     In an embodiment a neural network model with hidden layers and backpropagation as shown in  FIG.  13 A  may be used in the tokenization step to convert textual input to tokenized input. The inputs in this case would be the text keywords in the form of the description  405 , listing title  402 , listing category name  408 , and listing brand name  412 , where each of these inputs may have its own designated model. By using such a classification technique, it may be possible to create a system of nodes with weights. This system of nodes with weights may be used to give a reliable prediction of a numerical score for the user&#39;s input. 
     The different components of the neural network model shown in  FIG.  5 B  will herein be explained, according to some embodiments. The input layer  1302   a  contains nodes  1  to which represent inputs into the model. Each of these nodes corresponds to a different aspect of the string entered. In particular, a string value, as inputted in  405 ,  402 , etc., is first tokenized into words, and the tokenized words are stemmed. 
     Training data may be used (from previous descriptions  405 , listing titles  402 , etc.), where full sentences of the string value may be transformed. Such a transformation may tokenize each word, create word stems, etc. After enough training data is used, there may be a collective library of word stems, some of which are associated with only one type of brand, product titles of a certain category, listing titles of a certain category, or descriptions of a certain category. 
     For example, if a description is related to a brand of particular designer handbag, it may contain unique word stems that are only present in such a category. Thus the weightage of words may be different depending on their multiplicity, appearance across multiple categories, etc., such that when an input string in  402 ,  405 ,  408 , or  412  is parsed apart, one input node may correspond to each word of such a string. Then, these nodes can be compared to the library of word stems associated with past descriptions  405 , listing titles  402 , listing category names  408 , or listing brand names  412 . 
     For example, if Ugg Boots is a company headquartered in Australia, the stem ‘Aus’ may be in the library of word stems array associated with the category  408  of shoes or boots, Thus if the user enters “Wearable contraption from Australia” as the input string in description  406 , node  1  of the input layer  1302   a  for the neural network model for processing descriptions  406  may represent the word stem “Wear”, node  2  may represent “contraption”, node  3  may represent “from,” and node  4  may represent the word stem “Aus.” These nodes may then be compared to the word stems from the training library (called “bag of words”), wherein nodes  1  through  3  maybe assigned the value 0 if they do not match up with any word stems in the bag of words, and node  4  may be assigned the value 1 if it does match up with a word stem in the bag of words (in this example it matches ‘Aus’ from above for previous listings of Ugg Boots). 
     In practical terms, the input is parsed through and correlated with a series of 0&#39;s and 1&#39;s where 1&#39;s correspond to words that are in the bag of words. The bag of words may be assembled by parsing through word stems from previous listings  112  for the titles  402 , descriptions  406 , categories  408 , and brands  412 . In an embodiment, the bag of words may be for previous listings in categories  408  that are deemed similar. In the above example, the bag of words may take into account all previous listings  112  in the categories of footwear (shoes, boots, athletic cleats, etc.) for forming a bag of words. Alternatively, a bag of words may be created for previous listings in all categories  408 . 
     Through repeated rounds of the neural network being trained with training data, each stem may have a different weight w ij  associated with the stem going to the next layer, and eventually to the output layer  1306   a . This is because some words in the bag of words may have a stronger association with particular descriptions, titles, categories, or brands based on the type of product offered, and thus may be more important than others. Importance may be gauged by several metrics in this regard as well. A stronger association for bag-of-words may be gauged based on the multiplicity and appearance of a word stem over multiple past titles, descriptions, categories, or brands for a particular type of product. 
     On the other hand, the tf-idf algorithm may be used in a situation where highly frequent words may start to dominate in the document (e.g. larger score) but do not contain much “informational content” to the model as rarer but domain specific words. In this case words such as “the” or “from” which may appear frequently are penalized, whereas word stems like “Aus” which may provide greater informational content because they signal a headquarter location of a company like Ugg Boots. 
     The metrics used are term frequency, which is a scoring of the frequency of the word in a document, and inverse document frequency, which is a scoring of how rare the word is across documents, wherein the score is a weighting. For example, the inverse document frequency of a frequent term such as “the” is likely to be low, whereas the inverse document frequency of a rare term is high. As a result, the w ij  associated with the stem going to the next layer of the neural network model  1300   a  for terms that have a low inverse document frequency across multiple descriptions  406 , titles  402 , categories  408 , or brands  412 , may be weighted more highly if the tf-idf weighting algorithm is used. The word2vec algorithm of block  1208 , on the other hand, represents word stems as vectors, and will not be discussed further herein. 
     Output layer  1306   a  of the neural network model  1300   a  may include only one node  1 , containing a normalized score from 1-10. A score of 10 may indicate a high correspondence with a particular type of product and therefore correspond to a higher degree of searchability or ease-of-finding 
     Based on the inputs and weights from each node to the other (w ij  as shown in  FIG.  5 B ), the results of the output layer are tabulated, and the normalized score is output to the output node  1  in the output layer  1306   a . In this case. In using the tf-idf algorithm since ‘Aus’ may have a particular association with Ugg Boots (if Ugg Boots is headquartered there as in the hypothetical example described above), the weights from the input layer node  4  to the hidden layer  1304   a  may carry more weight than front the input layer nodes  1 - 3  to the hidden layer  1304   a , as discussed above. This means, Input layer  4  may contribute in a greater manner to the normalized score in the output layer  1306   a . If there are multiple nodes such as node  4 , then this may contribute to a greater normalized score, wherein the normalization process occurs in comparison to previous titles  402 , descriptions  406 , categories  408 , or brands  412  from previous listings  116 . As described above, a separate scoring model may be used for the title  402 , category  408 , brand  412 , and description  406 . 
     In traversing from the input layer  1302   a  to the output layer  1306   a , as shown in  FIG.  13 A  there may also be several hidden layers  1304   a  present. The number of hidden layers  1304   a  may be preset at one or may be a plurality of layers. If the number of hidden layers  1304   a  is one (such as shown in  FIG.  13 A ), the number of neurons in the hidden layer may be calculated as the mean of the number of neurons in the input and output layers. 
     This is derived from an empirically-based rule of thumb in ease of calculating weights across layers. According to an additional rule of thumb. In an embodiment to prevent over-fitting, where the number of neurons in input layer  1302   a  is N i  and the number of neurons in the output layer is N o , and the number of samples in the training data set of all word stems associated with the model (for either titles  402 , descriptions  406 , categories  408 , or brands  412 ) is N s , then the number of neurons N h  in one hidden layer may be kept below 
                       N   h     =       N   s       (     α   *     (       N   i     +     N   o       )       )         ,           (     equation   ⁢         1     )               
where α is a scaling factor (typically ranging from 2-10). In this manner, the number of free parameters in the model may be limited to a small portion of the degrees of freedom in the training data. In order to prevent overfitting.
 
     From the input layer, based on the weights from each node in the input layer  1302   a  to the hidden layer  1304   a  shown in  FIG.  13 A , there may be a sigmoidal transfer function in going from the input layer  1302   a  to the hidden layer  1304   a . Initially, the weights w ij  may be initialized to random values between 0 and 1. An input node word-stem that corresponds to a word stem in the bag of words or tf-idf algorithms may then be propagated according to these weights (forward-propagation), wherein the hidden layer  1304   a  forms the first outputs for the neurons of the input layer  1302   a . For example, Inputs given as neuron  1  and  2  in the input layer  1302   a  in the example above would be multiplied respectively by 0 in the bag of words algorithm because they do not correspond to a word stem in the hag of words, whereas nodes  3  and  4  may be multiplied by 1 because the word “from” and “Aus” do correspond to word stems in the bag of words. 
     In this case, node  3  of the input layer  1302   a  may be multiplied by weights w 31  and w 32 , etc., until w 3j  because it does correspond to a word stem in the hag of words, and node  4  of input layer  1302   a  is multiplied by weights w 41  and w 42 , etc., until w 4j , respectively. In the same manner. With respect to the bag of words algorithm, as discussed above, because the word “from” in node  3  may appear more frequently than the word “Aus” in descriptions  406 , titles  402 , brands  412 , or categories  408  of previous listings  116 , the weights of node  3  may be higher than that of node  4 , since the bag of words algorithms gauges importance based on multiplicity. By contrast, with respect to the tf-idf algorithm, the weights of node  4  of the input layer  1302   a , may be higher than those of node  3 , because the inverse document frequency (rarity) of the word “Aus” may be higher across descriptions  406 , titles  402 , brands  412 , or categories  408  than that of the word “from” in node  3 . 
     Subsequently, these nodes in input layer  1302   a  would be summed to form the output to the hidden layer  1304   a  (e.g. node  1  in the hidden layer in the example above would be the sum of w 11, +w 21 +w 31 +w 41 =w 41  since only node  4  has a word stem in the hag of words). Then the node  1  at the hidden layer  1304   a  may take this net value and transfer this value further. In the same manner, to the output layer  1306   a . At each output layer (hidden layer  1304   a  with respect to input layer  1302   a , and output layer  1306   a  with respect to hidden layer  1304   a ) transfer functions comprising the sigmoid activation function 
                 S   ⁡   (   x   )     =     1     1   +     e     -   x             ,         
hyperbolic tangent function
 
               tanhx   =         e     2   ⁢   x       -   1         e     2   ⁢   x       +   1         ,         
or smooth rectified linear unit (SmoothReLU) function f(x)=log(1+e x ) may be used to transfer outputs.
 
     In the example above, the output given from the input layer  1302   a  to neuron  1  of the hidden layer  1304   a  would be inputted as the activation value to be transferred at the hidden layer  1304   a  to one of the transfer functions described above, and the output would form the value of neuron  1  of the hidden layer  1304   a  to be given onward as input to the output layer  1306   a , and multiplied by respective weights to the neuron  1  of the output layer. In this manner, full forward propagation of input nodes  1  through I in the input layer  1302   a  may be achieved to the output layer  1306   a.    
     Then, to conduct backpropagation, error is calculated between the expected outputs and the outputs forward propagated from the network. In training the neural network, k-fold cross validation, may be used, particularly when the data sets are small. For k-fold cross-validation, for example, there could be an aggregated set of sentence descriptions all input by the user that are known to be associated with a particular product category with respect to different associated word stems for each group, comprising all the components described above. This set of sentence descriptions may be shuffled and split into a k number of groups (e.g., 5 groups if k is 5, each holding a particular number of results (normalized score measures of importance) and corresponding associated word stems). Then, for each unique group (of the 5 groups in the example above), the group can be held out as a test data set, with the remaining groups of aggregated sentence descriptions and corresponding normalized score measures of importance being used to train the classifier. 
     To transfer the error, the error signal to propagate backwards through the network is given by error=(expected normalized score−output score)*transfer_derivative(output score), wherein transfer_derivative is the derivative of the transfer function used (sigmoid, hyperbolic, or SmoothReLU). 
     The error signal for a neuron in the hidden layer  1304   a  is then calculated as the weighted error of each neuron in the output layer  1306   a , according to the weights from the output layer to the neuron in the hidden layer  1304   a . Similarly, the error signal from the hidden layer is then propagated back to the input layer  1302   a . Once the errors are calculated for each neuron in the network via the back propagation method described, the errors are used to update the weights according to the formula new_weight=old_weight+learning_rate*error*input. Here, the old weight variable is the previous given weight in the model, the learning_rate variable is a value from 0 to 1 that specifies how much to change the old weight to correct for the error (and consequently how fast the model “learns” or adjusts its weights over time), the error variable is the error calculated by the backpropagation procedure, and the input variable is the value of the input that caused the error. 
     After or as the text data is being tokenized and scored as described above, categorical data  1204  may also be parsed and encoded. Categorical data in  1204  may be present in the form of brand ID, category ID, shipping fee payer (whether seller  104  is paying), and condition (e.g., fair, used, like new, or new). This information may be calculated in step  608  in the process of  FIG.  6   . That is, based on the user&#39;s input of brand  412  or category  408 , an actual brand or category, and their respective ID&#39;s may be determined. The ID&#39;s may be numerical in nature, wherein an ID of 1 corresponds to brand A or category A, an ID of 2 corresponds to brand B or category B, etc. These ID&#39;s, if sent as inputs to regression models used in  1212  as-is, may improperly skew the data. For example, if a Brand C was assigned an ID of 3, a Brand B was assigned an ID of 2, and a Brand A was assigned an ID of 1, if the ID itself was sent as an input to a regression model, the regression model may presuppose that the price value of Brand C may be greater than Brand B, which in turn may be greater than Brand A, because 3&gt;2&gt;1. To avoid such an interpretation by the model, one hot encoding may be used to perform binarization of the category. That is, the information may be arranged in a binary vector as shown below 
     
       
         
           
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Brand A 
                 Brand B 
                 Brand C 
               
               
                   
               
             
            
               
                 1 
                 0 
                 0 
               
               
                 0 
                 1 
                 0 
               
               
                 0 
                 0 
                 1 
               
               
                   
               
            
           
         
       
     
     As shown in Table 1, Brands A, B, and C can be represented by a binary vector of 0 and 1 values. As a result because the presence of either brand does not result in a numerical value which is greater than other brands, any potential skewing of the regression model is avoided. Such one hot encoding can be used for both the Brand ED and Category ID, which are sent as inputs to the regression model in block  1212 . 
     Additional categorical data  1204  may also be sent to such a regression model. For example, the shipping fee payer value and the condition may be sent to the regression model. The shipping fee payer value may simply be a Boolean value that is input from the user, which represents whether the seller  104  is responsible for shipping (wherein the Boolean value may be TRUE or 1) or whether the seller  104  is not responsible for shipping costs (wherein the Boolean value may be FALSE or 0). 
     Lastly, the condition  414  may be input by the user as one of, e.g., four categories. These categories may indicate the condition of an FSO  106 , such as “Poor”, “Fair”, “Good,” “Like New”, and “New.” In an embodiment, “Mint Condition” may used instead of “New.” As each of these verbal categories is commensurate with a different value of condition, with Used having the least value and Mint Condition or New having the most value, simple label encoding may be used, wherein each of these labels may be assigned a numerical value (e.g, “Poor” may be 0.1, “Fair” may be 0.2, “Good” may be 0.4, “Like New” may be 0.6, and “Mint Condition” or “New” may be 0.8). Any scale of predefined numbers (e.g., ranging from 0 to 1) may used in this manner to encode the condition labels of the FSO  106 . As a result, when this information is passed to the regression model, it can account for a plurality of prospective buyers likely willing to pay more for a product that is in a “Like New” condition as opposed to a “Poor” condition. Alternatively, the condition  414  may be pre-input by the seller  104  in a numerical form, as part of numerical data  1206  described below, such that the label encoding is not needed. 
     Numerical data  1206  may also be sent to a regression model in block  1212  that may be used. Numerical data  1206  may comprise data that is present as part of the listing information  126  and price history  124  of past listings  116 . In the same category  408  as FSO  106  or in all categories  408 . Such numerical data may comprise the year, month, and day that previous listings  116  were first offered for sale, as well as the year month and day that such previous listings were actually sold. 
     From this numerical data two key statistics may be calculated. First, the degree to which the data is recent may be ascertained from the year month and day that a previous listing  116  was sold. The previous listing may be assigned a value between 0 and 1 proportional to the distance between the year month and day that a previous listing  116  was sold, and the current listing of FSO  106  (including after it is listed, as the optimal price  506  may be evaluated after a seller  104  lists FSO  106 , as discussed with reference to  FIG.  5   ). 
     For example, if the previous listing  116  was sold very recently, the distance between the year month and day that a previous listing  116  was sold and the current listing of FSO  106  will be very small, resulting in a value close to 1. On the other hand, if the previous listing  116  was sold a long time back, the distance between the year month and day that a previous listing  116  was sold and the current listing of FSO  106  will be very large, resulting in a value close to 0. To calculate the proportional figure, the earliest previous listing  116  may be used, according to the formula: 
     
       
         
           
             
               
                 date 
                 ⁢ 
                     
                 of 
                 ⁢ 
                     
                 current 
                 ⁢ 
                     
                 listing 
               
               - 
               
                 date 
                 ⁢ 
                     
                 of 
                 ⁢ 
                     
                 previous 
                 ⁢ 
                     
                 listing 
                 ⁢ 
                     
                 116 
               
             
             
               
                 date 
                 ⁢ 
                     
                 of 
                 ⁢ 
                     
                 current 
                 ⁢ 
                     
                 listing 
               
               - 
               
                 date 
                 ⁢ 
                     
                 of 
                 ⁢ 
                     
                 earliest 
                 ⁢ 
                     
                 previous 
                 ⁢ 
                     
                 listing 
                 ⁢ 
                     
                 116 
               
             
           
         
       
     
     This formula may be used to give a value corresponding to the degree of recent-ness of the previous listing, which may be sent along with other data of each previous listing  116  to a regression model used in block  1212 . Second, from the numerical data in  1206 , the age of listing for the previous listing  116  when it was sold can be calculated simply by subtracting the date the previous listing  116  was sold and the date the previous listing  116  was initially offered. In this manner, these two statistics can be sent to a regression model used in block  1212 . 
     Along with the previously described items which may be sent to a regression model in block  1212 , further computed parameters, called tabulated parameters  1216 , may be gathered by running metrics on the previous listings  116 , on the basis of comparison to other previous listings  116  in the same category  408  as the FSO, or in all categories  408 . For example, using the numerical data  1206 , the scarcity of a particular item sold in previous listing  116  (e.g., corresponding to a particular category  408 ) may be determined. If there were not many items present in the time period which the previous listing  116  was sold (within a predetermined number of days, e.g. 7 days), then the scarcity value of the previous listing may be proportionately higher. Conversely, if there were many items present in the time period which the previous listing  116  was sold (within a predetermined number of days, e.g. 7 days), then the scarcity value of the previous listing may be proportionately lower. The basis of comparison may be the least scarce item of all previous listings  116 , e.g. in an embodiment according to the formula: 
     
       
         
           
             1 
             - 
             
               
                 
                   
                     
                       amount 
                       ⁢ 
                           
                       of 
                       ⁢ 
                           
                       items 
                       ⁢ 
                           
                       present 
                       ⁢ 
                           
                       in 
                       ⁢ 
                           
                       same 
                       ⁢ 
                           
                       category 
                       ⁢ 
                           
                       408 
                     
                   
                 
                 
                   
                     
                       
                         within 
                         ⁢ 
                             
                         predetermined 
                         ⁢ 
                             
                         days 
                       
                       , 
                       
                         for 
                         ⁢ 
                             
                         previous 
                         ⁢ 
                             
                         listing 
                         ⁢ 
                             
                         116 
                       
                     
                   
                 
               
               
                 
                   
                     
                       amount 
                       ⁢ 
                           
                       of 
                       ⁢ 
                           
                       items 
                       ⁢ 
                           
                       present 
                       ⁢ 
                           
                       in 
                       ⁢ 
                           
                       same 
                       ⁢ 
                           
                       category 
                       ⁢ 
                           
                       408 
                     
                   
                 
                 
                   
                     
                       
                         within 
                         ⁢ 
                             
                         predetermined 
                         ⁢ 
                           
                         days 
                       
                       , 
                       
                         for 
                         ⁢ 
                             
                         least 
                         ⁢ 
                             
                         scarce 
                         ⁢ 
                             
                         listing 
                         ⁢ 
                             
                         116 
                       
                     
                   
                 
               
             
           
         
       
     
     Furthermore, the parameter of a number of views of previous listing  116  may also be recorded by summing, e.g., listing information  126  including times that the listing  116  may have been accessed by prospective buyers  108 . 
     Regression models used in block  1212  with the previously sent inputs will now be discussed. Each of these regression models may output a predicted price. Based on such a predicted price, analysis of a price range may occur as it did in the first overarching paradigm and the second overarching paradigm. Here as well, an inter-quartile range of values may be calculated using previous listings  116  in the same category  152  of the product, or using all products. In an embodiment, this calculated interquartile range may be shifted so that the predicted price from the regression models may lie at the center of the range, wherein the range serves as a measure of variability as described above. Alternately, the price range may be calculated by a neural network model itself as will be described. 
     Firstly a neural network regression model may be discussed. The model is as shown in  FIG.  13 B , with input layer  1302   b , hidden layer  1304   b , and output layer  1306   b . The model operates principally in the same manner as disclosed above with respect to  FIG.  13 A , and may use the same transfer functions, etc. In an embodiment, the rule of thumb discussed with regard to  FIG.  13 A  for calculating the number of nodes in the hidden layer  1304   b  may also be used. 
     However, alternately. In another embodiment (the one which will be discussed herein), two nodes may be used for the hidden layer. As shown, there may be a single weight x i  for each of a predetermined number of nodes  1  to i (e.g.,  1 - 3  as shown in  FIG.  13 B ) to Node  1  of the hidden layer  1304   b . Similarly there may be a single weight y j  for each of a predetermined number of nodes i+1 to j (e.g.,  4 - 6  as shown in  FIG.  13 B ) to Node  2  of the hidden layer  1304   b . Finally, both of the hidden layer nodes in  1304   b  may combine, with weights p and q, respectively, at the output layer  1306   b , to generate an output score at Output Layer  1306   b.    
     The first node of the hidden layer  1304   b , Node  1 , represents price drivers, or ordinary factors which may influence the price of a listing. The second node of the hidden layer  1304   b , Node  2 , represents multiplying factors which may have an exponential influence on the price of a listing. The weights p and q of the hidden layer nodes, may reflect this arrangement by being weighted such that weight p of Node  1  of hidden layer  1304   b  to output layer  1306   b  may be less than the weight q of Node  2  of hidden layer  1304   b  to output layer  1306   b . When parameters are first assessed, the neural network model  1300   b  may be category ID dependent. That is, there may be a separate neural network model for each different category  152  of product. This embodiment is described herein. Alternately, if the product of items has pricing which more largely reflects general trends for all products, then an embodiment may be used wherein all product listings  116  in all categories  152  are considered. 
     Having a separate neural network model for each different category  152  of products may be particularly useful when there are trends present in the pricing of some categories of products, that are associated with features that are used in the machine learning model, that are not present in other categories. 
     For still other categories of products, such as luxury items, extra steps need to be taken to make sure the proper data of previous listings  116  is being analyzed. For example, the previous listings  116  in a category  152  may be associated with luxury goods, determined through the category ID as part of the categorical data  1204  described earlier. In this case, all previous listings  116  may be authenticated. If a certain number of previous listings  116  may be found to be fake and not authentic, then these listings can be excluded from the training of the regression model. 
     Within the category  152  specific neural network regression model, the nodes are now described. Input nodes  1  to i may include at least one of measures such as scarcity, number of views, scores for description  406 , title  402 , brand  412 , and category  408  after tokenization, which may be generated by another neural network model as described above. The measures above in input nodes  1  to i may also include the condition  414  of the product, converted through label encoding as described above, the SKU of the product, the seasonality or perishability (as a value of −1 or 1, e.g., 1 if not perishable/seasonable and −1 if perishable seasonable), the slider input from slider  1018  of GUI  1000  (as a value of −1 if positioned at the end of maximize time, 1.5 if positioned at the end of maximize profit, and 0 at the center), and the presence of a sell-by condition from fields  1014  and  1016  of GUI  1000  (wherein if there is a sell-by condition, then the input node value may be a negative value commensurate to the number of days, wherein a shorter number of days results in a greater negative value). As discussed above, when a charity option is selected, the field  1016  may automatically be filled out and the field  1014  may be checked, meaning that the presence of a sell-by condition in the neural network model may also take the selection of the charity option into account. 
     The values of the measures for input nodes present in  1  to i concerning seasonality or perishability, the slider input from slide  1018  of GUI  1000 , and the presence of a sell-by condition are herein explained. As explained above, nodes  1  to i affect ordinary pricing conditions, that is, the demand of the product. When a product is deemed to be perishable, this has a negative of effect on pricing or demand on the product. In that the seller  104  may want to sell such a product before it perishes or goes out-of-season, as explained above. 
     As a result, if such a condition is present, to adjust the overall demand lower (and hence the overall price result lower) a negative value is input at the input node. With a lower overall output from nodes  1  to i in the input layer  1302   b  to the hidden layer  1304   b , the eventual output value of Node  1  of  1304   b  to the output layer  1306   b  is consequently lowered. This in effect lowers the initial offer price, which is needed to ensure the sale of a product before it perishes or goes out-of-season. The value is not restricted to −1 in the case of a perishability/seasonality being present, and can be adjusted as needed to effect a greater/lesser decrease in price. 
     Similarly the slider input from the slider  1018  of GUI  1000  is present as a node ranging from −1 to greater than 1. In the case of maximization of profit, the seller  104  has in effect indicated that they are willing to wait even longer than normal for the product to be sold. In such a case, the demand may be increased or boosted, to a certain degree, by making the node value greater than 1, which as explained above has an effect on the price outputted by Node  1  of the Output Layer  1306   b  which serves to increase the price. In the case of maximization of time (the other end of the slider  1018  as shown in GUI  1000 ), the value may be −1 for the same reason as with the perishability/seasonality node as explained above. In order to sell the product at a faster rate than normal, the demand may be artificially lowered by a negative value, resulting in a slightly lower optimal price value at Node  1  of  1306   b  than normal, which would encourage buyers to buy such a product at a faster rate. 
     An alternate embodiment may be present in the case of a sell-by condition given a particular number of days from the field  1016  of GUI  1000 . As discussed above, for each previous listing  116 , there is a metric calculated indicating the number of days from when the listing was offered until when it was sold. In this case, a bagging or bootstrapping approach may be followed on the basis of such a metric. That is, previous listings that have a value for this metric that is greater than the number of days from field  1016  of GUI  1000  may be excluded from the training of the neural network model in  FIG.  13 B , and in conducting backpropagation for the adjustment of weights x i , y j , p, and q. 
     By doing so, previous listings  116  that were sold after the number of days which the user is planning to sell by are excluded from training the model. In this way the model can capture the trends associated with such a subset of previous listings  116  (e.g., lower demand and lower prices in order to sell faster). In this embodiment, a window of days may also be used. 
     For example, a seller  104  may indicate in field  1016  that they want to sell-by 0 days, which means they would want to sell immediately. In this case, a bootstrapping approach where a window of 5 days is used may allow for all previous listings  116  that were sold proximate to the immediate initial offering day can be considered. Instead of only listings  116  from day 0. The use of such a window can allow for the consideration of a greater amount of data where there are few data points or previous listings  116  available. 
     In this manner, as described above, the artificial neural network of  FIG.  13 B  allows for deep learning of multiple layers and multiple neurons (nodes) to “learn” about the interactive effects of these variables on listing prices. As discussed above with respect to the neuron network model in  FIG.  13 A , the weights of the nodes at each layer can be adjusted by the comparison of the price output by node  1  of the output layer  1306   b  with designated actual optimal price values for the previous listings  116  in the subset that is used to train the model. In this manner, weights may be adjusted in each training iteration via back-propagation to reduce errors. 
     The nodes i+1 to j of the input layer  1302   b  (e.g., nodes  4  to  6  in input layer  1302   b  of  FIG.  13 B ) will herein be described. As explained above, these input nodes have an effect which may be exponential on price, and as such output to Node  2  of the Hidden Layer  1304   b , which accounts for scaling factors. Firstly a measure of picture-worthiness might be used as one of these nodes i+1 to j. Picture worthiness may be the result of an output of the use of a neural network, such as that shown in  FIG.  13 A . Each picture that is used for a listing  112  may have input data which is used at input nodes of an input layer  1302   a.    
     For example, there may be a node  1  which denotes if the brightness level of the picture exceeds a certain threshold (which may be a Boolean value of 1 or 0, 1 if yes 0 if no). Similarly there may be a node  2  which denotes if a certain amount of threshold distance is present from the camera to the object (which may be a Boolean value as with brightness above). 
     There may also be a node  3  denoting if the effect of a shadow is pronounced (which may be a Boolean value of 1 or 0, 0 if yes 1 if no). Any number of additional inputs at the input layer  1302   a  may be present, and such a neural network may follow the rules of thumb discussed above with regard to  FIG.  13 A . The output of such a neural network of the type shown in  FIG.  13 A  may be a single node  1  at the output layer  1306 , which depicts a score of the value of the picture. If the score is higher, then that means the picture may be more valuable in attracting attention to the listing  112 , and ultimately boosting price. 
     As described, each picture that is used for a listing  112  (both for previous listings  116 , used as training data, and the current listing  112  for the FSO  106 , which would be used as test data) may thus have input data values at nodes  1  to i of input layer  1302   a  that are used for determining a picture worthiness score. In an embodiment, the picture with the maximum score may be automatically displayed on the GUI  1000  as the default listing picture  1002 . Next, the scores for all of the pictures may be averaged, and normalized, on a scale of 1-10 indicating picture worthiness. Any other scale may also be used (such as 0 to 1, etc.). This normalized picture worthiness statistic may then be used at an input node of input nodes i+1 to j (corresponding to nodes  4 - 6  in  FIG.  13 B ). 
     Similar to photo-worthiness, the output of another neural network similar to  FIG.  13 A  may be used for determining social favorability or the trending nature value of the FSO  106 . Social favorability may be used as an input node in one of nodes  1  to i which has an effect on ordinary pricing, whereas the trending nature value may be used as a scaling factor in one of input nodes i+1 to j. 
     For both of these measures, separate models which resemble the neural network of  FIG.  13 A  may be used. First, with respect to both models (for social favorability and trending nature value), a bag of words may first be created. Such a bag of words may be created by taking original textual string data of the listing  112  of the FSO  106  and previous listings  116  of the same category. Including the description  406 , category  408 , brand  412 , and title  402 , and finding relating posts that are deemed to be favorable matching this text from social media content services such as FACEBOOK, TWITTER, SINA WEIBO, AMEBA. INSTAGRAM, etc. From these related posts, a bag of words comprising most commonly found word stems, as described above with respect to  FIG.  13 A , can be created. Then, using the bag of words approach, or the tf-idf approach, as discussed above with respect to block  1208  of  FIG.  12   , the model may be trained to output a score of social favorability at a single output node  1  in an output layer  1306   a  of such a model. 
     For the trending nature value, the social favorability model may be used with a bagging approach. For example, the social favorability model may be trained using previous listings with a sold date (from numerical data  1206  as discussed above) that is within a window of a predetermined number of years only, and this window may be moved over time (e.g. training of the social favorability model using previous listings from  116  in a window for 3 years, from 2000-2003, 2003-2006, 2006-2009, 2009-2012, etc.), with a new neural network model used for each different window. 
     To have a greater capture on recent trends, a smaller window may be used (e.g., 1 month), but corresponds as well to an increase in processing time, so a balance optimal for the seller  104  may be determined and used. Then, the input test data from the listing  112  corresponding to FSO  106  may be used with respect to each created model for each respective different window, and may output a social favorability score. Because the textual data of the FSO  106  is the same input data that is applied to each model, if there is a jump in social favorability scores between models, this is considered to be a jump in the rate of change over time of the social favorability of the products in the product category  152 , and the use of language associated with the bag of words may therefore attract an exponentially greater (or lesser if it is trending negatively due to a manufacturer defect, etc.) demand, and boost price. In an embodiment, such a plurality of social favorability models may be created in one month windows, for example, leading up to the current listing time of the FSO  106  (or age of listing if it has already been listed), wherein the most recent average rate of change (e.g., over the last 3 months) of the social favorability score, may normalized on a scale of −1 to 1 (or any other numerical scale) and be used as the trending nature value in an input node of input nodes i+1 to j in  FIG.  13 B . 
     Finally, another node that is considered to be a scaling factor is the degree to which the training data of previous listings  116  is recent. In training the model of  FIG.  13 B , recent data may be emphasized as more valuable in an embodiment, because recent data may have more of a bearing on the optimal offer price in current times, as compared to historic data, due to differences in the buying and selling climate. To create this greater emphasis, the metric of the degree of recent-ness may be used as an input node of input nodes i+1 to j in  FIG.  13 B , as a scaling factor. Alternately, all of input nodes  1  to j may be multiplied by the metric of the degree of recent-ness, such that previous listings that are more recent may have more of an impact on the optimal price that is output by Node  1  of the Output Layer  1306   b.    
     In this manner, the neural network of  FIG.  13 B  may be trained by the training data comprising previous listings  116  of the same category  152  as the FSO  106  (or of all categories  152  in the alternate embodiment described above), and then backpropagated to adjust the weights x i , y j , p, and q, accordingly. Then the trained model can be used with the input values for nodes  1  to j associated with the listing  112  of the FSO  106 . Ultimately, an optimal price is output by Node  1  of  1306   a  when used with these test input values. 
     This calculated optimal price is then output back to the GUI  1000  in field  1004 , as shown in  FIG.  10   . The price range of such a price may be calculated using the approach of the first or second paradigm as described above, or may be calculated using the neural network model of  FIG.  13 B . To calculate the price range using the neural network model of  FIG.  13 B , a leaving-out technique is used. In the leaving-out technique, a randomly chosen fraction of the connections of the neural network model are turned off, or pruned, from the model. 
     An example is shown with reference to  FIG.  13 A , wherein for a drop-out of 0.25, assuming there are 8 connections from the input layer  1302   a  nodes to the nodes of the hidden layer  1304   a,  2 of these (in bold) may be randomly pruned. Similarly, 2 connections from nodes from the hidden layer  1304   a  to the output layer  1306   a  (in bold) may also be pruned. By repeating this process for a predetermined number of iterations, training each of these “left-out” neural network models, and observing the output scores from the output layer  1306   b  of each of these models, a price range can be constructed. 
     For example, if 100 such models are made with dropout as 0.25, then 100 different predictions may be made using the input values associated with the listing  112  for FSO  106 . Using these different predictions of output score, if 50 of these fall within a certain range, then this may be a 50% price range that will be used. Again, this is a measure of variability, and may be shifted such that the predicted optimal price is at the center of such a price range. The percentage of dropout used may be compared to known price ranges present with regard to previous listings  116 , such that it may provide a suitable prediction for price range. 
     Thus, the optimal price and the price range (with the optimal price at the center of the price range. In an embodiment) may be output to the GUI  1000 . The price range (which covers the middle 50% of score values) may be highlighted in a color and displayed along a horizontal axis  1010 , where values below and above this range may be highlighted in different respective colors. A slider may be set at the center of this horizontal axis as shown in  FIG.  10    corresponding to the optimal price, and an accompanying message may be shown through a message bubble  1006  indicating that the range is suitable for FSO  106  to be sold while maximizing both profit and minimizing time for the product to sell. 
     A view curve  1008 , which may be calculated based on historic data of number of views from block  1216  as discussed above for previous listings  116  in the same category, may be superimposed upon the price range of axis  1010 , with a displayed vertical dashed line from the maximum number of views. In this manner, the user may know at which price within the price range they can likely achieve the most views from, while at the same time maximizing profit and minimizing the time needed to sell the item. 
     Similar products within the category  152  for FSO  106  that have a sold price within the calculated price range for FSO  106 , may be displayed in field  1012  as shown. Finally, an auto-adjust checkbox  1020  may be shown with respect to field  1020  on GUI  1000 . The presence of the checkbox, when checked can provide an intuitive way for the user to maximize profit, or minimize the time-to-sale (“Maximize Time”), as shown on slider  1018 . That is when a user drags the slider to the Maximize Profit end of the slider  1018 , the slider of axis  1010  may be automatically adjusted to go to the right end of the optimal price range (middle 50%, although it can be any other predetermined range in other embodiments), at $400 as shown in  FIG.  10   . Conversely, when a user drags the slider to the Maximize Time end of slider  1018 , the slider of axis  1010  may be automatically adjusted to the left end of the optimal price range of axis  1010 , at $300 as shown in  FIG.  10   . 
     As shown in block  1212  of  FIG.  12   , a random forest regression model or a boosted decision tree regression model may also be used in place of the neural network regression model. Such models will be described with reference to  FIG.  14   . These models are based on conditional evaluations. First, for training data comprising past listings  116 . Input data is evaluated at a decision split  1402 . In the tree  1400  shown the split is based on the condition  1402  of a listing  112 . 
     Thus if a product is in Like New condition, it may proceed down the right path of the tree, and if the product is in an Old condition, it may proceed down the left path of the tree. If the product is Old, it&#39;s Make  1404  may be examined, where if the product is of a Make B, then the training score falls in box  1410 . At the end of the training process the boxes  1408 ,  1410 ,  1412 , and  1414  are averaged, such that only one output value remains at the end of each path. Then when the test input data is used with the decision tree  1400 , the output value is the optimal price  506 . In decision trees, the best performing features from nodes  1  to j in the neural network regression model may be taken as split nodes (the features that minimize variance), and this results in one tree for a category  142 . 
     However. In random forest regression, these features are selected randomly, and multiple trees are created. The features which comprise nodes  1  to j in the neural network model may be taken into consideration, and a randomized threshold of features (e.g., 5 features) is used to create the Decision tree. A plurality of such trees may be created, and their scores may be averaged and output as the optimal score  506 . If the first or second paradigm is used, as described above the seller  104  may inform the pricing module  138  (via the specifications  154 ) that the seller  104  wishes to sell within a predetermined number (e.g., 20 days) of the age of the listing. Assume the example category decay curve  202  shown in  FIG.  2    applies to the category  152  of the FSO  106  being offered. In the example of  FIG.  2   , decay point  208 B of 80% is closest to listing age  210 =20 days. Thus, the pricing module  138  may apply this decay point  208 B—that is 80%—to thereby suggest an offer price  404  of $8 (that is, 0.8×$10). The seller flexibility curve  304  associated with the seller  104  can also or alternatively be applied in the same way to generate the offer price  404 . 
     Similar to the first and second paradigm, the third paradigm after generation of the price range, may also have an associated confidence threshold (denoted as ct_c) for the optimal price and price range. Such a confidence threshold may depend on the number of past listings  116  that are used to train the neural network regression model, as well as the number of past listings  116  that are used in generation of the price range. 
     Depending on the confidence threshold, a decision to use the first, second, or third paradigm may be made. For example, a threshold value for decision making may be set (e.g., 0.6). If the third paradigm has a greater confidence threshold statistic (e.g., ct_c is 0.8) than the decision threshold, and ctb, et a are under the decision threshold, then the third paradigm may be used. If multiple paradigms have confidence threshold values above the decision threshold, then the paradigm with the highest confidence threshold measure may be used. Finally, if none of the paradigms have confidence threshold values above the decision threshold, then a weighted average of all three optimal prices and price ranges generated by the three paradigms may be used. For example, the optimal price may be calculated per the formula: 
     
       
         
           
             
               
                 
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     The lower and upper bounds of the price range for the first through third paradigms may be weighted in a similar manner, with lower bound a-c and upper bound a-c replacing the optimal price a-c values in the formula above. In these formulas, ‘a’ corresponds to the first paradigm, ‘b’ corresponds to the second paradigm, and ‘c’ corresponds to the third paradigm. 
     Other embodiments for generating offer prices for FSOs  106  are described in U.S. patent application titled “Inventory Ingestion And Pricing System,” Ser. No. 16/288,203, filed Feb. 28, 2019, which is herein incorporated by reference in its entirety, and any of those embodiments may be used herein. 
     Referring again to  FIG.  6   . In  612 , the listing module  134  generates one or more listings  112  for the FSO  106  being offered. The different listings  112  may have varying titles, descriptions, pictures and offer prices  404 . As discussed above, the offer prices  404  in the listings  112  may be the seller&#39;s original price  504  provided in  604 , the optimal price  506  generated by the pricing module  138  in  610 , and prices in between based on, for example, ZOPAs  508  associated with the seller  104  and/or the category  152  of the FSO  106  being offered that was determined in  608 . 
     For example, with reference to the above example discussed for ZOPA  508 B, multiple listings may be generated covering offer prices P 2 -P 5  simultaneously. In an effort to expedite buyer agreement. In another example, as discussed with respect to the artificial intelligence aspects above, some pictures may be deemed to be more valuable than others per the neural network analysis. In this case, pictures which do not cross a particular threshold of value may be excluded from additional generated listings. 
     For example, if the original output picture worthiness value for a picture is less than an acceptance threshold, it may be excluded from an additionally generated listing. In addition, the social media neural network may be used to assess similar data points in the same product category  152 , to determine if any particular description language may be associated with an application trending. For example, listing information  126  may be searched for descriptions or title language presented as inputs which correspond to a high social media output value from the social media demand neural network. For example, if the words “noveau leather” or “new” or “mint condition” may be associated with highly trending handbags in the social media neural network, then these words may also be used (if not used already in the original description  406  of a listing  112 ) in additional generated listings  112  If there are various words that are associated with highly trending products within a category, a plurality of listings  112  may be generated, one corresponding to each of these word inputs. 
     A further example of such operation of generating additional listings is shown in a flowchart  802  of  FIG.  8   . In  804 , the listing module  134  identifies potential buyers  108  of the FSO  106  being offered. For example, if the FSO  106  is a particular designer dress, a potential buyer  108  could be someone who has searched for or purchased dresses by that designer. As another example, if the FSO  106  is an IPHONE accessory, a potential buyer  108  could be someone who has recently purchased an IPHONE, or who has recently searched for IPHONE accessories. 
     In  806 , the listing module  134  generates at least some of the listings  112  for the FSO  106  being offered, so as to customize them for the potential buyers  108  identified in  804 . The listing module  134  may implement this by searching for past listings  116  of FSOs  106  purchased by the potential buyers  108 . Then, the listing module  134  may use the neural network model described, which may be trained on this subset of past listings  116 , and associated transaction information  118 . In this way, the neural network can analyze the past listings  116  to identify tendencies and preferences of the potential buyers  108  with regards to the features of the neural network model. Certain descriptions, phrases, picture types, or other listing information  126  may be associated with higher demand from the potential buyers  108 , and consequently may result in more profitability for the seller. For example, some buyers  108  may be more likely to purchase if the offer price  404  is within a certain range, or if the listing  112  includes pictures where scale is indicated, or if the listing  112  is less busy (for example, contains a shorter description). 
     In  808 . In addition to posting the listings  112  on the site  110 , the listing module  134  may bring the customized listings  112  to the attention of the respective potential buyers  108  (via email or text, or posting on the buyer  108 &#39;s home page on the site  110 ). 
     Other embodiments for automatically generating listings  112  are described in U.S. patent application titled “Inventory Ingestion And Pricing System,” Ser. No. 16/288,203, filed Feb. 28, 2019, and U.S. Provisional Application No. 62/900,764 titled “Automating The Creation Of Listings Using Augmented Reality Computer Technology,” filed Sep. 16, 2019, which are herein incorporated by reference in their entireties, and any of those embodiments may be used herein. 
     Referring again to  FIG.  6   . In  614 , after the listings  112  have been posted on the site  110  (and prior to the sale of the FSO  106  being offered), the smart nudge engine  140  periodically uses the ZOPA  508  associated with the current listing age  210  to suggest lower offer prices  404  to the seller  104 , so as to “nudge” the offer prices  404  into the applicable ZOPA  508  (to the extent the offer prices  404  are not already in the ZOPA  508 ). 
     As also discussed above. In determining the ZOPA at different ages of listing after the seller  104  has initially offered the FSO  106  for sale, the optimal price  506  may be periodically re-evaluated. When the optimal price  506  is re-evaluated at a later time, the inputs 1 to j of the neural network may be re-entered into the neural network regression model described above to determine optimal price. In this manner, the model may be able to capture altered demand due to market conditions. 
     For example, if a new product is out, the product perishes or is out of season, or the seller has now undergone financial difficulties and has dragged the slider  1018  in GUI  1000  to indicate that the seller would like to sell the product soon, each of these inputs can be accounted for in the model and adjust the model to suggest a new optimal price  506 . Furthermore, the emergence of a social media trend that may not have been present when the FSO  106  was initially offered for sale may also be captured by the re-evaluation of the optimal price  506  by the smart nudge engine  140 . 
     In an additional embodiment, if a sell-by condition exists, bagging or a bootstrap approach may be performed as described above with respect to the neural network model of  FIG.  13 B , wherein all previous listings that have a sold date that exceeds the value (in days) of the field  1016  in GUI  1000  in  FIG.  10    may be excluded from training the model. In the case where the optimal price is re-evaluated, the neural network model. In a further embodiment, may also take a bagging/bootstrap approach where all previous listings  116  that have a sold date before the current listing time may also be excluded. For example, the listing time with respect to ZOPA  508 B is 15 days. If the sell-by condition of  1016  in  FIG.  10    is 56 days, per this approach, when the optimal price is re-evaluated by the neural network model of  FIG.  13 B  at 15 days, all previous listings  116  that were sold prior to 15 days or after 56 days from their initial date of offering may be excluded from training the model. The model may then be retrained by previous listings that were sold between 15 days and 56 days from their initial date of offering, and then test data associated with the listing (including any updated fields in GUI  1000  of  FIG.  10   ) may be inputted as test data for nodes  1  to j of the model, and the updated optimal price  506  may be output. 
     By taking this updated bootstrap/bagging approach, it may be possible to capture trends that are specific to the age of listing  210 . In a still further embodiment, bagging/bootstrapping may be conducted between 0 days and the sell-by condition e.g., 56 days in field  1016 ) in one model, between 15 days and 56 days in another model, and the output of both models may be averaged. 
     An example of such operation is shown in a flowchart  902  of  FIG.  9   . In  904 , the smart nudge engine  140  accesses the ZOPA  508  applicable to the current listing age  210 . 
     In  906 , the smart nudge engine  140  determines new offer prices  404  for those listings  112  where the current offer price  404  is outside the applicable ZOPA  508 . The new offer prices  404  may vary among these listings  112 . 
     Also, in some embodiments, the smart nudge engine  140  may generate more aggressive offer prices  404  (that is, closer to the optimal price  506 ) if the seller  104  indicated the FSO  106  was being sold for charity, so as to sell the FSO  106  in a shorter period of time. Similarly, the pricing module  138  may generate a more aggressive (that is, lower) optimal price  506  in  610  (as an initial offer price  404 ) when the seller  104  has indicated the sale is for charity. The same is true where the seller  104  has indicated through the specifications  154  that the seller  104  wishes to sell the FSO  106  within a relatively short period of time. 
     Also in  906 , the smart nudge engine  140  suggests these new offer prices  404  to the seller  104 , where the new offer prices  404  are within the range of the applicable ZOPA  508  for the current listing age  210 . If the seller  104  agrees, then in  908 , the smart nudge engine  140  changes the offer price  404  in the listings  112 . In other embodiments, if so instructed by the seller  104  (in  604 ), the smart nudge engine  140  will automatically adjust the offer prices  404  as discussed above without seeking approval of the seller  104 . 
     Referring again to  FIG.  6   , Step  614  is repeated until the FSO  106  being offered sells. 
     In  616 , after the FSO  106  being offered sells, the payment from the buyer  108  is processed (and provided to designated charities, if the seller  104  elected the charity option in  606 ). All of the listings  112  associated with the FSO  106  that were generated in  612  are locked or deleted to prevent further purchase by other buyers  108 . 
     Example Computer System 
     Various embodiments and/or components therein can be implemented, for example, using one or more computer systems, such as computer system  1500  shown in  FIG.  10   . Computer system  1500  can be any computer or computing device capable of performing the functions described herein. For example, one or more computer systems  1500  or portions thereof can be used to implement any embodiments of  FIGS.  1 - 14   , and/or any combination or sub-combination thereof. 
     Computer system  1500  includes one or more processors (also called central processing units, or CPUs), such as a processor  1504 . Processor  1504  is connected to a communication infrastructure or bus  1506 . 
     One or more processors  1504  can each be a graphics processing unit (GPU). In some embodiments, a GPU is a processor that is a specialized electronic circuit designed to process mathematically intensive applications. The CiPU can have a parallel structure that is efficient for parallel processing of large blocks of data, such as mathematically intensive data common to computer graphics applications, images, videos, etc. 
     Computer system  1500  also includes user input/output device(s)  1503 , such as monitors, keyboards, pointing devices, etc., that communicate with communication infrastructure  1506  through user input/output interface(s)  1502 . 
     Computer system  1000  also includes a main or primary memory  1008 , such as random access memory (RAM). Main memory  1508  can include one or more levels of cache. Main memory  1508  has stored therein control logic (i.e., computer software) and/or data. 
     Computer system  1500  can also include one or more secondary storage devices or memory  1510 , Secondary memory  1510  can include, for example, a hard disk drive  1512  and/or a removable storage device or drive  1514 . Removable storage drive  1514  can be a floppy disk drive, a magnetic tape drive, a compact disk drive, an optical storage device, tape backup device, and/or any other storage device/drive. 
     Removable storage drive  1514  can interact with a removable storage unit  1518 . Removable storage unit  1518  includes a computer usable or readable storage device having stored thereon computer software (control logic) and/or data. Removable storage unit  1518  can be a floppy disk, magnetic tape, compact disk, DVD, optical storage disk, and/any other computer data storage device. Removable storage drive  1514  reads from and/or writes to removable storage unit  1518  in a well-known manner. 
     According to an exemplary embodiment, secondary memory  1510  can include other means. Instrumentalities or other approaches for allowing computer programs and/or other instructions and/or data to be accessed by computer system  1500 . Such means, instrumentalities or other approaches can include, for example, a removable storage unit  1522  and an interface  1520 . Examples of the removable storage unit  1522  and the interface  1520  can include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and/or any other removable storage unit and associated interface. 
     Computer system  1500  can further include a communication or network interface  1524 . Communication interface  1524  enables computer system  1500  to communicate and interact with any combination of remote devices, remote networks, remote entities, etc. (individually and collectively referenced by reference number  1528 ). For example, communication interface  1524  can allow computer system  1500  to communicate with remote devices  1528  over communications path  1526 , which can be wired and/or wireless, and which can include any combination of LANs, WANs, the Internet, etc. Control logic and/or data can be transmitted to and from computer system  1500  via communication path  1526 . 
     In some embodiments, a non-transitory, tangible apparatus or article of manufacture comprising a tangible computer useable or readable medium having control logic (software) stored thereon is also referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system  1500 , main memory  1508 , secondary memory  1510 , and removable storage units  1518  and  1522 , as well as tangible articles of manufacture embodying any combination of the foregoing. Such control logic, when executed by one or more data processing devices (such as computer system  1500 ), causes such data processing devices to operate as described herein. 
     Based on the teachings contained in this disclosure, it will be apparent to persons skilled in the relevant art(s) how to make and use embodiments of this disclosure using data processing devices, computer systems and/or computer architectures other than that shown in  FIG.  15   . In particular, embodiments can operate with software, hardware, and/or operating system implementations other than those described herein. 
     CONCLUSION 
     While this disclosure describes exemplary embodiments for exemplary fields and applications, it should be understood that the disclosure is not limited thereto, Other embodiments and modifications thereto are possible, and are within the scope and spirit of this disclosure. For example, and without limiting the generality of this paragraph, embodiments are not limited to the software, hardware, firmware, and/or entities illustrated in the figures and/or described herein. Further, embodiments (whether or not explicitly described herein) have significant utility to fields and applications beyond the examples described herein. 
     Embodiments have been described herein with the aid of functional building blocks illustrating the implementation of specified functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternate boundaries can be defined as long as the specified functions and relationships (or equivalents thereof) are appropriately performed. Also, alternative embodiments can perform functional blocks, steps, operations, methods, etc. using orderings different than those described herein. 
     References herein to “one embodiment,” “an embodiment,” “an example embodiment,” or similar phrases, indicate that the embodiment described can include a particular feature, structure, or characteristic, but every embodiment can not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it would be within the knowledge of persons skilled in the relevant art(s) to incorporate such feature, structure, or characteristic into other embodiments whether or not explicitly mentioned or described herein. Additionally, some embodiments can be described using the expression “coupled” and “connected” along with their derivatives. These terms are not necessarily intended as synonyms for each other. For example, some embodiments can be described using the terms “connected” and/or “coupled” to indicate that two or more elements are in direct physical or electrical contact with each other. The term “coupled.” however, can also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other.