Patent Publication Number: US-11651410-B2

Title: Inventory ingestion and pricing, including enhanced new user experience embodiments

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of U.S. patent application Ser. No. 16/288,199 titled “Inventory ingestion, Image Processing, and Market Descriptor Pricing System,” filed Feb. 28, 2019, and claims priority to U.S. Provisional Patent Application No. 62/769,752 titled “QR Code Based Shipping and Enhancing New Seller Experience,” filed on Nov. 20, 2018, both of which are herein incorporated by reference in their entireties. 
     This application is related to U.S. patent application Ser. No. 16/288,158 titled “Determining Sellability Score and Cancellability Score,” filed Feb. 28, 2019; U.S. patent application Ser. No. 16/288,203 titled “Inventory Ingestion and Pricing System,” filed Feb. 28, 2019; U.S. patent application Ser. No. 16/288,379 titled “Probabilistic Item Matching and Searching,” filed Feb. 28, 2019; and U.S. patent application Ser. No. 16/288,373 titled “Probabilistic Search Biasing and Recommendations,” filed Feb. 28, 2019, all of which are herein incorporated by reference in their entireties. 
    
    
     BACKGROUND 
     There are many websites and apps that allow users to sell new or used products. However, creating profiles or listings for each product to be sold is often cumbersome and time consuming for a user. This listing creation process may require both time and computer processing resources requiring a user to take pictures of a product, write up a description, determine pricing, etc. Additionally, listing creation by a user, particularly with regard to pricing, may consume substantial network bandwidth in multiple back-and-forth communications with a variety of websites, such as a marketplace, as a user performs searches for similar products and tries to determine how to price a product for sale. 
     As a result, this manual, resource intensive listing process may deter users from selling products they would otherwise be interested in selling. For example, a user may not have the time, computer processing resources, and/or communications bandwidth to generate a listing and/or determine pricing. This decision not to sell may cost these websites and apps potential commissions and listing fees that could have been earned through the posting and sale, cost the seller revenue that could have been earned through a sale, and cost potential buyers the enjoyment of the product they would have purchased. 
     BRIEF SUMMARY OF SELECTED EMBODIMENTS 
     In an embodiment, a computer-implemented method, system, and computer program product that receives an image including an object for sale from a mobile device are described. An electronic marketplace is determined to include one or more images of pre-identified objects that have sold. The object from the image is identified based on a comparison of the received image with the images of the one or more of the pre-identified objects from the electronic marketplace. The price for each of a subset of the one or more of the pre-identified objects that have sold is identified from the electronic marketplace. A sale price for the object for sale is generated based on the determined prices. The sale price for the object for sale is provided to the mobile device. 
     In an embodiment, a computer-implemented method, system, and computer program product that identifies an e-mail receipt identifying a purchased object are described. The e-mail receipt is parsed for the identification of the purchased object. A set of items similar to the purchased object is identified from an electronic marketplace. A price for a subset of the identified set of similar items that have sold on the electronic marketplace is determined. A sale price is generated for the purchased object based on the determined prices for the subset of similar items. The sale price is provided responsive to the identification of the purchased object. 
     In an embodiment, a computer-implemented method, system, and computer program product that receives a selection of a resell icon associated with a purchase history page of a retailer are described. An object associated with the purchase history page of the retailer that was purchased prior to receiving the selection is identified. A set of items similar to the identified purchased object is identified from an electronic marketplace. A price for each of at least a subset of the set of similar items that have sold on the electronic marketplace is determined. A sale price is generated for the purchased object based on the determined prices for the subset of similar items. The sale price is provided responsive to the identifying the purchased object. 
     In an embodiment, a computer-implemented method, system, and computer program product that determines an identification of a for sale object to be sold on an electronic marketplace are described. A set of items similar to the identification of the for sale object is identified from the electronic marketplace. A price of each of at least a subset of the set of similar items that have sold through the electronic marketplace is determined. A sale price for the for sale object is generated based on the determined prices of the items of the subset. An image for one or more of the subset of items is retrieved from the electronic marketplace. A market descriptor including the sale price and the retrieved one or more images is generated. The market descriptor is provided for display. 
     In an embodiment, a computer-implemented method, system, and computer program product for enhancing the experience for new sellers when creating listings are described. An embodiment operates by: enabling a new seller to create one or more new listings for an electronic marketplace (EM); determining that a number of the new listings satisfy a predetermined threshold; and enabling the new seller to acquire one or more for sale objects (FSOs) from associated eligible listing(s) on the EM at no or reduced cost. In an embodiment, the new seller is a user who has not previously sold a FSO on the EM, and the eligible listings are (1) listings having an offer price less than or equal to a predetermined maximum price; and (2) listings by users who have not previously sold a FSO on the EM. The new seller may create new listings manually, and/or via any combination of: automatic inventory ingestion based on analyzing images; automatic inventory ingestion based on analyzing emails; and/or automatic inventory ingestion based on analyzing retailer websites. 
     The details of these and other embodiments are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the subject matter of this disclosure will be apparent to persons skilled in the relevant art(s) from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings are incorporated herein and form a part of the specification. 
         FIG.  1    is a block diagram illustrating an example inventory ingestion engine and pricing system that identifies and prices for sale objects from images, according to some embodiments. 
         FIG.  2    is a flowchart illustrating operations of an example inventory ingestion engine and pricing system that identifies and prices for sale objects from images, according to some embodiments. 
         FIG.  3    is a block diagram illustrating an example inventory ingestion engine and pricing system that identifies and prices for sale objects from emails (such as email receipts), according to some embodiments. 
         FIG.  4    is a flowchart illustrating operations of an example inventory ingestion engine and pricing system that identifies and prices for sale objects from mails, according to some embodiments. 
         FIG.  5    is a block diagram illustrating an example inventory ingestion engine and pricing system that identifies and prices for sale objects from a retailer or other website, according to some embodiments. 
         FIG.  6    is a flowchart illustrating operations of an example inventory ingestion engine and pricing system that identifies and prices for sale objects from a retailer website, according to some embodiments. 
         FIG.  7    is a block diagram illustrating an example inventory ingestion engine and pricing system that identifies and prices for sale objects and provides market descriptors about the pricing, according to some embodiments. 
         FIG.  8    is a flowchart illustrating operations of an example inventory ingestion engine and pricing system that identifies and prices for sale objects and provides market descriptors about the pricing, according to some embodiments. 
         FIG.  9    is a block diagram illustrating example operations of an inventory ingestion engine and pricing system that identifies and prices for sale objects from images, according to some embodiments. 
         FIG.  10    is a block diagram illustrating example operations of an inventory ingestion engine and pricing system that identifies and prices for sale objects from email receipts, according to some embodiments. 
         FIG.  11    illustrates example listings in greater detail, according to some embodiments. 
         FIG.  12    is a flowchart illustrating operations for enhancing the experience for new sellers when creating listings, according to some embodiments. 
         FIG.  13    is 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 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 providing an inventory ingestion and pricing system. Also described herein are enhanced new user experience embodiments. 
     Automatic Inventory Ingestion Based on Analyzing Images 
       FIG.  1    is a block diagram  100  illustrating an example inventory ingestion engine and pricing system that identifies and prices for sale objects from images, according to some embodiments. System  100  may simplify the process of listing items for sale on an electronic marketplace (EM)  112 . 
     In an embodiment, an inventory engine (IE)  102  may receive an image  108  of a for sale object (FSO  106 ) from a mobile device  116 . For example, a user may use their mobile device  116  to take a picture or video of FSO  106 . FSO  106  may be an object, item, product, or collection or set of products that a user is considering listing for sale, individually or together. Example FSOs  106  include motor vehicles, electronics, clothes, appliances, furniture, office supplies, collector&#39;s items, etc. 
     IE  102  may identify FSO  106  from image  108  (and other information that may be received from mobile device  116 ) and generate a price  104  for the FSO  106  based on a pricing history of similar objects from EM  112 . The similar objects may include sold objects  110  and/or listed objects  120 . For example, based on the pricing history  128  of the similarly sold objects  110 , IE  102  may generate a projected sale price  104  for FSO  106 . 
     EM  112  may include a website or collection of webpages or listings  114  that enable users to list items for sale that are available for purchase. The listings  114  may include purchase now pricing, auction based pricing, or best offer requests or purchases, to name just some examples. 
     in an embodiment, IE  102  or EM  112  may track listing, purchase, and pricing histories for different items or objects listed and/or sold on EM  112 . For example, EM  112  may track how long an item has been listed  130 , the pricing history of an item between the time it is listed and sold  128 , geographic locations of sales and purchase, and other listing, sale, and purchase information. This information may be used by IE  102  in generating a projected or recommended price  104  for FSO  106 . 
     In an embodiment, IE  102  may also automatically generate and submit a listing  114  for FSO  106  on EM  112 . And upon a user request, or when a particular price target or alert  132  is reached, submit listing  114  to list FSO  106  for sale on EM  112 . 
     Image  108  may include one or more pictures or videos of an FSO  106 . For example, image  108  may include images taken from different angles, perspectives, or with different lighting or filters. In an embodiment, image  108  may include pictures of a model number, manufacturer name, serial number, receipt, or other object or feature or identifying information. In an embodiment, image  108  may include pictures taken of markings, including damage, on FSO  106 . 
     In an embodiment, image  108  may include pictures or a video of one or more objects of a collection. For example, a user may have a collection of baseball cards, and may take a picture of each card, and submit all of the pictures as a collection to be sold together. Or, image  108  may include a video taken of all the cards of the set. In an embodiment, image  108  may include any visual multimedia, including augmented realty or virtual reality based pictures, screenshots, renderings, and/or video. 
     In an embodiment, image  108  may include multiple different FSOs  106  that a user is interested to sell individually, in various sub-combinations, or as a set. For example, image  108  may include a picture of a user&#39;s living room, including a rug, couch, two end tables, a coffee table, and lamps that may be listed for sale together as an entire living room set, and/or separately as individual pieces on EM  112 . Or, for example, the two end tables may be sold together as subset of FSOs  116 . In an example embodiment, image  108  may include an image of two similar or identical end tables, and a quantity in the listing may be automatically set to two. 
     Mobile device  116  may include a mobile phone, laptop, gaming system, tablet, desktop, smart television, wearable, appliance or other computing device that is capable of taking pictures and/or video, and transmitting image(s)  108  to IE  102 . In an embodiment, mobile device  116  may include a camera that is capable of capturing image  108 . In an embodiment, mobile device  116  may be executing a web-based or local app that is connected to the cloud or other network of computers through which mobile device  116  sends and receives communications with IE  102 , including image  108  and pricing information  104 . 
     In an embodiment, IE  102  may receive image  108  and may automatically identify one or more FSOs  106  from image  106  through image recognition. To achieve this function, IE  102  may employ any well-known image recognition technique, process, module, apparatus, system, method, computer program product and/or technology. For example, IE  102  may compare image  108  to one or more images  118  from previously identified objects, objects currently listed for sale  120  on EM  112 , previously sold objects  110  from EM  112 , and/or third party databases, services or websites, to name just some examples. In an embodiment, IE  102  (or another matching system) may compare visual features of FSO  106  from image  108  to visual features of the other previously identified objects or images, including but not limited to images  118  from EM  112 , Based on the comparison, IE  102  may receive or determine an identification  122  of FSO  106  from image  108 . 
     In an embodiment, IE  102  may provide a request that a user confirm or correct the identification  122  of FSO  106 . In an embodiment, IE  102  may also request from the user additional information regarding one or more features  124  or possible features of FSO  106 . Example features  124  may include make, model, manufacturer, configuration, customization, color, serial number, condition indicators (e.g., poor, used, like new, new), geographic location, etc. 
     In an embodiment, IE  102  may identify multiple objects that may be visually similar to FSO  106 . IE  102  may request from the user additional information about one or more features  124  of FSO  106  that may distinguish between different subsets of the objects to more accurately identify ( 122 ) FSO  106 . 
     example, IE  102  may identify FSO  106  as being a tablet computer. IE  102  may identify a set of visually similar tablet computers with the manufacturer or model from EM  112 , and may request that the user provide additional information such as the amount of memory (e.g., 32 GB or 64 GB) or processing power of the FSO  106 . This may enable IE  102  to more accurately identify which subset of visually similar objects from EM  112  corresponds to FSO  106  for pricing purposes. Also or alternatively, IE  102  may request that the user use the mobile device  116  to take a picture (that is, an image  108 ) of particular identifying information, such as a model number, bar code, clothing label, etc., from FSO  106 . Embodiments for identifying objects that are the same or similar to FSO  106  are described in U.S. patent application Ser. No. 16/288,379 titled “Probabilistic Item Matching and Searching” and U.S. patent application Ser. No. 16/288,373 titled “Probabilistic Search Biasing and Recommendations” that were referenced above, and that are herein incorporated by reference in their entireties, and IE  102  may employ any of these incorporated embodiments (individually or in any combination with the teachings of this disclosure) to achieve the functions described herein. 
     IE  122  may generate a price  104  for FSO  106  based on comparing identification  122  and features  124  (if any) of FSO  106  to listed and/or sold objects  110 ,  120  from EM  112 . Price  104  may be a suggested or predicted price for which FSO  106  may sell on EM  112 . IE  102  may first identify similar objects from EM  112  based on a the identification  122  and features  124  comparison. IE  102  may then request the pricing histories  128  for the similarly identified objects. 
     Based on this pricing history of similarly listed  120  and/or sold objects  110 , IE  102  may generate an average, median, or range of prices  104  for FSO  106 . For example, price  104  may include a range of prices for which similar objects  110  have sold in the previous 4 weeks, or 6 months (or any other period of interest), and may account for geographic location of sales, particularly for larger items for which shipping costs increase prices. IE  102  may communicate the generated price  104  back to the app of mobile device  116 , in real-time, after receiving image  108 , to thereby inform the user of a suggested price (by doing so, the user may be better informed as to whether or not to list the FSO  106  for sale on the EM  112 ). 
     In an embodiment, for both sold objects  110  and listed objects  120 , EM  112  may include at least images  118 , features  126 , prices  128 , and sell times. Images  118  may include user submitted images for listings. Features  126  may include user approved or otherwise determined features for object that are or have been listed, and may include product descriptions in an embodiment, IE  102  may use images  118  and features  126  to identify items, products or objects from EM  112  that are similar to FSO  106 . 
     EM  112  may also track a pricing history  128  and sell time  130 . Pricing history  128  may include indications of when a user increased or decreased the price of an object during the course of listing. Sell time  130  may indicate how long a listing has been active, including when a listing was deactivated or reactivated, and how this may correspond to changes in the pricing  128 . For example, EM  112  may track that a particular product was initially listed for $100, that after 3 days, it was discounted to $85, that after a week, a 15% increase was added, and that 2 weeks after it was initially listed, the product sold for $94. 
     in an embodiment, IE  102  may automatically generate a listing  114  (or partial listing) for FSO  106 , without user request or involvement. Listing  114  may include a form that is used for submitting items for sale to EM  112 . In an embodiment, IE  102  may include image(s)  108 , generated price  104 , indicated features  124 , identification  122 , a general description of FSO  106  based on identification  122  (or listings of previously sold objects  110 ), default or preferred shipping terms, etc. in listing  114  for FSO  106 . A user may have the option of editing or customizing listing  114  as they see fit, including adding/removing features  124  or images  108  or editing the descriptions or price  104 . IE  102  may submit listing  114  to EM  112  and FSO  106  may be included as one of the listed objects  120 . 
     In an embodiment, IE  102  may enable a user to set a price alert  132  indicating a minimum price at which IE  102  takes an action on the user&#39;s behalf, such as submitting the listing  114  to EM  112 . For example, IE  102  may set a price alert  132  and monitor the average, range, or latest sell or list prices  128  of sold objects  110  (or listed objects  120 ). In an embodiment, alert  132  may include the indication of a particular date/time, or time period when the user wants price updates or listing  114  to be posted to EM  112 . 
     Based on alert  132 , IE  102  may periodically transmit notifications or updates to a user&#39;s e-mail address, phone number, app, or other contact information associated with a user account  134 . Based on new sales or listing from EM  112 , IE  102  may generate new prices  104 . The new prices may be compared against price alert  132 . For example, when price alert  132  is met or exceeded, IE  102  may automatically post listing  114  to EM  112  and transmit a notification to mobile device  116 , including a link to listing  114  (so that a user may view, edit, or cancel the listing) that the listing is active. 
     Listing  114  may be posted under a user account  134  associated with a user or mobile device  116 . User account  134  may include information such as: payment, bank account, name geographic location, seller/buyer ratings, user ID, password, score information, shipping preferences, or other information about a user who is submitting FSO  106 . In an embodiment, a user may see their listing and purchase history through accessing their user account  134  through a website or app on mobile device  116 . 
     In an embodiment, IE  102  may continue tracking prices  128  (and demand  136 ) of sold objects  110  (similar to FSO  106 ) and periodically send updates to mobile device  116  or an e-mail address associated with user account  134  until an alert  134  or listing  114  is cancelled, or FSO  106  is sold. When listing  114  is activated, deactivated, or FSO  106  is sold via EM  112 , IE  102  may automatically discontinue the price update notifications. 
     in an embodiment, IE  102  may generate a demand indicator  136  for FSO  106 . 
     Demand indicator  136  may include one or more measures of an estimated demand or supply/demand ratio for FSO  106 . In an embodiment, demand indicator  136  may indicate a median or range of sell times  130  for sold objects  110  at various price points. Demand indicator  136  may indicate how long currently listed objects  120  have been listed based on their accumulating sell time  130 . Also or alternatively, IE  102  may generate a sellability score and/or a cancellability score for FSO  106 . Sellability score is a measure of how likely the FSO  106  is likely to sell on the EM  112 , based on the strength of the associated listing and/or other factors. Cancellability score is a measure of how likely the listing of the FSO  106  will be cancelled by either the seller or the buyer, based on the seller&#39;s history and/or other factors. Embodiments for generating a sellability score and a cancellability score are described in U.S. patent application Ser. No. 16/288,158 titled “Determining Sellability Score and Cancellability Score,” that was referenced above, and that is herein incorporated by reference in its entirety, and IE  102  may employ any of these incorporated embodiments (individually or in any combination with the teachings of this disclosure) to achieve the functions described herein. 
     In an embodiment, EM  112  may track search requests  138  for different objects or categories of objects. For example, EM  112  may track how many or how often search requests  138  are received for objects that are similar to FSO  106 . IE  102  may determine whether the frequency of search requests  128  is increasing, decreasing, staying the same relative to a historical average or previous searches. In an embodiment, IE  102  may account for search request  138  information in generating demand indicator  136 . 
     In an embodiment, demand indicator  136  may include a score (e.g., within a range of 1-10) that incorporates various weighted or un-weighted factors to determine a demand for FSO  106 . Demand indicator  136  may include an indication of a price, purchase, or listing trend with regard to objects similar to FSO  106 . IE  102  may provide notifications of demand indicator  136  changes to mobile device  136 , similar to price  104  as described herein. In an embodiment, the periodic notifications and/or alert  132  may correspond to price  104  and/or demand indicator  136 . 
       FIG.  2    is a flowchart  200  illustrating operations of an example inventory ingestion engine and pricing system that identifies and prices for sale objects from images, according to some embodiments. Method  200  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.  2   , as will be understood by a person of ordinary skill in the art. Method  200  shall be described with reference to  FIG.  1   . However, method  200  is not limited to the example embodiments. 
     In  210 , an image including an object for sale is received. For example, IE  102  may receive image  108  from mobile device  116 . Image  108  may include multiple images of the same object  106 , or different objects  106 . The different objects  106  may be unrelated, or they may be from a collection or set of objects. In an embodiment, image  108  may include video or other multimedia of one or more FSOs  106 . The following steps are performed for each of the FSOs  106  in the image  108 . 
     In  220 , for an FSO  106  in the image  108  (called the current FSO  106 ), it is determined that an electronic marketplace includes one or more images and/or other information of pre-identified objects that have sold. For example, EM  112  may include listings of objects that are currently listed or were previously listed for sale  120 , including objects that have sold  110 . EM  112  may maintain or store information such as images  118 , feature and description  126 , price  128 , sell time  130  information, etc., for these current, expired, active, deactivated, and sold listings of objects  110 ,  120 . As noted above, the pre-identified objects are ones that are the same or similar to the current FSO  106 . Embodiments for identifying objects that are the same or similar to FSO  106  are described in U.S. patent application Ser. No. 16/288,379 titled “Probabilistic Item Matching and Searching” and U.S. patent application Ser. No. 16/288,373 titled “Probabilistic Search Biasing and Recommendations” that were referenced above, and that are herein incorporated by reference in their entireties, and IE  102  may employ any of these incorporated embodiments (individually or in any combination with the teachings of this disclosure) to achieve the functions described herein. 
     In  230 , the object from the image is identified using image recognition technology based on, for example, a comparison of the received image with the images of the pre-identified objects from the electronic marketplace. For example, IE  102  may compare image  108  with images  118  of objects  110 ,  120  from EM  112 . Based on an image  108 ,  118  comparison, IE  102  may identify the current FSO  106  without user input or description. In an embodiment, the identification  122  may be determined by an external system including both images  118  from EM  112 , and images from one or more other databases or systems of previously identified objects. In another embodiment, the object from image  108  may also be identified from images that are not retrieved from EM  112 . 
     In  240 , the price for each of a subset of the one or more of the pre-identified objects that have sold is determined from the marketplace. For example, IE  102  may determine prices  128  for the sold objects  110  from EM  112  that are the same or similar to the current FSO  106 . In an embodiment, IE  102  may request feature information  124  about the current FSO  106 . Then, for example, based at least in part on a feature  124 ,  126 , IE  102  may identify or request pricing information  128  for similarly sold and/or listed objects  110 ,  120  from EM  112 . 
     In  250 , a sale price for the object for sale is generated based on the determined prices. For example, from the determined prices  128  of sold objects  110  (and/or listed objects  12 ) that are the same or similar to the current FSO  106 , IE  102  may generate a price  104  for the current FSO  106 . Price  104  may include a suggested price, a predicted sale price, a range of prices for which similar objects have sold, and/or a pricing history of similarly listed and/or sold objects from EM  112  that are the same or similar (in both image  118  and/or features  126 ) to the current FSO  106 . In an embodiment, IE  102  may take a range of prices from the sold objects  110  and use a median or average price (of the range of prices) as a recommended sales price. 
     In  260 , the sale price for the object for sale is provided. For example, IE  102  may provide price  104  to mobile device  116 , or include price in a pre-generated listing  114  for the current FSO  106  on EM  112 . In an embodiment, IE  102  may continue providing price updates for the current FSO  106  based on alert(s)  132 , until a listing  114  has been activated, deactivated, or FSO  106  has sold. 
     In some embodiments, steps  220 - 260  may be repeated for each of the FSOs  106  contained in the image  108  obtained in step  210 . In this manner, listings and pricing information may be quickly, easily, and automatically generated for items contained in the user&#39;s house, such as the living room, garage, basement, office, hobby room, backyard, etc., or any other location. 
     Automatic Inventory Ingestion Based on Analyzing Emails 
       FIG.  3    is a block diagram  300  illustrating an example inventory ingestion engine and pricing system that identifies and prices for sale objects from email (such as email receipts), according to some embodiments. System  300  may simplify the process of listing products for sale on EM  112 , and may perform pricing and demand determination functionality similar to what is described above with respect to system  100  as described in reference to  FIGS.  1 - 2   . 
     In an embodiment, IE  102  may scan a user&#39;s e-mail account  302  or inbox and identify various e-mails or messages that may be receipts  304  for the purchase of various items and objects from one or more retailers. The e-mail receipt  304  may be an electronic message (e.g., email, webpage, etc.) that may include purchase information  306  about purchase of an item, such as retailer  306 A, subject  306 B, domain  306 C, purchased object  306 D, purchase price  306 E, and date  306 F. 
     In an embodiment, email receipt  304  may be any electronic message from a retailer or a payment service that collected and/or distributed payments on behalf of a user and/or retailer. Retailer information  306 A may include a name, physical address, web address, or other identifying information about the person or company from whom an item, product, or object was purchased. An electronic message  304  may include a subject area  306 B that indicates it is a receipt or includes other information identifying the contents of the e-mail. 
     Domain  306 C may indicate a web address or host associated with the purchased item or from whom the email receipt or electronic message  304  was received. In an embodiment, domain  306 C may indicate an electronic payment service that paid retailer  306 A on behalf of a user for the purchase of an item. Purchased object  306 D may include a description, features ( 124 ), condition, and/or identification  122  of what item(s) were purchased. Purchase price  306 E may indicate the price at which the product was purchased, and a date  306 F may be a date of shipment or purchase  306 F. 
     Purchase information  306  may be included in text  308  and/or metadata  310  of the e-mail receipt  304 . Text  308  may be included the body of the e-mail receipt as it appears (and is readable or visible) to in a user&#39;s inbox, e-mail account, app, or mobile device. Metadata  310  may include information about e-mail receipt  304  and may include information that is or is not visible in text  308 . While not visible to a user, metadata  310  may be accessible to applications such as apps (including an IE  102  app on a mobile device  116 ), or a browser. 
     In an embodiment, IE  102  may include a repository of one or more known formats  3012  of e-mail receipts  304  as they correspond to different retailers  306 A or domain  306 C, referred to collectively as domain  314 . Format  312  may include a pre-determined, pre-discovered, or otherwise known configuration of purchase data  306  within text  308  and/or metadata  310  of a particular e-mail receipt  304  associated with a particular company, domain, or retailer  306 A. 
     For example, a particular retailer  306 A may send out a similarly formatted messages or e-mail receipts  304  to different customers purchasing items from their website. IE  102  may determine an e-mail receipt  304  from the retailer  306 A or domain  306 C, and retrieve format  312  from a database or repository. Using format  312 , IE  102  may more quickly identify and retrieve purchase information  306  from e-mail receipt  304 , by identifying already known keywords  316  from text  308  or metadata  310  that indicate various purchase information  306 . 
     In an embodiment, IE  102  may scan an user&#39;s e-mail inbox for domains  306 C or retailers  306 A that correspond to a domain  314 . IE  102  may then use format  312  to process the e-mail receipt  304  for known domains  314 , which consumes fewer processing resources than having to perform full text and/or metadata searches for purchase information  306  from receipts  304 . In an embodiment, domain  314  may include subject line  306 B information as well, to distinguish between purchases and returns originating from the same retailer  306 A or domain  306 B. 
     Maintaining and using the list of domains  314  for which known format  312  have been identified, may save processing and memory resources in more quickly identifying e-mail receipts  304  from normal non-receipt e-mails or messages. Processing using format  312  may also save additional information in extracting or determining purchase information  306  from an identified e-mail receipt  304 . For example, if there are no known formats  312 , then a system would have to perform a full text scan of the same e-mails in the same formats repeatedly to both identify a potential e-mail receipt  304  and to extract information from an e-mail receipt. This may require and performing numerous text comparisons to identify which information is or is not present in the text  308  and/or metadata  310  of a particular e-mail. 
     If no corresponding format  312  exists for a particular domain  306 C or retailer  306 A, then IE  102  may scan text  308  and/or metadata  310  for one or more keywords  316  to determine whether or not the e-mail is an e-mail receipt  304 . Keywords  316  may include one or more words or phrases that are commonly associated with e-mail receipts  304  and purchase information  306 . 
     For example, IE  102  my scan the subject  306 B for keywords  316  such as: receipt, purchase, buying, order, shipping, or congratulations—any of which may indicate that a message is an e-mail receipt  304 . If, for example, one of these keyword  316  is identified in the subject line  306 B (or text  308 ) of an e-mail or message, then IE  102  may determine that the message is an e-mail receipt  304  that pertains to the purchase of an item or product. 
     IE  102  may scan the text  308  of the e-mail message  304 , for other keywords  316  corresponding to other purchase information  306 . For example, “$” may indicate a purchase price  306 E. Or, for example, keywords such as: computer, tablet, memory, laptop, monitor, shoes, jeans, shirt, may indicate various object  306 D that were purchased. IE  102  may identify a group of characters in a date format  306 F (e.g., MM/DD/YYYY) or identify a date of the e-mail receipt  304  from metadata  310 . 
     In an embodiment, IE  102  may record or track the retailer  306 A, subject  306 B, and/or domain  306 C information to generate formats for newly discovered domains  314 . Using machine learning, IE  102  may generate and compile a growing list of domains  314  and formats  312 . 
     Using the purchase information  306  extracted from the text  308  and/or metadata  310  of an e-mail receipt  304 , IE  102  may identify  122  a purchased object  306 D. IE  102  may identify sold and/or listed objects  110 ,  120  that are the same or similar to the identification  122  to generate a price  104  and/or demand indicator  136  for the purchased object  306 D corresponding to the identification  122 , as described above. Embodiments for identifying the same or similar items are described above. 
     IE  102  may use the extracted purchase information  306 , and information from EM  112 , to fill in a pre-configured shell for listing  114 , and enable the user to post the listing  114  to resell purchased object  306 D at the recommended or specified price  104 , or at any other price desired by the user. As described above, IE  102  may also enable alerts  132  to be set that monitor and update a price  104  as items or objects are listed and/or sold on EM  112 . 
     In an embodiment, IE  102  may periodically scan a user&#39;s inbox for new purchases, tracking which previous e-mails were already scanned, without having to scan them again. For example, IE  102  may record or store (in user account information  134 ) a date/time of the last or most recently received message that was processed from e-mail account  302 . Then, for example, during a subsequent processing, IE  102  may only process those messages that were received after the stored date/time. In another embodiment, a user may forward or submit an e-mail receipt  304  to IE  102  and receive in return an estimated or projected price  104 , listing  114 , and an option to resell purchased product on EM  112  without IE  102  performing a full or partial e-mail account  302 . 
       FIG.  4    is a flowchart  400  illustrating operations of an example inventory ingestion engine and pricing system that identifies and prices for sale objects from email receipts, according to some embodiments. Method  400  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.  4   , as will be understood by a person of ordinary skill in the art. Method  400  shall be described with reference to  FIG.  3   . However, method  400  is not limited to the example embodiments. 
     In  410 , an e-mail receipt identifying a purchased object is identified. IE  102  may receive authorization from a user to access the user&#39;s e-mail account, including the user&#39;s ID and password for access. IE  102  may access the user&#39;s email server or provider using any well-known methods, techniques, technologies, systems, processes, etc., existing now or developed in the future. Then, IE  102  may scan metadata  310  or a subject line  306 B information from the e-mails to determine whether the message is an e-mail receipt  304 . The e-mail receipt  304  may include a message that identifies a product that was purchased from a retailer  306 A. 
     In an embodiment, IE  102  may determine whether the e-mail receipt  304  is received from or otherwise associated with a known domain  314 . In an embodiment, the messages may include messages from a social media account. If IE  102  determines that the e-mail receipt  304  corresponds to a domain  314 , then format  312  may be used to extract purchase information  306  (including identifying purchased object  306 D) from e-mail receipt  304 . 
     In an embodiment, IE  102  may maintain a history or repository of email or receipt templates regarding a particular domain  314 . Then, IE  102  may cross-reference the date associated with a particular e-mail, to identify a specific template corresponding to the domain  314  for the email. The template may identify where the metadata  310  is located and/or how it is arranged. 
     If, however, no domain  314  match exists. IE  102  may determine whether the message is an e-mail receipt  304  or just a regular non-receipt message. In an example, IE  102  may scan the subject  306 D for keywords  316  indicating it may be a receipt, such as: receipt, purchase, congratulations, shipping, etc. If a receipt is identified, then IE  102  may scan the remaining text  308  and/or metadata to extract purchase information  306  based on one or more additional keywords  316 . 
     Or, for example, IE  102  may perform a domain lookup to identify whether a domain associated with the e-mail corresponds to a previously identified or known e-commerce website. If the domain is associated with an e-commerce website, then IE  102  may parse the subject or metadata of the e-mail for price and other receipt information. 
     in  420 , the e-mail receipt is parsed for an identification of the purchased object. For example, if email receipt  304  corresponds to a known domain  314 , IE  102  may determine purchased object information  306 D based on a known format  312  of e-mail receipt  304 . Or, for example, IE  102  may scan the text  308  of e-mail receipt for keywords  316  commonly associated with a purchased object  306 D such as: description, item, model number, serial number, computer, pants, boots, size, shirt, or different known manufacturers (e.g., Apple, Samsung, Kenneth Cole, etc.). 
     In  430 , a set of items that are the same or similar to the purchased object are identified from an electronic marketplace. For example, IE  102  may determine an identification  122  of the item or product purchased from purchase information  306  extracted from e-mail receipt  304 . As described above, IE  102  may identify listed objects  120  and/or sold objects  110  from EM  112  that are similar to identification  122  and features  124 . Embodiments for identifying objects that are the same or similar to FSO  106  are described in U.S. patent application Ser. No. 16/288,379 titled “Probabilistic Item Matching and Searching” and U.S. patent application Ser. No. 16/288,373 titled “Probabilistic Search Biasing and Recommendations” that were referenced above, and that are herein incorporated by reference in their entireties, and IE  102  may employ any of these incorporated embodiments (individually or in any combination with the teachings of this disclosure) to achieve the functions described herein. 
     In an embodiment, IE  102  may request additional information from a user confirming identification  122  and/or features  124 . The user may change, add, or remove identification  122  or features  124 . IE  102  may then identify similar objects from EM  112 . 
     In  440 , a price for each of a subset of the identified set of similar items that have sold on the electronic marketplace is determined. For example, IE  102  may determine which objects from EM  112  most closely resemble identification  112  and features  124  or purchased object  3061 ). IE  102  may request the pricing history  128  for those objects, which may include the price for which the objects  110  actually sold on EM  112 . 
     In  450 , a sale price for the purchased object is generated based on the determined prices for the subset of similar items that have sold. For example, IE  102  may select a subset of all the sold objects  110  that are similar to purchased object  306 D (e.g., ten objects) and generate a sales price  104 . The subset may include the most recently sold, the highest priced, the lowest priced, or random selections of objects from which to generate sales price  104 . 
     For example, if 100 similar cameras  110  may have sold in the previous 3 months, with identical features  124  to purchased object  306 D, IE  102  may randomly select 15 of the cameras and use those prices to generate price  104 . In an embodiment, IE  102  may determine a pricing trend (increasing, decreasing, flat) from the previous sales, and provide this with price  104 . In an embodiment, IE  102  may account for the listed prices of objects  120  as well in generating price  104 . 
     In  460 , the price is provided responsive to the identifying. For example, IE  102  may send a text or e-mail to the user identifying the purchased object  306 D, e-mail receipt  304 , and generated price  104 . In an embodiment, IE  102  may also provide demand indicator  136  information as well, which may include a demand or searching trend. IE  102  may automatically generate a listing  114  for purchased object  306 D on EM  112 , and may submit listing  114  upon a user request or a price alert  132 . 
     Automatic Inventory Ingestion Based on Analyzing Retailer Websites 
       FIG.  5    is a block diagram  500  illustrating an example inventory ingestion engine and pricing system that identifies and prices for sale objects from a retailer website, according to some embodiments. System  500  may simplify the process of listing products for sale on EM  112 , and may perform pricing and demand determination functionality similar to what is described above with respect to systems  100  and  300  as described in reference to  FIGS.  1  and  3   . 
     In an embodiment, a user may be viewing or accessing a retailer website  504  (such as AMAZON, EBAY, TARGET, WAYFAIR, etc.) using a browser  508 . Browser  508  may include a web browser or other app or application configured to operate on a mobile or other computing device that enables a user to view retailer website  504 . For example, browser may include an app associated with a particular retailer that is operating on a mobile device  116 . Retailer website  504  may include an electronic portal from which a user can purchase, has purchased, or can see previously purchased products (e.g., purchased object  506 ) from a retailer. 
     in an embodiment, plugin  510  may be a program that operates in conjunction or simultaneous with or as a part of browser  508 . In an embodiment, plugin  510  may be downloaded and installed and operate in the background while browser  508  is operating. Plugin  510  may be configured or authorized (by a user) to display pop-ups or other notifications to user, and/or receive information about user&#39;s activity from browser  508  and/or on retailer website  504 , including purchase history  512 A and other user account information. 
     In an embodiment, browser  508  may include or be configured with a plug-in (or other application)  510  that monitors a user&#39;s activity on browser  508  or on a particular website  504 . For example, plugin  510  may monitor which websites (e.g.,  504 ) and/or webpages  512  a user visits or requests. 
     In an embodiment, IE  102  (via plugin  510 ) may have authorized access to a purchase history webpage  512 A of retailer website  504 . Purchase history  512 A may include a webpage or screen in which retailer website  504  may provide information about one or more previous items or objects  506  that were viewed and/or purchased from or through retailer website  504 . In an embodiment, retailer website  504  may either sell products or items directly to a consumer or user, or may handle financial transactions related to the purchase or sale of items (e.g., purchased objects  506 ). In an embodiment, purchase history  512 A may include items or purchased objects  506  that were purchased from kiosks or store locations associated with a particular retailer, in addition or alternatively to purchased objects  506  from retailer website  504 . 
     In an embodiment, plugin  510  may generate or display, via browser  508 , a resell button  502  for each previously purchased object  506  listed in purchase history page  512 A. For example, if purchase history  512 A includes five purchased objects  506 , plugin  510  may generate a resell button  502  next to each purchased object  506  that is eligible for resale through EM  112 . In an embodiment, certain items or purchases may be prohibited from being resold on EM  112  (e.g., such services, downloaded electronic media (e.g., such as music files, electronic books, computer programs), or items with profanity or nudity). 
     In an embodiment, IE  102  may receive a notification when a user presses or otherwise selects a resell button  502  on a page  512  of a retailer website  504 . The notification may include purchase information  306  (e.g. of  FIG.  3   ) about the corresponding purchased object  506  from the retailer website  504 . IE  102  may determine an identification  122  and features  124  of the purchased object  506  based on this purchase information  306 . For example, the purchase information  306  may include manufacturer, model, images, serial number, size, color, or other information that may enable IE  102  to identify purchased object  506 . 
     As discussed above, IE  102  may perform an identification  122  and features  124  comparison to identify similarly listed  120  and sold objects  110  in EM  12 , retrieve price information  128 , and generate a price  104  for purchased object  506 . Upon a user request or later based on an alert  132 , IE  102  may automatically generate and submit a listing  114  for the purchased object  506  on EM  112 . 
     In an embodiment, the resell button  502  may be any visual indicator or icon that indicates a purchased object  506  may be eligible for resale on EM  112 . In an embodiment, resell button  502  may include the generated or estimated resell price  104  (or range of prices) for a particular purchased object  506 . In an embodiment, purchase history  512 A may include a resell all button that enables a user to resell all the items eligible for resale on EM  112  from purchase history  512 A. The resell all button may include a price or range of prices  104  for selling all the eligible items of purchase history  512 A. In an embodiment, plugin  510  may enable a user to select or deselect a collection or set of objects  506  to be sold on EM  112 , individually or as a set/collection. 
     Or, for example, plugin  510  may enable a user to select a subset of items to be resold individually and/or as a collection of items on EM  112 . For example, two matching nightstands may be sold as a collection, whereby both must be purchased in the same transaction or are offered on the same listing  114  on EM  112 . 
     In an embodiment, plugin  501  may generate price  104  (e.g., via resell button  502 ) without receiving a request from a user to check the price. For example, IE  102  may scan the purchase history information  512 A associated with a user account, and identify and price the objects  506  available for resale. IE  102 , via plugin  510 , may similarly indicate that certain purchased objects  506  are not eligible for resale, or listing  114  and pricing information must be determined manually. 
     In an embodiment, IE  102  may generate a pre-pay value  514 . Pre-pay value  514  may include a monetary price, tokens, credits, or discounts that may be transferred or applied to a user account  134  at a time of listing  114  or prior to purchased object  506  actually being sold on EM  112 . Pre-pay value  514  may provide a user an additional incentive for reselling the purchased object  506  on EM  112 . 
     In an embodiment, a user may be purchasing a new item or object  516  from retailer website  504 , which may include a listed purchase price  518 . In an embodiment, EM  112  and retailer website  504  may include a partnership, relationship, or arrangement, such that pre-pay value  514  may be offered as a credit or discount on a pending purchase from a checkout page  512 B of retailer website  504 . 
     For example, plugin  510  may detect that a user is on checkout page  512 B of retailer website  504 . IE  102  (via plugin  510 ) may provide a notification that if one or more purchased objects  506  (from purchase history  512 A) were listed for resale, user may receive the generated price  104 , or that pre-pay value  514  may be applied as a credit or discount on the pending purchase of new object  516 . 
     For example, a user may be purchasing a new laptop for $1000 on checkout page  512 B. However, the user may have previously purchased a laptop (or any other item, such as shoes, a camera, etc.) for $750. IE  102  may determine that the resell price of the old laptop is now $500. IE  102  may further compute or generate a pre-pay value of $375 for listing the older laptop on EM  112  (and maintaining the listing  114  through a sale, otherwise the $375 may be credited back to the user account  134  of a user). 
     Then, for example, a user may purchase the new laptop for $1000−375=$625. The user account  134  may later be credited for any money greater than $375 for which the laptop sells on EM  112 . If, however, the user sells the old laptop for $300, the user account  134  may be charged $75. In an embodiment, IE  102  may generate pre-pay value  514  based on a number of different factors such as the estimated resale price  104 , demand indicator  136 , and a trust score  520  associated with user account  134  to determine pre-pay value  514 . 
     Trust score  520  may be a user&#39;s rating on EM  112 , and may include factors such as the condition of previously sold products, a timeliness of shipment of sold products or payment for purchases, and other factors. In an embodiment, a user with a higher trust score  520  may be provided a higher pre-pay value  514  relative to a user with a lower trust score  520  or history of transactions on EM  112 . In an embodiment, trust score  520  may include an indication of how much money is available in a user account or current balance. 
       FIG.  6    is a flowchart  600  illustrating operations of an example inventory ingestion engine and pricing system that identifies and prices for sale objects from a retailer website, according to some embodiments. Method  600  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  600  shall be described with reference to  FIG.  5   . However, method  600  is not limited to the example embodiments. 
     In  610 , a selection of a resell icon associated with a purchase history page of a retailer is received. For example, IE  102  may receive a selection of resell icon  502  from a purchase history page  512 A of retailer website  504 . Retailer website  504  may include a website viewable in a web browser  508  and/or via an app on a mobile device  116 . 
     In an embodiment, IE  102  may retrieve the purchase history information from retailer website  504 , and identify  122  purchased objects  506  and prices  104  associated with the selected resell icon and based on EM  112 . Plugin  510  may monitor which websites a user browses (using browser  508 ). When a user requests purchase history page  512 A, plugin  510  may generate, provide, or indicate that a resell icon  502  is to be displayed next to one or more purchased objects  506  (that may have been previously identified and priced by IE  102 ). 
     In  620 , an object associated with the purchase history of the retailer that was purchased prior to receiving the selection is identified. For example, IE  102  may receive a notification indicating which purchased object(s)  506  from purchase history  512 A a user wants to sell on EM  112 . IE  102  may identify any unidentified objects  506 , or may update the pricing (if needed) for any previously identified and priced objects  506 . 
     In  630 , a set of items that are the same or similar to the identified purchased object is identified from an electronic marketplace. For example, IE  102  may identify sold objects  110  from EM  112  that are similar to purchased object  506 . The similarity may include visually similar objects, objects from the same manufacturer, model, or the same category of products. The sold objects  110 , from EM  112 , may include images  118  and features  126  that are similar to purchased object  506 . This may include identifying any newly sold objects  110  that were not accounted for in a previous pricing model  104 . Embodiments for identifying objects that are the same or similar to FSO  106  are described in U.S. patent application Ser. No. 16/288,379 titled “Probabilistic Item Matching and Searching” and U.S. patent application Ser. No. 16/288,373 titled “Probabilistic Search Biasing and Recommendations” that were referenced above, and that are herein incorporated by reference in their entireties, and IE  102  may employ any of these incorporated embodiments (individually or in any combination with the teachings of this disclosure) to achieve the functions described herein. 
     In  640 , a price for each of at least a subset of the set of similar items that have sold is determined from the electronic marketplace. For example, IE  102  may determine a price  128  for the sold objects  110  that are similar to purchased object  506 . This may include performing a feature comparison between features  124  of purchased object  506  and features  126  of sold objects  110 . 
     In  650 , a sale price for the purchased object is generated based on the determined prices of the subset. For example, as described above, IE  102  may generate a resale price  104  for purchased object  506 . The resale price  104  may include a guaranteed sale price, a projected price, or a range of prices, and may account for a user&#39;s trust score  520  on EM  112 . In an embodiment, a higher trust score  520  may result in a higher projected price  104 . 
     In  660 , the sale price is provided responsive to the identifying the purchased object. For example, the price  104  may be provided to a user via browser  508  as a pop-up, or as a notification on purchase history page  512 A. In an embodiment, price  104  may be provided on a checkout page  512 B of retailer website, and may be deducted (at least in part) from the purchase price  518  of a new object  516  as pre-pay value  514 , thus effectively providing a user a discount on a new object in exchange for selling purchased object  506  on EM  112 . 
     Providing Market Descriptors About Pricing For Objects 
       FIG.  7    is a block diagram  700  illustrating an example inventory ingestion engine and pricing system that identifies and prices for sale objects, and provides market descriptors about the pricing, according to some embodiments. System  700  may simplify the process of listing products for sale on EM  112 , and may perform pricing and demand determination functionality similar to what is described above with respect to systems  100 ,  300 , and  500  as described in reference to  FIGS.  1 ,  3 , and  5   . 
     In an embodiment, IE  102  may generate a market descriptor  702  that provides detailed information or context about the existing market for an FSO  106  on EM  112 . Rather than just providing a projected or estimated resale price  104  for FSO  106 , market descriptor  702  may include additional information from actual sales that have occurred on EM  112 , regarding sold objects  110 A,  110 B,  110 C that are similar to FSO  106  based on image  108  or feature information (including descriptions)  124  comparisons. 
     In an embodiment, market descriptor  702  may include images of a selection of sold objects  110  that are identified as being similar to FSO  106 . The selected objects of market descriptor  702 , may include the most recently sold objects  110 , the highest priced objects  110 , the objects  110  that are most similar to FSO  106  (in image  118 A or features  124 ), the objects  110  are similarly geographically located, etc. In another embodiment, market descriptor  702  may include listed objects  120  in addition or alternatively to sold objects  110 A-C. In an embodiment, the sold objects  110 A-C from market descriptor  702  may have been used to generate price  104 . 
     The sold objects (or listed objects)  110 A-C of market descriptor may include images  118 A, price  128 A, sell times  130 , and links  704 . Image  118 A may include one or more images from EM  112  from a listing  114  of the sold object  110 A. Price  128 A may include a sold price, or may include a range of prices for which the object has been or is currently listed. Sell time  130  may include a date of sale or how long the object is/was listed prior to be sold. 
     In an embodiment, market descriptor  702  may include a link  704  to a listing  114  corresponding to the sold object  110 A in EM  112 . For example, by clicking link  704  or entering the address of link  704  in a browser, a user may see the actual listing of the object on EM  112 . In an embodiment, link  704  may route a user to a list or listings  114  of the sold objects  110  and/or listed objects  120  that were accounted for in generating price  104 . 
       FIG.  8    is a flowchart  800  illustrating operations of an example inventory ingestion engine and pricing system that identifies and prices for sale objects and provides market descriptors about the pricing, according to some embodiments. Method  800  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.  8   , as will be understood by a person of ordinary skill in the art. Method  800  shall be described with reference to  FIGS.  1 ,  3 ,  5 , and  7   . However, method  800  is not limited to the example embodiments. 
     In  810 , an identification of a for sale object to be sold on an electronic marketplace is determined. For example, IE  102  may receive an image  108  of FSO  106 , an e-mail receipt  304 , or other indication of a purchased object  506 , and identify a FSO  106  that a user wants to resell on EM  112 . 
     In  820 , a set of items similar to the identification of the for sale object is identified from the electronic marketplace. For example, as described above, IE  102  may identify a set of sold items  110  from EM  112  that are similar to FSO  106 . Embodiments for identifying objects that are the same or similar to FSO  106  are described in U.S. patent application Ser. No. 16/288,379 titled “Probabilistic Item Matching and Searching” and U.S. patent application Ser. No. 16/288,373 titled “Probabilistic Search Biasing and Recommendations” that were referenced above, and that are herein incorporated by reference in their entireties, and that is herein incorporated by reference in its entirety, and IE  102  may employ any of these incorporated embodiments (individually or in any combination with the teachings of this disclosure) to achieve the functions described herein. 
     In  830 , a price for each of at least a subset of the set of similar items that have sold through the electronic marketplace is determined. For example, IE  102  may identify a price  128  of the objects that have been listed on and sold through EM  112 , or one or more other websites associated with EM  112 . 
     In  840 , a sale price for the for sale object based on the determined prices of the items of the subset is generated. For example, as discussed above, IE  102  may generate a price  104  for the FSO  106  based on the pricing histories  128  of listed  120  and/or sold objects  110 . 
     In  850 , an image for one or more of the subset of items is retrieved from the electronic marketplace. For example, the images  118  of the sold objects  110  may be retrieved from EM  112 . 
     In  860 , a market descriptor comprising the sale price and the retrieved one or more images is generated. For example, IE  102  may generate a market descriptor  702  including a selection of the objects  110 ,  120  which are similar to FSO  106 . Market descriptor  702  may include images  118 A, prices  128 A, sell times  130 , and links  704 . In an embodiment, market descriptor  702  may include a link that redirects a browser or app to a list of the identified objects of EM  112  that are identified as being similar to FSO  106 . In an embodiment, this may only include recently listed or sold objects e.g., within the previous 90 days). 
     In  870 , the market descriptor is provided for display to the user. For example, IE  102  may display market descriptor  702  in an app or website, or e-mail or mobile phone, or next to image  108  of FSO  106 , to name just some examples. 
       FIG.  9    is a block diagram illustrating example operations of an invent ingestion engine and pricing system that identifies and prices for sale objects from images, according to some embodiments.  FIG.  9    is generally related to  FIGS.  1  and  2    and the associated teachings contained herein. Features and functionalities as described in the system  900  may be performed by the computing system  100  as described above with respect to  FIG.  1   . 
     In  910 , an example EM  112  is displayed as it may appear in an app on a mobile device  116 . EM  112  may display various products for sale that have been uploaded by various users and/or that are being sold on EM  112 . EM  112  may be operating through an app on mobile device  116  and/or a website. A user may select a camera button that may enable a user to select one or more previously taken pictures, or as illustrated in the example, take a new picture of an object. 
     In  920 , the camera of a mobile device  116  (when operated through the EM  112  app) may identify a location where a user should center a FSO  106  in the camera&#39;s view. When the camera takes a picture, or detects that an object has been placed in the noted area, IE  102  may begin analyzing the FSO  106 . In an embodiment, the app may take a picture of the object which may be uploaded to one or more servers, such as a cloud computing system, for analysis. As discussed above, IE  102  may then identify the FSO  106  based on the image  108 . 
     Assume, for example, that the object is a phone. Based on identification  122  of the object (FSO  106 ) as being a phone, IE  102  may identify various sold objects  110  and/or listed objects  120  that are similar to FSO  106  (that is, that are phones). In an embodiment, IE  102  may provide a market descriptor  702  that includes images of a selection of the listed and/or sold objects. 
     As shown in  940 , market descriptor  702  may include two sold objects (phones  942 A,  942 B), and one listed object (phone  942 C). The market descriptor  702  may include images of the items, descriptions, a date sold (or time listed) and/or links to listings from EM  112 . Price  104  may be 0 which may indicate that more information may be needed from a user to generate a price. 
     In an embodiment, additional feature information  124  may be required, that would enable IE  102  to generate a more accurate price determination. In an embodiment, a user may have selected phone  942 C as the exemplary to generate the price  104 , which may be added as the price as illustrated in  950 . 
     As further illustrated in  950 , to generate another listing, the user may scan a collection of four pants which are valued or priced at $35 each. In this example, the user or IE  102  may generate a discount for purchasing all four pants as a collection or set. Then, for example, instead of being priced at $140, the collection may be priced at $120. As illustrated in  960 , the total for the scanned items may be $560. 
     The add to sell list button  502  may authorize IE  102  to generate or submit a listing  114  to sell FSO  106  on EM  112 . In  970 , a user may have selected to sell the phone from  940 , and may be prompted to include additional information in listing  114 . This may generate an updated suggested price, or the user may enter her own price and submit the listing  114  to EM  112 . 
       FIG.  10    is a block diagram  1000  illustrating example operations of an inventory ingestion engine and pricing system that identifies and prices for sale objects from email receipts, according to another example embodiment.  FIG.  10    is generally related to  FIGS.  3  and  4    and associated teaches contained herein. Features and functionalities as described in the system  1000  may be performed by the computing system  300  as described above with respect to  FIG.  3   . 
     In  1010 , an example EM  112  is displayed in an app or browser of a mobile device  116 . EM  112  may display various products for that sale that have been uploaded by various users and/or that are being sold on EM  112 . EM  112  may be operating through an app on mobile device  116 . A user may select a camera button that may initiate functionality to enable a user to list products for sale. 
     In  1020 , a user may be prompted with the opportunity to have their electronic message account (e.g., e-mail or social media account) scanned for items that may be sold on EM  112 . 
     In  1030 , a user may select which accounts are to be scanned. The example illustrates e-mail inboxes, cloud or network storage locations, and local disks or memory locations. 
     In  1040 , a user may be provided an option to connect to one or more e-mail accounts  302 , and authorize the EM  112  to retrieve information from those accounts. The user may be asked to provide account information (e.g., userid, password information) or to otherwise login to the account, granting IE or the app access to the messages. 
     In  1050 , as described above, IE  102  may scan the e-mail account  302  for e-mail receipts  304  for both known-domains  314  and unknown domains  314 . In an embodiment, IE  102  may only scan for known-domains  314 , and may flag unknown domains  314  for subsequent manual processing by a user. In an embodiment, IE  102  may provide real-time updates of the receipts  304  that are found in the e-mail account  302  during an e-mail scan process. 
     In  1060 , the scan of the e-mail account  302  for the e-mail receipts  304  may be completed, and IE  102  may generate prices  104 . In another embodiment, price generation for identified receipts may be conducted simultaneously with scanning for email receipts. 
     In  1070 , the prices for the identified FSOs  106  may be provided to the user. IE  102  may provide a price (or range of prices) for selling all the identified items or objects. 
     In an embodiment, IE  102  may store the previous scan. Then, for example, during a subsequent e-mail scan (which may occur periodically, or upon user request), the e-mail scan would occur quicker and consume fewer resources than the initial scan. IE  102  may update some of the prices, if a threshold period of time has passed or additional similar objects have been sold on EM  112 , while other prices may not be updated. The app may show changes in prices and items from previous scans. A user may select particular items to sell, or for which to receive price updates. 
     Enhanced New User Experience Embodiments 
     Embodiments for automatically generating listings  114 ,  141  for offering for sale objects (FSOs)  106  on the electronic marketplace (EM)  112  were described above. Also provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for enhancing the experience of new sellers such that they are further encouraged to generate new listings  1106  for selling FSOs  106  on the EM  112 . The new sellers may use any combination of the above-described embodiments to automatically generate such new listings  1106 , and/or may generate some or all of the new listings  1106  manually. 
     Referring again to  FIG.  1   , in some embodiments, the inventory engine (IE)  102  includes a new seller enhancement module  137 . The IE  102  also includes a listing database  139  that stores listings  141 . These listings  141  were created by sellers to offer their FSOs  106  for sale on the EM  112 , using (for example) any combination of the automatic embodiments described above, and/or using well known manual listing creation techniques, approaches, methods, systems, modules and/or technologies. The listings  141  may include listing(s)  114  associated with user account(s)  134  (as described above). It is noted that new seller enhancement module  137  and the listing database  139  may be in and used with any of the embodiments described above (including, for example and without limitation, the embodiments of  FIGS.  1 ,  3 ,  5   , and/or  7 ). 
       FIG.  11    illustrates the listings  141  in greater detail, according to some embodiments. The listings  141  may include listings  1102  from first time sellers. A first time seller is defined herein as a seller who has created one or more listings  114 , but who has not yet sold any FSO  106  on the EM  112  via those listings  114 . Some or all of those first time seller listings  1102  may qualify as eligible listings  1108 , as described below. Also, some of those first time seller listings  1102  may be new listings  1106  from new sellers, as described below. 
     The listings  141  may also include listings  1104  from non-first time sellers. A non-first time seller is defined herein as a seller who has previously sold one or more FSOs  106  on the EM  112 . 
     In some embodiments, to grow the number of sellers and listings  141 , the new seller enhancement module  137  may implement incentives for certain users of the EM  112 . Such certain users may include users who have not previously created a listing  141  for the EM  112 , or who have created less than a predetermined number of listings  141  on the EM  112 , or who have not created a listing  141  on the EM  112  for a predetermined period of time, or who has created one or more listings  141  on the EM  112  but none have sold, or who registered with the EM  112  after a certain date. Such certain users may be called herein new sellers. New sellers are not limited to the above examples, but may instead include any user defined by the EM  112  and/or new seller enhancement module  137 . Listings  141  created by new sellers are referred to as  1106 , as shown in  FIG.  11   . As noted, to be a new seller, the user also must never have previously sold a FSO  106  on the EM  112  (thus, new sellers once they create a listing  1106  also become first time sellers, as indicated in  FIG.  11   ). 
     In some embodiments, the incentive implemented by the new seller enhancement module  137  may be characterized as follows. If a new seller generates X new listings  1106 , then the new seller may be allowed to purchase one or more FSOs  106  offered in certain ones of listings  141  on the EM  112  at no or reduced cost. The certain listings may be referred to herein as eligible listings  1108  (as shown in  FIG.  11   ). In one example embodiment, X may be 4, although X may be any other integer value. 
     In some embodiments, a listing  141  may qualify as an eligible listing  1108  if: (1) the listing  141  has an offer price (such as a buy it now price) less than a predetermined maximum amount, such as but not limited to $25; and (2) the listing  141  is a listing  1102  by a first time seller (that is, the listing  141  was generated by a seller who has never sold anything on the EM  112 ). The latter requirement has an advantageous practical effect, because in this way, two (rather than one) sellers are provided with incentives to begin and continue selling on the electronic marketplace  112 : (1) the new seller who is generating multiple new listings  1106  and is being given FSOs  106  from eligible listings  1108  at no or reduced cost, and (2) the first time sellers associated with the eligible listings  1108  who achieve their first sale on the EM  112 . In some embodiments, only requirement (2) must be satisfied for a listing  141  to be an eligible listing  1108 . 
       FIG.  12    is a flowchart  1202  illustrating operations for enhancing the experience for new sellers when creating new listings  1106 , according to some embodiments. 
     In  1204 , a new seller (as defined above above) may create X new listings  1106  offering FSOs  106  for sale on the EM  112 , with X being an integer greater than 0. The new seller may generate these new listings  1106  manually, and/or automatically using one or more of the embodiments described above. 
     In  1206 , the new seller enhancement module  137  may determine whether the new seller created enough new listings  1106  to qualify for the incentive program described herein. That is, the new seller enhancement module  137  may determine if X is equal to or greater than a predetermined threshold associated with the incentive program. In some embodiments, the threshold may be equal to 4, but any other integer greater than 0 could alternatively be used. In some embodiments, the new seller must create new listings  1106  equal to or greater than the threshold within a predetermined period of time (as established by the new seller enhancement module  137 ). The time period may be 1 day, a week, a month, or any other time period. 
     If the new seller qualifies for the incentive program, then the new seller enhancement module  137  may provide the new seller with the ability to purchase one or more FSOs  106  of eligible listings  1108  at no cost, or reduced cost. For example, the new seller enhancement module  137  may provide the new seller with coupons, credits, promotions, etc., that may be applied at checkout when purchasing FSOs  106  of eligible listings  1108 . 
     As noted above, the operation of the new seller enhancement module  137  has a number of practical effects that are implemented by the computing technology and operations discussed herein. For example, as indicated by  1208 , two sellers are provided with incentives to begin and continue selling on the electronic marketplace  112 : (1) the new seller who is generating multiple new listings  1106  and is being given FSOs  106  from eligible listings  1108  at no or reduced cost, and (2) the first time sellers associated with the eligible listings  1108  who achieve their first sale on the EM  112 . 
     It is noted that new listings  1106  from new sellers qualify as listings  1102  a first time sellers, and thus may also qualify as eligible listings  1108  if they satisfy the requirements described above. If they qualify as eligible listings  1108 , then the incentive program described herein may result in a quicker sale, thereby furthering the practical effect described above. 
     As soon as a new user listing  1106 , an eligible listing  1108 , or a first time seller listing  1102  sells, then the other listings  1102  of that seller become non-first time seller listings  1104 . The new seller enhancement module  137  manages such storage and classification of the listings  141  in the listings database  139 . 
     Various embodiments may be implemented, for example, using one or more well-known computer systems, such as computer system  1300  shown in  FIG.  13   . One or more computer systems  1300  may be used, for example, to implement any of the embodiments discussed herein, as well as combinations and sub-combinations thereof. 
     Computer system  1300  may include one or more processors (also called central processing units, or CPUs), such as a processor  1304 . Processor  1304  may be connected to a communication infrastructure or bus  1306 . 
     Computer system  1300  may also include customer input/output device(s)  1303 , such as monitors, keyboards, pointing devices, etc., which may communicate with communication infrastructure  1306  through customer input/output interface(s)  1302 . 
     One or more of processors  1304  may be a graphics processing unit (GPU). In an embodiment, a GPU may be a processor that is a specialized electronic circuit designed to process mathematically intensive applications. The GPU may 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  1300  may also include a main or primary memory  1308 , such as random access memory (RAM). Main memory  1308  may include one or more levels of cache. Main memory  1308  may have stored therein control logic (i.e., computer software) and/or data. 
     Computer system  1300  may also include one or more secondary storage devices or memory  1310 . Secondary memory  1310  may include, for example, a hard disk drive  1312  and/or a removable storage device or drive  1314 . Removable storage drive  1314  may 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  1314  may interact with a removable storage unit  1318 . Removable storage unit  1318  may include a computer usable or readable storage device having stored thereon computer software (control logic) and/or data. Removable storage unit  1318  may be a floppy disk, magnetic tape, compact disk, DVD, optical storage disk, and any other computer data storage device. Removable storage drive  1314  may read from and/or write to removable storage unit  1318 . 
     Secondary memory  1310  may include other means, devices, components, instrumentalities or other approaches for allowing computer programs and/or other instructions and/or data to be accessed by computer system  1300 . Such means, devices, components, instrumentalities or other approaches may include, for example, a removable storage unit  1322  and an interface  1320 . Examples of the removable storage unit  1322  and the interface  1320  may 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  1300  may further include a communication or network interface  1324 . Communication interface  1324  may enable computer system  1300  to communicate and interact with any combination of external devices, external networks, external entities, etc. (individually and collectively referenced by reference number  1328 ). For example, communication interface  1324  may allow computer system  1300  to communicate with external or remote devices  1328  over communications path  1326 , which may be wired and/or wireless (or a combination thereof), and which may include any combination of LANs, WANs, the Internet, etc. Control logic and/or data may be transmitted to and from computer system  1300  via communication path  1326 . 
     Computer system  1300  may also be any of a personal digital assistant (PDA), desktop workstation, laptop or notebook computer, netbook, tablet, smart phone, smart watch or other wearable, appliance, part of the Internet-of-Things, and/or embedded system, to name a few non-limiting examples, or any combination thereof. 
     Computer system  1300  may be a client or server, accessing or hosting any applications and/or data through any delivery paradigm, including but not limited to remote or distributed cloud computing solutions; local or on-premises software (“on-premise” cloud-based solutions); “as a service” models (e.g., content as a service (CaaS), digital content as a service (DCaaS), software as a service (SaaS), managed software as a service (MSaaS), platform as a service (PaaS), desktop as a service (DaaS), framework as a service (FaaS), backend as a service (BaaS), mobile backend as a service (MBaaS), infrastructure as a service (IaaS), etc.); and/or a hybrid model including any combination of the foregoing examples or other services or delivery paradigms. 
     Any applicable data structures, file formats, and schemas in computer system  1300  may be derived from standards including but not limited to JavaScript Object Notation (JSON), Extensible Markup Language (XML), Yet Another Markup Language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or any other functionally similar representations alone or in combination. Alternatively, proprietary data structures, formats or schemas may be used, either exclusively or in combination with known or open standards. 
     In some embodiments, a tangible, non-transitory apparatus or article of manufacture comprising a tangible, non-transitory computer useable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system  1300 , main memory  1308 , secondary memory  1310 , and removable storage units  1318  and  1322 , 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  1300 ), may cause 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.  13   . In particular, embodiments can operate with software, hardware, and/or operating system implementations other than those described herein. 
     It is to be appreciated that the Detailed Description section, and not any other section, is intended to be used to interpret the claims. Other sections can set forth one or more but not all exemplary embodiments as contemplated by the inventor(s), and thus, are not intended to limit this disclosure or the appended claims in any way. 
     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. 
     The breadth and scope of this disclosure should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.