Automatic listing of items

Abstract

A method and system for automatically publishing a display on an e-commerce system is disclosed. In one aspect, a method includes: filtering historical display of items with similarities to a specific item; determining a prediction of a future selling price in a future time period for the specific item based on the filtered historical items, the determined prediction including future selling prices with different probabilities of completed sales; receiving a user selection indicating one or more predictions of future selling prices in one or more future time periods; and determining a time to initiate publishing of an online display for the specific item based on the selected one or more predictions.

Description

[0001] Priority claim

[0002] This application claims the benefit of priority to U.S. Application No. 16 / 185,667, filed on November 9, 2018, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure generally relates to digital communication technology. In particular, the present disclosure generally relates to a method for automatically publishing a listing based on one or more user-specified parameters. Background Art

[0004] Many people buy products with the plan to resell them after using them for a limited period of time. However, it is difficult for the buyer to predict the best time to sell. In addition, selling an item requires a certain amount of effort. Inertia may prevent the user from selling the item. Instead, the item may be given to a friend, sold locally, or donated to a charity. In some cases, even if the user has arranged an appropriate time to sell when purchasing the product, they may miss it.

[0005] The approaches described in this section are approaches that could be pursued, but are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In the accompanying drawings, which are not necessarily drawn to scale, similar reference numerals may describe similar components in different views. Similar reference numerals with different letter suffixes may represent different instances of similar components. The accompanying drawings generally illustrate various embodiments discussed in this document by way of example and not limitation.

[0007] The various figures in the drawings depict only example embodiments of the present disclosure and are not to be considered limiting of the scope of the present disclosure.

[0008] Figure 1 is an example user interface that may be implemented in at least some of the disclosed embodiments.

[0009] Figure 2 is an example user interface implemented by at least some of the disclosed embodiments.

[0010] Figure 3A Example notifications or alerts generated in one or more disclosed embodiments are shown.

[0011] Figure 3B Example notifications or alerts generated in one or more disclosed embodiments are shown.

[0012] Figure 4 A graph illustrating example forecasts of selling prices over time generated by one or more disclosed embodiments.

[0013] Figure 5 An example sold price forecast generated in some implementations is shown.

[0014] Figure 6 A user interface is shown that allows a user to select a price range over time for selling a product.

[0015] Figure 7 Example data structures implemented in at least some of the disclosed embodiments are shown.

[0016] Figure 8 Example data flows implemented in at least some of the disclosed embodiments are shown.

[0017] Fig. 9 Graphs showing three sets of predictions 904a - 904c of the market price of an item and the selling price of the item.

[0018] Fig.10 It is demonstrated how the forecasts made by embodiments of the present disclosure can be periodically updated based on market price information.

[0019] Fig.11 is a flow chart of a method for determining how to initiate an online listing of an item.

[0020] Fig.12 is a block diagram of an exemplary client-server based architecture.

[0021] Fig.13 is a block diagram illustrating a representative software architecture that may be used in conjunction with the various hardware architectures herein.

[0022] Fig.14 is a block diagram illustrating components of a machine capable of reading instructions from a machine-readable medium and performing any of the methods discussed herein, according to some example embodiments.

[0023] Fig.15 is a flow chart of a method for generating an alert based on a predicted resale date.

[0024] Fig.16 is an example of a notice of possible resale.

[0025] Fig.17 Example machine learning modules according to some examples of the present disclosure are shown. DETAILED DESCRIPTION

[0026] Reference will now be made in detail to specific example embodiments for carrying out the subject matter of the present invention. Examples of these specific embodiments are shown in the accompanying drawings. It will be understood that these examples are not intended to limit the scope of the claims to the embodiments shown. On the contrary, they are intended to cover alternatives, modifications and equivalents that may be included within the scope of the present disclosure. In the following description, specific details are set forth in order to provide a thorough understanding of the subject matter. Embodiments may be practiced without some or all of these specific details.

[0027] As discussed above, a product may have a useful life for a user. For example, a user purchases a product and uses the product for a period of time, after which the use of the product decreases. For example, a user may purchase a video game and use it frequently shortly after purchase. After a period of time, use of the video game may become less frequent for the user than when the video game was new. After the use of the product decreases, it may be in the user's interest to resell the product. Reselling the product enables the user to regain at least some of the costs spent during the purchase of the product. Selling unused items may also relieve the user of the burden of storing the unused items.

[0028] To address the issues discussed above, the disclosed embodiments predict the price and probability of selling of used goods at the time of purchase, and then automatically perform automatic resale at the optimal time. This helps buyers avoid the above problems when selling their previously purchased items. The disclosed embodiments also encourage buyers with resale plans to make purchase decisions. For example, if a buyer finds that the product he / she wants to buy is likely to be sold for 80% of the purchase price two years later, the buyer may go ahead with the purchase, which without this information may be too risky for the buyer and they avoid completing the purchase.

[0029] The disclosed embodiments promote the resale of items purchased by users. Some of the disclosed embodiments promote resale by predicting the future selling price of the item and suggesting to the user an appropriate time to sell the item based on the predicted selling price. The prediction of the future selling price is based in part on a database of completed sales maintained by the online display system. The prediction can be displayed in a chart of the user interface, allowing the user to intuitively understand how the value of their purchase changes over time, and allowing the user to determine an appropriate resale strategy. In some embodiments, a database of typical usage cycles for various products is maintained. Some embodiments issue an alarm or reminder to the user when the usage cycle expires to prompt the user to provide the product for resale.

[0030] Some embodiments provide an intuitive user interface for selecting a future selling price and / or a timeline for selling. For example, some embodiments receive touch input defining the corners of a rectangle on a chart. The rectangle defines an area covering a time period and the selling price during that time. In some embodiments, if the predicted selling price for the item enters the rectangle, an online listing is automatically published at the indicated time. The following figure illustrates one possible implementation of the method:

[0031] Some of the disclosed embodiments can understand product usage time based on the user's purchase and sale behavior. For example, a machine learning model that receives input including product type information, purchase date, and resale date can be used. The machine learning model can then generate a prediction of the resale date based on other provided product type information and purchase date. Various models can be used in various embodiments. For example, neural networks, decision trees, state vector machines, or other machine learning methods can be used in various embodiments. These predicted resale dates can then be used to trigger automatic listing of items for resale, or at least trigger a user prompt at a specific time, wherein the prompt asks the user whether he wants to list his product for resale.

[0032] Figure 1 104a is an example user interface implemented in at least some of the disclosed embodiments. The user interface 100 displays two options for selling an item. The first option 102a provides for selling an item based on a probability of sale. In other words, the disclosed embodiment generates a forecast for selling an item at a set of prices over a period of time. When the price is higher than the predicted price at a particular time, the probability that a sale will occur at that time will decrease. Similarly, when the price is lower than the predicted price at a particular time, the probability that a sale will occur at that time will increase. By selecting option 102a, the disclosed embodiment maintains a specific probability of sale by changing the selling price over time according to the predicted selling price. The specific probability of sale is entered via edit box 104a. In some embodiments, the predicted selling price is based on the historical selling price of similar products. Option 102b provides a disclosed embodiment for maintaining a fixed selling price over time. A fixed selling price is entered in list box 104b. List Items button 106a is used to list items using the strategy defined by radio buttons 102a to 102b. The cancel button 106b exits the user interface 100 without generating and publishing a display for the item.

[0033] Figure 2 is an example user interface implemented by at least some of the disclosed embodiments. Figure 2200 for selling an item to retain a specific remaining value. For example, a user may wish to retain 80% of the original value of the item. In some aspects, the original value is the purchase value. In other words, the user purchases the item for a first value, and then seeks to retain, for example, 80% of the first value when selling the item. User interface 200 allows the user to enter a retained value in edit box 202. The value can be entered as a percentage or an absolute amount.

[0034] Radio buttons 204a and 204b allow the user to select the probability of the sale retaining the original value. By selecting radio button 204a, the disclosed embodiment will post the listing of the item for sale within three months and provide a 95% probability of retaining the value shown in edit box 202. By selecting radio button 204b, the disclosed embodiment will post the listing of the item within six months and provide a 50% probability of retaining the value shown in edit box 202. Although Figure 2 The example embodiment of shows two choices with two sale probabilities, but other embodiments may provide 3, 4, 5, 6, 7, 8, 9, or 10 choices. In some aspects, the choices may not be selected via radio buttons, but rather via an edit box that allows entry of a sale probability (e.g., anywhere within a continuous range of 1% to 100%). In these aspects, a corresponding schedule for each sale probability may also be displayed by the user interface 200. Similarly, while in Figure 2 A probability of 95% and a probability of 50% are shown in FIG, but other embodiments may show different choices for the probability, such as 90%, 75%, and 60%.

[0035] User interface 200 also shows a checkbox 206. Checkbox 206 is configured to cause the disclosed embodiment to provide an alert to the user if the actual market price of item 201 deviates from the system prediction by more than a specified percentage. In other words, an alert is provided to the user if the market price deviates from the prediction that existed when user interface 200 defined the selling strategy by more than percentage 208. The alert may be provided via email, text message, phone call, social network message, or other messaging service. To facilitate detection of such conditions, the disclosed embodiment may periodically recalculate the prediction for item 201. It may be prudent for the user to re-evaluate the parameters they set for automatic resale if the new prediction deviates sufficiently from the prediction that existed when the user configured automatic resale as defined by user interface 200. For example, a user may set an automatic resale with the premise that if the user sells within three months, they have a 95% chance of retaining 80% of its value. The market price thereafter may indicate that after three months they only have a 40% chance of retaining 80% of its value. Given that the user is unable to retain as much value as they originally expected (e.g., they value the item higher than the revised market conditions dictate), the user may want to accept this new reality and sell their item as quickly as possible or perhaps decide to keep their item without reselling it.

[0036] Figure 3A 300 may be implemented as an email, text, social networking message, pop-up dialog box, or any other messaging interface. Alert 300 is for item 301. The alert includes message 302 indicating that an alert condition previously set for item 301 has been met. For example, message 302 may indicate that a condition has been met via Figure 2 The controls 206 and / or 208 of the user interface 200 previously established conditions.

[0037] Example alert 300 provides the user with three options 304a to 304c to select from. Selecting option 304a provides a review of the selling strategy for item 301. In some aspects, selecting option 304a, followed by selecting OK button 306a, causes user interface 200 to be displayed for item 301, thereby enabling the user to reconfigure the selling and publishing strategy for item 301. Selecting option 304b maintains the existing selling and publishing strategy. Option 304b essentially ignores alert 300. Selecting option 304c postpones the alert to a later time.

[0038] Figure 3BA second example notification or alert generated by one or more disclosed embodiments is shown. Alert 350 may be implemented as an email, text, social networking message, pop-up dialog box, or any other messaging interface. Alert 350 is for item 351. The alert includes message 352 indicating that a previously set alert condition for item 351 has been met. For example, message 352 may indicate that a previously set alert condition for item 351 has been met. Figure 2 The conditions established by controls 206 and / or 208 of user interface 200.

[0039] Example alert 350 provides three options 354a-354c for the user to select from. Each option 354a-354c is described via a corresponding message 364a-364c. Selecting option 354a updates the selling strategy to maintain the reserve value of item 351. Since the market price is lower than predicted, as indicated by message 352, it may be necessary to advance the selling date to maintain the previously held value of item 351. Figure 3B Message 364a indicates the new sale date (e.g., February 6, 2018). Option 354b is selected to maintain the existing sale date, and the user is aware that the retained value may be lower. If the existing sale date is maintained, message 364b provides an updated forecast of the retained value (e.g., $1,250). Option 354c is selected to postpone the alert for a predetermined amount of time.

[0040] Figure 4 A graph illustrating example forecasts of selling prices over time generated by one or more disclosed embodiments. Figure 4 The disclosed embodiments are shown generating an aggregate history 402 of selling prices for products similar to an item. History 402 is based on historical selling prices of similar items over time. For example, in some aspects, time zero 404 on chart 400 indicates when a group of similar products was newly purchased. With each progressive unit of time to the right in chart 400, the resale price of the product decreases as the depreciation curve or life cycle of the product increases. This is reflected in the past selling prices 402. Based on the history of selling prices, the disclosed embodiments generate a prediction of future selling prices for a particular item, represented as line 406 in chart 400. While line 406 shows a single prediction of the selling price at a single point in time, some aspects may generate a prediction interval that may provide a range of selling prices at a particular single point in time. The prediction interval may provide a level at which the selling price falls within a range or interval provided by the prediction. For ease of illustration, such an interval is shown in FIG. Figure 4 are simply shown as lines.

[0041] In determining a selling strategy for an item, some of the disclosed embodiments utilize the prediction represented by line 406. For example, user interface 200 generates specific options (e.g., 204a-204b) for preserving the value of the item (e.g., as specified in edit box 202) based on the prediction represented by line 406. As an example, line 406 represents a prediction of a 90% probability of selling at a determined price at a determined time.

[0042] Figure 5 An example sold price prediction generated in some embodiments is shown. Chart 500 shows three lines 502a-502c indicating three sold price predictions for an item. While each of the three lines 502a-502c typically conveys a single sold price prediction and a specific point in time, some of the disclosed embodiments may generate a prediction interval (which may provide a range or interval of sold prices at a single point in time) and a confidence level that the sold price at the single point in time falls within the provided range. For ease of illustration, Figure 5 The intervals are simplified and three lines 502a - 502c are shown, which may represent three different prediction intervals at three different confidence levels.

[0043] Each of lines 502c represents an additional (e.g., median) probability of selling at the indicated price at the indicated time. For example, since line 502a predicts a higher price than either line 502b or line 502c, the prediction of the selling price of line 502a may provide a lower probability of selling (first probability) than either line 502b (second probability of selling>first probability of selling) and line 502c (third probability of selling>first probability of selling and also>second probability of selling).

[0044] Figure 6 A user interface is shown that allows a user to select a price range over time for selling a product. Interface 600 shows a chart of predicted selling prices 602. User interface 600 shows that a user provides two touch inputs 604a and 604b that define a rectangle 606. Rectangle 606 defines an area of ​​a graph that represents a combination of time from an x-axis 608 and price from a y-axis 610. In some aspects, inputs 604a and 604b define a combination of time and price at which an item display can be posted. As shown, a prediction of future selling prices over time can be displayed on chart 600 to assist the user in selecting an area (e.g., rectangle 606).

[0045] Figure 7 Example data structures implemented in at least some of the disclosed embodiments are shown. The data structures discussed below are implemented as relational database tables in some aspects. In other aspects, the data structures are implemented as memory structures such as arrays or linked lists. Figure 7 It is intended to illustrate the nature of the data maintained by some of the disclosed embodiments and the relationship of the data to each other.

[0046] Table 700 stores display records. Display records store historical results of previous display of items that have been listed and / or sold in the item display system. Each display record stores item type 702, item subtype 704, item condition 706, listing date 708, listing price 710, selling price 712, and selling date 714. Table 700 is used by at least some of the disclosed embodiments to generate predictions of future selling prices over future time periods for items that are similar to the items included in the display records of table 700. In some aspects, the disclosed embodiments generate prediction intervals, confidence intervals, and / or credible intervals. In some aspects, intervals can be generated via one or more machine learning models. For example, a model can be trained by providing the model with parameters that describe the item (e.g., the category of the item, the type of the item, the subtype of the item, or other descriptive parameters). The model can also be provided with a selling price for a specific selling date. The model can then be used to predict the selling price of similar items on future selling dates. Refer to the following Fig.17 Discuss an example architecture for such a model.

[0047] Figure 7 Also shown is a prediction table or database 720. Each record of the prediction table includes an item type 722, an item subtype 724, a condition 726, a selling price 728, a selling date 729, and a confidence value 732. Each record in the prediction table 720 stores a predicted selling price 728 and a selling date 730 with a defined confidence 732. The records of the prediction table 720 can be generated by a prediction engine as discussed below. In some aspects, the selling price 728 can store a range of selling prices, such as a predicted interval at a time defined by the selling date 729. In other aspects, the selling price 728 can represent a single selling price 728, which in some aspects can be an average or midpoint of a predicted interval at the selling date 729.

[0048] Figure 7 Also shown is an item table or database 730. Item database 730 stores information related to items managed by one or more disclosed embodiments. Item database 730 includes an item identifier 734 that uniquely identifies an item. Item database 730 also includes an item type field 736, an item subtype field 738, a condition of the item 739, and a description of the item 742.

[0049] Figure 7Also shown is an example user item table 740. The user item table 740 records which users own which items. Each record in the user item table 740 includes a user identifier 744 that uniquely identifies the user and an item identifier 746 that uniquely identifies the item. The item identifier field 746 can be cross-referenced with the item identifier 734.

[0050] Figure 7 Also shown is a user settings table 750. The user settings table 750 includes a user identifier 752, an item identifier 754, and a sales policy 756. The user identifier 752 uniquely identifies a user and can be cross-referenced with the user identifier 744. The item identifier 754 uniquely identifies an item and can be cross-referenced with the item identifiers 746 and / or 734. The sales policy can define parameters to control the automatic listing of items corresponding to the item identifier 754 when specific parameters are met. As discussed below, the parameters stored in the sales policy field 756 can be set via the user interface 815 displayed by the user interface engine 805. For example, one or more of the user interfaces 100, 200, or 300 can set the parameters stored in the sales policy field 756.

[0051] Figure 7 Also shown is a display database 760. The display database stores items that are listed for sale. In some implementations, the items may be listed for sale on an e-commerce site. Display database 760 includes display identifier 762, item identifier 764, and price 766. Display identifier 762 uniquely identifies the display. Item identifier 764 uniquely identifies the item listed for sale and may be cross-referenced with one or more of item identifier 754, item identifier 746, or item identifier 734. Price 766 identifies the price at which the item (identified by item identifier 764) is listed for sale. In some aspects, the display database may be based on a prediction (e.g., generated by a prediction engine discussed below). Figure 4 and / or Figure 5 ) to set the price to 766.

[0052] Figure 8 Example data flows implemented in at least some of the disclosed embodiments are shown. Figure 8 The prediction engine 805 is shown reading the history database 700 to generate predictions stored in the prediction database 720. The user interface engine 810 reads the predictions 720 and information related to the user items 740 to generate one or more user interfaces 815. The user interface 815 includes, for example, the above-mentioned Figure 1 and Figure 2Example user interfaces 100 and / or 200 are shown. One result of user interface 815 may be user settings stored in user settings database 740. The user settings define listing criteria based on predictions stored in prediction database 720. Exhibition engine 820 reads data included in one or more of history database 700, prediction database 720, user item database 740, and user settings 750 to determine whether to initiate a listing for a particular item. If a listing is initiated, the exhibition is stored in exhibition database 760.

[0053] Each of prediction engine 805, user interface engine 810, and display engine 820 represents instructions that can be stored in electronic hardware memory. The instructions configure the hardware processing circuit to perform one or more functions discussed above with respect to each prediction engine 805, user interface engine 810, and / or display engine 820.

[0054] Fig. 9 A chart of three sets of predictions 904a to 904c for the estimated market price of an item and the selling price of an item is shown. The estimated market price over time is shown as line 902. The estimated market price 902 for a product can be generated based on the aggregation of multiple selling prices of a specific product type. In some aspects, multiple selling prices can be averaged or determined to generate a median selling price of the estimated market price 902. In some embodiments, the estimated market price can be based on predictions 904a to 904c. The first prediction for the selling price is shown as line 904a. The predicted selling price represented by line 904a is usually higher than the market price at a specific time. Therefore, the predicted selling price represented by line 904a has a lower probability of causing the sale of the item than the selling price represented by the second line 904b, and the second line 904b defines a predicted selling price lower than line 904a at a specific time. The selling price represented by line 904c may have a higher probability of completing the sale of the item than one of the selling prices represented by line 904a or line 904b. Implementations of the present disclosure may periodically update the predictions represented by lines 904a to 904c based on the additional selling price information. For example, as discussed above, parameters for automatic relisting and / or reselling of items may be based on a first version of one or more of lines 904a to 904c or 902. Updating one or more of the predictions represented by lines 904a to 904c may also cause a change in prediction 902, which may be generated based on aggregating predictions 904a to 904c.

[0055] Fig.10 It is demonstrated how the forecasts made by embodiments of the present disclosure can be periodically updated based on market price information. Fig.10The estimated market price of an item is shown over time, represented as line 1002. Lines 1004a and 1004b represent predictions of the selling price of the item over time at different confidence levels. Fig. 9 902 in , the estimated market price shown by line 1002 can be generated based on data representing multiple sales transactions at various market prices. In various aspects, the estimated market price can be based on an average or median value of the sales price during a period of time. Line 1004a represents a prediction at a first confidence level, and line 1004b represents a prediction at a second confidence level. The predictions represented by lines 1004a and 1004b are regularly updated based on the market price information represented by line 1002. For example, in the area of ​​the chart shown as 1006, the market price shows a decline beyond the prediction. This may be the result of various factors such as weather, the reputation of the item, etc. Fig.10 1000 shows that by at least some disclosed embodiments periodically updating their predictions based on market price information, a prediction for a given confidence level can maintain its confidence level over time. For example, if the predictions are not updated based on market price information, a prediction with a 95% confidence level on March 1st may have a lower confidence level (e.g., a 30% confidence level) at a later time. In embodiments where the selling price of an item is set based on a confidence level, updating the predictions helps ensure that the confidence level desired by the user for the sale is maintained over time.

[0056] Fig.11 is a flow chart of a method for determining how to initiate the publication of an online display of an item. Fig.11 One or more of the functions discussed may be performed by a hardware processing circuit. For example, an electronic memory may store instructions that, when executed by the hardware processing circuit, configure the hardware processing circuit to perform one or more of the functions discussed below.

[0057] In operation 1105, the historical display of items is filtered. The filtering identifies items from the historical display that have similarities to the particular item. For example, the identified items may have an item type (e.g., 722) and / or an item subtype (e.g., 724) that is equivalent to the particular item (e.g., via item type 735 and / or item subtype 738). Some aspects may have a condition (e.g., 726) that is similar or equivalent to the particular item (e.g., 739). In some aspects, the historical display is filtered to include a mix of products having different item types or item subtypes.

[0058] In operation 1110, a prediction of a future selling price in a future time period is determined for a specific item. The prediction is based on filtered historical items. For example, in some aspects, the filtered historical items display the selling price of the historical items of the sales transaction that occurred at a specific time. Based on this information, operation 1110 determines how the specific item depreciates (or appreciates) over time according to new conditions or according to given conditions. In some aspects, operation 1110 uses interval estimation such as confidence interval, credible interval, tolerance interval, prediction interval or likelihood interval to determine the prediction. In some aspects, a selling price prediction of different probabilities of a completed sales transaction is generated. Therefore, a first set of selling price predictions over time can be generated, which provides a 95% probability of selling items at a selling price and time defined by the prediction. A second set of selling price predictions over time can be generated, which provides an 80% probability of selling items at a selling price and time defined by the prediction; and so on.

[0059] In operation 1115, a user selection is received, which indicates one or more predictions of future selling prices in one or more future time periods. Some aspects of operation 1115 display a user interface (e.g., 100 and / or 200). The user interface may include a chart of at least some of the future selling prices predicted for a specific item determined in operation 1110 in a future time period. In these aspects, a touch input on the chart may be received. The touch input defines a rectangle covering the chart. The rectangle covers a subset of the future selling prices displayed on the chart and its corresponding future time period. This subset of future selling prices at a future time defines an acceptable selling standard for the item. This information may be stored in a user settings database (e.g., 750). For example, in some aspects, the selling strategy field 756 stores acceptable selling standards. These aspects may also monitor the current market price of items with similarities to a specific item. For example, some aspects periodically obtain records of items sold in a previous time period from the historical database 700, and the previous time period includes one or more minutes, hours, days, weeks, or months of a threshold number. The current market price of the item is then determined, for example, by averaging the prices at which similar items have sold over previous time periods.

[0060] Some aspects cause the following user interface to be displayed: the user interface is configured to receive a retention value for a particular item (e.g., 202). The user interface is also configured to display a plurality of controls, each of which is configured to select a different confidence interval for retaining the value within a different associated time period (e.g., 204a and 204b). In some aspects, the user interface provides a selectable control, wherein the selection of the control indicates that an alert is provided to the user if the actual market price of the item (e.g., via the monitoring of the market price described above) deviates from the forecast by more than a threshold amount. The threshold may also be configured via the user interface. The threshold may be provided as a percentage or an absolute number. In these aspects, operation 1115 receives a selection of a particular confidence interval of the different confidence intervals. Selecting a confidence interval also inherently selects a set of predictions. For example, as discussed below, if multiple predictions for multiple confidence intervals are generated in operation 1110, the selection of the confidence interval forms a prediction with the selected confidence interval associated with operation 1120.

[0061] Some embodiments may cause a user interface to be displayed that is configured to display a plurality of controls, each of which may be selected to define a different rule (e.g., 102a-102b) for setting a sale price for a particular item. At least one of the controls may be selected to define a rule for setting a sale price so as to maintain a fixed probability of completing the sale of the item over time based on a prediction. For example, as described above with respect to Figure 1 As discussed, user interface 100 provides a user with the ability to select a selling strategy that changes the selling price of an online listing of an item (e.g., 760) to maintain a fixed probability of selling over time. In other words, these embodiments, for example, vary the selling price of an online listing of an item (e.g., 760) to maintain a fixed probability of selling over time. Fig. 9 Any one of lines 904a to 904c or Fig.10 104a to 1004b) to set the selling price. As discussed above, the forecast based on which the probability of maintaining a fixed can be adjusted periodically based on new or recent historical sales transactions for the item (e.g., from the historical display database 700). The user interface is configured to receive input defining a fixed selling probability (e.g., via edit box 104a). The fixed selling probability determines a forecast, such as one of the forecasts represented by lines 904a to 904c to be applied in operation 1120. In some aspects, the user interface provides a selectable control (e.g., 102b) for setting a fixed selling price over time (e.g., which can be indicated via edit control 104b).

[0062] In operation 1120, a time to initiate an online display of a particular item based on the selected prediction is determined. In the above-discussed implementation of receiving a touch input defining a rectangle of acceptable selling prices and times, if the monitored market price falls within the rectangle defined by the chart, then an online display is initiated at that time. Initiating an online display may include generating a user interface including an item description and one or more pictures representing the item. The user interface may also indicate the selling price of the item. An online display may be published on an online display system, which provides users of the online display system with an opportunity to search for items based on one or more criteria. When the item meets one or more criteria, the online display of the item may be displayed on an electronic display. Since some implementations of process 1100 may be performed by a server component (e.g., application server 1240 discussed below), the publication of an online display may cause the online display to be displayed on a user device. For example, causing the display may include transmitting data to a user device that defines a user interface (e.g., XML or HTML data, or a combination of these) to be displayed on the user device.

[0063] Fig.12 is a block diagram of an exemplary client-server based architecture 1200. Fig.12 A client-server based architecture 1200 is depicted, but of course the inventive subject matter is not limited to such an architecture and may be equally well applied to, for example, event-driven, distributed, or peer-to-peer architecture systems. Fig.12 Various functional components that are not closely related to conveying an understanding of the subject matter of the present invention are omitted. In addition, it should be understood that although the singular meaning is discussed Fig.12 Various functional components are shown in FIG. 1 , but multiple instances of any of the various functional components may be employed.

[0064] The content publishing platform 1202, in the example form of a network-based system, provides server-side functionality to one or more client devices 1210 via a network 1204 (e.g., the Internet or a wide area network (WAN)). In some aspects, the content publishing platform 1202 can implement an item display system. The item display system can provide for the publication of e-commerce displays that facilitate providing items for sale.

[0065] Fig.12A web client 1212 (e.g., a browser), a client application 1214, and a programmatic client 1216 are shown, for example, executed on a client device 1210. The client device 1210 may include, but is not limited to, a mobile phone, a desktop computer, a laptop, a portable digital assistant (PDA), a smart phone, a tablet computer, an ultrabook, a netbook, a laptop, a multiprocessor system, a microprocessor-based or programmable consumer electronic product, a game console, a set-top box, or any other communication device that a user can utilize to access the content publishing platform 1202. In some embodiments, the client device 1210 includes a display module (not shown) to display information (e.g., in the form of a user interface). In another embodiment, the client device 1210 includes one or more of a touch screen, an accelerometer, a gyroscope, a camera device, a microphone, a global positioning system (GPS) device, etc. In one embodiment, the content publishing platform 1202 is a network-based marketplace, and its publication includes the publication of product listings available on the network-based marketplace (e.g., a network document).

[0066] One or more users 1206 may be humans, machines, or other devices that interact with client devices 1210. In an example embodiment, users 1206 are not part of the client-server based architecture 1200, but interact with the client-server based architecture 1200 via client devices 1210 or other devices. For example, user 1206 may provide input (e.g., touch screen input or alphanumeric input) to client device 1210, and the input is transmitted to content publishing platform 1202 via network 1204. In this case, content publishing platform 1202 transmits information (e.g., a user interface) to be presented to user 1206 to client device 1210 via network 1204 in response to receiving the input from user 1206. In this way, user 1206 can interact with content publishing platform 1202 using client device 1210.

[0067] The client device 1210 may include one or more client applications 1214 (also referred to as "apps"), such as, but not limited to, a web browser, a messaging application, an electronic mail (email) application, an e-commerce site application (also referred to as a marketplace application), etc. In some embodiments, if the e-commerce site application is included in the client device 1210, the application is configured to provide a user interface and at least some functionality locally, wherein the client application 1214 is configured to communicate with the content publishing platform 1202 as needed for data or processing capabilities that are not available locally (e.g., accessing a database of items available for sale, authenticating a user 1206, verifying a payment method). Conversely, if the e-commerce site application is not included in the client device 1210, the client device 1210 may use its web browser to access an e-commerce site (or a variant thereof) hosted on the content publishing platform 1202.

[0068] One or more portions of network 1204 may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the public switched telephone network (PSTN), a cellular telephone network, a wireless network, a WiFi network, a WiMax network, other types of networks, or a combination of two or more such networks.

[0069] Application program interface (API) server 1220 and web server 1222 are respectively coupled to application server 1240 and provide program and web interfaces respectively to application server 1240. Application server 1240 hosts prediction engine 805, user interface engine 810 and presentation engine 820, which may be embodied in hardware, software, firmware or any combination thereof.

[0070] The application server 1240 is in turn shown coupled to a database server 1224 that facilitates access to a database 1226. In the example implementation, the database 1226 is a server that stores information such as that described above. Figure 7 A storage device for information of any of the data structures discussed. According to an example implementation, database 1226 may also store digital item information.

[0071] In addition, third-party applications 1232 executing on third-party servers 1230 are shown as programmatically accessing content publishing platform 1202 via a programming interface provided by API server 1220. For example, third-party applications 1232 utilizing information retrieved from content publishing platform 1202 support one or more features or functions on a website hosted by a third party.

[0072] Fig.13 13 is a block diagram 1300 illustrating a representative software architecture 1302 that may be used in conjunction with the various hardware architectures described herein. Fig.13 is only a non-limiting example of a software architecture, and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture 1302 may be implemented in a system such as Fig.14 Executed on the hardware of machine 1400, Fig.14 The machine 1400 includes, among other things, a processor 1410, a memory 1430, and an I / O component 1450. A representative hardware layer 1304 is shown and may represent, for example, Fig.14 The representative hardware layer 1304 includes one or more processing units 1306 with associated executable instructions 1308. The executable instructions 1308 represent executable instructions of the software architecture 1302, including implementations of the methods and modules described above, etc. The hardware layer 1304 also includes a memory and / or storage module 1310 also having executable instructions 1308. The hardware layer 1304 may also include other hardware indicated by 1312, representing any other hardware of the hardware layer 1304, such as other hardware shown as part of the machine 1400 discussed below.

[0073] exist Fig.13 In the example architecture of , software architecture 1302 can be conceptualized as a stack of layers, in which each layer provides specific functionality. For example, software architecture 1302 may include layers such as operating system 1314, library 1316, framework / middleware 1318, application 1320, and presentation layer 1344. In operation, application 1320 and / or other components within the layer may call application programming interface (API) call 1324 through the software stack, and receive responses, return values, etc. shown as messages 1326 in response to API call 1324. The layers shown are representative in nature, and not all software architectures have all layers. For example, some mobile or dedicated operating systems may not provide framework / middleware layer 1318, while other mobile or dedicated operating systems may provide such a layer. Other software architectures may include additional layers or different layers.

[0074] The operating system 1314 may manage hardware resources and provide public services. The operating system 1314 may include, for example, a kernel 1328, services 1330, and drivers 1332. The kernel 1328 may serve as an abstraction layer between the hardware and other software layers. For example, the kernel 1328 may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, etc. Services 1330 may provide other public services to other software layers. Drivers 1332 may be responsible for controlling or interfacing with the underlying hardware. For example, depending on the hardware configuration, drivers 1332 may include display drivers, camera drivers, drives, flash memory drives, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Drivers, audio drivers, power management drivers, etc.

[0075] The library 1316 may provide a common infrastructure that may be utilized by the application 1320 and / or other components and / or layers. Compared to direct docking with the underlying operating system 1314 functions (e.g., kernel 1328, service 1330, and / or driver 1332), the library 1316 generally provides functions that allow other software modules to perform tasks in an easier manner. The library 1316 may include a system 1334 library (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematical functions, etc. In addition, the library 1316 may include an API library 1336, such as a media library (e.g., a library that supports the presentation and manipulation of various media formats such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), a graphics library (e.g., an OpenGL framework that may be used to present 2D and 3D graphics content on a display), a database library (e.g., SQLite that may provide various relational database functions), a web library (e.g., WebKit that may provide web browsing functions), etc. The library 1316 may also include a variety of other libraries 1338 to provide numerous other APIs to the applications 1320 and other software components / modules.

[0076] Framework / middleware 1318 (sometimes also referred to as "middleware") may provide a higher level common infrastructure that may be utilized by applications 1320 and / or other software components / modules. For example, framework 1318 may provide various graphical user interface (GUI) functions, advanced resource management, advanced location services, etc. Framework 1318 may provide a wide range of other APIs that may be utilized by applications 1320 and / or other software components / modules, some of which may be specific to a particular operating system or platform.

[0077] Applications 1320 include built-in applications 1340 and / or third-party applications 1342. Examples of representative built-in applications 1340 may include, but are not limited to, contact applications, browser applications, book reader applications, location applications, media applications, messaging applications, and / or game applications. Third-party applications 1342 may include any of the built-in applications as well as various other applications. In a specific example, third-party applications 1342 (e.g., applications created by entities other than the vendor of a particular platform using Android TM or iOS TM Software Development Kit (SDK) can be used to develop applications on platforms such as iOS TM 、Android TM , Mobile software running on the mobile operating system of the phone or other mobile operating system. In this example, third-party application 1342 can call API call 1336 provided by a mobile operating system such as operating system 1314 to facilitate the functions described herein.

[0078] Applications 1320 may utilize built-in operating system functionality (e.g., kernel 1328, services 1330, and / or drivers 1332), libraries (e.g., system 1334, API 1336 and other libraries 1338), framework / middleware 1318 to create a user interface for interacting with a user of the system. Alternatively or additionally, in some systems, interaction with the user may be performed through a presentation layer such as presentation layer 1344. In these systems, the application / module "logic" may be separate from the application / module for various aspects of interacting with the user.

[0079] Some software architectures make use of virtual machines. Fig.13 In the example of FIG. 1 , a virtual machine is shown by virtual machine 1348. A virtual machine creates a software environment in which applications / modules can run as they would on a hardware machine (e.g., such as a Fig.14 The virtual machine is run by the host operating system ( Fig.13 The virtual machine 1348 is hosted by an operating system 1314 in a virtual machine and typically, but not always, has a virtual machine monitor 1346 that manages the operation of the virtual machine and interfaces with the host operating system (i.e., operating system 1314). A software architecture is executed within the virtual machine, such as operating system 1314, libraries 1316, framework / middleware 1318, applications 1320, and / or presentation layer 1344. These layers of the software architecture executed within virtual machine 1348 may be the same as the corresponding layers previously described or may be different.

[0080] Fig.141 is a block diagram illustrating components of a machine 1400 capable of reading instructions from a machine-readable medium (e.g., a machine-readable storage device) and performing any one or more of the methods discussed herein, according to some example embodiments. Specifically, Fig.14 A diagrammatic representation of a machine 1400 in the example form of a computer system is shown within which instructions 1416 (e.g., software, programs, applications, applet, application (app) or other executable code) for causing the machine 1400 to perform any one or more of the methodologies discussed herein may be executed. These instructions transform a general purpose, unprogrammed machine into a specific machine that is programmed to perform the methods described and illustrated in the manner described herein. The machine 1400 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 1400 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. By way of non-limiting example, the machine 1400 may include or correspond to a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular phone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of sequentially or otherwise executing instructions 1416 that specify actions to be taken by the machine 1400. In addition, while only a single machine 1400 is shown, the term "machine" should also be taken to include a collection of machines 1400 that individually or jointly execute instructions 1416 to perform any one or more of the methodologies discussed herein.

[0081] The machine 1400 may include a processor 1410, a memory / storage device 1430, and an I / O component 1450, which may be configured to communicate with each other, for example, via a bus 1402. In an example embodiment, the processor 1410 (e.g., a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a radio frequency integrated circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 1412 and a processor 1414 that may execute instructions 1416. The term "processor" is intended to include a multi-core processor 1410, which may include two or more independent processors (sometimes referred to as "cores") that may execute instructions simultaneously. Although Fig.14 Multiple processors are shown, but the machine 1400 may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiple cores, or any combination thereof.

[0082] The memory / storage 1430 may include a memory 1432, such as a main memory or other memory storage device, and a storage unit 1436, both of which are accessed by the processor 1410, for example, via the bus 1402. The storage unit 1436 and the memory 1432 store instructions 1416 that embody any one or more of the methods or functions described herein. The instructions 1416 may also reside, completely or partially, within the memory 1432, within the storage unit 1436, within at least one of the processors 1410 (e.g., within a cache memory of the processor), or within any suitable combination thereof during execution thereof by the machine 1400. Thus, the memory 1432, the storage unit 1436, and the memory of the processor 1410 are examples of machine-readable media.

[0083] As used herein, "machine-readable medium" refers to a device capable of temporarily or permanently storing instructions and data, and may include, but is not limited to, random access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage devices (e.g., erasable programmable read-only memory (EEPROM)), and / or any suitable combination thereof. The term "machine-readable medium" should be deemed to include a single medium or multiple media (e.g., a centralized or distributed database or associated cache memory and server) capable of storing instructions 1416. The term "machine-readable medium" should also be deemed to include any medium or combination of multiple media capable of storing instructions (e.g., instructions 1416) for execution by a machine (e.g., machine 1400), such that when the instructions are executed by one or more processors of the machine (e.g., processor 1410), the machine performs any one or more of the methods described herein. Therefore, "machine-readable medium" refers to a single storage device or device, as well as a "cloud-based" storage system or storage network including multiple storage devices or devices.

[0084] Executable instructions and machine storage media

[0085] Various memories (i.e., 1430, 1432 and / or the memory of processor 1410) and / or storage unit 1436 may store one or more sets of instructions and data structures (e.g., software) embodying or utilized by any one or more of the methods or functions described herein. These instructions, when executed by processor 602, cause various operations to implement the disclosed embodiments.

[0086] As used herein, the terms "machine storage medium," "device storage medium," "computer storage medium" (collectively "machine storage medium") refer to the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and / or media (e.g., centralized or distributed databases and / or associated caches and servers) that store executable instructions and / or data, as well as a cloud-based storage system or storage network that includes multiple storage devices or devices. Thus, the terms should be considered to include, but are not limited to, solid-state memory and optical and magnetic media, including memory internal or external to the processor. Specific examples of machine storage media, computer storage media, and / or device storage media include non-volatile memory, including by way of example: semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGAs, and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms machine storage media, computer storage media, and device storage media expressly exclude carrier waves, modulated data signals, transient signals, and other such media, at least some of which are encompassed by the term "signal media" discussed below.

[0087] Signal medium

[0088] The term "signal medium" or "transmission medium" should be taken to include any form of modulated data signal, carrier wave, etc. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[0089] Computer readable medium

[0090] The terms "machine-readable medium," "computer-readable medium," and "device-readable medium" refer to the same thing and may be used interchangeably in this disclosure. These terms are defined to include both machine storage media and signal media. Thus, these terms include both storage devices / medium and carrier / modulated data signals.

[0091] I / O components 1450 may include a variety of components for receiving input, providing output, generating output, transmitting information, exchanging information, capturing measurements, etc. The specific I / O components 1450 included in a particular machine will depend on the type of machine. For example, a portable machine such as a mobile phone will likely include a touch input device or other such input mechanism, while a headless server machine will likely not include such a touch input device. It will be appreciated that I / O components 1450 may include Fig.141450. The grouping of I / O components 1450 by function is intended only to simplify the following discussion, and the grouping is by no means limiting. In various example embodiments, I / O components 1450 may include output components 1452 and input components 1454. Output components 1452 may include visual components (e.g., displays such as plasma display panels (PDPs), light emitting diode (LED) displays, liquid crystal displays (LCDs), projectors, or cathode ray tubes (CRTs)), acoustic components (e.g., speakers), tactile components (e.g., vibration motors, resistance mechanisms), other signal generators, and the like. Input components 1454 may include alphanumeric input components (e.g., keyboards, touch screens configured to receive alphanumeric input, optical keyboards, or other alphanumeric input components), point-based input components (e.g., mice, touch pads, trackballs, joysticks, motion sensors, or other pointing instruments), tactile input components (e.g., physical buttons, touch screens or other tactile input components that provide location and / or force of touch or touch gestures), audio input components (e.g., microphones), and the like.

[0092] In other example embodiments, the I / O component 1450 may include a biometric component 1456, a motion component 1458, an environmental component 1460, or an orientation component 1462, among a wide range of other components. For example, the biometric component 1456 may include components for detecting expressions (e.g., hand expressions, facial expressions, vocal expressions, body postures, or eye tracking), measuring biosignals (e.g., blood pressure, heart rate, body temperature, sweating, or brain waves), identifying people (e.g., voice recognition, retinal recognition, facial recognition, fingerprint recognition, or EEG-based recognition), etc. The motion component 1458 may include an acceleration sensor component (e.g., an accelerometer), a gravity sensor component, a rotation sensor component (e.g., a gyroscope), etc. The environmental component 1460 may include, for example, an illumination sensor component (e.g., a photometer), a temperature sensor component (e.g., one or more thermometers that detect ambient temperature), an acoustic sensor component (e.g., one or more microphones that detect background noise), or other components that may provide indications, measurements, or signals corresponding to the surrounding physical environment. The orientation component 1462 may include a position sensor component (e.g., a global positioning system (GPS) receiver component), an altitude sensor component (e.g., an altimeter or barometer that detects air pressure, from which the altitude can be obtained), an orientation sensor component (e.g., a magnetometer), etc.

[0093] A variety of technologies may be used to achieve communication. I / O components 1450 may include communication components 1464 that are operable to couple machine 1400 to network 1480 or device 1470 via coupling 1482 and coupling 1472, respectively. For example, communication components 1464 may include a network interface component or other suitable device to interface with network 1480. In other examples, communication components 1464 may include wired communication components, wireless communication components, cellular communication components, near field communication (NFC) components, Components (e.g. Low power consumption), Device 1470 may be another machine or any of a variety of peripheral devices (eg, a peripheral device coupled via a universal serial bus (USB)).

[0094] In addition, the communication component 1464 can detect the identifier or include a component operable to detect the identifier. For example, the communication component 1464 may include a radio frequency identification (RFID) tag reader component, an NFC smart tag detection component, an optical reader component (e.g., an optical sensor for detecting the following codes: one-dimensional barcodes such as universal product codes (UPC) barcodes, multi-dimensional barcodes such as quick response (QR) codes, Aztec codes, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D barcodes, and other optical codes), or an acoustic detection component (e.g., a microphone for identifying tagged audio signals). In addition, various information can be obtained via the communication component 1464, such as a location via Internet Protocol (IP) geolocation, a location via Internet Protocol (IP), ... The location of signal triangulation, the location of an NFC beacon signal via detection that can indicate a specific location, etc.

[0095] In various example embodiments, one or more portions of network 1480 may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of a public switched telephone network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, 1480, or a portion of network 1480, may include a wireless network or cellular network, and coupling 1482 may be a code division multiple access (CDMA) connection, a global system for mobile communications (GSM) connection, or other type of cellular or wireless coupling. In this example, coupling 1482 may implement any of various types of data transmission technologies, such as single carrier radio transmission technology (1xRTT), evolution data optimized (EVDO) technology, general packet radio service (GPRS) technology, enhanced data rates for GSM evolution (EDGE) technology, the third generation partnership project (3GPP) including 3G, fourth generation wireless (4G) networks, universal mobile telecommunications system (UMTS), high speed packet access (HSPA), worldwide interoperability for microwave access (WiMAX), long term evolution (LTE) standards, other data transmission technologies defined by various standard setting organizations, other long distance protocols, or other data transmission technologies.

[0096] The instructions 1416 may be sent or received over the network 1480 using a transmission medium via a network interface device (e.g., a network interface component included in the communication component 1464) and utilizing any of a number of well-known transmission protocols (e.g., the Hypertext Transfer Protocol (HTTP)). Similarly, the instructions 1416 may be sent or received using a transmission medium via a coupling 1472 (e.g., a peer-to-peer coupling) with the device 1470. The term "transmission medium" shall be deemed to include any intangible medium capable of storing, encoding, or carrying the instructions 1416 for execution by the machine 1400, and includes digital or analog communication signals or other intangible media to facilitate the communication of such software.

[0097] Fig.15 is a flow chart of a method for generating an alert based on a predicted resale date. Fig.15 One or more of the functions discussed may be performed by a hardware processing circuit. For example, an electronic memory may store the following instructions, which when executed by the hardware processing circuit configure the hardware processing circuit to perform the following Fig.15 One or more features discussed.

[0098] In operation 1505, a model is trained based on product type (e.g., 722) and product subtype (e.g., 724) information. The training may also be based on purchase date (e.g., 714) and / or resale date (e.g., 729) information. Training is performed for multiple items.

[0099] In operation 1510, a predicted resale date for the item is determined based on the trained model. The model may be provided with one or more of purchase date, item type, and item subtype information. Based on this information and based on the training performed in operation 1505, the model may predict a resale date for the item.

[0100] In operation 1515, an alert is generated based on the predicted resale date. Fig.16 An example of such an alert is shown. However, the alert may take one or more forms, including a text message, an email message, a pop-up dialog box, or other messaging methods.

[0101] Fig.16 is an example of a possible resale notification. Notification 1600 provides a prompt 1605 indicating that the useful time of a particular item may have passed. The notification also includes: a relist button 1610 that allows the user to relist the item; a postpone button 1615 that can postpone the listing for a period of time; and a button indicating that the user does not want to relist the item.

[0102] Fig.17 An example machine learning module 1700 according to some examples of the present disclosure is shown. The machine learning module 1700 uses a training module 1710 and a prediction module 1720. The training module 1710 inputs historical information 1730 into a feature determination module 1750a. The historical information 1730 can be labeled. Example historical information can include information about an item, including: item listing date, item type, item subtype, item condition indication, item relisting date, and other information about the item.

[0103] The feature determination module 1750a determines one or more features 1760 based on the historical information 1730. Generally speaking, the feature 1760 is a set of information that is input and is determined to predict a specific result. In some examples, the feature 1760 can be all of the historical activity data, but in other examples, the feature 1760 can be a subset of the historical activity data. The machine learning algorithm 1770 generates a model 1780 based on the features 1760 and the labels.

[0104] In the prediction module 1720, the current information 1790 can be input to the feature determination module 1750. The feature determination module 1750b can determine the same set of features or a different set of features as determined by the feature determination module 1750a based on the historical information 1730 based on the current information 1790. In some examples, the feature determination modules 1750a and 1750b are the same module. The feature determination module 1750b generates a feature vector 1715, which is input into the model 1780 to generate a likelihood of response score 1795. The training module 1710 can operate in an offline manner to train the model 1780. However, the prediction module 1720 can be designed to operate in an online manner. It should be noted that the model 1780 can be regularly updated via additional training and / or user feedback.

[0105] The machine learning algorithm 1770 can be selected from many different potential supervised or unsupervised machine learning algorithms. Examples of supervised learning algorithms include artificial neural networks, Bayesian networks, instance-based learning, support vector machines, decision trees (e.g., iterative bisection 3, C4.5, classification and regression trees (CART), chi-square automatic interaction detector (CHAID), etc.), random forests, linear classifiers, quadratic classifiers, k nearest neighbor methods, linear regression, logistic regression, and hidden Markov models. Examples of unsupervised learning algorithms include expectation maximization algorithms, vector quantization, and information bottleneck methods. Unsupervised models may not have training modules 1710. In an example embodiment, a regression model is used, and the model 1780 is a vector of coefficients corresponding to the learning importance of each of the features in the vectors 1760 and 1715 of the features. In order to calculate the score, the dot product of the vector of the coefficients of the feature vector 1715 and the model 1780 is obtained.

[0106] Some embodiments are described herein as including logic or multiple components, modules or mechanisms. A module may constitute a software module (e.g., a code implemented on a machine-readable medium or in a transmission signal) or a hardware module. A hardware module is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In an example embodiment, one or more computer systems (e.g., independent client or server computer systems) or one or more hardware modules (e.g., a processor or processor group) of a computer system may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.

[0107] In various embodiments, hardware modules may be implemented mechanically or electronically. For example, a hardware module may include dedicated circuits or logic that are permanently configured to perform certain operations (e.g., configured as a dedicated processor, such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC)). A hardware module may also include programmable logic or circuits that are temporarily configured by software to perform certain operations (e.g., as contained within a general purpose processor or other programmable processor). It will be understood that the decision to implement a hardware module mechanically in a dedicated and permanently configured circuit or in a temporarily configured circuit (e.g., configured by software) may be driven by cost and time considerations.

[0108] Thus, the term "hardware module" should be understood to include a tangible entity, i.e., an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a particular manner and / or perform certain operations described herein. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one time. For example, where the hardware modules include a general-purpose processor configured using software, the general-purpose processor can be configured as corresponding different hardware modules at different times. The software can configure the processor accordingly, for example, to constitute a specific hardware module at one time and to constitute different hardware modules at different times.

[0109] A hardware module can provide information to other hardware modules and receive information from other hardware modules. Therefore, the described hardware modules can be considered as being communicatively coupled. In the case of multiple such hardware modules being present simultaneously, communication can be achieved by signal transmission (e.g., by connecting suitable circuits and buses of the hardware modules). In an embodiment in which multiple hardware modules are configured or instantiated at different times, communication between such hardware modules can be achieved, for example, by storing information in a memory structure accessed by multiple hardware modules and retrieving information in the memory structure. For example, a hardware module can perform an operation, and the output of the operation is stored in a memory device to which the hardware module is communicatively coupled. Then, other hardware modules can access the memory device at a later time to retrieve and process the stored output. The hardware module can also initiate communication with an input or output device, and can operate on a resource (e.g., a collection of information).

[0110] The various operations of the example methods described herein may be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the related operations. Whether temporarily or permanently configured, such a processor may constitute a processor-implemented module that operates to perform one or more operations or functions. The modules mentioned herein may include processor-implemented modules in some example embodiments.

[0111] Similarly, the methods described herein may be implemented by processors at least in part. For example, at least some of the operations in the method's operation may be performed by one or more processors or processor-implemented modules. The performance of some operations in the operation may be distributed in one or more processors, not only residing in a single machine, but also deployed across multiple machines. In some example embodiments, one or more processors may be located in a single location (e.g., in a home environment, an office environment, or a server farm), and in other embodiments, processors may be distributed across multiple locations.

[0112] The one or more processors may also operate to support performance of related operations in a "cloud computing" environment or as "software as a service" (SaaS). For example, at least some of the operations may be performed by a group of computers (as an example of a machine including a processor), where the operations are accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., APIs).

[0113] Example embodiments may be implemented in digital electronic circuitry, or in computer hardware, firmware, or software, or in combinations of them. Example embodiments may be implemented using a computer program product, for example: a computer program tangibly embodied in an information carrier, for example in a machine-readable medium, for execution by a data processing apparatus, for example a programmable processor, a computer or computers, or for controlling the operation of the data processing apparatus.

[0114] The computer program may be written in any form of programming language including compiled or interpreted languages, and the computer program may be deployed in any form, including as a stand-alone program or as a module, subroutine, or other unit suitable for use in a computing environment. The computer program may be deployed to be executed on one computer or multiple computers at one site, or distributed across multiple sites and interconnected by a communication network.

[0115] In example embodiments, operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Method operations may also be performed by, and the apparatus of example embodiments may be implemented as, special purpose logic circuitry (e.g., FPGA or ASIC).

[0116] A computing system may include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship between the client and the server is generated by means of a computer program running on a respective computer and having a client-server relationship with each other. In an embodiment of a programmable computing system, it will be understood that both hardware architecture and software architecture are worthy of consideration. Specifically, it will be understood that the following selections may be design choices: whether to select to implement certain functions with permanently configured hardware (e.g., ASIC), or with temporarily configured hardware (e.g., a combination of software and a programmable processor), or with a combination of permanently configured hardware and temporarily configured hardware.

[0117] Although the embodiments of the present disclosure have been described with reference to specific example embodiments, it will be apparent that various modifications and changes may be made to these embodiments without departing from the broader scope of the subject matter of the present invention. Therefore, the specification and the accompanying drawings are considered to be illustrative rather than restrictive. The accompanying drawings forming a part of this article illustrate in an illustrative rather than restrictive manner the specific embodiments in which the present subject matter can be practiced. The illustrated embodiments are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be used and derived from the embodiments shown herein so that structural and logical replacements and changes may be made without departing from the scope of the present disclosure. Therefore, the specific embodiments are not adopted in a restrictive sense, and the scope of the various embodiments is limited only by the full scope of the appended claims and the equivalents referred to as such claims.

[0118] For convenience only, the term "invention" may be used herein to refer to these embodiments of the subject matter of the present invention individually and / or collectively, and if more than one invention or inventive concept is in fact disclosed, it is not intended to actively limit the scope of the present application to any single invention or inventive concept. Therefore, although specific embodiments have been shown and described herein, it should be understood that any arrangement calculated to achieve the same purpose can be replaced with respect to the specific embodiments shown. The present disclosure is intended to cover any and all adjustments or variations of the various embodiments. When the above description is reviewed, the combination of the above embodiments and other embodiments not specifically described herein will be obvious to those skilled in the art.

[0119] All publications, patents, and patent documents mentioned herein are incorporated herein by reference in their entirety, as if individually incorporated by reference. In the event of inconsistent usages between this document and those documents incorporated by reference, the usage in the incorporated references should be considered to supplement the usage herein. For conflicting inconsistencies, the usage in this document controls.

[0120] Herein, as is common in patent literature, the terms "a" or "an" are used to include one or more than one, regardless of any other instance or usage of "at least one" or "one or more." Herein, unless otherwise indicated, the term "or" is used to refer to a non-exclusive or, such that "A or B" includes "A but not B," "B but not A," and "A and B." In the appended claims, the terms "including" and "in which" are used as the plain English equivalents of the respective terms "comprising" and "wherein." In addition, in the appended claims, the terms "including" and "comprising" are open-ended, that is, systems, apparatus, articles, or processes that include elements other than those listed after such terms in a claim are still considered to fall within the scope of the claim.

Claims

1. A method comprising: A display of historical objects that filters out objects that have similarities to a specific object; Based on the filtered historical items, determining a prediction of a future selling price in a future time period for the specific item, the determined prediction including future selling prices with different probabilities of completing the sale; receiving a user selection indicating one or more predictions of future selling prices within one or more future time periods; as well as determining a time to initiate publishing of an online display for the particular item based on the selected one or more predictions, Wherein, the method further comprises: causing display of a user interface including a graph of future selling prices of the particular item during the future time period; receiving a touch input, the touch input defining a rectangle superimposed on the chart; determining a subset of future sale prices and a corresponding subset of future time periods displayed on the chart based on the rectangles; and monitoring the current market prices of items similar to the specific item, Wherein, determining the time to initiate the publication of the online display of the specific item includes: if the monitored current market price falls within the rectangle, initiating the publication of the online display of the specific item at a time within a subset of the future time period corresponding to a future selling price equivalent to the monitored current market price.

2. The method according to claim 1, further comprising: causing display of a user interface configured to receive a retention value for the particular item for the user, the user interface further configured to display a plurality of controls, each control configured to select a different confidence interval for retaining the value over a different associated time period; receiving a selection of a particular confidence interval among the different confidence intervals; as well as A display for the specific item is generated on the online display system according to the selected specific confidence interval during the associated time period.

3. The method according to claim 1, further comprising: causing display of a user interface configured to display a plurality of controls, each control selectable to define a different rule for setting a selling price for the particular item, one of the controls selectable to define a rule that sets the selling price to maintain a fixed probability of a completed sale of the item over time based on the prediction; as well as receiving an input selecting one of the plurality of controls, The generation of the display sets the selling price of the specific item in the display according to the rules defined by the selected control.

4. The method according to claim 3, wherein: The user interface is also configured to display a second control selectable to define a second rule that sets the sale price to maintain a second fixed probability of a completed sale of the item over time based on the prediction.

5. The method according to claim 1, further comprising: Monitor recently completed displays of items to determine the current market value of said particular items; comparing the current market value to a predicted value for a current time period in a forecast of future selling prices; as well as If the difference between the current market value and the predicted value meets one or more criteria, an alert is generated.

6. The method according to claim 5, wherein: Generating an alert includes causing display of a notification user interface configured to display at least two selectable controls, a first control configured to indicate, when the first control is selected, that a sales policy for an item is to be reviewed, and a second control configured to indicate, when the second control is selected, that the sales policy is to be maintained.

7. The method according to claim 1, wherein: Determining the forecast includes interval estimation.

8. The method according to claim 7, wherein: Determining the prediction includes determining one or more of a confidence interval, a credible interval, a tolerance interval, a prediction interval, or a likelihood interval.

9. A system comprising: Hardware processing circuit; a hardware memory storing instructions that, when executed, configure the hardware processing circuitry to perform operations comprising: A display of historical objects that filters out objects that have similarities to a specific object; Based on the filtered historical items, determining a prediction of a future selling price in a future time period for the specific item, the determined prediction including future selling prices with different probabilities of completing the sale; receiving a user selection indicating one or more predictions of future selling prices within one or more future time periods; and determining a time to initiate publishing of an online display for the particular item based on the selected one or more predictions, The operation further includes: causing display of a user interface including a graph of future selling prices of the particular item during the future time period; receiving a touch input, the touch input defining a rectangle superimposed on the chart; determining a subset of future sale prices and a corresponding subset of future time periods displayed on the chart based on the rectangles; and monitoring the current market prices of items similar to the specific item, Wherein, determining the time to initiate the publication of the online display of the specific item includes: if the monitored current market price falls within the rectangle, initiating the publication of the online display of the specific item at a time within a subset of the future time period corresponding to a future selling price equivalent to the monitored current market price.

10. The system of claim 9, the operations further comprising: causing display of a user interface configured to receive a retention value for the particular item for the user, the user interface further configured to display a plurality of controls, each control configured to select a different confidence interval for retaining the value over a different associated time period; receiving a selection of a particular confidence interval among the different confidence intervals; as well as A display for the specific item is generated on the online display system according to the selected specific confidence interval during the associated time period.

11. The system of claim 9, the operations further comprising: causing display of a user interface configured to display a plurality of controls, each control selectable to define a different rule for setting a selling price for the particular item, one of the controls selectable to define a rule that sets the selling price to maintain a fixed probability of a completed sale of the item over time based on the prediction; as well as receiving an input selecting one of the plurality of controls, The generation of the display sets the selling price of the specific item in the display according to the rules defined by the selected control.

12. The system according to claim 11, wherein: The user interface is also configured to display a second control selectable to define a second rule that sets the sale price to maintain a second fixed probability of a completed sale of the item over time based on the prediction.

13. The system of claim 9, the operations further comprising: Monitor recently completed displays of items to determine the current market value of said particular items; comparing the current market value to a predicted value for a current time period in a forecast of future selling prices; as well as If the difference between the current market value and the predicted value meets one or more criteria, an alert is generated.

14. The system according to claim 13, wherein: Generating an alert includes causing display of a notification user interface configured to display at least two selectable controls, a first control configured to indicate, when the first control is selected, that a sales policy for an item is to be reviewed, and a second control configured to indicate, when the second control is selected, that the sales policy is to be maintained.

15. The system according to claim 9, wherein: Determining the forecast includes interval estimation.

16. The system of claim 15, wherein: Determining the prediction includes determining one or more of a confidence interval, a credible interval, a tolerance interval, a prediction interval, or a likelihood interval.

17. A computer-readable medium comprising instructions that, when executed, configure a hardware processing circuit to perform operations comprising: A display of historical objects that filters out objects that have similarities to a specific object; Based on the filtered historical items, determining a prediction of a future selling price in a future time period for the specific item, the determined prediction including future selling prices with different probabilities of completing the sale; receiving a user selection indicating one or more predictions of future selling prices within one or more future time periods; as well as determining a time to initiate publishing of an online display for the particular item based on the selected one or more predictions, The operation further includes: causing display of a user interface including a graph of future selling prices of the particular item during the future time period; receiving a touch input, the touch input defining a rectangle superimposed on the chart; determining a subset of future sale prices and a corresponding subset of future time periods displayed on the chart based on the rectangles; and monitoring the current market prices of items similar to the specific item, Wherein, determining the time to initiate the publication of the online display of the specific item includes: if the monitored current market price falls within the rectangle, initiating the publication of the online display of the specific item at a time within a subset of the time period corresponding to a future selling price equivalent to the monitored current market price.

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