DELIVERY ESTIMATE PREDICTION AND VISUALIZATION SYSTEM

• US 20140330741A1
• Filed: 12/21/2013
• Published: 11/06/2014
• Est. Priority Date: 05/03/2013
• Status: Abandoned Application

• Current Assignee: eBay Inc.
• Original Assignee: Muhammad Arif Iqbal
• Inventors: Stepin, Maxim, Bialynicka-Birula, Iwona, Iqbal, Muhammad Arif, Sidhom, Benjamin
• Application Number:
  US14/138,028
• Publication Number:
  US 20140330741A1
• Time in Patent Office: Days
• US Class Current: 705/341
• CPC Class Codes: G06Q 10/0838 Historical data

Abstract

A first delivery estimate prediction model and a second delivery estimate prediction model are generated using historical data from an online marketplace. Transaction information related to an item listed in the online marketplace is determined. A first time estimate is calculated by applying the transaction information to the first delivery estimate prediction model. A second time estimate is calculated by applying the transaction information to the second delivery estimate prediction model. A delivery time estimate is generated based on the lowest of the first time estimate and the second time estimate.

Claims

1. A system comprising:

a delivery estimate model module configured to generate a first delivery estimate prediction model and a second delivery estimate prediction model using historical data from an online marketplace;

a storage device comprising the historical data from the online marketplace; and

a delivery estimate prediction module configured to;

determine transaction information related to an item listed in the online marketplace;

calculate a first time estimate by applying the transaction information to the first delivery estimate prediction model;

calculate a second time estimate by applying the transaction information to the second delivery estimate prediction model; and

generate a delivery time estimate based on the lowest of the first time estimate and the second time estimate.

2. The system of claim 1, wherein the transaction information comprises a seller identification, a method of shipment, a shipping origin location of the item, a shipping destination location of the item, an order date and time of the item, and a price of the item, wherein the delivery time estimate represents the maximum number of business days for shipping and handling.

3. The system of claim 1, wherein the historical data comprises seller identifications, corresponding methods of shipment, corresponding shipping origin location of items, corresponding shipping destination locations of items, corresponding order dates and times of items, and corresponding prices of items.

4. The system of claim 1, wherein the delivery estimate prediction module further comprises:

an item location prediction module configured to predict a shipping origin location of the item based on an identification of the seller.

5. The system of claim 1, wherein the first delivery estimate prediction model predicts the lowest number of business days x such that a shipping origin location of the item is known, a seller of the item has at least one transaction with tracking information during a training period of the first delivery estimate prediction model, the seller has shipped within y business days at least a fraction p of the time during the training period, a combination of a shipping method, a corresponding three-digit zip code origin prefix, a corresponding three-digit zip code destination prefix resulted in at most z days in shipping time at least a fraction p of the time during the training period, wherein x is the sum of y and z, and p is a model parameter specified individually for each possible value of x.

6. The system of claim 1, wherein the second delivery estimate prediction model predicts the total handling and shipping time using a Naï

ve Bayes algorithm trained with variables from an identification of the seller, a shipment method, a distance between an origin and destination zip codes, the first three digits of the destination zip code, an expected payment hour of day, an expected payment date, a leaf category identifier of the item, and a price of the item, the total handling and shipping time is the lowest number of days x such that the confidence that the item arrives within x days from payment is greater or equal than s times the confidence that the item will arrive later, wherein s is a parameter defined individually for each possible value of x.

7. The system of claim 1, wherein the storage device is configured to store data for the first and second delivery estimate prediction models as a series of key value pairs.

8. The system of claim 1, wherein the delivery estimate prediction module is configured to calculate the first time estimate using a p model algorithm before the second time estimate using a Naï

ve Bayes algorithm.

9. The system of claim 1, wherein the delivery estimate prediction module further comprises a multiple additive regression tree module further configured to:

separate data related to a third delivery estimate prediction model into two data sets comprising a training data set and a validation data set;

use the training data for training the third delivery estimate prediction model;

evaluate the third delivery estimate prediction model using the validation data set after adding each decision tree to generate a validation error;

iterate the use of the training data and the evaluation of the third delivery estimate prediction model until the validation error stops decreasing or starts increasing; and

calculate a third time estimate by applying the transaction information to the third delivery estimate prediction model.

10. The system of claim 1, further comprising:

a graphical visualization module configured to generate a graphical display of delivery dates and corresponding delivery probabilities.

11. A method comprising:

generating, using a processor of computer, a first delivery estimate prediction model and a second delivery estimate prediction model using historical data from an online marketplace;

storing the historical data from the online marketplace in a storage device;

determining transaction information related to an item listed in the online marketplace;

calculating a first time estimate by applying the transaction information to the first delivery estimate prediction model;

calculating a second time estimate by applying the transaction information to the second delivery estimate prediction model; and

generating a delivery time estimate based on the lowest of the first time estimate and the second time estimate.

12. The method of claim 11, wherein the transaction information comprises a seller identification, a method of shipment, a shipping origin location of the item, a shipping destination location of the item, an order date and time of the item, and a price of the item, wherein the delivery time estimate represents the maximum number of business days for shipping and handling.

13. The method of claim 11, wherein the historical data comprises seller identifications, corresponding methods of shipment, corresponding shipping origin location of items, corresponding shipping destination locations of items, corresponding order dates and times of items, and corresponding prices of items.

14. The method of claim 11, further comprising:

predicting a shipping origin location of the item based on an identification of the seller.

15. The method of claim 11, wherein the first delivery estimate prediction model predicts the lowest number of business days x such that a shipping origin location of the item is known, a seller of the item has at least one transaction with tracking information during a training period of the first delivery estimate prediction model, the seller has shipped within y business days at least a fraction p of the time during the training period, a combination of a shipping method, a corresponding three-digit zip code origin prefix, a corresponding three-digit zip code destination prefix resulted in at most z days in shipping time at least a fraction p of the time during the training period, wherein x is the sum of y and z, and p is a model parameter specified individually for each possible value of x.

16. The method of claim 11, wherein the second delivery estimate prediction model predicts the total handling and shipping time using a Naï

ve Bayes algorithm trained with variables from an identification of the seller, a shipment method, a distance between an origin and destination zip codes, the first three digits of the destination zip code, an expected payment hour of day, an expected payment date, a leaf category identifier of the item, and a price of the item, the total handling and shipping time is the lowest number of days x such that the confidence that the item arrives within x days from payment is greater or equal than s times the confidence that the item will arrive later, wherein s is a parameter defined individually for each possible value of x.

17. The method of claim 11, further comprising:

storing data for the first and second delivery estimate prediction models as a series of key value pairs in the storage device; and

calculating the first time estimate using a p model algorithm before the second time estimate using a Naï

ve Bayes algorithm.

18. The method of claim 11, further comprising:

separating data related to a third delivery estimate prediction model into two data sets comprising a training data set and a validation data set;

using the training data for training the third delivery estimate prediction model;

evaluating the third delivery estimate prediction model using the validation data set after adding each decision tree to generate a validation error;

iterating the use of the training data and the evaluation of the third delivery estimate prediction model until the validation error stops decreasing or starts increasing; and

calculating a third time estimate by applying the transaction information to the third delivery estimate prediction model.

19. The method of claim 11, further comprising:

generating a graphical display of delivery dates and corresponding delivery probabilities based on the delivery time estimate.

20. A non-transitory computer-readable storage medium storing a set of instructions that, when executed by a processor, cause the processor to perform operations, comprising:

generating a first delivery estimate prediction model and a second delivery estimate prediction model using historical data from an online marketplace;

storing the historical data from the online marketplace in a storage device;

determining transaction information related to an item listed in the online marketplace;

calculating a first time estimate by applying the transaction information to the first delivery estimate prediction model;

calculating a second time estimate by applying the transaction information to the second delivery estimate prediction model; and

generating a delivery time estimate based on the lowest of the first time estimate and the second time estimate.