User interface generation for transacting goods

• US 10,650,437 B2
• Filed: 06/01/2015
• Issued: 05/12/2020
• Est. Priority Date: 06/01/2015
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Tsao, Vivien
• Primary Examiner(s): Allen, William J
• Assistant Examiner(s): Heath, Maria S. P.
• Application Number:
  US14/727,237
• Publication Number:
  US 20160350838A1
• Time in Patent Office: 1,807 Days
• CPC Class Codes: G06Q 30/0635 Processing of requisition or of purchase orders
• CPC Class Codes: G06Q 30/0641 Shopping interfaces
• CPC Class Codes: G06Q 30/0643 Graphical representation of items or shoppers

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for user interface generation for transacting goods are disclosed. In one aspect, a method includes receiving order data, user data, and other data. The method further includes determining, for a particular user, an order confidence score that corresponds to a likelihood that the particular user ordered a particular good from a particular vendor on a particular day. The method further includes receiving vendor-type mapping data. The method further includes determining a good-type confidence score that corresponds to the likelihood that the particular good or the particular vendor is associated with a particular type of good. The method further includes determining a composite confidence score. The method further includes storing the composite confidence score, data identifying the particular user, data identifying the particular type of good, and data identifying the particular date.

Claims

1. A computer-implemented method comprising:

receiving, over a network, from one or more computers, and by an order detector of a backend order processing system that includes (a) the order detector, (b) an event correlator, (c) an order database manager, (d) an order/marketing manager, (e) a pattern detector, and (f) a user interface generator, (i) vendor data that specifies, for each vendor transaction, a vendor, goods sold, a total transaction price, and a transaction date and (ii) user data that specifies, for each user transaction, a vendor, a user identifier, and a transaction date;

based on the vendor data and the user data, determining, by the event correlator of the backend order processing system and for each vendor transaction, an order confidence score that corresponds to a likelihood that a particular user ordered a particular good from the vendor on a particular date by selecting a representation of the particular good from a default user interface that includes additional representations of other goods;

based on the order confidence scores, determining, by the order database manager of the backend order processing system, for each user, for each good, and for each vendor, a composite confidence score that reflects a likelihood that the user ordered the good from the vendor by selecting a representation of the good from the default user interface;

storing, by the order database manager of the backend order processing system, the composite confidence scores that each reflect the likelihood that the respective user ordered the respective good from the respective vendor;

based on the composite confidence scores that each reflect the likelihood that the respective user ordered the respective good from the respective vendor, generating, by the order/marketing manager of the backend order processing system and for each respective user, a rule for adjusting the default user interface, the rule specifying to promote goods that the respective user is less likely to select than other goods;

receiving, by the pattern detector of the backend order processing system, data indicating that a given user is accessing a goods ordering application for a given vendor, the good ordering application being configured to present a default user interface that includes goods offered by the given vendor;

in response to the data indicating that the given user is accessing the goods ordering application for the given vendor and based on the rule specifying to promote goods that the given user is less likely to select than other goods, adjusting, by the user interface generator of the backend order processing system, the default user interface for the goods ordering application by automatically moving representations of goods that the given user is less likely to select than other goods to more prominent positions in the default user interface; and

providing, by the user interface generator of the backend order processing system and for output in the goods ordering application for the given vendor, the adjusted user interface.

2. The method of claim 1, wherein adjusting the default user interface for the goods ordering application by automatically moving representations of goods that the given user is less likely to select than other goods to more prominent positions in the default user interface comprises:

based on the rule for adjusting the default user interface, adjusting the default user interface for the good ordering application by moving the representations of goods that the given user is less likely to select to a top of the default user interface.

3. The method of claim 1, wherein each rule is configured to increase a total transaction price.

4. The method of claim 1, wherein adjusting the default user interface for the goods ordering application by automatically moving representations of goods that the given user is less likely to select than other goods to more prominent positions in the default user interface comprises:

based on the rule for adjusting the default user interface, adjusting the default user interface for the good ordering application by decreasing prices of goods that the given user is less likely to purchase.

5. The method of claim 1, wherein:

each composite confidence score reflects a likelihood that the user ordered the good from the vendor on a particular day, and

the rule for adjusting the default user interface specifies to promote goods that the respective user is less likely to purchase than other goods on a particular future day.

6. The method of claim 1, wherein receiving the user data that specifies, for each user transaction, a vendor, a user identifier, and a transaction date comprises accessing, by a social medial crawler of the backend order processing system, social media posts and reviews that indicate a purchase from the vendor and a corresponding user identifier.

7. The method of claim 1, wherein determining, for each user, for each good, and for each vendor, a composite confidence score that reflects a likelihood that the user ordered the good from the vendor comprises identifying patterns among the vendor data and the user data according to the order confidence scores.

21. The method of claim 1, wherein:

receiving the vendor data that specifies, for each vendor transaction, a vendor, goods sold, a total transaction price, and a transaction date comprises;

receiving, from vendor computing devices, the vendor data that specifies, for each vendor transaction, a vendor, goods sold, a total transaction price, and a transaction date, and

receiving the user data that specifies, for each user transaction, a vendor, a user identifier, and a transaction date comprises;

receiving, from user computing devices, the user data that specifies, for each user transaction, a vendor, a user identifier, and a transaction date.

8. A system comprising:

one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising;

receiving, over a network, from one or more computers, and by an order detector of a backend order processing system that includes (a) the order detector, (b) an event correlator, (c) an order database manager, (d) an order/marketing manager, (e) a pattern detector, and (f) a user interface generator, (i) vendor data that specifies, for each vendor transaction, a vendor, goods sold, a total transaction price, and a transaction date and (ii) user data that specifies, for each user transaction, a vendor, a user identifier, and a transaction date;

based on the vendor data and the user data, determining, by the event correlator of the backend order processing system and for each vendor transaction, an order confidence score that corresponds to a likelihood that a particular user ordered a particular good from the vendor on a particular date by selecting a representation of the particular good from a default user interface that includes additional representations of other goods;

based on the order confidence scores, determining, by the order database manager of the backend order processing system, for each user, for each good, and for each vendor, a composite confidence score that reflects a likelihood that the user ordered the good from the vendor by selecting a representation of the good from the default user interface;

storing, by the order database manager of the backend order processing system, the composite confidence scores that each reflect the likelihood that the respective user ordered the respective good from the respective vendor;

based on the composite confidence scores that each reflect the likelihood that the respective user ordered the respective good from the respective vendor, generating, by the order/marketing manager of the backend order processing system and for each respective user, a rule for adjusting the default user interface, the rule specifying to promote goods that the respective user is less likely to select than other goods;

receiving, by the pattern detector of the backend order processing system, data indicating that a given user is accessing a goods ordering application for a given vendor, the good ordering application being configured to present a default user interface that includes goods offered by the given vendor;

in response to the data indicating that the given user is accessing the goods ordering application for the given vendor and based on the rule specifying to promote goods that the given user is less likely to select than other goods, adjusting, by the user interface generator of the backend order processing system, the default user interface for the goods ordering application by automatically moving representations of goods that the given user is less likely to select than other goods to more prominent positions in the default user interface; and

providing, by the user interface generator of the backend order processing system and for output in the goods ordering application for the given vendor, the adjusted user interface.

9. The system of claim 8, wherein adjusting the default user interface for the goods ordering application by automatically moving representations of goods that the given user is less likely to select than other goods to more prominent positions in the default user interface comprises:

based on the rule for adjusting the default user interface, adjusting the default user interface for the good ordering application by moving the representations of goods that the given user is less likely to select to a top of the default user interface.

10. The system of claim 8, wherein each rule is configured to increase a total transaction price.

11. The system of claim 8, wherein adjusting the default user interface for the goods ordering application by automatically moving representations of goods that the given user is less likely to select than other goods to more prominent positions in the default user interface comprises:

based on the rule for adjusting the default user interface, adjusting the default user interface for the good ordering application by decreasing prices of goods that the given user is less likely to purchase.

12. The system of claim 8, wherein:

each composite confidence score reflects a likelihood that the user ordered the good from the vendor on a particular day, and

the rule for adjusting the default user interface specifies to promote goods that the respective user is less likely to purchase than other goods on a particular future day.

13. The system of claim 8, wherein receiving the user data that specifies, for each user transaction, a vendor, a user identifier, and a transaction date comprises accessing, by a social medial crawler of the backend order processing system, social media posts and reviews that indicate a purchase from the vendor and a corresponding user identifier.

14. The system of claim 8, wherein determining, for each user, for each good, and for each vendor a composite confidence score that reflects a likelihood that the user ordered the good from the vendor comprises identifying patterns among the vendor data and the user data according to the order confidence scores.

15. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:

receiving, over a network, from one or more computers, and by an order detector of a backend order processing system that includes (a) the order detector, (b) an event correlator, (c) an order database manager, (d) an order/marketing manager, (e) a pattern detector, and (f) a user interface generator, (i) vendor data that specifies, for each vendor transaction, a vendor, goods sold, a total transaction price, and a transaction date and (ii) user data that specifies, for each user transaction, a vendor, a user identifier, and a transaction date;

based on the vendor data and the user data, determining, by the event correlator of the backend order processing system and for each vendor transaction, an order confidence score that corresponds to a likelihood that a particular user ordered a particular good from the vendor on a particular date by selecting a representation of the particular good from a default user interface that includes additional representations of other goods;

based on the order confidence scores, determining, by the order database manager of the backend order processing system, for each user, for each good, and for each vendor, a composite confidence score that reflects a likelihood that the user ordered the good from the vendor by selecting a representation of the good from the default user interface;

storing, by the order database manager of the backend order processing system, the composite confidence scores that each reflect the likelihood that the respective user ordered the respective good from the respective vendor;

based on the composite confidence scores that each reflect the likelihood that the respective user ordered the respective good from the respective vendor, generating, by the order/marketing manager of the backend order processing system and for each respective user, a rule for adjusting the default user interface, the rule specifying to promote goods that the respective user is less likely to select than other goods;

receiving, by the pattern detector of the backend order processing system, data indicating that a given user is accessing a goods ordering application for a given vendor, the good ordering application being configured to present a default user interface that includes goods offered by the given vendor;

in response to the data indicating that the given user is accessing the goods ordering application for the given vendor and based on the rule specifying to promote goods that the given user is less likely to select than other goods, adjusting, by the user interface generator of the backend order processing system, the default user interface for the goods ordering application by automatically moving representations of goods that the given user is less likely to select than other goods to more prominent positions in the default user interface; and

providing, by the user interface generator of the backend order processing system and for output in the goods ordering application for the given vendor, the adjusted user interface.

16. The medium of claim 15, wherein adjusting the default user interface for the goods ordering application by automatically moving representations of goods that the given user is less likely to select than other goods to more prominent positions in the default user interface comprises:

based on the rule for adjusting the default user interface, adjusting the default user interface for the good ordering application by moving the representations of goods that the given user is less likely to select to a top of the default user interface.

17. The medium of claim 15, wherein each rule is configured to increase a total transaction price.

18. The medium of claim 15, wherein adjusting the default user interface for the goods ordering application by automatically moving representations of goods that the given user is less likely to select than other goods to more prominent positions in the default user interface comprises:

based on the rule for adjusting the default user interface, adjusting the default user interface for the good ordering application by decreasing prices of goods that the given user is less likely to purchase.

19. The medium of claim 15, wherein:

each composite confidence score reflects a likelihood that the user ordered the good from the vendor on a particular day, and

the rule for adjusting the default user interface specifies to promote goods that the respective user is less likely to purchase than other goods on a particular future day.

20. The medium of claim 15, wherein receiving the user data that specifies, for each user transaction, a vendor, a user identifier, and a transaction date comprises accessing, by a social medial crawler of the backend order processing system, social media posts and reviews that indicate a purchase from the vendor and a corresponding user identifier.