ITEMIZED RECEIPT EXTRACTION USING MACHINE LEARNING

• US 20120330971A1
• Filed: 06/26/2012
• Published: 12/27/2012
• Est. Priority Date: 06/26/2011
• Status: Abandoned Application

• Current Assignee: Itemize Corporation
• Original Assignee: Itemize Corporation
• Inventors: Thomas, James, Contractor, Gopali, Packer, Thomas L., Haley, Michael A.
• Application Number:
  US13/532,863
• Publication Number:
  US 20120330971A1
• Time in Patent Office: Days
• US Class Current: 707/748
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A method, including retrieving a transaction receipt, wherein the transaction receipt includes unstructured data. Features indicating details of the transaction are extracted from the unstructured data, and using a receipt language model, weights are applied to the features. Based on the features and the weights, labels are associated with tokens in the receipt, and the receipt language model is updated with the extracted features, the applied weights and the associated labels.

Claims

1. A method, comprising:

retrieving, by a computer, a transaction receipt comprising unstructured data;

extracting features indicating details of the transaction from the unstructured data;

applying, using a receipt language model, weights to the features;

associating, based on the features and the weights, labels with tokens in the receipt, the tokens comprising values stored in the unstructured data; and

updating the receipt language model with the extracted features, the applied weights and the associated labels.

2. The method according to claim 1, wherein the unstructured data is selected from a list comprising unformatted text, hypertext markup language formatted text, and data extracted from an image of a physical receipt.

3. The method according to claim 1, wherein retrieving the unstructured data comprises associating an email account with a user, identifying an email in the account comprising a transaction receipt, and retrieving the identified email.

4. The method according to claim 3, and comprising updating a profile of the user with the extracted transaction details.

5. The method according to claim 1, wherein the labels comprise descriptions of the values.

6. The method according to claim 1, wherein each of the extracted values is selected from a list comprising a merchant name, an item name, an item description, and item category, an item price, a sales tax amount, a shipping charge, a handling charge, a discount, and adjustment and a total transaction amount.

7. The method according to claim 6, wherein the receipt language model accesses a database comprising one or more merchants, and wherein the merchant name does not match any of the one or more merchants in the database.

8. The method according to claim 1, wherein updating the receipt language model comprises calculating an accuracy score based on the associated labels.

9. The method according to claim 8, and comprising updating the weights based on the accuracy score.

10. The method according to claim 8, and comprising manually revising the identified features upon the accuracy score being below a specified threshold.

11. An apparatus, comprising:

a memory configured to store a transaction receipt comprising unstructured data; and

a processor configured to extract features indicating details of the transaction from the unstructured data, to apply, using a receipt language model, weights to the features, to associate, based on the features and the weights, labels with tokens in the receipt, the tokens comprising values stored in the unstructured data, and to update the receipt language model with the extracted features, the applied weights and the associated labels.

12. The apparatus according to claim 11, wherein the processor is configured to select the unstructured data from a list comprising unformatted text, hypertext markup language formatted text, and data extracted from an image of a physical receipt.

13. The apparatus according to claim 11, wherein the processor is configured to retrieve the unstructured data by associating an email account with a user, identifying an email in the account comprising a transaction receipt, and retrieving the identified email.

14. The apparatus according to claim 13, wherein the processor is configured to update a profile of the user with the extracted transaction details.

15. The apparatus according to claim 11, wherein the labels comprise descriptions of the values.

16. The apparatus according to claim 11, wherein the processor is configured to select each of the extracted values from a list comprising a merchant name, an item name, an item description, and item category, an item price, a sales tax amount, a shipping charge, a handling charge, a discount, and adjustment and a total transaction amount.

17. The apparatus according to claim 16, wherein the receipt language model accesses a database comprising one or more merchants, and wherein the merchant name does not match any of the one or more merchants in the database.

18. The apparatus according to claim 11, wherein the processor is configured to update the receipt language model by calculating an accuracy score based on the associated labels.

19. The apparatus according to claim 18, wherein the processor is configured to update the weights based on the accuracy score.

20. The apparatus according to claim 18, and comprising manually revising the identified features upon the accuracy score being below a specified threshold.

21. A computer software product comprising a non-transitory computer-readable medium, in which program instructions are stored, which instructions, when read by a computer executing a user interface, cause the computer to retrieve a transaction receipt comprising unstructured data, to extract features indicating details of the transaction from the unstructured data, to apply, using a receipt language model, weights to the features, associate, based on the features and the weights, labels with tokens in the receipt, the tokens comprising values stored in the unstructured data, and to update the receipt language model with the extracted features, the applied weights and the associated labels.