Method for predicting business income from user transaction data

Patent metadata

Field	Value
Publication number	US-10997672-B2
Application number	US-201715610596-A
Country	US
Kind code	B2
Filing date	May 31, 2017
Priority date	May 31, 2017
Publication date	May 4, 2021
Grant date	May 4, 2021

Abstract

A method includes obtaining data related to a plurality of historical transactions, where each historical transaction is associated with a label based on a click stream created by the first user, generating a vector of features from the data related to each historical transaction, training, using the vectors and labels, a multinomial classifier to generate a probability that a specific transaction belongs to a specific classification with respect to income, obtaining data related to a new transaction from a financial stream for a second financial account of a second user of the financial service, generating a new vector of features from the data related to the new transaction, determining a classification with respect to income for the new transaction, and presenting the classification to the second user for review in a view of a graphical user interface.

First claim

What is claimed is:

1. A computer implemented method, comprising:
generating a vector of features from data related to a historical transaction;
generating a probability that the historical transaction belongs to a specific classification with respect to income;
training a multinomial classifier using the vector, a label associated with the historical transaction, and the probability;
assigning weights to a sparse matrix made up of a plurality of vectors that include the vector to train the multinomial classifier;
obtaining data related to a new transaction from a data stream for an account of an online service;
splitting the data related to the new transaction into a set of unigrams;
generating a new vector of features from the data related to the new transaction, the new vector including a set of values that correspond and are assigned to the set of unigrams;
determining a classification with respect to income for the new transaction by applying the multinomial classifier to the new vector;
labeling the new transaction with the classification;
presenting the classification to a view of a graphical user interface; and
populating, using the classification, one or more fields of a form that is maintained by the online service.

2. The method of claim 1 , further comprising:
receiving a reclassification of the new transaction;
populating, using the reclassification instead of the classification, one or more fields of the form that is maintained by the online service; and
updating, using the reclassification, the multinomial classifier.

3. The method of claim 1 ,
wherein at least one of the features has been filtered using a custom stop-word dictionary developed through empirical testing of the multinomial classifier, and
wherein the classification is based on a probability generated by applying the multinomial classifier to the new vector.

4. The method of claim 1 , wherein one of the features identifies a week day on which the historical transaction occurred.

5. The method of claim 1 , further comprising:
applying a threshold based on a precision-recall curve to the probability when determining the classification with respect to income for the new transaction.

6. The method of claim 1 , further comprising the operation of:
retraining the multinomial classifier at an end of a predetermined period using transactions which occurred during the predetermined period.

7. The method of claim 1 , wherein the online service is a massively multi-user online service.

8. The computer implemented method of claim 1 , further comprising:
obtaining data related to a plurality of historical transactions,
wherein each historical transaction is associated with a second account provided by the online service and with a label based on a click stream of graphical user interface interactions, and
wherein the label identifies the historical transaction as belonging to a specific classification with respect to income.

9. The method of claim 8 , further comprising:
obtaining data related to tax filing for a plurality of accounts of the online service;
mining the data related to tax filing and the historical transactions using a clustering technique to identify potential sources of income; and
training the multinomial classifier to generate a probability that a specific transaction is one of the potential sources of income.

10. A non-transitory computer-readable storage medium storing instructions, which when executed, perform operations as follows:
generate a vector of features from the data related to each historical transaction;
generate a probability that the historical transaction belongs to a specific classification with respect to income;
train a multinomial classifier using the vector, a label associated with the historical transaction, and the probability;
assign weights to a sparse matrix made up of a plurality of vectors that include the vector to train the multinomial classifier;
obtain data related to a new transaction from a data stream for an account of an online service;
split the data related to the new transaction into a set of unigrams;
generate a new vector of features from the data related to the new transaction, the new vector including a set of values that correspond and are assigned to the set of unigrams;
determine a classification with respect to income for the new transaction by applying the multinomial classifier to the new vector;
label the new transaction with the classification;
present the classification to a graphical user interface; and
populate, using the classification, one or more fields of a form that is maintained by the online service.

11. The non-transitory computer-readable storage medium of claim 10 , further comprising instructions to:
receive a reclassification of the new transaction;
populate, using the reclassification instead of the classification, one or more fields of the form that is maintained by the online service; and
update, using the reclassification, the multinomial classifier.

12. The non-transitory computer-readable storage medium of claim 10 ,
wherein at least one of the features has been filtered using a custom stop-word dictionary developed through empirical testing of the multinomial classifier, and
wherein the classification is based on a probability generated by applying the multinomial classifier to the new vector.

13. The non-transitory computer-readable storage medium of claim 10 , wherein one of the features identifies a week day on which the historical transaction occurred.

14. The non-transitory computer-readable storage medium of claim 10 , further comprising instructions to:
apply a threshold based on a precision-recall curve to a probability when determining the classification with respect to income for the new transaction.

15. The non-transitory computer-readable storage medium of claim 10 , further comprising instructions to:
retrain the multinomial classifier at an end of a predetermined period using transactions which occurred during the predetermined period.

16. The non-transitory computer-readable storage medium of claim 10 , wherein the online service is a massively multi-user online service.

17. The non-transitory computer-readable storage medium of claim 10 , further comprising instructions to:
obtain data related to a plurality of historical transactions,
wherein each historical transaction is associated with a second account provided by the online service and with a label based on a click stream of graphical user interface interactions, and
wherein the label identifies the historical transaction as belonging to a specific classification with respect to income.

18. The non-transitory computer-readable storage medium of claim 17 , further comprising instructions to:
obtain data related to tax filing for a plurality of accounts of the online service;
mine the data related to tax filing and the historical transactions using a clustering technique to identify potential sources of income; and
train the multinomial classifier to generate the probability that a specific transaction is one of the potential sources of income.

19. A system comprising:
a processor;
a storage storing instructions which, when executed by the processor, perform operations as follows:
generate a vector of features from the data related to each historical transaction;
generate a probability that the historical transaction belongs to a specific classification with respect to income;
train a multinomial classifier using the vecto

Assignees

• Chen Meng
• Pei Lei
• Jennings Zachary Grove
• Ho Ngoc Nhung Thi
• Intuit Inc

Inventors

• Chen Meng
• Pei Lei
• Jennings Zachary Grove
• Ho Ngoc Nhung Thi

Classifications

• G06N20/00

  Machine learning · CPC title

• G06Q40/123 Primary

  Tax preparation or submission · CPC title

• G06F16/2465

  Query processing support for facilitating data mining operations in structured databases · CPC title