Intuit, Inc.

Ngoc Nhung Thi Ho

Meng Chen

Lei Pei

Zachary Grove Jennings

Bei Huang

A method that predicts business income from user transaction data. A multinomial classifier is trained, using a vector of features from data related to a historical transaction and a label associated with the historical transaction, to generate a probability that the historical transaction belongs to a specific classification with respect to income. Data related to a new transaction is split into a set of unigrams. A new vector of features is generated from the data related to the new transaction. The new vector includes a set of values that correspond and are assigned to the set of unigrams. A classification with respect to income is determined for the new transaction by applying the multinomial classifier to the new vector. The new transaction is labeled with the classification. One or more fields of a form that is maintained by an online service is populated using the classification.

Method for predicting business income from user transaction data

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.

A method for recommendation model integration includes obtaining, using a first model, multiple text scores ranking multiple software applications according to a first user submitted terms in a source software application, and obtaining, using a second model, multiple feature scores ranking the software applications according to multiple features of the software applications. The method further includes integrating the text scores and the feature scores to generate multiple combined scores for the software applications, and presenting, to a user, the software applications according to the combined scores.

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Mountain View, CA, United States of America

Ngoc Nhung Thi Ho