Patent Document ID: 9697474
Application ID: 14096050
Patent Flag: 1

Claim One:
1. A computer-implemented method comprising: obtaining training data sets that each include (i) data identifying a publisher, an author, and one or more words in a title of a respective article, (ii) for each of multiple topics associated with a predefined set of topics, data identifying the topic, and (iii) for each of the multiple topics associated with the predefined set of topics, data indicating that the respective article either relates or does not relate to the topic; training, using the training data sets, a predictive model for generating, given (i) data identifying a publisher, an author, and one or more words in a title of a given article and (ii) data identifying a given topic, a likelihood that the given article relates to the given topic; receiving data identifying a particular article; obtaining data identifying a publisher, an author, and one or more words in a title of the particular article; generating, for each of the multiple topics associated with the predefined set of topics, a test data set that includes (i) data identifying the publisher, the author, and the one or more words in the title of the particular article, and (ii) data identifying the topic; providing, to the predictive model and for each of the multiple topics associated with the predefined set of topics, (i) the data identifying the publisher, the author, and the one or more words in the title of the particular article, and (ii) data identifying the topic; based on providing, for each of the multiple topics associated with the predefined set of topics, (i) the data identifying the publisher, the author, and the one or more words in the title of the particular article and, (ii) the data identifying the topic, receiving, from the predictive model and for each of the multiple topics associated with the predefined set of topics, a score that reflects a likelihood that the particular article relates to the topic; selecting a plurality of topics associated with the predefined set of topics based on the scores; and for each topic of the selected plurality of topics associated with the predefined set of topics, designating the particular article as relating to the topic based on the score that reflects the likelihood that the particular article relates to the topic.