Abstract: A method for automatically recommending to a user of a software application one or more categories of a plurality of different categories of tax deductible expenses includes providing input data to a trained machine learning model and receiving output from the trained machine learning model based on the input data. The output includes a recommendation for the user that includes (i) one or more categories of the plurality of different categories of tax deductible expenses; and (ii) a plurality of examples of tax deductible expenses for each of the one or more categories. The method includes receiving feedback from the user on the recommendation and generating updated training data for the trained machine learning model based on the feedback.

Abstract: Systems and methods for tracking anonymous visitors of an online website or mobile app are disclosed. The browsing activity by an anonymous visitor of the online website or mobile app is converted into features and a visitor-identifier associated with the browsing activity generated by the anonymous visitor is determined. The features are stored with the visitor-identifier in a super-visitor-state before the visitor-identifier is associated with a super-visitor-identifier. After the visitor-identifier is associated with the super-visitor-identifier, the features are stored with the super-visitor-identifier in the super-visitor-state. After the visitor-identifier is associated with the super-visitor-identifier, the features associated with the visitor-identifier in the super-visitor-state may be combined, e.g., aggregated, with the features associated with the super-visitor-identifier and the visitor-identifier may be removed from the super-visitor-state.