Intuit, Inc.

Zhicheng Xue

Wei W Wang

Alexander S Ran

Christopher Z Lesner

Marko Rukonic

Meng Chen

Lei Pei

Linxia Liao

Eric King Loong Shiu

Yueyue Gu

Steve Shibuya

Certain aspects of the present disclosure provide techniques for generating a user interface to prompt users of a software application to perform an action in the software application. The method generally includes generating historical transaction time gap data for transactions in the account. A probability distribution is generated based on the historical time gap data. The probability distribution represents a probability that a transaction related to the account has been performed after an elapsed time from a previous transaction. A probability that an unrecorded transaction exists for an account based on the probability distribution and a time difference between a most recent transaction and a current time. The probability that an unrecorded transaction exists is determined to exceed a threshold probability, and a user interface is generated and displayed to a user of the software application including a prompt for the user to enter new transactions for the account.

Method and system for generating user interfaces to prompt users to perform an activity in a software application based on transaction time analysis

Certain aspects of the present disclosure provide techniques for generating a human readable summary explanation to a user for an outcome generated by a complex machine learning model. In one embodiment, a risk assessment service can receive a request from a user in which a risk model of the risk assessment service performs a specific task (e.g., determining the level of risk associated with the user). Once the risk model determines the risk associated with the user, in order to comply with regulations from a compliance system, the risk model can provide a user with an explanation as to the outcome for transparency purposes.

Explainable complex model

The invention relates to a method for characterizing time series. The method includes obtaining a time series that includes data points for consecutive time intervals, decomposing the time series into a trend component, a seasonal component, and a residual component, and clustering the seasonal component into a number of clusters. Each of the consecutive time intervals is assigned to one of the clusters based on a level of the data points of the seasonality component in the time interval. The method further includes labeling one of the clusters as baseline with the baseline cluster containing more time intervals than any one of the other clusters, determining cluster labels for the other clusters based on the level of the data points of the time intervals assigned to the other clusters; and for each time interval, storing the corresponding cluster label in a vector.

Method and system for characterizing time series

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Mountain View, CA, United States of America

Zhicheng Xue