Intuit, Inc.

Akshay Ravindran

Avinash Thekkumpat

Shylaja R Deshpande

Raja Sabra

A method including preprocessing natural language text by cleaning and vectorizing the natural language text. A first machine learning model (MLM) extracts negative reviews. A first input to the first MLM is the natural language text and a first output of the first MLM is first probabilities that the negative reviews have negative sentiments. The method also includes categorizing the negative reviews by executing a second MLM. A second input to the second MLM is the negative reviews. A second output of the second MLM is second probabilities that the negative reviews are assigned to categories. The method also includes identifying, using a name recognition controller and based on categorizing, a name of a software application in the negative reviews and sorting the negative reviews into a subset of negative reviews relating to the name. The software application is adjusted based on the subset of negative reviews.

Using machine learning to identify hidden software issues

A method including receiving natural language text. A negative review is extracted from the natural language text using a first machine learning model (MLM). A first input to the first MLM is the natural language text and a first output of the first MLM is a first probability that the negative review has a negative sentiment. The negative review includes an instance of the natural language text having a corresponding negative sentiment probability above a threshold value. The negative review is categorized by executing a second MLM. A second input to the second MLM is the negative review. A second output of the second MLM is a second probability that the negative review is assigned to a category. A name of a target of the negative review is identified using the name recognition controller and the negative review. The name of the target and the category are provided.

Growing community of inventors

Mountain View, CA, United States of America

Akshay Ravindran