Intuit, Inc.

Shrutendra Harsola

Vikas Raturi

Naveen Kumar Kaveti

Sourav Prosad

Viswa Datha Polavarapu

Arnab Chakraborty

Poorvi Agrawal

Vignesh Thirukazhukundram Subramaniam

An ensemble of machine learning models used for real-time prediction of text for an electronic chat with an expert user. A global machine learning model, e.g., a transformer model, trained with domain specific knowledge makes a domain specific generalized prediction. Another machine learning model, e.g., an n-gram model, learns the specific style of the expert user as the expert user types to generate more natural, more expert user specific text. If specific words cannot be predicted with a desired probability level, another word level machine learning model, e.g., a word completion model, completes the words as the characters are being typed. The ensemble therefore produces real-time, natural, and accurate text that is provided to the expert user. Continuous feedback of the acceptance/rejection of predictions by the expert is used to fine tune one or more machine learning models of the ensemble in real time.

Ensemble of machine learning models for real-time predictions in expert electronic chats

Certain aspects of the present disclosure provide techniques for training and using a machine learning model to extract relevant textual content for custom fields in a software application from freeform text samples. An example method generally includes generating, via a natural language processing pipeline, a training data set from a data set of freeform text samples and field entries for a plurality of custom fields defined in a software application. A first machine learning model is trained to identify custom fields for which relevant data is included in freeform text. A second machine learning model is trained to extract content from the freeform text into one or more custom fields of the plurality of custom fields defined in the software application and identified by the first machine learning model as custom fields for which relevant data is included in the freeform text.

Extracting content from freeform text samples into custom fields in a software application

A method predicts custom fields from text. Transaction text is normalized from transaction data to generate normalized text. A field prediction and a type prediction are selected using prediction data and the normalized text. The prediction data is generated using a machine learning model trained to identify field predictions from free form text. The field prediction and the type prediction are presented to a client device. In response to user input from the client device, the transaction data is updated with the field prediction.

Predicting custom fields from text

Systems and methods for real-time churn prediction using a flexible and robust machine learning system to predict user churn propensities in real-time using user activity data. In addition to churn propensity prediction, systems and methods may provide insights based on user activity data intelligence.

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Bengaluru, India

Shrutendra Harsola