Microsoft Technology Licensing, LLC

Christopher Lawrence Laterza

Manoj Kumar Rawat

Karan Singh Rekhi

Sathia Prabhu Thirumal

Pranav Jayant Farswani

Natarajan Arumugam

Girish Kumar

David Benjamin Levitan

Andres Felipe Salcedo

Erfan Najmi

Jin Luo

Gregory Lawrence Brake

A method and system for generating synthetic privacy preserving training data for training a language classifier machine-learning (ML) model includes receiving a request to generate the synthetic privacy-preserving training data for the language classifier ML model, retrieving labeled training data associated with training the language classifier ML model, providing the labeled training data, one or more privacy parameters, and a domain type associated with the labeled training data to a synthetic data generation ML model, the synthetic data generation ML model being configured to generate synthetic training data in a privacy-persevering manner, receiving synthetic privacy-preserving training data as an output from the synthetic data generation ML model, and providing the synthetic privacy preserving training data to the language classifier ML model for training the language classifier ML model in classifying text.

Generating private synthetic training data for training machine-learning models

A system and method for creating a machine learning (ML) classifier for a database uses a weakly-supervised training data set created automatically from database items on the basis of a human-created keyword set. The automatically created training data set is used to construct one or more deep learning classifier checkpoints, which can then be compared with one another and with a classifier based on the original keyword set in order to select a classifier for use by other users viewing the database.

Rule-based machine learning classifier creation and tracking platform for feedback text analysis

A system and method for online user feedback management are provided. The method includes receiving online user feedbacks for a product from a plurality of users. A plurality of topics for the product are identified from the online user feedbacks. For each topic, the received online user feedbacks are categorized into a plurality of groups based on a rating score provided in each online user feedback for the product and semantic analysis of each online user feedback for the product. A net promoter score (NPS) uplift for each topic is calculated, where the NPS uplift measures an improvement in a NPS for the product if issues related to the topic are resolved. A priority topic is identified based on the NPS uplift for each of the topics. The priority topic is then prioritized in resolving issues related to the topics included in the online user feedbacks.

Net promoter score uplift for specific verbatim topic derived from user feedback

Growing community of inventors

Issaquah, WA, United States of America

Christopher Lawrence Laterza