Company Filing History:
Years Active: 2019-2020
Title: Innovations by Crystal Shi in Mobile Device Prediction
Introduction
Crystal Shi is an accomplished inventor based in Los Angeles, CA. She has made significant contributions to the field of mobile device technology, particularly in the area of predictive modeling using machine learning. With a total of two patents to her name, her work focuses on enhancing the efficiency and performance of mobile device predictions.
Latest Patents
Crystal Shi's latest patents include "Systems and methods for predicting lookalike mobile devices" and "Systems and methods for using geo-blocks and geo-fences to discover lookalike mobile devices." Both patents provide innovative methods and systems that utilize mobile device location events and machine learning to generate predictive classification and regression models for lookalike prediction. These systems efficiently use various types of location events, offering improved scale and performance. They also benefit from the advantages of a machine learning platform, such as automatic adaptation to different seed lists, the addition of new features, and adjustments to changes in data statistical properties.
Career Highlights
Crystal Shi is currently employed at Xad, Inc., where she continues to develop her innovative ideas and contribute to advancements in mobile technology. Her work has positioned her as a key player in the industry, and her patents reflect her expertise and forward-thinking approach.
Collaborations
Some of her notable coworkers include Can Liang and Pravesh Katyal, who collaborate with her on various projects at Xad, Inc. Their teamwork fosters an environment of creativity and innovation, further enhancing the impact of their collective work.
Conclusion
Crystal Shi's contributions to mobile device prediction through her innovative patents demonstrate her expertise and commitment to advancing technology. Her work not only improves the efficiency of mobile device predictions but also showcases the potential of machine learning in this field.