Seattle, WA, United States of America

Dinesh Mandalapu


Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2023

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1 patent (USPTO):

Title: Dinesh Mandalapu: Innovator in Predictive Systems

Introduction

Dinesh Mandalapu is an accomplished inventor based in Seattle, WA (US). He has made significant contributions to the field of predictive systems, particularly in the area of item influence prediction. His innovative work has garnered attention in the tech industry, especially through his association with Amazon Technologies, Inc.

Latest Patents

Dinesh holds a patent titled "Scalable product influence prediction using feature smoothing." This patent discloses systems and methods to implement an item metric prediction system that predicts a metric for an item using a feature-based model built from other similar items. The system is designed to predict item influence values (IIVs) of items, indicating the expected amount of subsequent transactions caused by an initial transaction of the items. The process involves distributing a sample of item transaction data to multiple task nodes that execute in parallel to determine the observed IIVs from the transaction data. A new IIV is then determined for items with low confidence levels by selecting a set of similar items and tuning the parameters of a feature-based model to fit the observed IIVs. Ultimately, a new IIV with a high confidence level is obtained using this model.

Career Highlights

Dinesh's career is marked by his role at Amazon Technologies, Inc., where he has been instrumental in developing advanced predictive systems. His work has not only contributed to the company's innovative edge but has also set a benchmark in the industry for predictive analytics.

Collaborations

Dinesh has collaborated with notable colleagues such as Karthik Sundaresan Gurumoorthy and Vineet Shashikant Chaoji. These collaborations have further enriched his work and expanded the impact of his innovations.

Conclusion

Dinesh Mandalapu is a prominent figure in the realm of predictive systems, with a focus on scalable product influence prediction. His contributions through patents and collaborations highlight his commitment to innovation in technology.

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