Cupertino, CA, United States of America

Hanzhao Lin

This inventor holds 1 USPTO granted patent and 2 published patent applications. Top assignee: Google Inc.. Active years: 2026.

USPTO Granted Patents = 1 

% Patents Active = 100.0

Average Co-Inventor Count = 12.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Hanzhao Lin: Innovator in Large Language Models

Introduction

Hanzhao Lin is a prominent inventor based in Cupertino, CA (US). He has made significant contributions to the field of artificial intelligence, particularly in the area of large language models (LLMs). His innovative work focuses on enhancing the efficiency and effectiveness of these models.

Latest Patents

Hanzhao Lin holds a patent titled "Instruction following in large language models to reduce computational resource consumption." This patent involves implementations that improve the instruction-following capabilities of LLMs through techniques such as instruction decomposition, self-evaluation, and progressive refinement. The system can obtain natural language (NL) based input, generate multiple candidate responses, and evaluate these responses based on the instructions provided. The process allows for the progressive refinement of candidate responses until specific termination criteria are met. In some cases, the NL based input can be sourced from client devices or databases, enhancing the fine-tuning of the LLM.

Career Highlights

Hanzhao Lin is currently employed at Google Inc., where he continues to push the boundaries of AI technology. His work is instrumental in developing more efficient models that can better understand and respond to human instructions.

Collaborations

Hanzhao collaborates with talented colleagues such as Ragha Kotikalapudi and Swaroop Mishra, contributing to a dynamic and innovative work environment.

Conclusion

Hanzhao Lin's contributions to the field of large language models exemplify the importance of innovation in technology. His patent work not only enhances the capabilities of AI systems but also paves the way for more efficient computational processes.

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