Palo Alto, CA, United States of America

Lianjie Cao

This inventor holds 19 USPTO granted patents and 7 published patent applications, primarily in Parallel Processing Optimization. Top assignee: Hewlett Packard Enterprise Development LP. Active years: 2019-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Lianjie Cao. Retrieved from https://idiyas.com/inventor/lianjie-cao

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 19 

% Patents Active = 100.0

Average Co-Inventor Count = 3.9

ph-index = 3

Forward Citations = 46(Granted Patents)


Location History:

  • San Jose, CA (US) (2020)
  • Palo Alto, CA (US) (2019 - 2023)
  • Milpitas, CA (US) (2023 - 2024)

Company Filing History:


Years Active: 2019-2026

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Areas of Expertise:
Parallel Processing Units
DMA Transfers
Multi-Tenant Communication
Sharded Data Parallel
Persistent Memory
Containerized Applications
Resource Configuration
Machine Learning
Network-Aware Allocation
Edge Site Deployment
Microservices Assignment
Virtual Network Functions
19 patents (USPTO):Explore Patents

Title: Lianjie Cao: Innovator in Machine Learning and Network Optimization

Introduction

Lianjie Cao is a prominent inventor based in Palo Alto, CA, with a remarkable portfolio of 11 patents to his name. His work predominantly revolves around optimizing resource allocation for machine learning workloads and enhancing service function chain selection in 5G networks. His innovative contributions have significant implications for both the fields of machine learning and telecommunications.

Latest Patents

Cao's latest patents reflect his cutting-edge research and development efforts. One of his notable inventions includes a system and method for resource configuration optimization for machine learning workloads. This invention provides techniques for optimally allocating resources across multiple tasks, particularly for training jobs in machine learning. By generating potential resource configurations, the approach utilizes Bayesian optimization to select the configurations that yield the fastest job completion times.

Another significant patent focuses on employing machine learning-based approaches for service function chain selection in 5G networks. This system addresses the dynamic nature of network performance by using adaptive Bayesian optimization to determine the most optimized assignments of incoming network requests to service function chains. These advancements aim to minimize response times and maximize throughput for various network service requests, showcasing his expertise in the intersection of machine learning and telecommunications.

Career Highlights

Lianjie Cao is currently affiliated with Hewlett Packard Enterprise Development LP, where he applies his expertise to develop innovative solutions in resource management and network optimization. His contributions have been vital in advancing the capabilities of machine learning applications and telecommunications systems.

Collaborations

Cao collaborates with talented professionals in his field, including Puneet Sharma and Faraz Ahmed. Together, they work on pushing the boundaries of technology through collaborative research, driving advancements that benefit industries relying on efficient machine learning and networking solutions.

Conclusion

Lianjie Cao’s inventive spirit and pioneering work in machine learning and network optimization underscore his importance as an inventor. His patents not only demonstrate his technical acumen but also reflect the potential for significant advancements in both machine learning and telecommunications. As technology continues to evolve, innovations like those of Cao will play a crucial role in shaping the future of these dynamic fields.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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