For the Inventor, By the Inventor

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San Jose, CA, United States of America

Scaleflux, Inc.

USPTO Granted Patents = 37

Trademarks = 1

Forward Citations = 84

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20 inventors (with patents filed for the assignee):

Yang Liu (29 out of 30 patents)

Tong Zhang (24 out of 30 patents)

Fei Sun (23 out of 58 patents)

4. Hao Zhong (17 out of 48 patents)

5. Qi Wu (8 out of 19 patents)

6. Jiangpeng Li (5 out of 5 patents)

7. Mark Vernon (4 out of 6 patents)

8. Kelly Fitzpatrick (4 out of 4 patents)

9. Yong Peng (3 out of 7 patents)

10. Qing Li (2 out of 19 patents)

11. Wenzhong Wu (2 out of 2 patents)

12. Duy Nguyen (2 out of 6 patents)

13. Wentao Wu (1 out of 37 patents)

14. Thad Omura (1 out of 2 patents)

15. Prathamesh Amritkar (1 out of 1 patent)

Core Competencies:

DRAM Modules

Data Compression

Solid-State Drives

Error Correction

Storage Efficiency

In-Memory Computation

Data Deduplication

Flash Memory

Hybrid Software-Hardware

Data Mapping

Performance Enhancement

Non-Volatile Memory

37 patents: Sort by Forward Citations

1. 12748537 - Reducing energy comsumption of self-managed DRAM modules (2026-09-29),

2. 12737114 - Self-managed dram modules with built-in adaptive data compression (2026-09-15),

3. 12681639 - Self-managed DRAM modules with built-in data compression and tiered caching (2026-07-14),

4. 12650791 - Storage-memory fusion drive with direct processor-storage access data path (2026-06-09),

5. 12619492 - Burst error correction for reed-solomon codes (2026-05-05),

6. 12572414 - Hybrid error correction system design for multimode solid-state drives (2026-03-10),

7. 12572286 - Reducing energy consumption of self-managed DRAM modules (2026-03-10),

8. 12339745 - Solid-state drive with multimode compression and error correction (2025-06-24),

9. 12112049 - Reducing energy consumption of self-managed DRAM modules (2024-10-08),

10. 11762698 - Techniques to enable stateful decompression on hardware decompression acceleration engines (2023-09-19),

11. 11386002 - Enhancing solid-state storage device speed performance through stream-aware garbage collection (2022-07-12),

12. 11347586 - Realizing high-speed and low-latency RAID across multiple solid-state storage device with host-side FTL (2022-05-31),

13. 11347422 - Techniques to enhance storage devices with built-in transparent compression for mitigating out-of-space issues (2022-05-31),

14. 11301476 - Using in-storage computation to improve the performance of hash join for database and data analytics (2022-04-12),

15. 11262942 - Integrating host-side storage device management with host-side non-volatile memory (2022-03-01),

Company Overview

Scaleflux, Inc. is a technology company based in San Jose, California, specializing in innovative memory solutions that enhance data storage and processing capabilities. The company focuses on developing advanced storage-class memory technologies that bridge the gap between traditional memory and storage systems, enabling faster data access and improved performance for various applications. Scaleflux aims to address the growing demands of data-intensive environments, providing solutions that support cloud computing, big data analytics, and enterprise storage systems.

Profile summary synthesized from public USPTO records and patent filings.

Company Badge

USPTO Data Jan 1 1976 - Oct 13 2026

37 Patents

#230 in San Jose, CA

#2,190 in California

#9,262 in US

#18,281 in the World

San Jose, CA, United States of America

Scaleflux, Inc.

Forward Citations = 84

20 inventors:

Yang Liu (29 out of 30 patents)

Tong Zhang (24 out of 30 patents)

Fei Sun (23 out of 58 patents)

4. Hao Zhong (17 out of 48 patents)

5. Qi Wu (8 out of 19 patents)

IDiyas Innovation Intelligence. (2026). Company Patent Portfolio: Scaleflux, Inc.. Retrieved from https://idiyas.com/company/scaleflux

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

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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