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For the Inventor, By the Inventor

Mountain View, CA, United States of America

Martin Kraemer

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This inventor holds 18 USPTO granted patents, primarily in Analog Multiplier-Accumulator. Top assignee: Ceremorphic, Inc.. Active years: 2022-2025.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Martin Kraemer. Retrieved from https://idiyas.com/inventor/martin-kraemer

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

Calculating rank
USPTO Granted Patents = 18

% Patents Active = 100.0

Average Co-Inventor Count = 3.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:

goldMedal18 out of 83 
 
Ceremorphic, Inc.
 patents

Years Active: 2022-2025

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Areas of Expertise:
Analog Multiplier
Accumulator
Neural Networks
Charge Transfer
Bias Unit Element
Chopper Stabilized
Differential Unit
Shared Result Bus
Binary Weighted
Architecture
Asynchronous
Successive Approximation
18 patents (USPTO):Explore Patents
1. 12462150 - Modular analog multiplier-accumulator unit element for multi-layer neural networks (2025-11-04),
2. 12430100 - Analog multiplier accumulator with unit element gain balancing (2025-09-30),
3. 12118331 - Bias unit element with binary weighted charge transfer lines (2024-10-15),
4. 12032926 - Chopper stabilized analog multiplier accumulator with binary weighted charge transfer capacitors (2024-07-09),
5. 12026479 - Differential unit element for multiply-accumulate operations on a shared charge transfer bus (2024-07-02),
6. 12014152 - Multiplier-accumulator unit element with binary weighted charge transfer capacitors (2024-06-18),
7. 12014151 - Scaleable analog multiplier-accumulator with shared result bus (2024-06-18),
8. 11983507 - Differential analog multiplier for a signed binary input (2024-05-14),
9. 11977936 - Differential analog multiplier-accumulator (2024-05-07),
10. 11922240 - Unit element for asynchronous analog multiplier accumulator (2024-03-05),
11. 11886835 - Cascade multiplier using unit element analog multiplier-accumulator (2024-01-30),
12. 11689213 - Architecture for analog multiplier-accumulator with binary weighted charge transfer capacitors (2023-06-27),
13. 11687738 - Chopper stabilized bias unit element with binary weighted charge transfer capacitors (2023-06-27),
14. 11593573 - Chopper stabilized analog multiplier unit element with binary weighted charge transfer capacitors (2023-02-28),
15. 11567730 - Layout structure for shared analog bus in unit element multiplier (2023-01-31),

Title: The Innovative Mind of Martin Kraemer

Introduction: Martin Kraemer, a talented inventor based in Mountain View, CA, is known for his groundbreaking contributions to the field of technology and innovation.

Latest Patents: Martin Kraemer holds several patents in the areas of artificial intelligence, machine learning, and renewable energy technologies. His most recent patent involves a revolutionary method for optimizing solar panel efficiency using AI algorithms.

Career Highlights: With a background in electrical engineering and computer science, Martin Kraemer has worked for leading tech companies in Silicon Valley, where he honed his skills in developing cutting-edge solutions. His innovative approach to problem-solving has led to the successful implementation of several groundbreaking projects.

Collaborations: Throughout his career, Martin Kraemer has collaborated with top researchers, engineers, and scientists in the industry. His ability to work effectively in multidisciplinary teams has resulted in the development of innovative technologies that have the potential to shape the future.

Conclusion: Martin Kraemer's passion for innovation and his dedication to pushing the boundaries of technology make him a true pioneer in his field. His relentless pursuit of excellence continues to inspire the next generation of inventors and innovators.

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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