Nano Dimension Announces Another Deep Learning AI Patent Granted to its DeepCube Technology

Rhea-AI Summary

Nano Dimension (NNDM) announces the granting of a patent for technology developed by its AI group, DeepCube, enabling better training and optimization of decentralized deep learning-based AI models. This milestone enhances the company's commercialization of its industrial AI solution by addressing the challenge of training models on new customer data without direct access to the data or model.

Insights

Intellectual Property Analyst

The granting of the Neural Network Mimicking Patent to Nano Dimension Ltd. signifies a strategic enhancement in the realm of industrial AI and AME. It presents a competitive edge by resolving the conundrum of customer data sensitivity, which is a widespread hurdle in AI deployment. By enabling on-premise model training without the need for data transfer, Nano Dimension can potentially catalyze widespread adoption of their AI solutions, particularly where data privacy is paramount. This technology could lead to a paradigm shift in how AI is integrated into various industries, potentially opening up new markets for the company.

From an intellectual property standpoint, this patent fortifies Nano Dimension's portfolio and can act as a deterrent against competitors seeking to enter the same space. Patents are a form of intangible asset that can significantly augment a company's valuation, especially when they protect innovative technologies with broad applications. Investors should note the potential for increased barriers to entry for competitors, which can translate to a stronger market position for Nano Dimension.

Market Analyst

From a market perspective, the implications of such a patent are multifaceted. Firstly, the ability to train AI models on the edge, directly at the client's site, aligns with the increasing demand for edge computing solutions, which is a growing segment within the technology sector. Secondly, the continuous self-improvement aspect of the AI platform could lead to significant operational efficiencies for customers, which in turn, may result in higher customer satisfaction and retention rates for Nano Dimension.

Moreover, the expansion of DeepCube's capabilities beyond additive manufacturing into a comprehensive industrial AI platform indicates Nano Dimension's ambition to diversify and capture a larger share of the AI market. This move could potentially attract a broader range of investors interested in the industrial AI space and may lead to increased stock market activity surrounding NNDM.

Data Privacy Consultant

The aspect of data privacy and security is becoming increasingly critical in the digital age and Nano Dimension's patent directly addresses this concern. The ability to train AI models without exposing sensitive data is a significant advantage in industries such as healthcare, finance and defense, where confidentiality is non-negotiable. By mitigating the risk of data breaches and ensuring compliance with stringent data protection regulations, the patented technology could facilitate new partnerships and collaborations with entities that were previously hesitant to adopt AI solutions due to data privacy issues.

For stakeholders, this innovation translates into a potentially expanded customer base and a stronger trust in Nano Dimension's products, which is a important factor in long-term business relationships. The emphasis on data privacy could also serve as a unique selling proposition that differentiates Nano Dimension from competitors in the AI industry.

The Patent Relates to Model Training and Optimization on a Decentralized Networks

The Milestone Represents a Critical Innovation to Enable the Company’s Commercialization of Its Industrial AI Solution

Waltham, Mass., March 19, 2024 (GLOBE NEWSWIRE) --  Nano Dimension Ltd. (Nasdaq: NNDM) (“Nano Dimension” or the “Company”), a leading supplier of Additively Manufactured Electronics (“AME”) and multi-dimensional polymer, metal & ceramic Additive Manufacturing (“AM”) 3D printing solutions, today announced that a patent was granted for technology developed by its industrial artificial intelligence (“AI”) group, DeepCube, that enables better training and optimization of decentralized deep learning-based AI models.

The U.S. patent, formally titled System and method for mimicking a neural network without access to the original training dataset or the target model, (the “Neural Network Mimicking Patent”) addresses one of the core challenges of deploying AI models in the real-world, specifically continuously training models on new data when that data belongs to a customer. In the industry at-large, dealing with new customer data has often been a limitation due to sensitivity and confidentiality concerns that limit data shareability. The new patent addresses this challenge by ultimately training and improving the AI models on customers’ premises, without Nano Dimension having direct access to the new data or model.

This patent is another key component in Nano Dimension’s efforts to transform DeepCube from a solely in-house AI group to a leading industrial AI solution provider. Nano Dimension has already made progress in this initiative, having announced agreements and memorandums of understanding (“MOUs”) with several parties. DeepCube is currently developing an end-to-end AI platform for industrial usage that is not only limited to additive manufacturing. The software platform is intended to run autonomously on customers’ premises, and continuously improve itself, such that the more it is used, the higher the accuracy will become.

Importantly, the Neural Network Mimicking Patent is not just about software, but also hardware. The training infrastructure at the core of this innovation exclusively uses Nvidia graphics processing units (“GPUs”), while the deployed inference models are currently optimized for Nvidia GPUs, along with Intel and AMD central processing units (“CPUs”).

Nano Dimension’s DeepCube alone has 50 patent applications filed, of which 27 have already been granted. These patent applications are filed in 8 different jurisdictions, providing a truly global IP protection.

Yoav Stern, Chief Executive Officer and Member of the Board of Directors of Nano Dimension, stated: “Nano Dimension’s leadership in AI continues to make progress. In fact, this has been the case since we acquired DeepCube three years ago, but we are proud that these developments are becoming more public. Most importantly, these milestones are evident of how we are moving closer to having a full fledged commercialized industrial AI solution that will take the deep learning-based AI we have developed for proprietary use to the broader industrialized markets.”

About Nano Dimension

Nano Dimension’s (Nasdaq: NNDM) vision is to transform existing electronics and mechanical manufacturing into Industry 4.0 environmentally friendly & economically efficient precision additive electronics and manufacturing – by delivering solutions that convert digital designs to electronic or mechanical devices - on demand, anytime, anywhere.

Nano Dimension’s strategy is driven by the application of deep learning based AI to drive improvements in manufacturing capabilities by using self-learning & self-improving systems, along with the management of a distributed manufacturing network via the cloud.

Nano Dimension has served over 2,000 customers across vertical target markets such as aerospace and defense, advanced automotive, high-tech industrial, specialty medical technology, R&D and academia. The Company designs and makes Additive Electronics and Additive Manufacturing 3D printing machines and consumable materials. Additive Electronics are manufacturing machines that enable the design and development of High-Performance-Electronic-Devices (Hi-PED®s). Additive Manufacturing includes manufacturing solutions for production of metal, ceramic, and specialty polymers-based applications - from millimeters to several centimeters in size with micron precision.

Through the integration of its portfolio of products, Nano Dimension is offering the advantages of rapid prototyping, high-mix-low-volume production, IP security, minimal environmental footprint, and design-for-manufacturing capabilities, which is all unleashed with the limitless possibilities of additive manufacturing.

For more information, please visit www.nano-di.com.

Forward Looking Statements

This press release contains forward-looking statements within the meaning of the “safe harbor” provisions of the Private Securities Litigation Reform Act of 1995 and other Federal securities laws. Words such as “expects,” “anticipates,” “intends,” “plans,” “believes,” “seeks,” “estimates,” and similar expressions or variations of such words are intended to identify forward-looking statements. For example, Nano Dimension is using forward-looking statements in this press release when it discusses the potential benefits of the Neural Network Mimicking Patent, its efforts to transform DeepCube to a leading industrial AI solution provider and the ability to provide a full fledged commercialized industrial AI solution that will take the deep learning-based AI Nano Dimension has developed for proprietary use to the broader industrial market. Because such statements deal with future events and are based on Nano Dimension’s current expectations, they are subject to various risks and uncertainties. There can also be no assurance that other pending patent applications will be granted and, even if granted, it may be in a narrower scope than requested. Actual results, performance, or achievements of Nano Dimension could differ materially from those described in or implied by the statements in this press release. The forward-looking statements contained or implied in this press release are subject to other risks and uncertainties, including those discussed under the heading “Risk Factors” in Nano Dimension’s annual report on Form 20-F filed with the Securities and Exchange Commission (“SEC”) on March 30, 2023, and in any subsequent filings with the SEC. Except as otherwise required by law, Nano Dimension undertakes no obligation to publicly release any revisions to these forward-looking statements to reflect events or circumstances after the date hereof or to reflect the occurrence of unanticipated events. References and links to websites have been provided as a convenience, and the information contained on such websites is not incorporated by reference into this press release. Nano Dimension is not responsible for the contents of third-party websites.

NANO DIMENSION INVESTOR RELATIONS CONTACT
Investor Relations | ir@nano-di.com

FAQ

What patent was granted to Nano Dimension (NNDM) related to AI technology?

Nano Dimension (NNDM) announced the granting of a patent for technology developed by its AI group, DeepCube, enabling better training and optimization of decentralized deep learning-based AI models.

What is the title of the U.S. patent granted to Nano Dimension (NNDM)?

The U.S. patent granted to Nano Dimension (NNDM) is formally titled System and method for mimicking a neural network without access to the original training dataset or the target model.

How does the Neural Network Mimicking Patent address the challenge of deploying AI models in the real world?

The Neural Network Mimicking Patent addresses the challenge of continuously training models on new customer data without Nano Dimension having direct access to the data or model, thus enabling better optimization of AI models.

What is DeepCube currently developing besides additive manufacturing?

DeepCube is currently developing an end-to-end AI platform for industrial usage that is not to additive manufacturing, intended to run autonomously on customers' premises and continuously improve itself.

How does the Neural Network Mimicking Patent contribute to Nano Dimension's efforts in transforming DeepCube into an industrial AI solution provider?

The Neural Network Mimicking Patent is a key component in Nano Dimension's efforts to transform DeepCube into a leading industrial AI solution provider by enabling better training and optimization of AI models on customers' premises.