Our 8th deep learning patent is a first.

On Octobert 9, 2018, Intraspexion’s patent family achieved a first: Our 8th patent is the first U.S. patent to use the terms “deep learning” and blockchain in the legally relevant field of patent Claim(s). For more detail, see the Blockchain group, below.

Our 1st patent is the parent to the others.

On July 1, 2016, with assistance from co-founders Dan Cotman and Obi Iloputaife of CotmanIP Law Group PLC (Pasadena, CA), founder Nick Brestoff filed the provisional application for our first patent.

On September 27, 2016, the formal application was filed for “Using Classified Text and Deep Learning Algorithms to Identify Risk and Provide Early Warning.”

On December 12, 2016, as a first Office Action, we received a Notice of Allowance.

On January 24, 2017, our first patent issued as U.S. Pat. No. 9,552,548.

Based on this 1st patent, we conceived additional inventions and began writing continuations-in-part. We submitted seven applications. They’ve all been approved and they all relate back to our 1st patent. As a result, each member of Intraspexion’s patent family has the following characteristics:

Priority Date: July 1, 2016.

Expiration Date: September 27, 2036.

Our patents-first strategy.

Our first instinct was to build the software system to implement our first patent. We did.

However, because Intraspexion’s leadership believed that “deep learning” was a form of Artificial Intelligence (AI) that was hot and getting hotter, we also adopted a unique AI “patents-first” strategy.

With a current family of 8 deep learning patents, we’ve succeeded. Nevertheless, we believe that there’s much more to do.

Context.

Let’s put our 8 patents in context. We’ve been keeping a list of “Who’s Got Deep Learning Patents,” and, with a request via the Contact Us page, will provide an Excel spreadsheet to you free of charge

Our patent searches are always the same. In the legally-relevant field of Claim(s) [ACLM], we search for “deep learning” OR “deep neural” OR “multi-layer neural” AND by the Issue Date [ISD], going year by year.

Year over Year results: 2013 (3), 2014 (4), 2015 (4), 2016 (36), 2017 (81), 2018 (162), and 2019 (through the current date of March 19, 2019) (61). Looking back, the deep learning patent “land rush” began in 2016.

As of the current date, the Leaderboard Top 10 standings are as follows:

1.    IBM (34)

2. Google (28)

3. Siemens Healthcare (20)

4. Microsft (19)

5. Amazon.com, Amazon Technologies and
A9.com (subsidiary) (8)

5. INTRASPEXION (8)

5. Samsung Electronics (8)

8. Adobe Systems Incorporated (6)

8. Intel Corporation (6)

8. NEC (6)

8. Nuance Communications (6)

The Intraspexion patent family.

General Risk

On January 24, 2017, Patent No. 9,552,548 was issued and assigned to Intraspexion. The title is “Using Classified Text and Deep Learning Algorithms to Identify Risk and Provide Early Warning.”  

On September 12, 2017, No. 9,760,850 was issued. This patent goes by the same title but has different claims.

Specific Risks

On September 5, 2017, the USPTO issued patents related to identifying risks in connection with a Product Defect (No. 9,754,205), Contract Invalidity (No. 9,754,206), Entertainment and Publishing projects (No. 9,754,219), and Medical Diagnoses (No. 9,754,220).  

Financial Advantages

On September 5, 2017, the USPTO also issued a patent for “Using Classified Text and Deep Learning Algorithms to Identify Support for Financial Advantage and Provide Notice” (No. 9,754,218).

Note: This patent is for identifying a financial advantage to obtain rather than a risk to avoid.

Blockchain

On October 9, 2018, the USPTO issued Patent No. 10,095,992 for "Using Classified Text, Blockchain and Deep Learning to Identify Low-Frequency Adverse Situations, and Provide Early Warning."

Note: This patent is the first patent to use blockchain to enable deep learning, and solves the problem for adverse situations where the number of training examples is small, e.g., violations of the Foreign Corrupt Practices Act, Product Liability litigation, and losses due to the theft of trade secrets.