Frequently Asked Questions

What is Intraspexion®?

Intraspexion is a legal tech company organized as a Delaware for-profit corporation, with headquarters near Seattle. Intraspexion currently offers a patented Deep Learning software system to identify litigation risks.

What is “Deep Learning”?

Artificial Intelligence (AI) a broad term. A sub-category of AI is Machine Learning (ML). A sub-category of ML is Neural Networks. A sub-category of Neural Networks is Deep Learning (formally a “multi-layer” Neural Network). As the result of academic breakthroughs beginning in 2012, such Deep Learning neural networks have enabled computers “to learn” from either images or words. 

Take driverless vehicles and images: after training runs consisting millions of miles, a Deep Learning “brain” can "understand" the camera and Lidar images well enough to accurately control the mechanical aspects of the vehicle, i.e., the gas pedal, brakes, and steering wheel.

How does Intraspexion use Deep Learning?

Please see the discussion at the Tab for How It Works. We discussed how we use “deep learning” there. 

What’s the value of identifying a True Positive?

There are two “values.” First, a True Positive is an impetus to begin an internal investigation. Then, law department personnel, perhaps with the assistance of outside counsel, may use email threading and/or access internal databases to decide whether a potential lawsuit is brewing. Then they can advise a control group executive and (hopefully) nip the risk in the bud. Why find the “smoking guns” after the lawsuits are filed? 

What’s the second value?

The second value is that, when True Positives are identified, a Deep Learning system “learns” to be an even better (more accurate) filter. Our system enables human reviewers to tag and store both True Positives and False Positives in a database. When there’s a sufficient amount, we add them to the training set, and the “model” (the “engine”) becomes more specific to the company. 

What’s the first way the training becomes company-specific?

Our “generic” training dataset consists of attorney-vetted documents from previously filed lawsuits in a specific litigation category without regard to the identity of the defendant. And for each prospective client, we may also extract the attorney-vetted documents in previously filed lawsuits against that prospective client.   

How “accurate” is your system?

As we described in How It Works, the system's been tested with Enron emails. (We can’t tell you about customer experience, which we never share.) After assessing 20,401 emails, the system reported only 25 of them as "related" to the risk of employment discrimination. The fraction of 25 out of 20,401 means that system said “never mind” as to about 99.88% of the emails it processed, and "take a look" as to about one-eighth of one percent. Think of it: You’re closest to your company’s emails, but you wouldn’t look at a mind-numbing number like 20,000 emails every day. But 25? Yes, that’s doable.

How is Intraspexion’s system deployed?

Intraspexion offers on-prem or in-the-cloud (using AWS or Azure) solutions.

What technical assistance do you provide when the system is deployed?

We provide step-by-step instructions, including both text and screen shots.

Do you store any customer data?

No.

Do you forward any customer data to anyone other than a user?

No.

Do you have Office 365 connectivity?

Yes.

Do you have any direct competitors?

No.

Because of your patents?

Yes.

Where does Intraspexion stand with them?

For context, the growth rate of patents issued by the USPTO with “deep learning” (or “deep neural” or “multi-layer neural”) in the Claims field has been explosive: in 2013 (3); 2014 (4); 2015 (4); 2016 (36); 2017 (81); 2018 (162); 2019 (thru Jan. 15, 2019) (15).    

Of the “deep Learning” patents issued from January 1, 2013 through January 15, 2019, the Top 5 patent owners (“Assignees") are IBM (29); Google (26); Microsoft (18); Siemens Healthcare (17); and both Amazon (8) and Intraspexion (8). 

To receive a copy of the current spreadsheet, which we update every Tuesday, just use the form at Contact Us.

What are the key differences between what Intraspexion does and the tools used in eDiscovery?

1. With eDiscovery, the personnel in corporate law departments are forced to look backward for custodians of documents that are potentially relevant to an already-filed lawsuit. Such responsive documents and timeframes are limited to the allegations and claims in the lawsuit. Intraspexion scans emails from every yesterday, so the scope is limited only by the number of specific risks for which an enterprise wants an “early warning.”

2. In eDiscovery, practitioners are accountable to the opposing parties and to the court. This is the “defensibility” issue. Intraspexion is preventive in nature, so corporate law personnel are only accountable to the company.     

3. In general, eDiscovery, by definition, is reactive in nature. Intraspexion enables the corporate law department to be proactive.

Aren’t there other ways internal investigations can be triggered?

Yes. Someone on the way out might say, “You’ll be hearing from my attorney.” Or the company may have a “hot line.”

Besides "employment discrimination," what other litigation threats does Intraspexion detect?

Because every company has employees, "employment discrimination" is our initial risk category. As attorneys familiar with litigation in federal court already know, there are many business-relevant litigation categories in PACER, e.g., breach of contract, fraud, antitrust, to name only a few. We can train a Deep Learning model for any category of litigation pain, and we can build a generic version in a very short period of time. 

What Technical assistance do you provide?

We provide step-by-step instructions.

What's your pricing structure?

Each prospective customer has a unique litigation profile. Their prevention priorities differ. As a result, we have no set pricing structure. We customize our pricing and we keep our relationship with each customer confidential.

What about the privacy concerns of company employees?

First, we don't monitor personal devices. However, where a “computer technology resource" (CTR) policy is in place, and an employee is aware of the CTR policy, and agrees to it, it's generally settled law that an employee has no reasonable expectation of privacy when a company monitors its own company-owned computers. See Holmes v. Petrovich Development, LLC, 191 CAl.App.4th 1047, 119 Cal.Rptr.3d 878 (2011) (deciding that not even the attorney-client privilege bars monitoring).

For the sake of customer confidence and transparency, can you tell us anything about the software you use?

Yes. We are proud to say that our indexing and visualization capabilities are powered by dtSearch®, www.dtsearch.com. We also use Global Vectors for Word Representation (GloVe) and Google's TensorFlow. Both GloVe and TensorFlow are “open-source.” GloVe and TensorFlow are included in our system under an Apache 2.0 license, the terms of which are here and are incorporated herein in full by this reference. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.