using technology assisted review for insurance defense

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877.557.4273 catalystsecure.com A primer on the law and technology for insurance defense professionals WEBINAR TAR for Smart People Gordon J. Calhoun, Esq. John Tredennick, Esq. Presenters &

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Page 1: Using Technology Assisted Review for Insurance Defense

877.557.4273catalystsecure.com

A primer on the law and technology for insurance defense professionals

WEBINAR

TAR for Smart People Gordon J. Calhoun, Esq.

John Tredennick, Esq.

Presenters

&

Page 2: Using Technology Assisted Review for Insurance Defense

We welcome your questions via the chat feature.

Our Speakers

Gordon serves as Chair of the firm's E-Discovery Practice and specializes in representing clients in a variety of

complex, large exposure litigated matters. He also manages the Insurance Regulatory and Reinsurance Division of

the firm’s Insurance Coverage and Bad Faith Practice Group.

Partner, Lewis Brisbois Bisgaard & Smith LLP

Gordon J. Calhoun

John founded Catalyst, which designs, hosts and services the world’s fastest and most powerful document

repositories for large-scale discovery and regulatory compliance. He was named by the American Lawyer as one of

the top six e-discovery trail blazers in its special issue on the “Top 50 Big Law Innovators” of the past 50 years.

Founder and CEO, Catalyst

John Tredennick

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What is Technology Assisted Review?

1. A process through which humans work with a computer to teach it to identify relevant documents.

2. Ordering documents by relevance for more efficient review.

3. Stopping the review after you have reviewed a high percentage of documents. [Optional]

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Is This New?

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We Already Use It

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What is the Process?

1. Collect and process your files

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Shredding the Documents

12

3

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What is the Process?

1. Collect and process your files

2. Train the system (seeds)

3. Rank the documents

WHY RELEVANT?

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How Does it Work?

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How Does it Work?

Support Vector Machines Naïve Bayes K-Nearest Neighbor Geospatial Predictive Modeling Latent Semantic

"I may be less interested in the science behind the "black box” than in whether it produced responsive documents with reasonably high recall and high precision.“

Magistrate Judge Andrew Peck

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How Does it Work?

Support Vector Machines Naïve Bayes K-Nearest Neighbor Geospatial Predictive Modeling Latent Semantic

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What Goes on Under the Hood?

The computer builds a big, complex search!

What terms are most likely to be associated with good documents?

What terms are most likely to be associated with bad documents?

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What is the Process?

1. Collect and process your files

2. Train the system (seeds)

3. Rank the documents

4. Continue your review

5. Test as you go

6. Stop when finished

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A Circular Process

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What Are the Savings?

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What Are the Savings?

Number of documents in the review

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What Are the Savings?

Percentage of relevant documents found

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What Are the Savings?25

Linear Review

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What Are the Savings?

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Review 12% and get 80% recall

What Are the Savings?

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Review 24% and get 95% recall

What Are the Savings?

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When Can I Stop?

1. When you run out of relevant documents

2. Courts have approved 70% and higher

3. Prove recall by sampling the non-reviewed documents

4. Sample size and margin of error

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Case Study: Large Production Review

Collection: 2.1 million documents Initial richness: 1% Review team used CAL Review richness: 25 to 35%

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Result: 98% Recall

Review: Less than 10%

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Japanese client faces U.S. litigation

Case Study: IP Litigation

Over 3.6 million documents collected

Six relevant per 1,000 documents

Started with 10,000 already-tagged training documents

Used Insight Predict and its CAL protocol

Batch richness grew to 35%

Stopped review when relevant documents stopped coming

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Results

Found 80% relevant documents after just 20% review

Reviewed 500,000 of 3.6 million total

Saved 80% of the review costs

Saved 80% of the review time

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How Can I Use it?

Prioritized Review

Studies show that review teams get through documents more quickly when similar documents are reviewed together.

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How Can I Use it?

Outbound productions

Reduce review costs and time dramatically by establishing a cutoff point and stopping review.

Save up to 90% on review costs!

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How Can I Use it?

In-bound productions

In-bound productions allow your team to find hot documents for depositions and trial.

Find relevant documents in a fraction of the time!

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How Can I Use it?

Early case/document assessment

The SEC and DOJ actively use TAR to get a quick handle on document produced in their investigations.

Greg Buckles, eDJ Analyst

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How Can I Use it?

Focus team on multiple issues during review to save time and effort

Issue review

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How Can I Use it?

Non-English documents

TAR works effectively on any language so long as you tokenize.

裁判所はどこにありますか ?

Where is the courthouse?

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TAR 1.0: One-Time Training

1. Rolling uploads?

2. Subject matter experts?

3. Low richness collections?

4. Only for big cases?

Problems

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Review = training

TAR 2.0: Continuous Training

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CAL: Solving Real-World Problems

1. One time training

2. Rolling uploads

3. Subject matter experts

4. Low richness collections

5. Only for big cases

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Simple and flexible

CAL Training

1. Train with anything you want

2. Use as many or as few as you want

3. Keep searching and feeding throughout the

process

4. Makes use of every attorney decision on

documents

5. Contextual diversity will help find what you

don’t know

6. QC helps ensure consistent training

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Continuous active learning (CAL) is more effective than one-time

training used in TAR 1.0

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Continuous Active Learning vs. One-Time Training

TAR 2.0 Savings

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What Have the Courts Said?

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Go Ahead, Dive In!

“This judicial opinion now recognizes that computer-assisted review is an acceptable way to search for relevant ESI in appropriate cases.”

Da Silva Moore v. Publicis Groupe (S.D.N.Y. 2012)

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Go Ahead, Dive In!

“This judicial opinion now recognizes that computer-assisted review is an acceptable way to search for relevant ESI in appropriate cases.”

Da Silva Moore v. Publicis Groupe (S.D.N.Y. 2012)

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Approving Predictive Coding

Global Aerospace v. Landau Aviation (Virginia) April 23, 2012

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Unanimous Approval

Nat’l Day Laborer Org. Network v. U.S. Immigration & Customs Enforcement Agency, No. 10 Civ. 2488 (SAS), 2012 WL 2878130 (S.D.N.Y. July 13, 2012).

EORHB, Inc. v. HOA Holdings, LLC, No. 7409-VCL (Del. Ch. Oct. 15, 2012).

Global Aerospace, Inc. v. Landow Aviation, L.P., No. CL 61040 (Vir. Cir. Ct. Apr. 23, 2012).

In re Actos (Pioglitazone) Prods. Liab. Litig., MDL No. 6:11-MD-2299 (W.D. La. July 27, 2012).

2012

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Unanimous Approval

2013Gordon v. Kaleida Health, No. 08-CV-378S(F), 2013 WL 2250579 (W.D.N.Y. May 21, 2013).

In re Biomet M2a Magnum Hip Implant Prods. Liab. Litig., 2013 U.S. Dist. LEXIS 84440 (N.D. Ind. Apr. 18, 2013).

In re Biomet M2a Magnum Hip Implant Prods. Liab. Litig., 2013 U.S. Dist. LEXIS 172570 (N.D. Ind. Aug. 21, 2013).

EORHB, Inc. v. HOA Holdings, LLC, No. 7409-VCL, 2013 WL 1960621 (Del. Ch. May 6, 2013).

Gabriel Techs., Corp. v. Qualcomm, Inc., No. 08CV1992 AJB (MDD), 2013 WL 410103 (S.D. Cal. Feb. 1, 2013).

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Unanimous Approval

2014

Bridgestone Americas, Inc. v. Int. Bus. Machs. Corp., No. 3:13-1196 (M.D. Tenn. July 22, 2014). 

Dynamo Holdings Ltd. P’ship v. Comm’r of Internal Revenue, Nos. 2685-11, 8393-12 (T.C. Sept. 17, 2014).

FDIC v. Bowden, No. CV413-245, 2014 WL 2548137 (S.D. Ga. June 6, 2014).

In re Bridgepoint Educ., Inc., No. 12cv1737 JM (JLB), 2014 WL 3867495 (S.D. Cal. Aug. 6, 2014).

Progressive Cas. Ins. Co. v. Delaney, No. 2:11-cv-00678-LRH-PAL, 2014 WL 2112927 (D. Nev. May 20, 2014).

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The Law

“The case law has developed to the point where it is black letter law that if a party wants to utilize TAR for document review, courts will permit it.”

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Discussion and Q&A

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Let’s continue the discussion.

Thank you!

[email protected]

Gordon J. Calhoun

[email protected]

John Tredennick