Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 1 of 201
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`Exhibit 35
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`Exhibit 35
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 2 of 201
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` 1 UNITED STATES DISTRICT COURT
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` 2 SOUTHERN DISTRICT OF NEW YORK
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` 3 CIVIL ACTION NO. 1:14-cv-02396-PGG
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` 4
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` 5 - - - - - - - - - - - - - - - - - - - - - - - -
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` 6 NETWORK-1 TECHNOLOGIES, INC.,
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` 7 Plaintiff,
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` 8 vs.
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` 9 GOOGLE, INC., and YouTube,
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`10 Defendants.
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`11 - - - - - - - - - - - - - - - - - - - - - - - -
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`12
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`13 PROSECUTION/ACQUISITION BAR MATERIALS
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`14 CONFIDENTIAL OUTSIDE ATTORNEYS ONLY
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`15 HIGHLY CONFIDENTIAL SOURCE CODE
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`16 TRANSCRIPT OF THE STENOGRAPHIC NOTES OF
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`17 THE DEPOSITION OF SANJIV KUMAR
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`18 October 15, 2019
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`19
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`20
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`21
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`22
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`23
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`24 LORRAINE B. ABATE, Notary Public
` 456335
`25
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 3 of 201
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` 1 UNITED STATES DISTRICT COURT
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` 2 SOUTHERN DISTRICT OF NEW YORK
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` 3 CIVIL ACTION NO. 1:14-cv-02396-PGG
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` 4
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` 5 - - - - - - - - - - - - - - - - - - - - - - - -
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` 6 NETWORK-1 TECHNOLOGIES, INC.,
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` 7 Plaintiff,
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` 8 vs.
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` 9 GOOGLE, INC., and YouTube,
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`10 Defendants.
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`11 - - - - - - - - - - - - - - - - - - - - - - - -
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`12
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`13 PROSECUTION/ACQUISITION BAR MATERIALS
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`14 CONFIDENTIAL OUTSIDE ATTORNEYS ONLY
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`15 HIGHLY CONFIDENTIAL SOURCE CODE
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`16 TRANSCRIPT of the stenographic notes of
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`17 the deposition of SANJIV KUMAR in the above-entitled
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`18 matter, as taken by and before LORRAINE B. ABATE, a
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`19 Certified Shorthand Reporter and Notary Public of the
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`20 State of New York and Registered Professional
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`21 Reporter, held at the offices of Williams & Connolly,
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`22 LLP, 650 Fifth Avenue, New York, New York, on October
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`23 15, 2019, commencing at 8:59 a.m., pursuant to
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`24 Notice.
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`25
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 4 of 201
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` 1
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` 2 A P P E A R A N C E S:
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` 3
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` 4 RUSS AUGUST & KABAT, ESQS.
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` 5 Attorneys for the Plaintiff
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` 6 12424 Wilshire Boulevard, 12th Floor
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` 7 Los Angeles, California 90025
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` 8 BY: BRIAN LEDAHL, ESQ.
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` 9 MARC FENSTER, ESQ.
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`10 (310)826-7474
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`11 bledahl@raklaw.com
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`12 mfenster@raklaw.com
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`13
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`14 WILLIAMS & CONNOLLY, LLP
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`15 Attorneys for the Witness
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`16 725 Twelfth Street NW
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`17 Washington DC 20005
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`18 BY: GRAHAM SAFTY, ESQ.
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`19 SAMUEL BRYANT DAVIDOFF, ESQ.
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`20 (202) 434-5548
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`21 gsafty@wc.com
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`22 sdavidoff@wc.com
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`23 A L S O P R E S E N T:
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`24 James Brady, Videographer
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`25 Demarron Berkley, Esq. Google In-House Counsel
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 5 of 201
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` 1
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` 2 I N D E X
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` 3
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` 4 WITNESS EXAMINATION BY PAGE
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` 5 Sanjiv Kumar Mr. Ledahl 4, 174
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` 6 Mr. Safty 168
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` 7
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` 8 E X H I B I T S
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` 9 EXHIBIT PAGE
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`10 1 August 2015 Quantization Based Inner
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`11 Product Search (QUIPS)with LUT 16
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`12 Option Presentation 66
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`13 2 Siberia Primer Description 76
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`14 3 ScaM: Scalable Matching, A Research
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`15 Perspective Presentation 81
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`16 4 ScaM 2.0 Documents 94
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`17 5 ScaM:Scalable Matching Presentation 103
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`18 6 Content ID Siberia A ScaM
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`19 Perspective Presentation 111
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`20 7 ScaM: Scalable Matching Technical
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`21 Deep Dive Presentation 154
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`22 8 Amended Joint Claim Construction Chart 169
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`23 DIRECTIONS NOT TO ANSWER
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`24 PAGE
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`25 174-176
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2 THE VIDEOGRAPHER: Today's date is
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` 3 October 15th, 2019. The time is 8:59 a.m. My
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` 4 name is Jim Brady. I'm the videographer. We're
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` 5 here today at Williams & Connolly, 650 5th
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` 6 Avenue, New York, New York. We're here today in
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` 7 the matter of Network-1 Technology versus
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` 8 Google. Today's witness' name is Sanjiv Kumar.
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` 9 May I ask now that the attorneys please
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`10 introduce themselves and for the court reporter
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`11 to swear in the witness.
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`12 MR. LEDAHL: Brian Ledahl from Russ
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`13 August & Kabat on behalf of the plaintiff.
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`14 MR. SAFTY: Graham Safty from Williams &
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`15 Connolly on behalf of defendants Google and
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`16 YouTube. With me is Sam Davidoff, also from
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`17 Williams & Connolly on behalf of defendants.
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`18 S A N J I V K U M A R,
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`19 Having been first duly sworn by a Notary
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`20 Public of the State of New York, was
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`21 examined and testified as follows:
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`22 EXAMINATION BY MR. LEDAHL:
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`23 Q. Good morning, Mr. Kumar.
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`24 A. Good morning.
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`25 Q. Have you ever had your deposition taken
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`4
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2 before?
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` 3 A. No.
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` 4 Q. Okay. I'm going to go over a couple of
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` 5 ground rules, if you will, to hopefully make the
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` 6 process just a little bit smoother as we go through
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` 7 the day.
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` 8 First, as you probably noted already, we
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` 9 have a court reporter who takes down a transcript of
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`10 all of the things that we say during the deposition.
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`11 There are a couple of things I'm going to ask you to
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`12 try to remember to do to make that transcript as
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`13 clear and as accurate as possible.
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`14 The first is if you could wait until I
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`15 finish my questions to begin answering, and
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`16 similarly, I'll try to wait until you finish your
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`17 answers to begin asking my next question. That way,
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`18 only one of us is talking at a time, because it's
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`19 very difficult for the court reporter to take down
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`20 more than one of us at a time.
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`21 Do you understand?
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`22 A. Yes.
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`23 Q. The second is that because nods of the
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`24 head or phrases like um-hum and uh-uh are extremely
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`25 difficult to accurately capture on a transcript, I'm
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` 2 going to ask you to do your best to remember to say
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` 3 yes or no, as opposed to using those expressions. I
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` 4 may try to remind you if I notice it, but it's not me
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` 5 being rude. I just want to make sure we have an
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` 6 accurate record.
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` 7 Do you understand?
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` 8 A. Yes, I do.
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` 9 Q. Okay. At any point today, if you don't
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`10 understand my question, please let me know. I'll do
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`11 my best to clarify. Otherwise, if you answer, I'll
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`12 assume you understood the question. Fair enough?
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`13 A. Yes.
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`14 Q. Do you understand that the oath you took
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`15 a few moments ago is the same oath you would take as
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`16 if you were testifying in court in front of a judge
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`17 and a jury?
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`18 A. Yes, I do.
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`19 Q. And you understand that oath carries the
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`20 same penalties of purgery as if you were testifying
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`21 in court?
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`22 A. Yes, I do.
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`23 Q. Are you on any medication, or is there
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`24 any other health reason that would prevent you from
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`25 giving your best and most accurate testimony today?
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2 A. No.
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` 3 Q. Okay. As we go along today, we'll take
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` 4 breaks periodically, but if at some point you need to
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` 5 take a break for some reason, please let me know and
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` 6 I'll do my best to accommodate that as quickly as
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` 7 possible. I will ask, however, that we not take
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` 8 breaks while there's a question pending to you.
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` 9 Do you understand?
`
`10 A. Yes.
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`11 Q. Okay. Where are you currently employed,
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`12 Mr. Kumar?
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`13 A. Google.
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`14 Q. And where physically do you work at
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`15 Google?
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`16 A. New York office.
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`17 Q. And is that located at -- where in New
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`18 York City is that located?
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`19 A. It is Chelsea. 15th Street and Eighth
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`20 Avenue.
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`21 Q. And how long have you been employed at
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`22 Google?
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`23 A. More than 14 years.
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`24 Q. So since around 2005?
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`25 A. Yeah.
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` 2 Q. What is your current job title at
`
` 3 Google?
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` 4 A. I'm distinguished scientist.
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` 5 MR. DAVIDOFF: Could you maybe just speak
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` 6 up a little bit.
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` 7 Q. When you first started at Google in
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` 8 2005, what was your job title or function?
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` 9 A. Research scientist.
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`10 Q. And did you have a particular area of
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`11 focus or application?
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`12 A. Yes. I work in machine learning.
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`13 Q. And at some point, did your job title
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`14 change from research scientist?
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`15 A. I have been promoted, so these are
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`16 different levels of designations.
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`17 Q. And has your focus always been in
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`18 machine learning or have there been various different
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`19 areas over time?
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`20 A. It has been primarily, machine learning.
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`21 But also, allied areas like computer vision.
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`22 Q. I assume you went to university
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`23 somewhere.
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`24 So starting with that, can you tell me
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`25 where you went to university.
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` 2 A. I did my Ph.D. at Carnegie Melon, New
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` 3 York City.
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` 4 Q. And where did you do your undergraduate?
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` 5 A. I did my undergrad in India.
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` 6 Q. What was the focus of your Ph.D.?
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` 7 A. It was machine learning and computer
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` 8 vision.
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` 9 Q. When did you receive your Ph.D.?
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`10 A. 2005.
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`11 Q. And was Google your first job after your
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`12 Ph.D.?
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`13 A. Yes.
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`14 Q. Have you ever worked anywhere else
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`15 professionally other than Google?
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`16 A. I have.
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`17 Q. Where else?
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`18 A. Before Ph.D., I was with National
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`19 Robotics Engineering Consortium. It was affiliated
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`20 with CMU before starting my Ph.D.
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`21 And before that, I worked at National
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`22 University of Singapore in Department of Surgery.
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`23 Q. What did you do at the National
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`24 University of Singapore?
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`25 A. We were developing a robot.
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2 Q. At some point during your time at
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` 3 Google, did you have any involvement with what's
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` 4 generally referred to as the Content ID system that's
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` 5 used at YouTube?
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` 6 A. Yes.
`
` 7 Q. When was the first time you had some
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` 8 involvement or connection to Content ID?
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` 9 A. It was a while ago. Somewhere around
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`10 2014-ish, we started a collaboration on designing a
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`11 new system.
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`12 Q. And when you say designing a new system,
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`13 was it your understanding there was already an
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`14 existing Content ID system?
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`15 A. There was an existing match system. I
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`16 don't know if it was called Content ID system.
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`17 Q. And you said existing match system; is
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`18 that right?
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`19 A. That's right.
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`20 Q. Okay. Did you have any involvement with
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`21 the pre-existing match system?
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`22 A. No.
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`23 Q. Did you develop any familiarity with the
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`24 operation of the pre-existing system?
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`25 A. We had few discussions, but I did not
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` 2 personally dive into the details of the system. I
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` 3 knew big picture of how the system worked.
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` 4 Q. Was it your understanding that the
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` 5 pre-existing system used locality sensitive hashing
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` 6 to identify matches?
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` 7 A. Yes.
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` 8 Q. Did you have a particular point of
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` 9 contact or someone you interfaced with to gain some
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`10 understanding of the pre-existing system?
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`11 A. We did not really go into details of
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`12 that system. I talked to few engineers, but it was a
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`13 very quick discussion because they were just unhappy
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`14 with the system in general.
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`15 Q. What was the -- can you explain for me
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`16 the motivation to design a new system.
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`17 MR. SAFTY: Objection to the extent it
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`18 calls for speculation.
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`19 A. They wanted a more accurate system.
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`20 Q. And what do you mean by more accurate;
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`21 can you explain what that connotes.
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`22 A. Which can find the content reuse in a
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`23 more effective way.
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`24 Q. And what was your role in the design of
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`25 a new system?
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` 2 A. I was the technical lead of -- from
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` 3 machine learning side and research, and I was guiding
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` 4 the big picture and the experiments, but I did not go
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` 5 into implementation.
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` 6 Q. Did that new system have a name or a
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` 7 project designation of some kind?
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` 8 A. It was called Siberia.
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` 9 Q. And you said you started working on that
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`10 sometime around 2014?
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`11 A. That's where initial discussions
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`12 started.
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`13 Q. Was there some person who was sort of
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`14 the lead on the Siberia project to develop this new
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`15 system?
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`16 A. This project had a lot of people. I
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`17 talked to many people. Are you looking for specific
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`18 names?
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`19 Q. Yes.
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`20 A. I talked to, for example, Hanna Pasula,
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`21 Matthias Konrad, and many other engineers there.
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`22 Q. Did you ever deal with Johan Granstrom?
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`23 A. Yes.
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`24 Q. What was his role?
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`25 A. He was one of the designers of the --
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` 2 one of the engineers working from the Content ID
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` 3 side.
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` 4 Q. And what was the sort of approach to
`
` 5 this new system that made it different?
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` 6 MR. SAFTY: Objection. Vague and
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` 7 ambiguous.
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` 8 A. Let me understand what question means.
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` 9 Q. Sure. So what -- the old system, I
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`10 think you indicated was, generally speaking, a
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`11 locality sensitive hashing-based system.
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`12 What was the new approach that was being
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`13 implemented in the Siberia system?
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`14 MR. SAFTY: Same objection.
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`15 A. Do you want me to go through the
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`16 technical details?
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`17 Q. Well, let's start at a high level.
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`18 Is there a way you would be able to
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`19 describe what was new or different about it at a
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`20 general level?
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`21 A. So it was not based on locality
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`22 sensitive hashing.
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`23 Q. The new system?
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`24 A. The new system. New system was based on
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`25 primarily, quantization.
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` 2 Q. What do you mean by quantization?
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` 3 A. You take a value, and instead of keeping
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` 4 the full position, you quantize it in buckets.
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` 5 Q. And can you give me an example or
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` 6 explain how that works in a general sense.
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` 7 A. Suppose we have a value 4.3, but instead
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` 8 of keeping 4.3, we'll keep 4, 5, 6, and it will be
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` 9 mapped to 4. If it is 4.7, let's say it will be
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`10 mapped to 5. So that is an example of quantization.
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`11 Q. And how was quantization used to -- in a
`
`12 general sense, in the new match system, the Siberia
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`13 system?
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`14 A. So we call the process
`
`
`
`
`
`
`
`
`
`19 Q. Now, you mentioned
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`20 Was that an approach that was
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`21 implemented from the beginning of the development of
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`22 this Siberia system or did that come along the way?
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`23 A. It was from very beginning.
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`24 Q. So what was it that was being
`
`
`
` in the Siberia system?
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` 2 MR. SAFTY: Objection. Vague and
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` 3 ambiguous.
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` 4 A. The input was the embedding.
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` 5 Q. And was that embedding a value or a
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` 6 vector that was extracted from the underlying videos?
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` 7 A. So when you say videos, is it video
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` 8 content, audio content, melody content? There are
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` 9 many.
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`10 Q. Fair enough. So let's break that down.
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`11 So you mentioned I think three
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`12 categories; video, audio and melody content; is that
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`13 right?
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`14 A. That's right.
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`15 Q. Okay. And those are three separate sort
`
`16 of -- I've heard them referred to as channels that
`
`17 are used for matching in YouTube's Content ID system;
`
`18 is that right?
`
`19 A. Yes.
`
`20 Q. So let's start with video.
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`21 In the case of video, what's an
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`22 embedding?
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`23 A. It is a representation of chunks of
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`24 videos. It could be a shot, it could be a frame, it
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`25 could be any entity.
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` 2 Q. And when you say a shot, is that a
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` 3 collection of similar frames?
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` 4 A. That will be one way of defining shots.
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` 5 Q. How would you define shots?
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` 6 A. It is any chunk of video which you think
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` 7 should be represented together.
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` 8 Q. And by chunk, do you mean chunk in time
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` 9 space?
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`10 A. That's right.
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`11 Q. So you could represent an individual
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`12 frame or you might represent some -- more than one
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`13 frame together if that time space could be
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`14 represented by a single embedding; is that fair?
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`15 A. Yes.
`
`16 Q. In the case of audio, what are the
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`17 embeddings?
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`18 A. It's again, a bunch of audio frames and
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`19 where we learn -- or the system learns a
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`20 transformation which represents this collection of
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`21 audio frames.
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`22 Q. And so the embedding is the
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`23 transformation that represents that group of audio
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`24 frames?
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`25 A. That's right.
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` 2 Q. And are those audio frames in the form
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` 3 of spectrograms?
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` 4 A. I do not remember exactly how they
`
` 5 extracted the embeddings, because that was another
`
` 6 team. Our work started after giving these
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` 7 embeddings.
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` 8 Q. Okay. And then for melody, which I
`
` 9 think was the third category you mentioned, what was
`
`10 the nature of the embedding for melody?
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`11 A. Nature of embedding? I'm trying to
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`12 understand.
`
`13 Q. Sure. You've talked about the videos,
`
`14 the embeddings being representations of a frame or
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`15 group of frames. For audio, I think you similarly
`
`16 said the embedding was a representation of a frame or
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`17 a group of frame.
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`18 What's the embedding for melody?
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`19 A. Same way as audio. Group of frames,
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`20 audio frames.
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`21 Q. So the melody embeddings are
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`22 representations of a group of audio frames also?
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`23 A. That's right.
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`24 Q. So I think you started out by saying
`
`25 that
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 20 of 201
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` were these embeddings; is that correct?
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2
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` 3 A. Yes.
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` 4 Q. So walk me through, if you could, what
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` 5 is done
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` 6 A. So embeddings are seen as a vector in a
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` 7 space. Do I need to define vector?
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` 8 Q. That's okay. I think I -- I'll go back
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` 9 if I need to, but go ahead for now.
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`10 A. And we take the vector, we
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`17 Q. And what's the process to, as you said,
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`19 A. That's just take the vector. There are
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`20 various versions of this. One can
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 21 of 201
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2 Q. And are there transformations that are
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` 3 applied to the embeddings that are used in Content ID
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` 4 before
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` 5 A. I don't exactly recall. Maybe in -- out
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` 6 of these three systems, some form may use it. I
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` 7 don't remember exactly, but this is a variant, and it
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` 8 may be used in one of the variants.
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` 9 Q. Okay. Now, am I correct that the -- in
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`10 the Siberia Content ID system, the embeddings used
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`11 are
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`12 A. That's right.
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`13 Q. How many
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`15 A. For which system are we talking?
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`16 Q. Well, so let's go piece by piece.
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`17 So for video, how many
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`19 A. I have to remember exactly. There are
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`20 -- every
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`25 Q. So just to clarify, a moment ago you
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 22 of 201
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 4 A. Yes.
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` 5 Q. Okay. And so -- so basically, that
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` 9 A. That's right.
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`10 Q. And what's the
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`12 A. So again, there are several variants. I
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`13 can give you general, broad picture of how it is
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`14 done.
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`15 Q. Sure.
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`16 A. So for instance, we have a database of
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`17 let's say, 1 billion embeddings.
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 23 of 201
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2
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` 3 Q. And the
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` 6 A. That's right.
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` 7 Q. Now, going back, we talked about the
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` 8 vectors and
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` 9 For audio, what's the -- those are also
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`10
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`11 A. That's right.
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`12 Q. For the embeddings. Okay. And what's
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`13 the
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`14 A. I don't remember exactly because it is a
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`15 parameter in the system, how -- right now it is. But
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`16 I believe it is
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`17 Q. And is that the same for melody?
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`18 A. Melody -- I don't recall in melody what
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`19 exact
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`20 is this.
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`21 Q. When you say it's a parameter in the
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`22 system, how is that parameter set?
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`23 A. There is a speed and resource,
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`24 resources, how many machines we use. The speed of
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`25 the system and the accuracy. So depending on what
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` we use, but I won't be surprised if it
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` for video.
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` as well.
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 24 of 201
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2 operating point we want, we do experiments with
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` 3 different primary settings and pick what we want for
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` 4 a particular system.
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` 5 Q. Is that something that's set as a
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` 6 parameter but can be changed within the Content ID
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` 7 system or is that something that was -- that were
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` 8 optional parameters, but it was sort of hard designed
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` 9 into the system once you identified the parameter
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`10 that you wanted?
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`11 A. I have to understand the question.
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`12 Q. Sure. Sure. So you talked about the
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`13 possibility of experimenting with different
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`14 parameters based on resources and speed and accuracy,
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`15 things like that.
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`16 A. Yes.
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`17 Q. And what I'm trying to understand is is
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`18 the parameter, for example, how many
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`19 something that you experimented with, but once you
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`20 found the value you what, it was set, sort of as a
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`21 fixed variable in the Content ID system, or is that a
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`22 parameter that, for example, can be changed in the
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`23 particular search or particular coding?
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`24 Do you understand?
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`25 A. So when we were doing the experiments,
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` you use
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 25 of 201
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2 early design of the system, we experimented quite a
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` 3 lot from our side and our -- in the sense from
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` 4 research side. And we gave them the best parameters
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` 5 we thought about, but they do lot of experiments
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` 6 inside their own team. So I wouldn't be surprised
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` 7 based on different scenarios they changed these
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` 8 parameters.
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` 9 Q. So you don't know how that's set in the
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`10 actual production system?
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`11 A. We can check the code and get to know
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`12 what system it is.
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`13 Q. And where would you look in the code to
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`14 know how that parameter is set of
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`15 are used?
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`16 A. There are clear config files for each
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`17 match system.
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`18 Q. Did you say config?
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`19 A. That's right.
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`20 Q. Like C-O-N-F-I-G?
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`21 A. C-O-N-F-I-G. This is a short form of
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`22 configuration files.
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`23 Q. And those config files would set these
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`24 kinds of parameters?
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`25 A. That's right.
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 26 of 201
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` for the
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` is indexed.
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2 Q. So you mentioned the
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` 4 match system.
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` 5 After those
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` 6 what happens next, what's the next system?
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` 7 A. Next step is index of the database.
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` 8 Once we know these
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`10 Q. And how is that index structured?
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`11 A. Let me understand your meaning of
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`12 structured.
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`13 Q. Sure. You mentioned that you have -- I
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`14 think it's
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`15 A. That's right.
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`16 Q. And those are then -- I guess I want to
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`17 understand what is it that's indexed?
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`18 A. So I can give you example.
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`19 Q. Sure, please.
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`20 A. So we take one vector. This is the
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`21 embedding vector.
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`22 Q. Yes.
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`23 A. From the database, and we take, let's
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`24 say
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`25 example, and then we find the
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` is that right?
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` for
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`24
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 27 of 201
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` is that right?
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` in this example.
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` correct?
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 6 Q. And so that
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` 8 A. With the
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` 9 Q. So a given embedding would have
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`10 obviously,
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`11 A. Yes.
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`12 Q. And would they all have
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`15 A. No. Let me understand your question
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`16 about location. What do you mean by location here?
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`17 Q. Well, so you have the
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`20 A. Yes.
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`21 Q. I guess I'm trying to understand what
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`22
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`23 A. These
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`24 Q. So each chunk has
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` Then I assume you have
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` and so on.
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` correlates to. Is it --
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`are independent.
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 28 of 201
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` what is
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` That's what I'm trying to understand. Is
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 2 A. That's right.
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` 3 Q. Okay. And so are
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` 4 indexed for? So in other words -- strike that.
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` 5 Is the index built from
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` 7 A. Each
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` 9 Q. And what's the -- what's the nature
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`11 it --
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`12 A. These are
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`13 Q. And what's the relationship between
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`15 A. I have to understand this question. You
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`16 are looking for how do we
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`18 Q. Yes. That's what I'm -- I guess what
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`19 I'm trying to understand is what is it that
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`23 A. So it
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`25 if a
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` For example,
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`Case 1:14-cv-02396-PGG-SN Document 241-10 Filed 11/12/20 Page 29 of 201
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` 1 CONFIDENTIAL - October 15, 2019 - CONFIDENTIAL
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` 4 Q. So the
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