I/P Engine, Inc. v. AOL, Inc. et al

Filing 528

Declaration re 527 Reply to Response to Motion, of Joshua L. Sohn in Support of the Reply in Support of Defendants' Motion for Summary Judgment by AOL Inc., Gannett Company, Inc., Google Inc., IAC Search & Media, Inc., Target Corporation. (Attachments: # 1 Exhibit 34, # 2 Exhibit 35, # 3 Exhibit 36, # 4 Exhibit 37, # 5 Exhibit 38)(Noona, Stephen)

EXHIBIT 35

Page 1
UNITED STATES DISTRICT COURT
EASTERN DISTRICT OF VIRGINIA
NORFOLK DIVISION
---------------------------------x
I/P ENGINE, INC.,
Plaintiff,
v.

Civil Action No.:
2:11-cv-512

GOOGLE INC.,
Defendant.
---------------------------------x
CONFIDENTIAL - ATTORNEYS' EYES ONLY
Videotaped 30(b)(6) Deposition
of
JAIME G. CARBONELL, Ph.D.
Washington, D.C.
Friday, September 21, 2012
9:04 a.m.

Reported by:

Amy E. Sikora, RPR, CRR, CSR-NY, CLR

Job No. CS1338951

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A.

Excuse me a second.

2

Q.

No.

3

A.

Sorry.

4

Q.

Okay.

No problem.
Go on.
So then, the next sentence of

5

column 14 says, "Alternatively, a human editor

6

could assign scores according to judgments made

7

by such editor."

8
9
10

Do you see that?
A.

That's right.

Q.

So that sentence follows the sentence

11

that we just read that said, "For example, the

12

scores can be initially set to correspond with

13

the frequency of the term occurrence in the

14

article"; correct?

15

A.

Correct.

16

Q.

Okay.

So then, do you understand that

17

"Alternatively, a human editor could assign

18

scores according to judgments made by such

19

editor" refers to, among other things, the key

20

term scores?

21

MR. JACOBS:

22

Calls for speculation.

23

A.

Objection as to form.

I cannot tell if -- to which scores

24

they refer to.

25

scores.

It could refer to the key term

It could refer to scores assigned to
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other terms selected by an editor.

2

Q.

Okay.

3

A.

Editors typically exercise their

4
5

editorial capacity.
Q.

Understood.

Okay.

So let's just --

6

let's take an example where the editor can assign

7

initial values to the key term scores; okay?

8

A.

Uh-huh.

9

Q.

And you agree that the key term scores

10

are derived from terms that appear in the

11

articles; correct?

12
13
14

MR. JACOBS:
Q.

Objection.

Or the key terms are -- are derived

from terms that appear in the articles; correct?

15

A.

That's what that paragraph says.

16

Q.

Okay.

So let's go back to your

17

example in paragraph -- or excuse me, in

18

footnote 5, to paragraph 106 of your report.

19

A.

Yes.

20

Q.

You used an example of an item that

21

was clicked on a thousand times in response to a

22

query involving a particular key term; correct?

23

A.

Yes.

24

Q.

And that key term was initialized with

25

a value of one; right?
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A.

Correct.

2

Q.

So what you're saying as well, after a

3

thousand clicks, that key term value will be

4

1,001?

5

A.

That's right.

6

Q.

1/10th of one percent of that value

7

would be in response to the initial content-based

8

initialization; correct?

9
10
11

A.

Correct.

Q.

And the rest of it you say would be

based on collaborative feedback data; correct?

12

A.

Correct.

13

Q.

So now, let's take an example where

14

the editor initialized that key term to a

15

thousand, to have a value of a thousand; okay?

16

A.

Okay.

17

Q.

Okay.

So there that initialization of

18

the key term to a value of a thousand would be a

19

content-based initialization; correct?

20

A.

If the editor assigned a value of a

21

thousand and if it was a key term that was

22

contained inside the article.

23
24
25

Q.

Right.

Then it would be a

content-based initialization; correct?
A.

With the two provisos I just gave,
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2

yes.
Q.

Okay.

So then, in the same example,

3

if that article then was clicked a thousand times

4

in response to a query that contained that -- the

5

same key term, the value would be a thousand plus

6

a thousand; correct?

7

A.

If you were to modify the example in

8

that particular way.

9

example in other ways as well.

10

Q.

No.

One could modify the

Understood.

But that's -- based

11

upon this reading of column 14 of Culliss, that's

12

a fair modification of the example; correct?

13
14

MR. JACOBS:
A.

Objection as to form.

In the -- in that case, the initial

15

usage, the content would dominate, and there

16

would be no significant effect from the

17

popularity part.

18

Q.

Right.

But after a thousand clicks,

19

the popularity portion, what you're calling the

20

collaborative feedback, would be 50 percent of

21

the value, and the content-based would be

22

50 percent of the value; correct?

23
24

A.

At that -- at that point in time.

At

the earlier point, the content would dominate.

25

To follow through with your example,
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A.

Yeah.

2

Q.

Okay.

3

A.

That's why I can't agree when you say

4

he does it one specific way.

5

know.

6

Q.

Okay.

7

I don't -- I don't

Understood.

So now, let's go back to your report,

8

which we've marked as Exhibit 1, and look at

9

paragraph 108.

There you say, "Third, Culliss

10

does not disclose filtering each informon for

11

relevance to the query or filtering the combined

12

information for relevance to at least one of the

13

query and the first user."

14

Do you see that?

15

A.

I see that.

16

Q.

And then further down, you say that --

17

and I'm paraphrasing here because I don't want to

18

just read the whole thing -- that Culliss

19

discloses ranking, but not filtering; correct?

20

A.

That's correct.

21

Q.

Okay.

So can you explain to me what

22

you mean by the difference between ranking and

23

filtering?

24

A.

Yes.

25

Q.

Please do.
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A.

Ranking is putting a set of elements,

2

whether they be documents or other items, in

3

order.

4

from a score, a relevant score.

5

popularity score.

6

criteria.

That order would typically be derived

7

It can be a

It can be based on other

So ranking is -- is essentially you

8

start with a set, and you end up with an ordered

9

set.

Same set in order.

10

Filtering is the process where you

11

examine elements of a set one at a time, and you

12

determine whether or not they qualify according

13

to some filtering criterion.

14

Then that set is divided into two

15

sets, one of which is the filtered or accepted

16

set, one of which is the rejected or filtered out

17

set.

18

Q.

So let me have an example.

If you

19

have a -- a criterion that says, I want to review

20

the top 10 something, then you rank whatever the

21

candidates are, and you display the top 10, is

22

that ranking or is that filtering?

23

A.

That is ranking.

24

Q.

Okay.

25

Even though you used a

criterion to decide which ones were going to be
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displayed and which ones were not going to be

2

displayed; correct?

3

A.

That -- that criterion is not based

4

on -- it's not an absolute criterion.

5

based on -- it is based on relative properties of

6

the members of the set where they -- where they

7

belong in the ranking.

8
9

Q.

It is not

So you think that in order for

something to be filtering, it has to -- there has

10

to be a decision being made concerning only the

11

properties of that individual member of the set?

12
13
14

A.

Right.

Say yea or nay, depending on

those properties.
Q.

So if you're including some decision

15

concern -- some criteria concerning properties of

16

other members of the set in your decision to say

17

yea or nay, then you're not filtering?

18

A.

If you're comparing this one to the

19

other members of the set in your -- in your

20

criteria, these comparative criterion, then

21

you're not filtering.

22

subsetting.

Bowman calls that

23

Q.

What -- what was that last part?

24

A.

There are -- one of the other

25

references uses a different term for it.
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your index, along with the key terms, a rating

2

label; correct?

3
4
5

A.

You can include a rating label, yes.

It could be in the index.
Q.

Okay.

So then it goes -- the Culliss

6

reference goes on to say, "The rating key term is

7

considered appropriate for all ages while the

8

rating key term X rated is considered appropriate

9

only for adults."

10
11
12

Do you see that?
A.

Where does he say that?

There it is.

I found it.

13

Q.

It's like line 12, maybe.

14

A.

Yes, I see that.

15

Q.

Then at line 15 it says, "The articles

16

are initially associated with one or more of

17

these key terms by any possible manner, such as

18

by human judgment or default association."

19

Do you see that?

20

A.

I see that.

21

Q.

So do you understand a human could

22

initially decide this is G-rated content or this

23

is X-rated content in the article?

24

A.

Yes.

25

Q.

Okay.

And then if we go down a little

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bit further about line 23, which is the next

2

paragraph there in column 11.

3

the rating key terms can be incorporated into the

4

index of key terms and included in the

5

association of the comparison score and, if used,

6

the key term probability score."

7

It says, "Moreover

Do you see that?

8

A.

I see that.

9

Q.

So the -- the rating can be an

10

additional value in the key term index; correct?

11

A.

Yes.

12

Q.

And then in the example that's

13

provided here, if we go down about line 39, it

14

says, "The invention operating separately from or

15

in addition to the manner described above would

16

permit or require the user to enter a rating key

17

term in the search query."

18

Do you see that?

19

A.

I see that.

20

Q.

So there you understand that you could

21

have an example where you put in key terms alpha

22

and gamma, and in addition you say, I want G

23

rated; right?

24
25

A.

You would -- not exactly, but close to

what you said.

You would provide three key terms

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2

in the query:
Q.

Alpha, gamma, G or G rated.

Okay.

And -- and we talked about

3

earlier, alpha and gamma can be associated with

4

words that are in the article; correct?

5

A.

Yes, they can be.

6

Q.

So then, the next sentence there,

7

beginning about line 41, says, "The invention

8

would operate in a similar manner for the rating

9

key terms as described above for the key terms

10

alone, whereby the search activity of the user

11

would alter the key term scores and key term

12

total scores for the rating key terms."

13

Do you see that?

14

A.

I see that.

15

Q.

So, in other words, users -- you'd

16

have a key term score associated with the rating

17

that's initialized at some particular value;

18

right?

19

A.

Yes.

20

Q.

Yeah.

It seems to be once here.
In the example that's shown,

21

for example, in the index, you know, surrounding

22

line 35 of column 11, they're initialized to the

23

values of one; correct?

24

A.

Yes.

25

Q.

And then continuing with the example,
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one of the key terms that the user would enter in

2

the query in this embodiment is to include a

3

rating key term; correct?

4

A.

You're referring to 47 through 55?

5

Q.

Correct.

6

A.

Yeah, that's what it says there.

7

Q.

And so that rating key term score

8

would be altered by both whether an article is

9

returned, as well as whether it's selected by the

10

user as per the previous examples that we

11

discussed; correct?

12

A.

Yes.

Under the embodiment where it

13

keeps both scores with a slash notation as -- as

14

exemplified here, that would be correct.

15

Q.

Okay.

So then, if we look toward the

16

bottom of column 11, continuing onto the top of

17

column 12, it says, "In this manner," peo --

18

"people looking for X-rated material will

19

identify and effectively label that material as X

20

rated.

21

screened entirely from the rating key term of

22

G rated by precluding articles entirely from the

23

search results which have a key term probability

24

score or comparison score for the rating key term

25

X rated above a predetermined threshold."

Such X-rated material can then be

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Right, you see that?

2

A.

I see what it says here, yes.

3

Q.

So in what's described there, the --

4

the rating key term will be evaluated for each

5

article individually; correct?

6

A.

It -- he's talking about putting in

7

the ratings as -- in the same way you would put

8

other data associated with the -- with the

9

article such as key terms.

10

Q.

Understood.

But here in the part that

11

I just read at the bottom of column 11, the

12

rating key term for each of the articles is

13

evaluated to determine whether the article will

14

be displayed independently of any of the other

15

articles in the set; correct?

16

A.

It doesn't say that it's evaluated

17

with respect to whether the article will be

18

displayed.

19

Q.

Well, it says, "In this manner, people

20

looking for X-rated material will identify and

21

effectively label that material as X" -- "as X

22

rated.

23

screened entirely from the rating key term of

24

G rated by precluding articles entirely from the

25

search results which have a key term probability

Such X-rated material can then be

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score or comparison score for the rating key term

2

X rated above a predetermined threshold"; right?

3

A.

That's what it says.

4

Q.

So isn't that saying that the rating

5

key term score will be evaluated independently

6

for each article to determine whether that

7

article will be screened entirely from the search

8

results?

9

A.

The -- the X rated or the G-rated key

10

term will have a score, and then that score can

11

be modified over time, depending on the feedback

12

from the user and the key term probability score

13

or comparison score above a predetermined

14

threshold, and then that -- that key term, then,

15

can be used as -- as a factor or criterion in

16

determining what to show or what to -- what to --

17

not to show, it doesn't say show.

18

say?

19

What does he

From the search results.
Q.

Right.

And if we look -- let's just

20

look further.

21

more light on this in the example.

22

Maybe we can shed a little bit

Continuing in column 12, about line 6,

23

it says, "For example, suppose article A3

24

contained adult content, and articles A1 and A2

25

contained not adult content, which would not be
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users."

2

Do you see that?

3

A.

I see that.

4

Q.

So where is it that you believe that

5
6

that has to be a content-based query?
A.

It's implicit on the third limitation

7

content-based filter system for combining the

8

information from the feedback system and the

9

information from the scanning system.

If -- the

10

feedback system being the -- the popularity or

11

user-based replies, and so, therefore, if that

12

needs to be combined with something different,

13

that leads to the implication of the scanning

14

system would be the content based.

15

Q.

But the -- the last element of

16

content-based filter system, that's a separate

17

element from the scanning system, you agree?

18
19
20

A.

The content-based filter system is

different from the scanning system.
Q.

Right.

And, in fact, the

21

content-based filter system receives an input

22

from the scanning system; correct?

23

A.

Right.

And receives an input from

24

the -- the -- what is it called?

25

system.

The feedback

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2

Q.

Right.

So it receives inputs from

both of those systems?

3

A.

That's right.

4

Q.

So let's look back at the claim 1 of

5

the '420 patent for a minute.

6

A.

Okay.

7

Q.

So if you look at -- keep your report

8
9
10

open to that page 24 where you were just reading.
A.

Yes.

Q.

So element A says, "A system for

11

scanning a network to make a demand search for

12

informons relevant to a query from an individual

13

user."

14

Do you see that?

15

A.

I see that.

16

Q.

So if you look at paragraph 102, and I

17

think we covered this earlier, you don't offer an

18

opinion in your report that the element I just

19

read is absent from Culliss; correct?

20

A.

Right.

21

Q.

Okay.

But then if we look at the

22

first element of claim 1, for example, the '664

23

patent, it reads, "A scanning system for

24

searching for information relevant to a query

25

associated with the first user in a plurality of
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users"; correct?

2

A.

Right.

3

Q.

And in particular, you say the aspect

4

of that element that requires searching for

5

information relevant to a query associated with a

6

user is absent from the Culliss reference?

7

A.

Only to the extent that that is

8

interpreted to mean that that is a content-based

9

search.

10

Q.

So then going back to claim-- the

11

first element of claim 10 of the '420 patent, you

12

don't interpret that element, "A system for

13

scanning a network to make a demand search," as

14

requiring a content-based search; right?

15

A.

Not necessarily, because there is

16

another element that talks about the content --

17

content-based filter system receiving the

18

information.

19

could be interpreted to be contained within this

20

second element.

21

Q.

So the content-based component

Okay.

Well, in the '664 patent, the

22

third element has a content-based filter system;

23

correct?

24

A.

Yes.

25

Q.

But you don't think in the '664 patent
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that the content-based aspect can be found in the

2

third element?

3

A.

Well, to the degree that that is found

4

in the third element, that would suffice.

5

degree that the -- that the searching

6

content-based filtering.

7

Q.

Okay.

To the

So let's go to the Culliss

8

reference now, which we marked as Exhibit 4, and

9

sticking on this searching for information

10

relevant to a query associated with the first

11

user.

12

A.

Right.

13

Q.

Can you look at figure 1 of the

14

Culliss reference?

15

A.

Yes.

16

Q.

So box 10 says, "Receive first search

17

query from first user and identify related

18

articles."

19
20

And box 20 says, "Present articles
related to first search query to a first user."

21

Do you see that?

22

A.

Yes.

23

Q.

So you don't think that those boxes

24

from figure 1 describe searching for information

25

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terms; correct?

2

specifying each of the terms of the query to

3

produce a ranking value for the item?

Because the claim has queries

4

A.

5

other terms.

6

I'm just -- just -- clarifying.

7
8
9
10

That's right.

Q.

It could also specify

Basically, I'm agreeing with you.

Yeah, yeah.

the same page.

Okay.

I think we're on

So let's -- and maybe I can go --

A.

Okay.

Q.

-- back and explore something else and

11

make sure of that.

12

you have the first two steps which are basically

13

identical to the first two steps we read in 28;

14

right?

So if you look at claim 22,

15

A.

It's a method claim, but yes.

16

Q.

Right.

17

But the steps themselves are

the same?

18

A.

Right.

19

Q.

Okay.

So then that last element says,

20

"For a plurality of items identified in the query

21

result, combining ratings of frequencies with

22

which users select the item in earlier queries

23

specifying one or more terms of the query" --

24

A.

Right.

25

Q.

-- "to produce a ranking value for the
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item."

2

Do you see that?

3

A.

I see that.

4

Q.

So in claim 28, we're talking about

5

each of the terms in the query, whereas in 22

6

it's one or more; correct?

7
8

A.

In 22, it's one or more.

reread 28.

9
10

Let me

I believe you are right.
Q.

Yeah.

So let's just take an example.

11

If somebody put in the query "lightweight running

12

shoes"?

13

A.

Light weight, one word or two words?

14

Q.

Let's just say it's one.

15

A.

And running shoes, two words.

16

Q.

Correct.

17

Lightweight running shoes.

So then under claim 28, in order to

18

get this ranking score, I would go back and sum

19

the item scores for queries that also included at

20

least those three terms:

21

and shoes; correct?

22

A.

Lightweight, running,

I believe that's the right reading.

23

Let me read it again because this is a little

24

confusing.

25

Yeah.

It says "satisfying" -- sorry,

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"to select an item in earlier queries," specify,

2

not satisfying, "specifying each of the terms on

3

the query to produce a ranking value."

4
5

Yes.

So that would be -- be all the

Okay.

So it would be all the terms.

terms.

6

Q.

7

But you -- you could have a situation where the

8

subsequent term, just sticking with the example,

9

would be like blue lightweight running shoes?

10

A.

Right.

11

Q.

But you would still go back and look

12

at queries for the item scores associated with

13

queries that ran at least the terms

14

"lightweight," "running," "shoes"?

15

A.

Correct.

16

Q.

Okay.

17

A.

Uh-huh.

18

Q.

So claim 29 is dependent from

19

So now let's look at claim 29.

claim 28; right?

20

A.

Yes.

21

Q.

So I think we covered this earlier.

22

It includes all the limitations of 28, plus

23

what's added in claim 29; right?

24
25

A.

Do you agree?

That's what a dependent claim means,

yes.
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Q.

Okay.

So then claim 29 says, "The

2

computer readable medium of claim 28 wherein the

3

contents of the computer readable medium further

4

cause the computer system to perform the step of

5

adjusting the ranking value produced for each

6

item identified in the query result to reflect

7

the number of terms specified by the query that

8

are matched by the item."

9

Do you see that?

10

A.

I see that.

11

Q.

Okay.

So you agree that the system of

12

claim that's in claim 29 has to be covered by

13

claim 28 as well; right?

14
15
16

A.

It has to be covered by -- it's an

additional limitation on top of page 28, yes.
Q.

Right.

So anything that would fall

17

within claim 29 also has to fall within claim 28;

18

right?

19

A.

Yes.

20

Q.

Okay.

So the way I understood your --

21

the discussion earlier concerning your

22

understanding of Bowman is that the summing of

23

the ranking scores is always a function of the

24

items that are in the index table; correct?

25

A.

In the rating table, correct?
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patent that we marked as Exhibit 3.

2

A.

Okay.

3

Q.

And in claim 1, I think the element

4

you're referring to there is "a scanning system

5

for searching for information relevant to a query

6

associated with the first user in a plurality of

7

users."

8
9
10

Do you see that?
A.

I see that.

Q.

Now, let's pull up the '6 -- or,

11

excuse me, the '420 patent again.

12

MR. NELSON:

And that one, just for

13

the record, we marked as Exhibit 2 to your

14

deposition.

15

A.

I have it.

16

Q.

That first element in claim 10, again,

17

is "a system for scanning a network to make a

18

demand search for informons relevant to a query

19

from an individual user."

20

Do you see that?

21

A.

I see that.

22

Q.

Now, if you look back to paragraph 78

23

of your report, also there on page 17, you don't

24

offer an opinion in your report that the first

25

element of claim 10 is absent from the Bowman
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reference; correct?

2

A.

Correct.

3

Q.

Okay.

So does the difference between

4

your opinion on the first element of claim 1, for

5

example, the '664 patent, go back to what we

6

talked about earlier with respect to the Cullis

7

reference, is dependent upon an interpretation

8

that that first element of claim 1 of the '664

9

patent requires some kind of content-based

10
11

analysis?
A.

That's correct.

Yeah, to the degree

12

that it requires content-based analysis, that

13

belongs there.

14

then it's -- I'm not objecting to it.

15

Q.

Okay.

To the degree that it doesn't,
So then the next element that

16

you have listed in paragraph 80 that you believe

17

is absent from the Bowman reference with respect

18

to the asserted claims of the '664 patent is

19

"combining the information from the feedback

20

system with the information from the scanning

21

system and filtering the combined information for

22

relevance to at least one of the query and the

23

first user."

24
25

Do you see that?
A.

Yes.
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Q.

And then you list the corresponding

2

element right after that from claim 26 of the

3

'664 patent; correct?

4
5
6

A.

Right.

Which I believe is a method

equivalent to that claim.
Q.

Agreed.

So is your opinion that the

7

element from the '664 patent that we just

8

identified from paragraph 80 of your report is

9

absent because you don't believe that Bowman does

10
11
12
13

filtering or content-based analysis?
A.

That's correct.

That's for the same

reasons that we just discussed for the '420.
Q.

Okay.

Okay.

Let's put that one aside

14

for -- well, let's just hopefully just put it

15

aside.

16

A.

You mean, Bowman aside?

17

Q.

Yeah.

18

Okay.

So let's turn to your report,

19

which we've marked as Exhibit 1.

20

particularly on page 32, I want to start there.

And

21

A.

Under the obviousness, yes.

22

Q.

Yeah.

Under the obvious -- I think

23

the obviousness starts on page 31 under Roman

24

numeral heading XIV; correct?

25

A.

Oh, yes.

You're right.

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