I/P Engine, Inc. v. AOL, Inc. et al
Filing
512
Declaration re 511 Opposition, of Howard Chen in Support of Defendants' Opposition to Plaintiff's Daubert Motion, and Fourth Motion in Limine to Exclude Lyle Ungar's New Theory of Invalidity and Opinions Regarding Claim Construction by AOL Inc., Gannett Company, Inc., Google Inc., IAC Search & Media, Inc., Target Corporation. (Attachments: # 1 Exhibit 1, # 2 Exhibit 2, # 3 Exhibit 3, # 4 Exhibit 4, # 5 Exhibit 5, # 6 Exhibit 6, # 7 Exhibit 7, # 8 Exhibit 8, # 9 Exhibit 9, # 10 Exhibit 10, # 11 Exhibit 11, # 12 Exhibit 12, # 13 Exhibit 13)(Noona, Stephen)
EXHIBIT 11
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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Q.
Do you have any estimate at all?
2
A.
I can give you an estimate, but I
3
cannot guarantee its accuracy.
4
Q.
Understood.
5
A.
It would be between one and 200 hours.
6
Q.
Okay.
7
8
MR. NELSON:
Let's mark this next one
here.
9
(Carbonell Exhibit No. 4, copy of U.S.
10
Patent No. 6,006,222, marked for identification
11
as of this date.)
12
Q.
Okay.
I'm showing you now what I've
13
had marked as Carbonell Deposition Exhibit No. 4.
14
It is a -- United States Patent No. 6,006,222.
15
Do you see that?
16
A.
Yes.
17
Q.
And the named inventor is Gary
18
Culliss.
Do you see that?
19
A.
I see that.
20
Q.
I think -- well, actually, let me just
21
ask that first.
22
before?
23
A.
Yes, I have.
24
Q.
Do you recall about how long you spent
25
Have you reviewed Exhibit No. 4
reviewing it?
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A.
I reviewed the asserted prior art as a
2
group as well as individually.
3
to say collectively, I mean, how my time
4
distributed among the various items of prior art.
5
Q.
Okay.
So it's difficult
But do you feel like you have a
6
good understanding of what we've marked here as
7
Carbonell Deposition Exhibit No. 4?
8
9
10
A.
I have read it more than once.
Yes,
the answer to your question.
Q.
Okay.
Now, if you look to page 24 of
11
your report that we marked as Exhibit No. 1, you
12
have a section that's headed "XII."
13
Do you see that?
14
A.
Yes.
15
Q.
And this section concerns what we've
16
marked as Carbonell Deposition Exhibit No. 4;
17
correct?
18
A.
Correct.
19
Q.
Okay.
The -- here in parentheticals
20
and throughout this section of your report, you
21
refer to it as "the Culliss reference"; is that
22
right?
23
A.
Just as "Culliss."
24
Q.
Okay.
25
So I'll -- is it okay for the
deposition, if I just refer to it as Culliss,
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then, so that we use the same terminology used in
2
your report?
3
A.
Yes.
4
Q.
Okay.
Now, in paragraph 102, you
5
express a summary of your opinions concerning the
6
Culliss reference with respect to the '420
7
patent; correct?
8
A.
Correct.
9
Q.
And there are two elements that you
10
identify in -- let's just start with independent
11
claim 10 that you believe are not found in the
12
Culliss reference; correct?
13
A.
Correct.
14
Q.
Okay.
Now, with respect to inde --
15
independent claim 10, at least, of the '420
16
patent, and you have it there in front of you, if
17
you want to look at it, in your report you didn't
18
express any opinions concerning elements of
19
claim 10 beyond the two that you addressed here
20
or you list here in paragraph 102; correct?
21
A.
That's correct.
22
Q.
So, then --
23
A.
Let me -- let me put claim 10 up here
24
25
so that I can refresh my memory.
Q.
Yup.
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Q.
Okay.
Well, let's -- let's -- let's
2
go back to column 3 towards the bottom, about
3
line 61 for a moment.
4
You see it says, "These articles" --
5
that are given the generic labels A1, A2, A3.
6
You see that?
7
A.
Right.
8
Q.
"These articles are each associated
9
with one or more of these key terms by any
10
conceivable method of association, such as
11
through indexing all words or through meta tag
12
headers containing keywords selected by the
13
author or the editor."
14
A.
Right.
15
Q.
Okay.
16
A.
Yes, I see that.
17
Q.
So you agree that one of the ways that
You see that?
18
Culliss teaches that the key terms can be
19
selected is by indexing words in the article;
20
correct?
21
A.
22
tables.
23
Q.
That's how we would initialize the
Okay.
Now, if we go over to column 4,
24
about line 10, it says, "The invention will
25
accept a search query from a user, and a search
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engine will identify key terms which match the
2
search query"?
3
A.
Yes.
4
Q.
"These key terms which match the
5
search query are called matched key terms.
6
search engine then identifies in any conceivable"
7
matter -- "manner, the articles which are
8
associated with the matched key terms."
9
The
Do you see that?
10
A.
I see that.
11
Q.
So do you understand that to mean that
12
the key term scores are used by the search engine
13
to do that matching in response to the search
14
query that we just looked at at the top of
15
column 4?
16
A.
I do not know.
If the key term scores
17
reach zero or some very small number, it would be
18
normal to -- they would be ranked very low, and
19
Culliss could return them or could not return
20
them.
21
has an entry in the table, then the scores are
22
not material.
23
it.
24
25
If Culliss were to return everything that
Q.
I do not know which way he does
Okay.
So you don't know which -- one
way or the other is what you're saying?
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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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Q.
Oh, okay.
2
A.
We can get to it later.
3
Q.
Okay.
Good.
Okay.
So let's look at -- back to the
4
I understand what you're
saying now.
5
6
Culliss reference, which is Exhibit No. 4 to your
7
deposition, and I want to look at column 11 here.
8
So you see here there's a section that
9
10
begins about line 6 or 7, something like that,
the heading is "Ratings"?
11
A.
Yes.
12
Q.
And it says, "The invention can also
13
be used to organize articles by ratings.
14
end, the key terms of the index may additionally
15
or alternatively comprise rating key terms
16
represented by the generic labels G rated and X
17
rated, for example."
18
To this
Do you see that?
19
A.
Yes.
20
Q.
So you understand that Culliss is
21
saying, in addition to the key terms used in the
22
index, you can include a rating index?
23
24
25
A.
You can include rating labels.
It
didn't say index, but -Q.
Oh, that's fair.
You can include in
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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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of interest to users searching with the rating
2
key term of X rated."
3
Do you see that?
4
A.
Right.
5
Q.
So in this example, the users that are
6
performing the query, one of the key terms they
7
include is a rating key term; correct?
8
A.
That's right.
9
Q.
Okay.
Then it goes on to say, "After
10
several users have performed searches, the index
11
may look like this," and then provides an
12
example.
13
14
15
16
Do you see that?
A.
Provides an example where X rated was
preferred for -- for article A3.
Q.
17
For article A3, correct.
And then it continues in the
18
description, beginning at line 20 of column 12,
19
"While the article A3 containing adult content
20
was clearly of interest to the G-rated crowd, it
21
was also clearly of interest to the X-rated
22
crowd.
23
the article A3 (i.e., prevent the squib from
24
being displayed from search queries containing
25
the rating key term of G rated)."
Accordingly, the invention would screen
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2
Do you see that?
A.
I see what he says, but I don't -- I
3
don't see how the invention would -- would screen
4
it, because it also has a high -- high
5
probability value under his example.
6
Q.
Right.
Well, what it says is when it
7
has a high probability value of being X rated, it
8
will be screened from key queries containing a
9
G-rated key term; correct?
10
A.
Under this example, it would have a
11
high probability value for both X rated and
12
G rated.
13
it's 45 over 45 for one.
14
over 22 or, you know, .95 or whatever that number
15
actually is.
16
Q.
Slightly higher for X rated, because
Right.
In the other case, 21
And what -- what it's saying
17
is because the article A3 has a high probability
18
for X rated, it will be screened from queries --
19
meaning not displayed, from queries that contain
20
the G-rated term; correct?
21
22
23
A.
The -- the method disclosed or taught
here does not -- would not have that effect.
Q.
Well, that's exactly what it says;
24
right?
25
content was clearly of interest to the G-rated
"While the article A3 containing adult
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crowd."
2
In other words, little kids may be
3
wanting to look at it, but they're not supposed
4
to be looking at it; right?
5
A.
Right.
Like the -- even if you divide
6
the -- the users into two groups, the G rated and
7
the X-rated crowd here, what's taught in the
8
invention would not have that -- that result.
9
Q.
Even where it says, "Accordingly, the
10
invention would screen the article A3 (prevent
11
the squib being displayed from search queries
12
containing the rating key term of G rated)";
13
right?
14
A.
The methods that are described before
15
will not have the effect of performing that
16
screening.
17
Q.
Sure they will.
If your -- if your
18
threshold is -- if it has a high probability of
19
being X rated, I'm not going to show it to
20
G rated; correct?
21
22
23
MR. JACOBS:
Objection.
Asked and
answered.
A.
Yeah.
The -- the method described
24
here where you show by -- by the scores will not
25
have that effect.
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Q.
You don't think that this will screen
2
article A3 from -- from queries that include the
3
G-rated key term?
4
5
6
MR. JACOBS:
Objection.
Asked and
answered.
A.
The method described of generating
7
these tables, calculating the probability or the
8
incremental values, will not have the desired
9
effect or the stated effect of screening one and
10
11
permitting the other.
Q.
Well, sure it will, if your threshold
12
is -- does it have a high probability of being
13
X-rated content; correct?
14
A.
That is not the way the method here
15
works.
16
calculating the scores between what the terms in
17
the user preference is and ranking according to
18
those scores.
19
The method here works is -- works by
Q.
Right.
But this screening of X rated
20
from G rated is based solely upon the score
21
associated with the rating key term; correct?
22
A.
Yeah.
The -- the part here that is
23
inconsistent with the earlier teachings is,
24
accordingly, the invention would screen the
25
article A3.
The way that the invention is
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described, it will not have the effect of
2
screening that article.
3
Q.
Okay.
So you -- you think that both
4
the G-rated probability and the X-rated
5
probability have to be used in order to determine
6
whether you're going to screen the article in the
7
embodiment described in column 11 and 12 here,
8
that's what you're saying?
9
A.
It's essentially by looking at all of
10
the terms associated with the article.
11
need to devise an additional or new or different
12
mechanism to have the desired effect.
13
Q.
You would
Well, let's look at the next one.
14
the other hand" -- this is -- begins at
15
column 26.
16
terms for articles A1 and A2 under the X-rated
17
key term are low and suggests that those
18
articles, A1 and A2, do not contain adult
19
content.
20
displayed in response to a search query
21
"On
containing a rating key term of G rated."
22
"On the other hand, the rating key
Accordingly, these articles could be
Do you see that?
23
A.
Yes, I see what it says there.
24
Q.
So, in other words, the screening of
25
X rated content, meaning determining whether to
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display it to G-rated users, is based upon the
2
probability that a particular article has X-rated
3
content in this example; correct?
4
A.
The -- the method described here of
5
tabulating and calculating sums or probabilities
6
and then using that to score which items have a
7
higher score and which ones have a -- have a
8
lower score would not have the effect of using
9
the presence of some item to serve as a way of
10
11
excluding that item.
Q.
Well, of course it would.
We have --
12
we have the first example that's given that says
13
article A3 has a high probability, 45 out of 45,
14
right, of having X-rated content; correct?
15
16
17
A.
And that's what it says, yes, in the
example.
Q.
Right.
And so in that example,
18
it's -- in column 12, the Culliss reference says,
19
I'm not going to show that to people who included
20
a G-rated key term in their query; correct?
21
A.
The -- the mechanism -- that's what it
22
says what he wants to do.
23
disclosed here will not have the effect that he
24
desires.
25
Q.
The mechanism
Well, if you -- if you base -- well,
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let's just continue with the example.
2
Then we have the example of articles
3
A1 and A2 that have a low probability of
4
including X-rated content; correct?
5
A.
According to the example.
6
Q.
Right.
And in that instance, that low
7
probability of including X-rated content leads to
8
the conclusion that I can show those articles or
9
display those articles to a user, including the
10
G-rated key term in their query; correct?
11
A.
That's what it says here.
The Culliss
12
mechanism for generating the scores and so on
13
would not have that effect, either.
14
Q.
Well, the mechanism that's described
15
here is, take the X-rated probability and
16
determine whether you're going to display that
17
particular article to someone who does a G-rated
18
search; correct?
19
20
21
A.
here.
There's no real mechanism described
There's a desired outcome described here.
Q.
But you agree that in the example
22
that's shown here that we just looked at in
23
column 11 and 12, that the -- the determination
24
of whether to screen an article, meaning not
25
display it, is done on an article-by-article
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basis?
2
3
MR. JACOBS:
A.
Objection as to form.
The -- the -- no, not necessarily.
4
The -- Culliss discloses a mechanism for ranking
5
articles based on user feedback with some
6
initialization step, then he goes on to add
7
additional features like G rated and X rated and
8
collects statistics on those.
9
So if people who put X-rated items
10
liked a particular item a considerable number of
11
the time, and people who liked G rated also liked
12
it, that does not mean that that article
13
necessarily is -- is -- well, first of all, it
14
doesn't mean it's an X-rated article, it just
15
simply means it is liked by both crowds.
16
Q.
It says here, right, "While the
17
article A3 containing adult content was clearly
18
of interest to the G rated" cloud -- "crowd, it
19
was also clearly of interest to the X-rated
20
crowd.
21
the article A3 (i.e., prevent the squib from
22
being displayed from search queries containing
23
the rating key term of G rated."
24
25
Accordingly, the invention would screen
Do you see that?
A.
Right.
I agree that it says that.
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Q.
Right.
So the article has a high
2
probability of being X rated, so it's not shown
3
in response to a query including a G-rated search
4
term; correct?
5
6
7
MR. JACOBS:
Objection as to form.
Asked and answered.
A.
Right.
So Culliss would need a
8
different mechanism beyond the one described here
9
to have the desired effect.
10
Q.
The mechanism being determining
11
whether there's a high probability that it's X
12
rated; correct?
13
A.
The mechanism being how the -- the
14
ranking and selection are done compared to his
15
method of keeping these tables about all of the
16
other terms.
17
Q.
Yeah.
The probability associated with
18
whether the article has X-rated content is
19
determined at least in part by the feedback from
20
users that included an X-rated rating key term in
21
their query; correct?
22
A.
The way that the -- that the Culliss
23
works, that would be one of the criterion that
24
would be combined with -- with the others to
25
determine the pertinence of that article to the
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2
3
query.
Q.
That would be the only criterion;
correct?
4
5
MR. JACOBS:
A.
Objection as to form.
Culliss is -- developed a method where
6
he combines criteria from all the terms
7
associated with the article.
8
12, he mentions an example where one particular
9
criterion may dominate or maybe dominate over
Then at the top of
10
others.
11
describe under what cases when a criterion would
12
be used instead of the combination of the
13
criterion.
14
combination of the criterion.
15
He doesn't provide any mechanism to
Q.
His description throughout is a
So you don't think that beginning at
16
line 20, where he says article A3's got a high
17
level of interest to the X-rated crowd, so I'm
18
not going to show it to the G-rated crowd, and
19
conversely, articles A1 and A2 have a low level
20
of interest to the X-rated crowd, so I am going
21
to show it to the G-rated crowd describes a
22
mechanism?
23
24
25
MR. JACOBS:
A.
Objection as to form.
Article A2, for example, has no
ratings with respect to the G-rated crowd, so
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it's not clear whether they would like to see
2
item A2 or not.
3
Q.
Right.
Because I'm only -- in the
4
example that's shown here, I'm only basing it on
5
the interest that the X-rated crowd had in the
6
article; correct?
7
A.
So you're saying that the lack of
8
interest of the X-rated crowd on A1 and A2 makes
9
that a higher candidate for -- more eligible
10
11
candidate for showing it to other people.
Q.
To the G-rated crowd, that's what it
12
says right here.
13
key terms for articles A1 and A2 under the
14
X-rated key term are low and suggests those
15
articles A1 and A2 do not contain adult-rated
16
content.
17
displayed in response to a search query
18
containing a rating key term of G rated; right?
19
A.
On the other hand, the rating
Accordingly, these articles could be
Right.
So what you're describing here
20
is using a low probability as a positive
21
criterion for showing something.
22
that he had described here had a way of favoring
23
low probabilities over high probabilities under
24
certain cases and not under others, and there was
25
a mechanism for selecting these things, then I
If the method
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would -- then that would make sense, it would be
2
consistent with his invention.
3
He's describing here a desired
4
outcome, but he's not providing a modification of
5
his mechanism that -- that would work -- that
6
would work in general.
7
Q.
Well, sure he is, isn't he?
Isn't he
8
saying that I'm going to look at the probability
9
results associated with a particular key term for
10
a particular article and determine whether that
11
has a high probability of being X rated or a low
12
probability of being X rated?
13
A.
So let's take another -- another term
14
here like table or wood table.
15
people that like wood tables and other people
16
that don't seem to -- another group of people
17
that also like wood tables, but they happen to
18
have another property, like they -- they like
19
classic furniture, and the first one likes modern
20
furniture, and they both like wood tables.
21
There are some
So in this case, because the people
22
who like classic furniture like wood tables, you
23
would never show that to somebody who likes
24
modern furniture, even if they also like wood
25
tables.
The mechanism you're describing would
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have that effect.
2
Q.
Wood tables are X rated?
3
A.
Substitute for X rated modern
4
5
6
7
furniture versus classical furniture.
Q.
Well, I'm trying to stick with the
example that's here, X rated and G rated.
A.
Yeah.
And I'm giving you another
8
example that is structurally equivalent to show
9
that there's no mechanism that is being offered
10
to address this -- this -- to address this
11
problem.
12
So if I have -- you're the classic
13
furniture guy, and I'm the modern furniture guy,
14
or the other way around, and we both like wood
15
tables, just like the G and the X rated both
16
liked A3 here, like some -- some article that
17
talks about wood tables, the fact that you are
18
now a member of a club different from mine or a
19
crowd different from mine, to use your word, that
20
means that I would no longer be able to see wood
21
tables because you got wood tables or vice versa.
22
Q.
Sure.
If you -- if you decided
23
that -- in your example that you were going to
24
screen -- in your implementation, you were going
25
to screen on the basis of whether you were a
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member of the classic furniture club or the
2
modern furniture club; right?
3
A.
4
tables.
5
Q.
Yeah, but they could both like wood
So what?
I mean, we can see right
6
here in article A3, little kids can like
7
pornography, too; right?
8
9
A.
I agree it's a laudable goal to not
display pornography to little kids.
10
Q.
Right.
11
A.
This is -- this is not a part of what
12
the Culliss method would necessarily do by
13
combining all of the -- all of the elements.
14
you were to add to Culliss the fact that having
15
some items then precludes -- having a high value
16
on some item precludes showing -- a high value in
17
some terms, excuse me, or feature precludes
18
showing items that were preferred by people who
19
had a different value for that term, then you
20
would get an invention that works very
21
differently and not very well in general than
22
what Culliss actually describes.
23
Q.
If
So what you're saying is because you
24
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25
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A.
Well --
2
Q.
Let me just finish so the record is
3
clear.
4
So you're saying because you can think
5
of a bad implementation not using the G-rated-
6
and the X-rated example, but using wood furniture
7
example, that Culliss doesn't describe a means
8
for screening articles based upon the results
9
associated with the rating key term?
10
11
12
MR. JACOBS:
Objection.
Misstates
testimony.
A.
Okay.
So these are not -- I'm not
13
describing an implementation.
14
outcome or a desired outcome, in this case an
15
undesired outcome that is analogous to the one
16
that is suggested here.
17
I am describing an
And I'm saying, in fact, that neither
18
one of these would be -- would result from an
19
implementation of the Culliss invention or the
20
Culliss patent describing these particular tables
21
that associate terms with items based on -- based
22
on popularity.
23
Q.
But in terms of the screening that's
24
described here in columns 11 and 12, the
25
screening's not based on any results associated
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2
3
4
5
6
with the other key terms; correct?
A.
The screening is -- that is described
here is essentially ignoring the other key terms.
Q.
Right.
So it's based upon -- solely
based upon the rating key term; correct?
A.
So, in general, in the invention how
7
would the system know which key term to associate
8
in order to do this kind of screening?
9
10
11
Q.
Because it -- you're saying that the
invention doesn't know one key term from another?
A.
The method described in the invention
12
does not use one key term and ignore all the
13
others throughout the description of the patent.
14
Q.
So are you saying that one of ordinary
15
skill in the art in 1998 couldn't implement a
16
system that looked at a particular key term like
17
a rating key term and made a judgment based upon
18
that key term?
19
20
MR. JACOBS:
A.
Objection as to form.
That would -- implementing a system
21
that -- that essentially looks at a single key
22
term, and then looks at the value of that key
23
term, and then decides whether to block it or not
24
is a mechanism which is separate from the
25
mechanisms described in the rest of Culliss.
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Q.
Right.
So the coding would be if
2
rating X-rated key term is above a threshold,
3
then block from G-rated queries; correct?
4
5
6
MR. JACOBS:
Objection as to form.
Incomplete hypothetical.
A.
The -- the method here would -- would
7
be, ignore everything else that -- that Culliss
8
does and then look at a single attribute; in
9
fact, look at the specific attributes that you
10
just described, and then make a decision based on
11
that particular attribute.
12
This is a little bit like saying that
13
if you have an automobile, and describe the
14
automobile invention, and -- and how to rate it,
15
and how to drive it, and so forth, and then you
16
also have -- add on to the automobile like if you
17
want to take a walk, put your shoes on, ignore
18
the automobile, jump out of the automobile, and
19
now whether you can walk or not depends upon
20
whether you have shoes.
21
22
Q.
Okay.
So that -- that isn't an
accurate example at all, is it?
23
So how about this example, would be a
24
better one to stick with your automobile example.
25
You have all sorts of factors:
Drivability,
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handling, engine power, model, make.
2
kinds of things; right?
3
4
All those
You also have color.
Now, the user says, I don't want a red
car.
It could happen all the time; right?
5
A.
That's right.
6
Q.
So in that instance, the user could
7
say any color but red.
8
to see it because I'm not going to buy it; right?
9
10
11
12
13
14
If it's red, I don't want
A.
Uh-huh.
Q.
So that would be filtering on the
criteria of the color of the car; correct?
A.
That would be the user doing that
filtering, not the system.
Q.
Right.
But, yes.
But you could implement a
15
system, they do this on -- they've done this on
16
search systems for quite some time now, haven't
17
they, where one of the criterion will be a
18
particular aspect of the product, in other words,
19
maybe the color in terms of cars, and then it
20
will display to you cars either only of that
21
color or, if you say, I don't want that color,
22
cars not of that color; correct?
23
MR. JACOBS:
24
Calls for speculation.
25
A.
Objection as to form.
There could be such a system.
That is
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2
not the mechanism described under Culliss.
Q.
Well, it is the mechanism described as
3
Culliss; right?
4
is indicative of whether the article has X-rated
5
content; correct?
6
7
8
9
10
You include a rating term that
MR. JACOBS:
Objection as to form.
Argumentative.
A.
One of the terms that is added to the
system is a rating term, yes.
Q.
Right.
And as described here in
11
columns 11 and 12, the purpose of that is to
12
determine whether an article is appropriate for
13
display to the G-rated audience; correct?
14
A.
15
rating term.
16
appropriate to display or not is -- is -- it's
17
not contained within that particular table.
18
Q.
The term that you're adding is the
Whether it's appro -- who it is
Well, but it says right at the
19
beginning of the example, going back to
20
column 11, about line 12, it says, "The rating
21
key term G rated is considered appropriate for
22
all ages, while the rating key term X rated is
23
considered appropriate only for adults"; right?
24
A.
That's what it says, right.
25
Q.
Right.
So G rated, I can show that to
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everybody.
2
adults; correct?
3
4
5
6
X rated, I can only show that to
That's what it's saying?
MR. JACOBS:
A.
Right.
Objection as to form.
So how would the system know
which are the adults and which are the G rated?
Q.
Well, because we know that one of the
7
key terms that you have to include is whether
8
you're searching for X-rated content or G-rated
9
content; correct?
10
11
12
13
MR. JACOBS:
A.
Objection as to form.
You could have a term in the query
that would be X rated or G rated, yes.
Q.
Right.
So you can use that as a proxy
14
for determining whether someone's an adult in
15
this instance; correct?
16
17
MR. JACOBS:
A.
Objection as to form.
If someone who's an adult was to
18
search for G rated or someone who's a child
19
states X rated when they shouldn't, you -- you
20
don't control that.
21
22
Q.
25
So what you're saying is
the system might not be perfect; right?
23
24
Understood.
MR. JACOBS:
Objection as to form.
Misstates testimony.
A.
Yeah.
I'm saying that this depends on
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