[00:00:00] Speaker 03:
We have four cases on the calendar this morning.

[00:00:20] Speaker 03:
Two cases, two patent cases, one from the district court and one from the patent office.

[00:00:26] Speaker 03:
We have a government employee case from the Merit Systems Protection Board, and we have a trademark case from the Patent Office, and that's submitted on the briefs, and that will not be argued.

[00:00:39] Speaker 03:
The first argued case is Clara Logic versus Form 3 Holdings, 2016, 1781.

[00:00:46] Speaker 03:
Mr. I don't see counsel.

[00:00:52] Speaker 03:
Mr. Rachario, please appear.

[00:00:56] Speaker 03:
Counsel, both sides should be at their places.

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So we are ready when you are, Mr. Pacheria.

[00:01:30] Speaker 03:
You have a 101 case.

[00:01:33] Speaker 01:
I do.

[00:01:43] Speaker 01:
May it please the court.

[00:01:45] Speaker 01:
My name is Nigam Acharyan.

[00:01:46] Speaker 01:
I represent Form Free Holdings Company in this appeal.

[00:01:50] Speaker 01:
The patent issue is the 243 patent, which dates back to 2008, when the lending industry was in crisis.

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And this patent attempted to provide a solution and did provide a solution at the time.

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It received numerous credits and awards.

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And the solution was a tangible, transformative method.

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Counsel, the claims speak to an algorithm engine.

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Where is that algorithm in the claims?

[00:02:24] Speaker 01:
The algorithm engine is... That's a series of rules that either a third party puts into the software or... So you're not claiming the rules.

[00:02:37] Speaker 04:
We are not claiming.

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So there's... If you take the rules out, what do you have left?

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We have the iterative process of taking... But without the rules, the process doesn't work.

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The process would not work without the rules.

[00:02:53] Speaker 04:
But you're not claiming the rules.

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We are not claiming the rules.

[00:02:56] Speaker 04:
So isn't that abstract?

[00:03:00] Speaker 04:
I mean, you're claiming something that doesn't even get off the ground.

[00:03:07] Speaker 01:
So we see it as an iterative process which takes in the data, electronically transforms it, and then uses those

[00:03:20] Speaker 01:
the algorithm, whether it's a third party's algorithm or a first party's or their own party's algorithm, and it tests the data against that algorithm so that we can tell whether the data is certified against that algorithm.

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So the fact that the algorithm is a third party group.

[00:03:43] Speaker 04:
It seems like, the pan is,

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is attempting to build a financial profile of a potential lender, let's say.

[00:03:52] Speaker 01:
Is that?

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A borrower.

[00:03:53] Speaker 04:
A borrower.

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Right.

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OK, a borrower.

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Attempting to build a financial.

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So that's where you want to get to.

[00:04:00] Speaker 01:
That's correct.

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Take out the algorithm.

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There is no profile anymore.

[00:04:08] Speaker 01:
There wouldn't be.

[00:04:09] Speaker 01:
But the whole question is whether

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the data conforms with the algorithm.

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The question is whether the claims are directed to an abstract idea.

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If you take out the algorithm, you cannot build this profile.

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Therefore, you're left with nothing.

[00:04:29] Speaker 04:
That's abstract, isn't it?

[00:04:30] Speaker 01:
Well, I would say that it's like deer.

[00:04:33] Speaker 01:
When you take the mathematical formula, all you are left with is

[00:04:38] Speaker 01:
a generic method for curing rubber.

[00:04:41] Speaker 01:
I would say it's similar to Bascom in that... But it was physically transformative, wasn't it?

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That's different than what you have here.

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In here, we have some physical transformation in that the data is... It's not physically transformative in that we do get a tangible report at the end.

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You've got data in and data out.

[00:05:06] Speaker 01:
together with the report showing certification against the standards.

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And so I think it's more like deer.

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It's similar to, well, it has a lot of similarities to AmDocs.

[00:05:21] Speaker 01:
The case where we're taking data from multiple sources, putting them together,

[00:05:31] Speaker 01:
and creating a billing report.

[00:05:34] Speaker 02:
Well, are you familiar with electric power group?

[00:05:37] Speaker 02:
Yes.

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So in that opinion, the court tried to draw from multiple cases from the court and come up with some principles.

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And it explained how collecting information, that's just abstract.

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It's an abstract idea.

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Then it went further and said,

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Taking that collected information and then analyzing it by a process that people go through in their minds and doing that on a computer is also just an abstract idea.

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And then it further said after going through that analysis of the information and displaying it is still without more just abstract.

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And so when I look at your claims, I feel like, okay, we're collecting financial information about an individual.

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We're applying some unstated rules to review that information to come and display a result.

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This is all computer implemented, indicating whether there's a risk problem with this financial information.

[00:06:53] Speaker 02:
That, to me, you can explain it to me why I'm wrong, but that, to me, follows the profile of what we explained in an electric power group.

[00:07:04] Speaker 02:
It falls within an abstract idea.

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What it looks like is we're doing an abstract process on a computer as opposed to, say,

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doing some kind of specific improvement to a computer that improves the computer as a tool.

[00:07:19] Speaker 02:
Now, having said all that setup, please explain to me where I'm off.

[00:07:24] Speaker 01:
So I think a couple of things here.

[00:07:26] Speaker 01:
So with electronic electric power grid, the whole idea was just collecting and analyzing information.

[00:07:34] Speaker 01:
Here, we are not doing that.

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What we're doing here is we are taking

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taking information that comes into the system.

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We're using that algorithm, whatever it may, that emphasizes a pattern of risk.

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Specifically, we're not looking at anything other than a pattern of risk.

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And we're marking certain exceptions.

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We're not saying whether those exceptions are per se more risky or less risky.

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And then we're

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having that same algorithm look at that data and then present that problematic report to a lender so that a lender knows, does this data conform to the standards that I have?

[00:08:24] Speaker 01:
And I think electric power grid was a different case in that it was simply gathering information, analyzing it, and presenting it to power

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in the power context.

[00:08:37] Speaker 02:
Okay.

[00:08:38] Speaker 02:
I'm sorry.

[00:08:39] Speaker 02:
I didn't quite understand the distinction there because if I could just summarize your description of your claimed invention, it's collecting information, analyzing that information to see if there's any trouble spots, and then presenting the trouble spots.

[00:08:56] Speaker 01:
First, it's collecting additional information from other sources and putting that together with the

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with the exceptions, as we call them, and then saying whether they're valid exceptions, saying are these exceptions that we should be concerned about or not concerned about?

[00:09:13] Speaker 02:
That's kind of like an analysis, right?

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About information?

[00:09:17] Speaker 01:
Yeah, I would look at it as more of a confirmation analysis.

[00:09:23] Speaker 02:
Okay, confirmation of information.

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Confirmation, and then presenting that in a way that a third party can act on this certified report.

[00:09:34] Speaker 01:
So I think it's a little different than simply just gathering information, collecting it, and then... And analyzing it, and then displaying it.

[00:09:43] Speaker 02:
Your invention is different from that.

[00:09:47] Speaker 01:
I think a couple of things that are key is this can't really be done for humans, and humans can't necessarily do this process.

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For one, we've gotten two big advantages out of this.

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One, it's faster.

[00:10:03] Speaker 01:
in that we can collect information from multiple sources and put them together in this report to say whether the data conforms with the standards.

[00:10:17] Speaker 01:
And second, we've taken out the human error part in that one of the biggest problems with the technology prior to this was humans introducing fraud into the system.

[00:10:28] Speaker 02:
Are you familiar with the invention in Alice Core?

[00:10:33] Speaker 02:
The Supreme Court decision?

[00:10:34] Speaker 02:
Yes.

[00:10:35] Speaker 02:
The Supreme Court rejected the idea that just being able to do something faster because it's computer implemented is going to save the day, right?

[00:10:44] Speaker 02:
There were thousands of currency exchanges going on on that system, and that wasn't persuasive, and so we're controlled by that.

[00:10:57] Speaker 01:
So I think here it's a combination of the iterative process, which was not in the ALA system.

[00:11:04] Speaker 01:
And the gathering from multiple sources, which also was not in the ALA system.

[00:11:12] Speaker 01:
And putting that all together faster.

[00:11:15] Speaker 01:
Faster is just one advantage of the system.

[00:11:17] Speaker 01:
It's not the advantage.

[00:11:19] Speaker 01:
It's the fact that you can actually do all of this.

[00:11:22] Speaker 01:
electronically and minimizing human error into the system.

[00:11:32] Speaker 03:
That's because computers work so fast.

[00:11:36] Speaker 01:
It's also part of the reason they're putting this algorithm on the system and then having the system gather the information.

[00:11:44] Speaker 01:
That's one of the big reasons, because data collection is not new to the financial industry.

[00:11:49] Speaker 01:
It's been there since the 60s.

[00:11:53] Speaker 01:
We're not claiming analyzing, we're not claiming that collecting the data and putting it in a form is new.

[00:12:00] Speaker 01:
We wouldn't consider any just taking the data and presenting it.

[00:12:07] Speaker 01:
We would not think that's new.

[00:12:09] Speaker 01:
It's the whole idea of taking the algorithm, putting it into a piece of software.

[00:12:15] Speaker 03:
You're talking about what's new.

[00:12:16] Speaker 03:
Novelty isn't the issue here.

[00:12:19] Speaker 03:
Abstractness is the issue.

[00:12:21] Speaker 01:
Yeah, so I think that the abstraction, or the lack of abstraction, is seen in that we're doing these concrete steps of putting the algorithm into the software, having the software confirm the exceptions, and then generating a report specifically telling us whether we

[00:12:49] Speaker 01:
we conform or we don't conform with the algorithm, which is different than just the pure collection of data.

[00:13:02] Speaker 03:
If you're ready to rebuttal, you can continue to use your time or save it.

[00:13:09] Speaker 01:
I will save it.

[00:13:13] Speaker 03:
Mr. Wellman.

[00:13:15] Speaker 00:
May it please the court, Arthur Wellman, on behalf of the Appellees and plaintiff below, Clara Logic.

[00:13:23] Speaker 00:
I want to clean up a couple of points, if I may.

[00:13:25] Speaker 00:
One is that in the reply brief, Form Free alleged that we had misconstrued their brief and referred to our unsuccessful motion to strike

[00:13:45] Speaker 00:
their evidence.

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As the court is probably aware, there wasn't an unsuccessful motion to strike the evidence, because we made no motion to strike the evidence.

[00:13:54] Speaker 03:
Why don't we get into the merits of the decision?

[00:13:58] Speaker 03:
Certainly.

[00:13:59] Speaker 03:
Are these claims abstract or not?

[00:14:02] Speaker 00:
They're absolutely abstract, Your Honors.

[00:14:04] Speaker 00:
And Your Honors, I think, correctly focused on the relevant authority.

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And I'm loathe to characterize to this court its own decisions, but for example,

[00:14:15] Speaker 00:
this court's decision in intellectual ventures rejected claims that analyze multiple sources of data.

[00:14:22] Speaker 00:
Just analyzing, as Judge Chen brought up, analyzing data and then going and getting more data and going and getting more data.

[00:14:29] Speaker 00:
That doesn't take it out of the realm of an abstract idea, nor is it, I would suggest, particularly inventive.

[00:14:37] Speaker 00:
As to whether a human can practice the claims, let's say I'm a lender who says,

[00:14:42] Speaker 00:
I don't want to lend to someone who's had three insufficient fund instances in the last six months.

[00:14:49] Speaker 00:
I pick up their bank statement.

[00:14:50] Speaker 00:
I look at it.

[00:14:51] Speaker 00:
The algorithm engine in my head says there's two.

[00:14:54] Speaker 00:
I pick up the next month's, and I see one.

[00:14:56] Speaker 00:
The algorithm engine in my head says that's three.

[00:14:59] Speaker 00:
Done.

[00:15:00] Speaker 00:
It reports to me, don't lend to that person.

[00:15:02] Speaker 00:
That's three insufficient fund instances.

[00:15:05] Speaker 00:
That's the height of abstraction.

[00:15:08] Speaker 00:
Going beyond that, Your Honors, I would suggest

[00:15:11] Speaker 00:
These claims are not only preemptive, they're co-optive.

[00:15:16] Speaker 00:
Humans have to be... What's the difference between those two?

[00:15:19] Speaker 00:
Preemptive, your honor, meaning that if someone comes up with a new method in the future for analyzing the data, a new algorithm, let's say three insufficient fund instances in six months or five in one year, this method would preclude them

[00:15:37] Speaker 00:
from implementing that on a computer, and that would preempt their idea, which is the issue of preemption.

[00:15:43] Speaker 00:
Co-optive is looking backwards, Your Honor, and that is it took my proprietary algorithm.

[00:15:52] Speaker 00:
It co-opted it.

[00:15:55] Speaker 00:
You have to have already had the algorithm because, as the Court has noted, the patent doesn't supply any algorithm, unlike in deer.

[00:16:02] Speaker 00:
The patent says, put in

[00:16:04] Speaker 00:
whatever it is that you think is a good idea to use to analyze their credit worthiness, and then the computer will run and chew through that data and tell you based on the criteria you provided whether or not that person is a good credit risk.

[00:16:20] Speaker 00:
And I think to suggest to this court or any other that it's somehow inventive or non-abstract to tell a company that's already gathering data by computer, hey, you should use your own method

[00:16:33] Speaker 00:
to analyze that data is not very innovative and is abstract.

[00:16:38] Speaker 02:
This looks like a data processing, data analysis invention.

[00:16:44] Speaker 02:
Is it your point of view that all data processing inventions are abstract?

[00:16:53] Speaker 00:
They may not all be abstract.

[00:16:56] Speaker 00:
So they may not fail at phase one, but for example, in the ENFISH case,

[00:17:04] Speaker 00:
That's a data processing software where a novel data structure was created and claimed, as this court has, I think, appropriately focused on, not just what you claim to have invented, but what is in the claim, what is recited there.

[00:17:20] Speaker 00:
Is it specifically claimed in a manner that is not preemptive?

[00:17:25] Speaker 00:
And in that case, the court said no.

[00:17:28] Speaker 00:
Of course, if yes, it's specifically enough claim that we're not as worried about preemption and it's innovative because you created this self-referential data structure which could then be used by a computer and that's what you're claiming.

[00:17:41] Speaker 00:
You're not claiming all ways of processing data and you're not claiming all ways of processing data faster.

[00:17:48] Speaker 00:
You're instead claiming this novel structure that you created that happens to enable that.

[00:17:54] Speaker 00:
But the claim is to the novel structure.

[00:17:56] Speaker 00:
So I'm not saying that all data processing patents would necessarily fail this court's jurisprudence and the court's jurisprudence in Alice.

[00:18:10] Speaker 00:
I hope this court doesn't have any issue with the admissibility of the evidence.

[00:18:15] Speaker 00:
There was an argument below that Mr. Acharya's citation to portions of his expert

[00:18:24] Speaker 00:
testimony saying, our expert said there was no other system that could do this.

[00:18:28] Speaker 00:
We came back in opposition and said, the expert didn't say that.

[00:18:32] Speaker 00:
And in reply, they said it was a scribbler's error, which notably they had repeated.

[00:18:37] Speaker 00:
But I want the court to realize that the portion of the record that they're now citing is Mr. Acharya's argument to the Patent and Trademark Office, which is also obviously inadmissible hearsay.

[00:18:51] Speaker 00:
I don't think it's particularly controversial that you need admissible evidence to oppose a motion for summary judgment, and that it was their burden below to show once an objection was made to it, which we did object, to show that the proffered evidence was admissible, which they didn't, and their burden on appeal to show that had the court rejected that evidence.

[00:19:15] Speaker 04:
I think the issue of real interest here is a one-on-one issue.

[00:19:20] Speaker 04:
Thank you, Your Honor.

[00:19:28] Speaker 00:
There were a couple of points I wanted to make on that issue, just following up on things that this Court has already raised.

[00:19:37] Speaker 00:
As Judge Lurie noted, without the rules, there's nothing.

[00:19:41] Speaker 00:
No algorithm is provided.

[00:19:44] Speaker 00:
And I also wanted to point out that their transformative step, and far from dear, recall that it was construed at their request.

[00:19:53] Speaker 00:
There's electronic data that's gathered.

[00:19:56] Speaker 00:
A computer reads it.

[00:19:57] Speaker 00:
and transforms the data into electronic data that a computer can read, which I would suggest to your honors is non-transformative or at least extremely subtle as to how that transforms anything.

[00:20:12] Speaker 00:
As Judge Lurie said, data comes in and data goes out.

[00:20:16] Speaker 00:
That data was readable when it came in by a computer because a computer read it, we know that.

[00:20:21] Speaker 00:
It's also readable by a computer when it comes out.

[00:20:24] Speaker 00:
So I don't see a transformation and certainly not a patentable transformation that would transform what is an abstract idea.

[00:20:32] Speaker 00:
And this court, as you've all analyzed, has said repeatedly that the analysis of data without more and reporting on that data is an abstract idea.

[00:20:41] Speaker 00:
I don't see anything here that would transform that.

[00:20:46] Speaker 00:
And with that,

[00:20:47] Speaker 00:
Given this court's questions, I would submit if the court has no further questions, I would thank the court and submit.

[00:20:53] Speaker 02:
Once the claim had recited some 15-step sequence of operations that the processor would undertake in order to assess whether there's financial risk in the data, would that be a patent-eligible invention?

[00:21:13] Speaker 00:
I don't want to quiz along the court's question, but I would suggest it depends on what the steps are.

[00:21:20] Speaker 00:
The steps here, I would submit, that were claimed were parsed down to a very minute detail.

[00:21:27] Speaker 00:
They have a step that says gather the data, then check the data, then apply the algorithm, then gather more data.

[00:21:34] Speaker 00:
And so you can break what is an abstract process and is the normal operation of any computer, get data, read, write, compare,

[00:21:44] Speaker 00:
Boolean right again, you can break that down into a bunch of steps.

[00:21:48] Speaker 00:
And so I don't think it's the number of steps per se that confer patentability, but rather the substance in those steps and what it is that those steps bring to the table.

[00:22:00] Speaker 00:
And again, as one example in ENFISH, if one of those steps is creating a heretofore unknown data structure and then utilizing that to analyze the data, I think that would be patentable

[00:22:13] Speaker 00:
you know, as this Court has said, it was patentable.

[00:22:15] Speaker 00:
So I don't, I don't think it's per se, Your Honor, is the number of the steps.

[00:22:21] Speaker 03:
Thank you, Counsel.

[00:22:22] Speaker 00:
Thank you very much.

[00:22:23] Speaker 03:
Mr. Charia has three plus minutes for rebuttal if he needs it.

[00:22:46] Speaker 01:
Your Honours, I submit that this case is different than just a mirror analysis prior of data.

[00:22:57] Speaker 01:
In that the cases where data was found with steps

[00:23:05] Speaker 01:
we're found to be abstract and not meeting step two.

[00:23:10] Speaker 04:
So counsel, do you agree that the inquiry here is where the claims are directed to an abstract idea?

[00:23:19] Speaker 01:
I do agree with that.

[00:23:20] Speaker 04:
So when we look at the claims and we say, what are the claims directed to?

[00:23:26] Speaker 04:
What do they embrace?

[00:23:27] Speaker 04:
What are they saying?

[00:23:29] Speaker 04:
And we know there's no algorithm involved.

[00:23:34] Speaker 04:
The claims are not directed to an algorithm.

[00:23:40] Speaker 04:
There's no rules.

[00:23:41] Speaker 04:
So aren't we just left with information?

[00:23:44] Speaker 01:
I think this is similar to the macro case in that where rules to... Well, in macro there were rules and the claims did address particular rules.

[00:23:59] Speaker 01:
So here, our rules are for the pattern of risk, as their rules were for the pattern of sound.

[00:24:05] Speaker 04:
Your rules are to the algorithm.

[00:24:10] Speaker 01:
The algorithm engine finds a pattern of risk in the... Right, your rules are to the algorithm, correct?

[00:24:15] Speaker 04:
Without the algorithm, there is no analysis, there is no transformation of data, there is no final report.

[00:24:23] Speaker 04:
Correct.

[00:24:24] Speaker 04:
So if the claims are not directed to an algorithm, then all we're left with is data, information.

[00:24:35] Speaker 01:
The fact that these are someone else's algorithms, I think doesn't change the abstract question.

[00:24:47] Speaker 01:
I think it's just like with macro.

[00:24:50] Speaker 01:
the pattern of sound.

[00:24:54] Speaker 04:
I could agree with you if the claims at least identify the algorithm or the steps, the rules that are followed.

[00:25:04] Speaker 04:
But we don't have that.

[00:25:07] Speaker 04:
So the patent which sits out to build a financial picture of a borrower.

[00:25:15] Speaker 04:
Correct?

[00:25:16] Speaker 04:
Of a borrower.

[00:25:17] Speaker 04:
It is rendered abstract because the claims can't do that.

[00:25:22] Speaker 04:
Because you don't have any rules.

[00:25:23] Speaker 04:
You have no way of analyzing the data.

[00:25:26] Speaker 04:
So it's abstract.

[00:25:28] Speaker 01:
So I would say that the rules here are the rules governing pattern of risk, which are well known in the industry.

[00:25:35] Speaker 01:
And we wouldn't want to claim those because each institute has its own proprietary set of rules.

[00:25:41] Speaker 01:
And so we're saying, let's help those institutes by putting your rules into a piece of software, having that piece of software transform the data you're coming in.

[00:25:54] Speaker 02:
So it's computerizing of prior practice?

[00:25:58] Speaker 02:
That's your invention?

[00:25:59] Speaker 01:
And adding and transforming it, and then ultimately allowing a report to say whether the data

[00:26:10] Speaker 01:
is certified against that algorithm, which is what that report would be included when a loan is sold or a loan is bought.

[00:26:22] Speaker 01:
Because one of the big problems with the lending industry was that nobody would know whether the algorithm was complied with.

[00:26:31] Speaker 01:
It would just have human errors and it would have various problems with it.

[00:26:37] Speaker 01:
And so here, we're actually saying, put it on a computer, which is, we think, not abstract.

[00:26:43] Speaker 01:
Have it checked, collect additional data, and then prepare the report that we'll add to the back of, we'll prepare the report that says the data is certified.

[00:26:54] Speaker 03:
Thank you, counsel.

[00:26:55] Speaker 03:
As you can see, your light is on.

[00:26:58] Speaker 03:
Do you take the case under advisement?